Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

Abstract

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.

The completion of various genome-sequencing projects and large-scale genomic studies has led to a wealth of available biological data. It is anticipated that this information will revolutionize our insight into the molecular basis of most common diseases by making it easier and quicker to identify genes with variants that predispose to disease (i.e., disease genes). At the moment, we are faced with many disease susceptibility loci, resulting from linkage or cytogenetic analyses, that cover extensive genomic regions. Usually, when the genes in these loci are assessed, positional candidate genes become apparent that can be linked to the phenotype being studied on the basis of their biological function.

However, the most obvious functional candidate gene from a disease locus does not always prove to be involved in the disease.^e.g.,1–5 Often, genes that would not have been predicted to be disease causing prove to be the true disease gene—for example, the BRCA1 gene in early-onset breast cancer.⁶ Moreover, although these disease genes might have been assigned biological functions, it is not always evident how these functions relate to disease. Finally, genes with unknown functions are often overlooked, as attention is paid only to well-studied genes for which functions and interactions have been identified or implicated, some of which can be related to the disease pathogenesis. For example, in Fanconi anemia, at least 10 disease genes were identified,⁷ but only a few had a known function. However, follow-up research^8–10 revealed that five of those genes function in the same protein complex. Another example is limb-girdle muscular dystrophy, in which many of the disease genes encode for proteins that are part of the dystrophin complex.¹¹ This emphasizes the importance of taking an unbiased approach to assessing positional candidate genes.

Faced with the absence of complete functional information for the majority of genes in susceptibility loci, it is difficult to prioritize the positional candidate genes correctly for further sequence or association analysis. However, high-throughput genomic work has now yielded relatively unbiased genomewide data sets^12–15 that comprise known metabolic, regulatory, functional, and physical interactions. There is, however, little integration of these diverse data sets into a coherent view of possible gene and protein interactions that can be used to investigate relationships between genes in different genetic loci. We have tried to address this problem by developing a functional human gene network that comprises known interactions derived from the Biomolecular Interaction Network Database (BIND)^,12 the Human Protein Reference Database (HPRD)^,13 Reactome^,15 and the Kyoto Encyclopedia of Genes and Genomes (KEGG).¹⁴

Since these data sets contain a limited number of known interactions, we implemented a Bayesian framework to complement these relationships with a large number of predicted interactions by relying on evidence for putative gene relationships based on biological process and molecular function annotations from the Gene Ontology database (GO).¹⁶ We further incorporated experimental data—namely, coexpression data derived from ∼450 microarray hybridizations from the Stanford Microarray Database (SMD)¹⁷ and the NCBI Gene Expression Omnibus (GEO),¹⁸ along with human yeast two-hybrid (Y2H) interactions¹⁹ and interactions based on orthologous high-throughput protein-protein interactions from lower eukaryotes.²⁰

Our interaction network was then used to test whether we could rank the best positional candidates in susceptibility loci on the basis of their interactions, assuming that the causative genes for any one disorder will be involved in only a few different biological pathways. This would be apparent in our network as a clustering of genes from different susceptibility loci, resulting in shorter gene-gene connections between disease genes than one would expect by chance (fig. 1). Our method (called “Prioritizer”) analyzes susceptibility loci and investigates whether genes from different loci can be linked to each other directly²¹ or indirectly.²² When we constructed artificial loci of varying size around susceptibility loci from 96 different genetic disorders (each containing at least three loci) and used Prioritizer in our most comprehensive gene network to rank the positional candidate genes for each locus, we were able to significantly increase the chance of detecting disease genes.

Figure 1.

Basic principles of the prioritization method for positional candidate genes with the use of a functional human gene network. The method integrates different gene-gene interaction data sources in a Bayesian way (left panel). Subsequently, this gene network is used to prioritize positional candidate genes, with all genes assigned an initial score of zero. In the example (right panel), three different susceptibility loci are analyzed, each containing a disease gene (P, Q, or R) and two nondisease genes. In each locus, the three positional candidate genes increase the scores of nearby genes in the gene network, by use of a kernel function that models the relationship between gene-gene distance and score effect. Genes within each locus are ranked on the basis of their eventual effect score, corrected for differences in the topology of the network (see the “Material and Methods” section).

Material and Methods

Functional Gene Network Reconstruction

As a basis for the gene network, we used annotations from Ensembl^,23 version 32.35, resulting in 20,334 known genes that physically map within the autosomes or chromosome X or Y. This yielded 206,725,611 potential gene-gene interactions.

On the basis of this set of genes, a comprehensive “gold standard” set of validated direct gene-gene relationships (true positives) was determined using both ^BIND (September 15, 2005) and HRPD (September 15, 2005) to extract human, curated protein-protein interactions, the proteins of which were mapped to Ensembl gene identifiers. In addition, all human pathways from Reactome (September 15, 2005) and KEGG (September 15, 2005) were used to derive direct interactions that were of transcriptional, physical, or metabolic origin, since pathways are usually composed of genes and proteins that interact with each other in various ways. We chose to allow interactions of physical, metabolic, and regulatory origin to be included within our network, because, for instance, mutations in either one of two genes encoding proteins in the same metabolic pathway or protein complex could lead to the same disease phenotype.

Because the true-positive gold standard only describes a limited number of relationships between a limited number of genes, we also used data from GO, coexpression data derived from microarray experiments, conserved protein-protein high-throughput data, and human Y2H interaction data to predict interactions of the remaining gene pairs. We used a Bayesian classifier, because these four types of data were of varying reliability and only contained information about a subset of the data. The classifier allows for combining dissimilar data sets, can deal with missing data, and uses conditional probabilities that can be well interpreted and that control for the varying reliability of the data sets.^24–29

Training of Bayesian Classifier on Gold Standard

For the prediction of interactions, we used a Bayesian classifier type that assumed all data sets had been binned. This operation was performed for each gene pair, and it determined, for each data set, to which bin the pair belongs. Because the number of bins per data set was limited, each bin contained many gene pairs. Subsequently, for each bin, we determined the likelihood ratio between the proportion of gene pairs known to interact and the proportion of gene pairs known not to interact. This measure indicates whether there is an over- or an underrepresentation of truly interacting gene pairs in the bin, which specifies the conditional probability estimates of the Bayesian classifier; thus, training of the classifier is straightforward.

However, to be able to train the classifier by determining likelihood ratios of sets of gene pairs, it was crucial that the gold standard, containing the aforementioned well-defined set of curated true-positive gene pairs, be complemented with a set of gene pairs for which there is strong evidence that they, or the proteins they encode, do not functionally interact (true negatives). As has been discussed by others,³⁰ the construction of this true-negative reference set is problematic, because it is impossible to be certain that two genes (i.e., their protein products) do not interact. However, by assuming that genes encoding for proteins localized within different cellular compartments are, in general, unrelated, it is possible to make a list of gene pairs that are unlikely to interact. The GO Cellular Component annotations were used to yield groups of gene pairs that have exclusive cellular component annotations. To overcome a strong selection bias in the classifier toward well-annotated genes (details provided in appendix A [online only]), only the 5,105 genes that were part of a true-positive gene pair at least three times were allowed to form true-negative gene pairs. We chose combinations of cellular organelles that were highly underrepresented (χ²=2,490; P<10^-10) within the true-positive set, which resulted in gene pairs for the following combinations: nucleus and extracellular matrix, protein complex and Golgi apparatus, protein complex and Golgi stack, non–membrane-bound organelle and Golgi stack, non–membrane-bound organelle and extracellular space, non–membrane-bound organelle and Golgi apparatus, extracellular region and organelle membrane, mitochondrion and extracellular matrix, extracellular space and organelle membrane, extracellular space and Golgi stack, organelle membrane and extracellular matrix, extracellular matrix and Golgi stack, extracellular matrix and ubiquitin ligase complex, and ubiquitin ligase complex and Golgi stack.

Preprocessing and Binning of Data Sets

To allow for Bayesian integration, the GO data, microarray coexpression data, and orthologous and human protein-protein interactions data were preprocessed and binned. Biological Process and Molecular Function GO annotations were derived from Ensembl, and two measures of relatedness for each of the two data sets were determined, resulting in a total of four different GO measures of relatedness. First, we determined, for each Biological Process GO term, how many of the genes had been assigned this term. Then, we determined which Biological Process GO terms were shared between the two components of each gene pair, for all the pairs. This led to the shared GO term that was annotated in the least number of genes, and its frequency of occurrence was used as a measure. GO terms GO:0000004 (biological process unknown) and GO:0005554 (molecular function unknown) were discarded, since genes that shared either of these highly unspecific terms should not be related to each other on the basis of this information. The same procedure was performed to generate the first measure of Molecular Function GO relatedness.

The second measure determined the maximal hierarchical depth at which a gene pair shared a Biological Process GO term. This hierarchical depth was defined as the shortest number of branches necessary to go from one Biological Process GO term back to the GO root. The same method was used to generate the maximum hierarchical depth of the Molecular Function GO sharing measure.

Coexpression between genes was determined in microarray data sets from GEO and SMD. Individual data sets comprised an experiment that contained at least 10 hybridizations. To ensure that the quality of the intensity measurements was reliable, various filtering steps were performed to exclude spots with low signal-to-noise ratios.³¹ Within the SMD data sets, intensity spots were filtered out that were either missing or contaminated, and the mean intensity of spots had to be at least 2.5 times higher than the average background signal of the microarray. Since GEO contains both ratiometric and Affymetrix single-spot intensity microarray data sets, we used different filtering strategies. The 5% of genes with the lowest maximal intensity were removed from the Affymetrix data sets. For both SMD and GEO, expression ratios were log₂ transformed. Microarray features missing ⩾25% of expression measurements in a data set after filtering were excluded. All features were assigned Ensembl gene identifiers by comparing their sequences to Ensembl transcripts with the use of SSAHA.³²

To determine which gene pairs showed coexpression, the mutual information was calculated between all the genes represented within each data set³³ if there were at least 10 nonmissing data points. As a preprocessing step, expression levels were ranked; this invertible reparameterization did not affect the mutual information. Next, for each pair of genes, the joint distribution of expression levels was estimated by calculating a histogram with overlapping windows. The range was divided into six windows, where each window extends to the center of the next window. The number of windows was chosen by optimizing the error rate for the mutual information derived from analytical probability densities.³³ In this way, each data point contributes to two windows, except at the extremities. Finally, on the basis of the resulting distribution, the mutual information (MI) between each pair of genes was calculated as MI(A,B)=H(A)+H(B)-H(A,B), where H(X) is the information-theoretic Shannon entropy.³⁴ For each microarray data set, the MI score was binned. This allowed the subsequent Bayesian classifier to determine the likelihood ratio, indicating whether gene pairs within each bin contained an overrepresentation of truly interacting gene pairs. Once the likelihood ratios had been determined for each data set, a receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated. Data sets that had a minimal AUC of 0.59 were combined in a naive way—for each gene pair, the likelihood ratios were multiplied by each other, resulting in a final microarray coexpression likelihood ratio for each gene pair.

Two orthologous protein-protein interaction data sets from Lehner and Fraser²⁰ were used to supplement the GO and microarray coexpression data. One data set contained computationally predicted human protein interactions that had been physically mapped within Ensembl genes. The second data set contained a subset of these protein pairs, to which Lehner et al. had assigned a higher confidence. Three bins were constructed: one containing the higher-confidence gene pairs, one containing the remaining lower-confidence pairs, and a third containing all the other unobserved gene pairs.

A human Y2H protein-protein interaction data set from Stelzl et al.¹⁹ was integrated by mapping the HUGO identifiers to Ensembl genes. Two bins were constructed: one containing the gene pairs for which a Y2H interaction was reported, and one containing all the other unobserved gene pairs.

Network Integration

The Bayesian classifier was employed to integrate the various binned types of data. We chose not to learn the Bayesian network structure from the data but to use a predefined Bayesian network structure, for which the conditional probabilities were determined by benchmarking the various data sets against the gold standard (fig. 2) (details provided in appendix A). We subsequently generated four gene networks. One network contained evidence for interaction based on the GO data (GO network). Another network contained evidence for interaction derived from integrating the microarray coexpression and predicted protein-protein interaction data in a naive way (MA+PPI network). A third network combined, in a naive way, the GO and MA+PPI networks (GO+MA+PPI network), and this was complemented with all known true-positive interactions in a final network (GO+MA+PPI+TP network). To relate interacting genes directly or indirectly, an all-pairs shortest path was calculated for each gene network.³⁵ This measure of the minimal path length between pairs of genes was used in the subsequent method to associate disease genes with each other.

Figure 2.

Integration of data sets in four gene networks. a, Data sets were benchmarked against a set of 55,606 known true-positive gene pairs derived from ^BIND, KEGG, HPRD, and Reactome and 800,608 true-negative gene pairs derived from GO. The Venn diagram indicates the data sources from which the true positives were derived and their degree of overlap. Numbers in parentheses indicate the number of interactions that are provided by each of the data sets. b, Potential gene-gene interactions derived from GO, microarray coexpression data, and human and orthologous protein-protein interaction data were integrated using a Bayesian classifier. The steps involved in building this classifier are shown.

Disease Analysis and Positional Candidate-Gene Prioritization

Prioritizer assesses whether genes residing within different susceptibility loci are close together within the gene network. This indicates that this method could also work with diseases for which only two loci have been identified. However, in such a case, there is a considerable probability that two genes, each residing in a different locus, would interact by chance. We therefore restricted the analysis to diseases for which at least three contributing disease genes had been identified. These diseases and disease genes were derived from the Online Mendelian Inheritance in Man (OMIM) database,³⁶ by text mining the first paragraphs of all ^OMIM disease entries as of March 1, 2005, and extracting the ^OMIM gene numbers contained within these paragraphs (table A1 in appendix A). The HUGO gene name was later extracted from these ^OMIM entries and was mapped to an Ensembl gene name. If, for any one disease, there were two disease genes situated at the same chromosome and positionally <200 genes apart, one of the two genes was randomly removed to ensure that no loci would overlap.

The diseases for which at least three disease genes remained after filtering were analyzed by artificially generating susceptibility loci around the disease genes, in a range from 50 to 150 genes, in steps of 50. All 20,334 genes were assigned an initial effect score of zero, and, subsequently, all loci were traversed. Using each gene network for all positional candidate genes residing in a particular locus, we determined whether any of these genes were functionally closely related to genes physically residing inside another susceptibility locus. If this was the case, the effect score of the related gene that was functionally close but physically in another locus increased (fig. 1), by use of the following Gaussian kernel scoring function:

where “distance” is defined as the all-pairs shortest path between the two genes. The kernel function width was chosen arbitrarily, but a sensitivity analysis showed that different widths did not influence the results much (data not shown). By applying this function, positional candidate genes that resided in different loci but that were functionally closely related in the gene network were assigned higher scores than positional candidate genes that were functionally far apart from each other. To correct for differences in topology of the gene network, an empiric P value was determined for each positional candidate gene through permutation of the other loci 500 times by reshuffling them across the genome and recalculating the effect scores. This permitted a probability density function to be determined per positional candidate gene, for which the empiric P value could be looked up. For each locus, the positional candidate genes were prioritized on the basis of this P value.

Results

Construction of a Functional Gene Network

The basis for our human gene network was a gold standard of validated gene-gene interactions (true positives) and a further set of gene-gene pairs that were deemed highly unlikely to interact (true negatives). To construct the set of true-positive gene pairs, 2,788 confirmed, direct, physical protein-protein interactions were derived from ^BIND; 18,176 confirmed human protein interactions were derived from HPRD; 22,012 direct functional interactions were derived from KEGG; and 16,295 interactions were derived from Reactome. This resulted in 55,606 unique true-positive gene relationships (fig. 2a). For the true-negative set, gene pairs were selected that encode for proteins localized in different cellular compartments. The combinations of cellular compartments were selected from their underrepresentation in the set of true positives (see the “Material and Methods” section). This resulted in 801,108 pairs, of which 500 were known to be true-positive gene pairs, and these were therefore removed from the set of true-negative gene pairs.

We trained the classifier on this gold standard and constructed functional human gene networks on the basis of GO data, microarray coexpression data, and inferred protein-protein interactions, as well as combinations of these. First, for each gene pair, we assessed whether the genes shared GO annotations, which were derived for 15,045 genes from Ensembl. Sharing of GO terms was based on the frequency of the least-common GO term shared between two genes and the maximal depth in the GO hierarchy at which two shared terms lay. Gene coexpression was calculated in 186 microarray data sets derived from GEO and 75 data sets from SMD. However, most of these data sets were not highly informative, as judged by their ability to identify true-positive gene interactions with a low false-positive rate. Because it is known that many classifiers perform best when a subset of features are used,^37,38 we used only four informative microarray coexpression data sets for classification,^39–42 each showing a minimal AUC of 0.59. In total, these data sets contained 461 microarray hybridizations. Finally, protein-protein interactions were derived from the Lehner and Fraser²⁰ data set containing human protein interactions predicted by mapping physical protein interactions from various Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans interaction data sets to orthologous human gene pairs. Of the 71,806 predicted gene pairs, we were able to physically map 62,635 gene pairs with both genes in the pair mapping to known Ensembl genes. A subset was defined by Lehner and Fraser²⁰ that contained 10,652 gene pairs deemed to be of higher confidence, of which 10,139 gene pairs could be mapped. In addition, we used 3,186 human protein-protein interactions identified by automated Y2H interaction mating by Stelzl et al.,¹⁹ of which 1,751 could be mapped to different Ensembl gene pairs.

We assessed the performance of our classifier on the basis of these various data sources in three different gene networks generated on the basis of a Bayesian framework, after preprocessing and binning of the data sets. As mentioned above, one network was generated solely on the basis of GO data (GO network), one network was based on both microarray coexpression and predicted protein-protein interaction data (MA+PPI network), and an overall network contained all three types of data (GO+MA+PPI network). ROC curves (fig. 3) show the performance of the reconstructed GO, MA+PPI, and GO+MA+PPI gene networks, which were constructed by cross-validating all data sets 10 times against the gold standard set, to mitigate overfitting (details provided in appendix A). When we compared the performance of the various gene networks, it became evident that the GO data set provided the most accurate evidence for interaction. The AUC was 88%, compared with 50% for an uninformative classifier. The ROC for the MA+PPI network shows that coexpression data derived from microarray expression, in conjunction with the orthologous protein-protein interaction data, correctly inferred functional interactions (AUC=68%), but to a lesser extent than the GO network. Nevertheless, as can be deduced from the GO+MA+PPI network, addition of the microarray coexpression and the orthologous protein-protein interaction data to the GO network improved slightly the accuracy of the network (AUC=90%). In accordance with most networks described in the literature thus far,⁴³ our reconstructed networks have a connectivity that follows a scale-free power-law distribution, which has also been demonstrated for other organisms.^44–46 This is most apparent when the topology of the MA+PPI network is assessed (see appendix A).

Figure 3.

ROC curve of the GO network, the MA+PPI network, and the combined GO+MA+PPI network. The baseline (solid gray line) indicates the performance of a classifier that would be totally uninformative.

To validate our network, we used a list of 2,574 Y2H interactions that recently became available⁴⁷ to assess whether our gene network had predicted an interaction for these gene pairs. We first mapped the set to Ensembl pairs and then removed all pairs that were in our gold standard true-positive set, to ensure that we only assessed newly identified interactions. This resulted in a set of 1,318 novel gene pairs.

We then assessed whether our gene network had predicted an interaction for these pairs. While Y2H interactions are known to regularly yield false-positive results,⁴⁸ we decided to test whether the distribution of likelihood ratios for these gene pairs was significantly different from a null distribution of 10,000 gene pairs sampled by generation of random gene pairs by selecting two genes at a time from the set of all individual genes that made up the Y2H gene pairs. The results show that the 1,318 Y2H gene pairs have a significantly higher likelihood ratio than the null distribution (P=.0003, by Wilcoxon Mann-Whitney test), which indicates that our gene network is capable of inferring as-yet-unknown interactions.

To allow researchers to look up known and predicted interactions and to identify the shortest routes between genes and susceptibility loci, we developed a Web tool, which is publicly available at the GeneNetwork Web site. The known and predicted interactions can be shown for each gene of interest, along with information about the source of evidence from which they were derived and how strong this evidence was. In addition, there are interactive graphs to visually explore how multiple genes interact with each other. All the data files (including the sets of true-positive and true-negative gene pairs) can be downloaded, along with a Java application programming interface, which can facilitate the development of new methods that use this gene network. Every 2 mo, we will update the gene network, on the basis of the most recent releases of the various repositories used in its construction.

Increased Functional Interactions Shown by Genes Associated with a Particular Disease

We first examined our hypothesis that genes associated with genetic disorders frequently share functional links, by assessing whether, for a disease, these causative genes were functionally more closely related to each other than a set of genes of equal size that were randomly selected from the full set of 345 unique disease genes of the 409 disease genes that were extracted from ^OMIM entries on disorders for which at least three causative genes were known. This set of disease genes was used as a background distribution to prevent bias, since the disease genes are generally better characterized than the complete set of genes in the network. We generated one extra network (GO+MA+PPI+TP network) that complemented the GO+MA+PPI network with all known true-positive gene pairs, and we calculated the shortest direct or indirect distance between all pairs of genes. In 76 (79%) of the 96 diseases, the total distance between all combinations of disease genes in one disease was, on average, lower than the total distance between all combinations of randomly selected disease genes in 10,000 permutations. This confirms our hypothesis that, in the majority of diseases, the causative genes are indeed closely related functionally.

Genes implicated in disease processes tend to be studied more than those not implicated, which could result in a bias in the gene network based on GO annotations, since these represent known functional annotations. To assess the degree to which this possible bias affected our gene network, we looked at network connectivity. The average number of direct interactions involving disease genes was 199, compared with an average of 203 for the other 11,875 genes that interacted with at least one other gene. This indicates that other genes are equally represented in the gene network, despite the fact that disease genes may have been studied more.

Increased Power to Detect Disease Genes Provided by a Functional Gene Network

Usually, researchers pick a limited number of candidate genes in susceptibility loci to follow-up, because it is too costly and labor intensive to analyze all the genes residing in these loci. As a result, these studies have a limited chance of finding disease-related variants, largely depending on the size of the loci and the number of genes selected. Using a test set of known disorders in a similar setup, we evaluated the ability of our reconstructed network to correctly prioritize positional candidate genes in a set of top-ranked candidate genes of typical size (5–10 genes). The test set consisted of 96 different disorders, for which a total of 409 disease genes (345 unique genes) had been identified. These were obtained from ^OMIM, with 3–10 disease genes per disease (average 4.3 genes per disease) (table A1 in appendix A). Of the diseases, 59 are of Mendelian origin, 17 have complex inheritance, and 20 are various types of cancer (table 1).

Table 1.

Overview of the 96 Diseases Studied with Prioritizer and the Number of Disease Genes per Disorder That Ranked in the Top 10 Genes per Susceptibility Locus, With Locus Widths of 100 and 150 Genes

			No. of Genes Ranking in Top 10at Locus Width of 100 Genes				No. of Genes Ranking in Top 10at Locus Width of 150 Genes
Disease Type and Disease	OMIM Number	No. of Genes	MA+PPI	GO	GO+MA+PPI	GO+MA+PPI+TP	MA+PPI	GO	GO+MA+PPI	GO+MA+PPI+TP
Mendelian inheritance (59 diseases):
Achromatopsia 2	216900	3	0	0	1	0	0	0	0	0
Achromatopsia 3	262300	3	0	0	1	0	0	0	0	0
Adrenoleukodystrophy, autosomal neonatal form	202370	5	0	4	2	4	0	4	2	3
Amyloidosis VI	105150	3	1	1	0	1	0	0	0	1
Amyloidosis, familial visceral	105200	3	0	0	0	0	0	0	0	0
Amyotrophic lateral sclerosis 1	105400	5	0	1	0	0	0	1	0	0
Atypical mycobacteriosis, familial	209950	5	1	4	2	4	1	1	0	1
Autonomic control, congenital failure of	209880	5	0	0	1	1	0	0	1	1
Bardet-Biedl syndrome	209900	8	0	2	2	3	0	1	1	1
Bare lymphocyte syndrome, type II	209920	4	1	0	1	4	0	0	1	4
Cardiomyopathy, familial hypertrophic	192600	9	0	7	4	4	0	7	3	3
Cholestasis, intrahepatic, of pregnancy	147480	3	1	0	0	0	0	0	0	0
Cholestasis, progressive familial intrahepatic 1	211600	4	1	1	1	0	0	1	1	1
Complex I, mitochondrial respiratory chain, deficiency of	252010	5	0	5	4	5	0	3	3	3
Coumarin resistance	122700	4	0	0	0	1	0	0	0	0
Dementia, Lewy body	127750	3	1	0	0	0	0	0	0	0
Epidermolysis bullosa junctionalis, disentis type	226650	4	1	0	1	0	0	0	0	0
Epidermolysis bullosa of hands and feet	131800	4	0	1	2	1	0	0	1	1
Fanconi anemia	227650	6	1	0	1	6	1	0	0	6
Fundus albipunctatus	136880	4	0	1	0	3	0	1	1	2
Generalized epilepsy with febrile seizures plus	604233	3	2	2	2	1	1	0	1	0
Glutaricaciduria IIA	231680	3	3	2	3	3	3	1	3	3
Hermansky-Pudlak syndrome	203300	6	0	1	0	0	0	0	0	0
Hirschsprung disease	142623	6	1	0	0	1	1	0	0	1
Hydrops fetalis, idiopathic	236750	4	0	0	1	0	0	0	1	0
Hypercholesterolemia, familial	143890	6	0	2	3	1	0	1	2	1
Hypertrophic neuropathy of Dejerine-Sottas	145900	4	0	2	2	2	1	1	1	1
Hypokalemic periodic paralysis	170400	3	0	0	0	2	0	0	0	0
Ichthyosiform erythroderma, congenital, nonbullous, 1	242100	3	0	2	2	1	0	1	1	1
Immunodeficiency with hyper-IgM, type 2	605258	3	0	1	0	2	0	1	0	1
Immunodeficiency with hyper-IgM, type 3	606843	3	0	2	0	2	0	1	0	1
Kartagener syndrome	244400	3	1	2	2	1	0	2	2	1
Keratosis palmoplantaris striata I	148700	3	0	1	1	3	0	1	1	3
Laron syndrome, type II	245590	3	0	1	0	2	0	0	0	1
Leber congenital amaurosis, type I	204000	7	0	3	2	1	0	0	1	2
Leigh syndrome	256000	6	3	4	3	3	1	4	3	3
Leukoencephalopathy with vanishing white matter	603896	5	5	5	5	5	5	5	5	4
Maple syrup urine disease, type IA	248600	4	1	1	1	4	0	0	0	3
Maturity-onset diabetes of the young	606391	5	1	0	1	3	1	0	1	0
Myasthenic syndrome, congenital, fast-channel	608930	3	0	2	2	3	0	2	2	3
Myasthenic syndrome, slow-channel congenital	601462	3	0	2	2	1	0	2	2	2
Myoclonic dystonia	159900	3	0	0	0	1	0	0	0	1
Nemaline myopathy 1, autosomal dominant	161800	3	0	1	1	0	0	1	1	0
Nesidioblastosis of pancreas	256450	3	0	1	0	0	0	1	1	0
Night blindness, congenital stationary	163500	3	0	0	1	0	0	0	0	0
Obsessive-compulsive disorder 1	164230	3	0	1	0	0	0	0	0	0
Ossification of the posterior longitudinal ligament of spine	602475	3	0	0	0	1	0	0	0	0
Osteopetrosis, autosomal recessive	259700	3	1	0	0	0	0	0	0	0
Peters anomaly	604229	4	0	0	1	2	0	0	0	0
Pituitary dwarfism III	262600	3	0	0	0	2	0	0	0	1
Progressive external ophthalmoplegia	157640	3	1	1	0	0	0	0	0	0
Pseudohypoaldosteronism, type I, autosomal recessive	264350	3	0	2	2	1	0	2	2	0
Pulmonary alveolar proteinosis	265120	3	0	2	1	0	0	2	1	0
Refsum disease, infantile form	266510	3	1	1	1	2	0	1	1	2
Reticulosis, familial histiocytic	267700	3	0	0	0	0	0	0	0	0
Rhizomelic chondrodysplasia punctata, type 3	600121	3	0	1	0	2	0	1	0	1
Stickler syndrome, type I	108300	3	0	0	0	2	0	0	0	0
Waardenburg-Shah syndrome	277580	3	0	1	1	1	0	2	1	0
Zellweger syndrome	214100	8	1	4	4	7	1	3	4	5
Complex inheritance (17 diseases):
Alzheimer disease	104300	8	0	1	0	3	0	1	1	2
Diabetes mellitus, non–insulin-dependent	125853	9	2	0	3	1	1	0	2	2
Elliptocytosis, Rhesus-unlinked type	130600	3	0	1	0	3	0	1	0	2
Graves disease	275000	3	0	1	0	1	0	0	0	0
Hypertension, essential	145500	7	1	1	0	0	0	0	0	0
Hypospadias	146450	3	0	0	0	1	0	0	0	1
IgA nephropathy	161950	4	1	0	0	1	1	0	0	1
Inflammatory bowel disease 1	266600	4	0	0	1	1	0	0	0	1
Longevity	152430	4	0	1	0	0	1	0	0	0
Lupus erythematosus, systemic	152700	4	0	0	0	0	0	0	0	0
Mycobacterium tuberculosis, susceptibility to infection by	607948	3	0	0	0	0	0	0	0	0
Myoclonic epilepsy, juvenile	606904	4	0	1	0	1	0	1	0	0
Obesity	601665	7	1	1	1	4	2	0	1	3
Osteoporosis, involutional	166710	5	0	1	1	0	0	3	1	2
Parkinson disease	168600	4	0	0	0	4	0	1	0	3
Rheumatoid arthritis	180300	5	0	0	0	0	0	0	0	1
Sudden infant death syndrome	272120	3	0	2	2	0	0	1	1	0
Heritable cancer (20 diseases):
Bladder cancer	109800	3	0	0	0	0	0	0	1	0
Breast cancer	114480	10	2	1	4	2	1	0	2	1
Chondrosarcoma	215300	4	1	1	0	2	0	0	0	1
Esophageal cancer	133239	8	1	0	1	5	1	0	0	2
Glioma of brain, familial	137800	6	1	1	1	0	2	1	0	0
Hepatocellular carcinoma	114550	3	0	0	0	1	1	0	0	0
Juvenile myelomonocytic leukemia	607785	4	0	3	2	1	0	1	1	1
Leiomyoma, uterine	150699	4	0	0	1	0	0	0	0	0
Lung cancer	211980	4	1	0	1	2	0	0	1	0
Lymphoma, non-Hodgkin, familial	605027	4	0	2	2	2	0	1	1	2
Medulloblastoma	155255	4	1	0	2	2	1	0	1	0
Myeloma, multiple	254500	4	1	1	0	0	1	0	0	1
Osteogenic sarcoma	259500	3	0	0	0	0	0	0	0	1
Pancreatic carcinoma	260350	6	1	1	1	0	1	0	1	0
Pheochromocytoma	171300	3	0	0	0	0	0	0	0	0
Prostate cancer	176807	9	1	0	1	0	0	0	0	0
Renal cell carcinoma, papillary	605074	3	1	0	0	1	1	0	0	1
Rhabdomyosarcoma 2	268220	3	0	0	0	0	0	0	0	0
Thyroid carcinoma, papillary	188550	5	2	0	0	0	1	0	0	0
Turcot syndrome	276300	3	2	2	2	1	2	2	3	2
Total		409	49 (12%)	99 (24%)	93 (23%)	138 (34%)	33 (8%)	67 (16%)	68 (17%)	98 (24%)

The ability of the functional human gene network to correctly prioritize known disease genes was assessed by creating artificial, nonoverlapping susceptibility loci around these disease genes. Since many genes in these loci have no known or predicted interactions in our network, we only assessed those genes for which interactions were predicted, to prevent a bias toward genes that were better represented in the underlying high-throughput data sets. This resulted in susceptibility loci of varying widths, containing 50, 100, or 150 genes, which were predicted to interact with at least one other gene. If, for any particular disease, two disease genes residing in the same chromosome yielded loci that were partly overlapping, one of the two loci was randomly removed.

For each locus, the genes were traversed, and, for each gene, we assessed whether there was another gene residing in a different locus that was nearby within the gene network. The effect scores (see the “Material and Methods” section) of each gene were affected by the gene in the other locus that had the shortest path to that gene. This procedure has the potential to preferentially identify genes with many interacting partners over genes that are less well connected, because a highly connected gene has a higher chance of interacting with a gene residing in another locus than a gene for which only a few interactions have been predicted. To overcome bias in the method toward genes that are highly connected, we corrected for differences in the network topology by permuting the susceptibility loci for each disease 500 times across the genome.

After all positional candidate genes were ranked on the basis of this permuted score, the results (see fig. 4 and table 1) indicated that this method was able to identify many of the disease genes in the top 5 or top 10 genes per locus. As expected from the ROC curves of the various gene networks (fig. 3), the performance of the MA+PPI network proved to be the least powerful. Nevertheless, the number of correctly ranked genes was higher than would be expected to occur by chance (fig. 4a and 4b; indicated by baseline) for many of the susceptibility loci widths. When assessing susceptibility loci that contained, on average, 100 genes, we found 8% and 12% of the disease genes were contained within the top 5 and top 10 per locus, respectively, compared with the 5% and 10% we would expect to find by chance. A lack of predictive performance of the MA+PPI network explains why the ranking did not improve considerably when this network was used, as is evident from inspection of the ROC curves (fig. 4c), which show the proportion of disease genes and nondisease genes that are returned when different sizes of sets of top-ranked genes per locus are assessed. For 86 of the 345 unique disease genes within the MA+PPI network, no interactions were predicted. Hence, they were ranked low, the more so because the 49, 99, or 149 other genes, residing together with each disease gene in the constructed susceptibility loci, had been selected on the premise that they interacted with at least one other gene. The GO network performed considerably better; when we used it to assess susceptibility loci that contained, on average, 100 genes, we found 16% and 24% of the disease genes were contained within the top 5 and top 10 genes per locus, respectively. The performance of the disease analysis was best when the inferred GO+MA+PPI network was complemented with the known true-positive interactions (GO+MA+PPI+TP network); with this network and an average susceptibility locus width of 100 genes, 27% and 34% of the disease genes were contained within the top 5 and top 10 per locus, respectively.

Figure 4.

Accuracy of positional candidate-gene prioritization. a and b, Percentage of the 409 disease genes that was ranked among the top 5 (a) or top 10 (b) genes per locus, after artificial susceptibility loci of varying widths around these genes were constructed and when different types of gene networks were used. The baselines (gray lines) indicate the percentage of disease genes expected to rank among the top 5 or top 10 genes by chance. c, ROC curves for susceptibility loci that contain 50, 100, or 150 genes.

We also assessed the probability of detecting at least one disease gene when only a fixed number of top-ranked genes per locus is followed up (fig. 5). When we employed the most comprehensive GO+MA+PPI+TP network and followed up all the top 5 or top 10 positional candidate genes for each disorder, using locus widths of 100 and 150 genes, we found at least one disease gene from these top sets of genes in 54% and 64% of the diseases, respectively, compared with 19% and 35% expected by chance. When we confined our analysis to diseases for which at least four or five disease genes were known, the performance of our method increased slightly (data not shown), because the true disease genes now interacted with more of the other true disease genes, increasing their overall scores.

Figure 5.

Probability of detecting at least one disease gene when a fixed number of top-ranked positional candidate genes—as ranked by Prioritizer—are followed up for each locus. Each locus contains either 100 or 150 genes, and the GO+MA+PPI+TP network was employed. The baselines (dashed lines) show the probability of detecting at least one disease gene if a fixed number of arbitrarily chosen genes in each locus are followed up.

Breast Cancer as an Example

We selected breast cancer as an example of how the various gene networks perform in a complex disease for which multiple disease genes have been identified. Artificial susceptibility loci, each comprising 100 genes, were constructed around 10 putative breast cancer genes described in ^OMIM (as of March 1, 2005). For each of the four networks, we then determined how many of the disease genes were ranked within the top 10 per locus. The MA+PPI network ranked two disease genes (PIK3CA and CHEK2) in the top 10, whereas the GO network ranked three (BRCA2, NCOA3, and CHEK2), and the GO+MA+PPI network ranked four (BARD1, PIK3CA, TP53, and CHEK) (fig. 6). However, the GO+MA+PPI+TP network, which integrates the most information, performed the worst; of the 10 disease genes now known, only 2 (BARD1 and BRCA1) were ranked in the top 10. This can be explained by the observation that the true-positive set contained many known interactions for these 10 breast cancer genes. As the ranking procedure corrects for the topology of the network, these disease genes, with a marked increase in the number of relationships with other genes in this most comprehensive network, were suddenly no longer ranked as high. This became evident when the genes were ranked using the GO+MA+PPI+TP network but the differences in topology were not corrected for: 9 of the 10 breast cancer genes were then in the top 10 per locus.

Figure 6.

Prioritizer analysis of breast cancer. Susceptibility loci, each containing 100 genes, were defined around 10 known breast cancer genes. The 10 highest-ranked genes for each locus are shown in the graph, with colors indicating the locus in which they reside. Use of the GO+MA+PPI network led to four breast cancer genes (PIK3CA, CHEK2, BARD1, and TP53 [circles]) being ranked in the top 10. Chr. = chromosome.

Prioritizer Availability

To allow researchers to analyze susceptibility loci of interest, we developed a Java application that can be downloaded, along with regularly updated gene network definition files and source code from the Prioritizer Web site. After a set of susceptibility loci has been entered, Prioritizer ranks the positional candidate genes in each locus by using the method described above in conjunction with one of the four gene networks. It can generate two- and three-dimensional graphs of the top-ranked positional candidate genes, which allows the user to visually inspect how the genes within the different loci interact with each other.

Discussion

In this study, we describe the construction of a functional human gene network of considerable accuracy (fig. 3; AUC=90%). As such, it can be used to assess interactions for a gene of interest through the bioinformatics tools that we have made available online. We have shown that, in cases where multiple genes underlie a disorder, these genes tend to have more functional interactions. When these functional interactions are employed to prioritize known disease genes in artificial susceptibility loci, the chance of detecting disease genes is increased considerably (2.8 fold).

In breast cancer, 4 of the 10 disease genes were ranked in the top 10 when the GO+MA+PPI network was applied, a fourfold enrichment over the single disease gene that would be picked up by chance. As has been discussed earlier, the correction for differences in topology is needed to prevent bias toward highly connected genes. However, this puts diseases in which underlying genes have a high degree of connectivity at a disadvantage, which was apparent in the analysis of breast cancer by use of the GO+MA+PPI+TP network. When this topology correction was omitted for breast cancer, the ranking of the disease genes improved considerably, to include 9 of the 10 genes. The availability of new high-throughput data sets will alleviate this problem in the future, by providing novel interactions for genes that currently have a low degree of connectivity, which will reduce the penalty on highly connected genes.

We noticed that the performance of Prioritizer was lower for complex disorders than for Mendelian disorders. This is likely caused by the fact that the etiology of complex diseases is more subtle and involves multiple pathways, so that most of the disease genes only confer a modest increased risk. Greater coverage of the gene network, leading to identification of relationships between genes that bridge the various pathways, could probably help to alleviate this problem.

When the accuracy of the various gene networks was assessed by investigation of their respective ROCs, it was envisaged that the GO+MA+PPI network would perform at least at a similar level in prioritizing disease genes as the GO network, because its AUC was greater. However, contrary to our expectation, when the positional candidate genes were prioritized, the disease genes in some diseases were ranked lower with the GO+MA+PPI network than with the GO network. One explanation could be that, within the microarray coexpression data sets (the main contributor to the MA+PPI network), we did not distinguish between coexpression and coregulation. As such, many direct interactions between genes were inferred, but a large proportion of these interactions were actually indirect. Methods have recently appeared^33,49 that could help remove some of these incorrectly inferred interactions.

In a somewhat comparable method by Turner et al.,²¹ positional candidate genes are prioritized by determining which genes share InterPro⁵⁰ domains and GO terms, as a measure to relate genes in susceptibility loci with each other. Our method extends this approach by also allowing for indirect relationships between individual disease genes, since Prioritizer uses the graph-theoretic distance between genes to relate them. Both approaches still rely largely on manual annotation, which is detrimental for genes that have not been investigated extensively. When no experimental evidence for interaction is available, there is only a small chance that these potential disease genes, residing in one specific susceptibility locus, will be associated with disease genes in other loci, since the sharing of GO or InterPro terms between these genes will be minimal. Although GO contributes the most to the performance of the Bayesian classifier, we should not depend entirely on a prediction if there is substantial evidence only from GO, while the evidence from the other data sets is lacking, for a specific gene pair, because the GO evidence has been inferred from the sharing of predominantly manually annotated terms, whereas the other sources rely more on direct biological measurements. It is expected that, when additional high-throughput data sets become available and their coverage of all possible functional interactions increases, GO evidence will be supplemented by experimental data, resulting in better predictions.

As such, an extensive and reliable functional gene network is crucial for good performance of our method. If this network is inaccurate or biased toward known genes, the ranking of true disease genes in the susceptibility loci will deteriorate. Several rapidly expanding data repositories are now becoming available that should help to improve our network. They include text mining methods,^51,52 which extract functional relationships from the literature, and methods that integrate results from high-throughput proteomic approaches.⁵³

Our gene network, which, in its current form, has been applied to genetic linkage analysis, can also be used for other applications. Recently, efforts have been made to prioritize positional candidate genes on the basis of their expression,⁵⁴ with the assumption that differences in expression behavior in comparisons of patients with controls may be due to cis-acting variants in the underlying genes. However, it has turned out that, in most genes, differences in expression are determined by genetic variation in genes located elsewhere.^55,56 The reconstructed functional gene network can help to relate the observed differences in gene expression to the underlying causative genetic variants in other genes, which might help in identifying the disease genes.

Prioritizer might also be well suited for genomewide SNP association studies. Technical improvements in conjunction with decreasing costs now allow researchers to perform these studies in complex diseases, thereby considerably increasing the resolution at which one can assess genetic variation. However, as the number of tested SNPs increases, the number of tested individuals required to achieve sufficient power will also rise. To help overcome this problem, a new statistical method has recently been developed⁵⁷ that combines evidence from the most-significant tests, under the assumption that there are multiple true associations in the disease under investigation. However, within this confined set, the majority of genes will still be false positives because of power issues. Our positional candidate-gene prioritization method can easily be adapted to help distinguish true disease-associated genes and false-positive genes, by assuming that the true disease genes are mostly functionally related and will therefore be closer to each other in the gene network than to the false-positive genes that have been randomly selected.

We have demonstrated that it is feasible to use gene networks to prioritize positional candidate genes in various heritable disorders with multiple associated genes, even when the susceptibility loci are fairly large. As such, this article and the proposed methods show that the integration of gene networks with various genetic studies can be useful in identifying disease genes. We envisage that improvements both in the quality of the data sets making up these gene networks and in the statistical methods incorporating the networks will result in new, genetically testable hypotheses.

Appendix A

Table A1.

All Disease Genes for the 96 Diseases Tested and How They Rank When a Locus Width of 50, 100, or 150 Genes Is Used

				MA+PPI		GO		GO+MA+PPI		GO+MA+PPI+TP
Locus Width and Disease Gene (Ensembl)	HUGO	Disease Name; Abbreviation	OMIMNumber	Rank	P	Rank	P	Rank	P	Rank	P
50 Genes:
ENSG00000091513	TF	Alzheimer disease; AD	104300	42	.24	27	.45	26	.46	2	.07
ENSG00000175899	A2M	Alzheimer disease; AD	104300	47	.89	21	.40	34	.59	1	.02
ENSG00000143801	PSEN2	Alzheimer disease; AD	104300	9	.02	3	.05	20	.53	27	.58
ENSG00000123384	LRP1	Alzheimer disease; AD	104300	50	1.00	16	.23	11	.17	5	.04
ENSG00000142192	APP	Alzheimer disease; AD	104300	25	.09	33	.48	25	.49	6	.15
ENSG00000130203	APOE	Alzheimer disease; AD	104300	26	.07	25	.71	44	.84	1	.00
ENSG00000010704	HFE	Alzheimer disease; AD	104300	40	.19	13	.57	31	.57	1	.00
ENSG00000080815	PSEN1	Alzheimer disease; AD	104300	4	.02	1	.06	26	.55	44	.78
ENSG00000101439	CST3	Amyloidosis VI	105150	3	.10	40	.81	31	.72	34	.82
ENSG00000165029	ABCA1	Amyloidosis VI	105150	50	1.00	47	.94	36	.81	24	.62
ENSG00000136156	ITM2B	Amyloidosis VI	105150	22	.74	47	.83	11	.40	45	.99
ENSG00000171560	FGA	Amyloidosis, familial visceral	105200	50	1.00	43	.79	28	.57	25	.54
ENSG00000090382	LYZ	Amyloidosis, familial visceral	105200	23	.43	35	.52	46	.95	23	.56
ENSG00000118137	APOA1	Amyloidosis, familial visceral	105200	5	.06	24	.32	46	.95	13	.45
ENSG00000124164	VAPB	Amyotrophic lateral sclerosis 1; ALS1	105400	12	.09	21	.35	34	.42	2	.03
ENSG00000003393	ALS2	Amyotrophic lateral sclerosis 1; ALS1	105400	50	1.00	24	.32	46	.62	39	.58
ENSG00000142168	SOD1	Amyotrophic lateral sclerosis 1; ALS1	105400	21	.10	1	.00	27	.23	36	.51
ENSG00000100285	NEFH	Amyotrophic lateral sclerosis 1; ALS1	105400	18	.11	7	.08	29	.21	28	.32
ENSG00000135406	PRPH	Amyotrophic lateral sclerosis 1; ALS1	105400	20	.08	50	1.00	9	.04	29	.21
ENSG00000112112	COL11A2	Stickler syndrome, type I; STL1	108300	14	.58	33	.78	46	.77	2	.04
ENSG00000139219	COL2A1	Stickler syndrome, type I; STL1	108300	50	1.00	22	.50	46	.77	12	.32
ENSG00000060718	COL11A1	Stickler syndrome, type I; STL1	108300	50	1.00	40	.78	24	.62	2	.04
ENSG00000139687	RB1	Bladder cancer	109800	37	.66	37	.58	11	.25	28	.46
ENSG00000068078	FGFR3	Bladder cancer	109800	32	.71	12	.23	30	.85	16	.35
ENSG00000174775	HRAS	Bladder cancer	109800	9	.25	3	.06	30	.48	38	.88
ENSG00000139618	BRCA2	Breast cancer	114480	27	.06	5	.01	9	.05	2	.00
ENSG00000138376	BARD1	Breast cancer	114480	12	.03	37	.32	2	.01	6	.01
ENSG00000124151	NCOA3	Breast cancer	114480	31	.17	5	.01	5	.01	8	.02
ENSG00000121879	PIK3CA	Breast cancer	114480	10	.02	32	.35	2	.01	25	.16
ENSG00000012048	BRCA1	Breast cancer	114480	11	.01	13	.08	6	.02	1	.01
ENSG00000170836	PPM1D	Breast cancer	114480	23	.07	22	.14	16	.06	9	.03
ENSG00000141510	TP53	Breast cancer	114480	11	.02	24	.07	4	.01	19	.13
ENSG00000023287	RB1CC1	Breast cancer	114480	20	.03	43	.60	48	.72	26	.17
ENSG00000183765	CHEK2	Breast cancer	114480	3	.01	8	.01	6	.02	17	.11
ENSG00000169083	AR	Breast cancer	114480	50	1.00	23	.15	22	.14	16	.14
ENSG00000105976	MET	Hepatocellular carcinoma	114550	33	.51	15	.22	7	.14	10	.16
ENSG00000168036	CTNNB1	Hepatocellular carcinoma	114550	16	.23	44	.89	11	.24	23	.48
ENSG00000141510	TP53	Hepatocellular carcinoma	114550	21	.51	15	.36	50	.93	49	.99
ENSG00000138109	CYP2C9	Coumarin resistance	122700	10	.30	41	.84	36	.71	10	.16
ENSG00000167397	VKOR1_HUMAN	Coumarin resistance	122700	16	.18	21	.48	39	.47	2	.02
ENSG00000198470	CYP2A6	Coumarin resistance	122700	32	.76	6	.09	40	.90	20	.33
ENSG00000101981	F9	Coumarin resistance	122700	50	1.00	19	.29	16	.50	6	.15
ENSG00000162992	NEUROD1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	8	.02	4	.01	49	.92	44	.53
ENSG00000121653	MAPK8IP1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	48	.41	20	.29	36	.52	3	.02
ENSG00000163581	SLC2A2	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	28	.14	10	.08	4	.02	38	.30
ENSG00000181856	SLC2A4	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	21	.08	15	.09	5	.03	1	.00
ENSG00000105221	AKT2	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	24	.09	31	.50	45	.58	23	.15
ENSG00000101076	HNF4A	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	4	.00	42	.52	16	.15	5	.04
ENSG00000142330	CAPN10	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	50	1.00	40	.50	27	.36	3	.00
ENSG00000135100	TCF1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	50	1.00	42	.43	38	.59	7	.04
ENSG00000104918	RETN	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	35	.18	24	.27	50	1.00	20	.14
ENSG00000171867	PRNP	Dementia, Lewy body; DLB	127750	23	.34	50	1.00	27	.64	22	.65
ENSG00000145335	SNCA	Dementia, Lewy body; DLB	127750	10	.10	47	.94	12	.40	31	.60
ENSG00000130203	APOE	Dementia, Lewy body; DLB	127750	40	.69	47	.95	22	.61	31	.74
ENSG00000070182	SPTB	Elliptocytosis, Rhesus-unlinked type	130600	50	1.00	2	.05	35	.99	1	.00
ENSG00000163554	SPTA1	Elliptocytosis, Rhesus-unlinked type	130600	23	.22	3	.07	4	.14	1	.00
ENSG00000004939	SLC4A1	Elliptocytosis, Rhesus-unlinked type	130600	12	.14	7	.16	22	.86	2	.03
ENSG00000186847	KRT14	Epidermolysis bullosa of hands and feet	131800	50	1.00	12	.03	1	.01	9	.01
ENSG00000186081	KRT5	Epidermolysis bullosa of hands and feet	131800	50	1.00	10	.03	9	.02	3	.00
ENSG00000132470	ITGB4	Epidermolysis bullosa of hands and feet	131800	50	1.00	19	.33	17	.22	43	.61
ENSG00000114270	COL7A1	Epidermolysis bullosa of hands and feet	131800	13	.13	20	.36	24	.30	13	.24
ENSG00000127870	RNF6	Esophageal cancer	133239	38	.65	29	.24	20	.21	24	.27
ENSG00000134982	APC	Esophageal cancer	133239	1	.00	33	.38	47	.55	29	.25
ENSG00000147889	CDKN2A	Esophageal cancer	133239	50	1.00	22	.22	14	.14	22	.46
ENSG00000008226	DLEC1	Esophageal cancer	133239	50	1.00	16	.21	6	.13	10	.15
ENSG00000061337	LZTS1	Esophageal cancer	133239	50	1.00	45	.73	20	.17	16	.12
ENSG00000141510	TP53	Esophageal cancer	133239	21	.37	42	.70	17	.30	2	.03
ENSG00000187323	DCC	Esophageal cancer	133239	50	1.00	12	.11	4	.08	5	.03
ENSG00000186153	NP_570606.1	Esophageal cancer	133239	22	.31	27	.36	30	.41	2	.01
ENSG00000140522	RLBP1	Fundus albipunctatus	136880	37	.38	15	.35	43	.79	1	.01
ENSG00000112619	RDS	Fundus albipunctatus	136880	50	1.00	23	.48	25	.50	12	.31
ENSG00000135437	RDH5	Fundus albipunctatus	136880	50	1.00	23	.39	31	.77	1	.02
ENSG00000130203	APOE	Fundus albipunctatus	136880	29	.58	49	.96	45	.83	23	.49
ENSG00000063169	GLTSCR1	Glioma of brain, familial	137800	41	.39	50	1.00	50	1.00	50	1.00
ENSG00000111087	GLI1_HUMAN	Glioma of brain, familial	137800	43	.57	9	.04	6	.03	22	.22
ENSG00000108231	LGI1	Glioma of brain, familial	137800	22	.17	50	1.00	50	1.00	50	1.00
ENSG00000147889	CDKN2A	Glioma of brain, familial	137800	50	1.00	36	.73	8	.08	20	.29
ENSG00000146648	EGFR	Glioma of brain, familial	137800	5	.04	15	.19	32	.41	43	.77
ENSG00000132170	PPARG	Glioma of brain, familial	137800	13	.11	32	.41	10	.07	29	.53
ENSG00000117298	ECE1	Hirschsprung disease	142623	28	.30	49	.71	26	.32	21	.30
ENSG00000136160	EDNRB	Hirschsprung disease	142623	50	1.00	41	.52	47	.79	5	.05
ENSG00000169554	ZFHX1B	Hirschsprung disease	142623	3	.01	10	.12	7	.05	5	.05
ENSG00000165731	RET	Hirschsprung disease	142623	9	.06	23	.27	24	.26	30	.30
ENSG00000124205	EDN3	Hirschsprung disease	142623	50	1.00	10	.12	21	.21	3	.05
ENSG00000168621	GDNF	Hirschsprung disease	142623	50	1.00	46	.79	15	.13	12	.16
ENSG00000130164	LDLR	Hypercholesterolemia, familial	143890	10	.29	2	.02	4	.03	1	.00
ENSG00000055955	ITIH4	Hypercholesterolemia, familial	143890	40	.71	45	.71	36	.44	49	.97
ENSG00000158874	APOA2	Hypercholesterolemia, familial	143890	50	1.00	23	.34	17	.23	3	.04
ENSG00000169174	PCSK9	Hypercholesterolemia, familial	143890	3	.04	27	.30	36	.56	12	.07
ENSG00000120915	EPHX2	Hypercholesterolemia, familial	143890	27	.27	41	.53	33	.40	10	.10
ENSG00000084674	APOB	Hypercholesterolemia, familial	143890	15	.28	3	.12	5	.10	7	.18
ENSG00000111664	GNB3	Hypertension, essential	145500	3	.02	14	.11	40	.42	20	.25
ENSG00000124212	PTGIS	Hypertension, essential	145500	31	.26	14	.22	32	.30	33	.41
ENSG00000087274	NP_789771.1	Hypertension, essential	145500	43	.36	26	.30	28	.33	47	.81
ENSG00000106258	CYP3A5	Hypertension, essential	145500	37	.46	8	.13	42	.61	10	.10
ENSG00000028137	TNFRSF1B	Hypertension, essential	145500	6	.03	19	.20	23	.21	28	.43
ENSG00000144891	AGTR1	Hypertension, essential	145500	47	.43	12	.09	33	.23	34	.30
ENSG00000135744	AGT	Hypertension, essential	145500	36	.22	11	.10	45	.53	8	.11
ENSG00000105227	PRX	Hypertrophic neuropathy of Dejerine-Sottas	145900	50	1.00	1	.00	1	.01	19	.32
ENSG00000109099	PMP22	Hypertrophic neuropathy of Dejerine-Sottas	145900	45	.66	32	.52	38	.37	8	.13
ENSG00000122877	EGR2	Hypertrophic neuropathy of Dejerine-Sottas	145900	5	.06	34	.68	27	.65	32	.78
ENSG00000158887	MPZ	Hypertrophic neuropathy of Dejerine-Sottas	145900	50	1.00	1	.02	1	.03	1	.03
ENSG00000169083	AR	Hypospadias	146450	50	1.00	36	.83	37	.83	40	.95
ENSG00000101871	MID1	Hypospadias	146450	50	1.00	4	.06	30	.63	2	.01
ENSG00000049319	SRD5A2	Hypospadias	146450	14	.22	23	.51	21	.48	20	.56
ENSG00000005471	ABCB4	Cholestasis, intrahepatic, of pregnancy; ICP	147480	50	1.00	29	.51	19	.27	18	.36
ENSG00000073734	ABCB11	Cholestasis, intrahepatic, of pregnancy; ICP	147480	50	1.00	42	.86	47	.92	13	.21
ENSG00000081923	ATP8B1	Cholestasis, intrahepatic, of pregnancy; ICP	147480	19	.41	6	.19	35	.61	34	.85
ENSG00000167768	KRT1	Keratosis palmoplantaris striata I	148700	48	.87	18	.29	33	.87	3	.01
ENSG00000134760	DSG1	Keratosis palmoplantaris striata I	148700	50	1.00	5	.13	18	.50	2	.01
ENSG00000096696	DSP	Keratosis palmoplantaris striata I	148700	44	.73	1	.01	1	.01	1	.00
ENSG00000111275	ALDH2	Leiomyoma, uterine	150699	37	.41	12	.21	46	.81	9	.28
ENSG00000143196	DPT	Leiomyoma, uterine	150699	47	.61	50	1.00	50	1.00	8	.08
ENSG00000182185	RAD51L1	Leiomyoma, uterine	150699	50	1.00	31	.51	15	.22	31	.64
ENSG00000164919	COX6C	Leiomyoma, uterine	150699	34	.35	17	.22	32	.42	14	.17
ENSG00000087237	CETP	Longevity	152430	17	.15	23	.31	11	.19	43	.91
ENSG00000108599	AKAP10	Longevity	152430	48	.67	7	.12	6	.08	16	.36
ENSG00000136869	TLR4	Longevity	152430	8	.06	4	.07	46	.90	8	.24
ENSG00000005421	PON1	Longevity	152430	50	1.00	5	.06	2	.03	10	.19
ENSG00000126594	DNASE1	Lupus erythematosus, systemic; SLE	152700	45	.71	16	.27	37	.66	27	.57
ENSG00000163599	CTLA4	Lupus erythematosus, systemic; SLE	152700	50	1.00	45	.85	50	1.00	14	.18
ENSG00000134242	PTPN22	Lupus erythematosus, systemic; SLE	152700	15	.19	12	.19	8	.12	35	.75
ENSG00000143226	FCGR2A	Lupus erythematosus, systemic; SLE	152700	14	.09	32	.47	37	.52	34	.50
ENSG00000168036	CTNNB1	Medulloblastoma	155255	4	.13	18	.48	2	.09	1	.02
ENSG00000134982	APC	Medulloblastoma	155255	1	.00	38	.63	1	.00	1	.01
ENSG00000117425	PTCH2	Medulloblastoma	155255	50	1.00	29	.65	12	.41	9	.18
ENSG00000107882	SUFU	Medulloblastoma	155255	19	.46	19	.40	13	.43	15	.30
ENSG00000107815	PEO1	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	29	.95	50	1.00	35	.92	26	.63
ENSG00000151729	SLC25A4	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	4	.38	19	.50	40	.82	29	.74
ENSG00000140521	POLG	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	38	.93	3	.01	25	.67	36	.79
ENSG00000136827	TOR1A	Myoclonic dystonia	159900	23	.39	24	.34	30	.59	26	.63
ENSG00000127990	SGCE	Myoclonic dystonia	159900	49	.87	35	.61	28	.56	26	.62
ENSG00000149295	DRD2	Myoclonic dystonia	159900	22	.48	14	.25	13	.24	11	.19
ENSG00000143632	ACTA1	Nemaline myopathy 1, autosomal dominant; NEM1	161800	45	.89	20	.67	8	.14	2	.06
ENSG00000143549	TPM3	Nemaline myopathy 1, autosomal dominant; NEM1	161800	5	.19	24	.60	5	.19	2	.05
ENSG00000198467	TPM2	Nemaline myopathy 1, autosomal dominant; NEM1	161800	27	.34	24	.47	1	.02	16	.41
ENSG00000179142	CYP11B2	IgA nephropathy	161950	50	1.00	7	.17	33	.88	30	.72
ENSG00000135744	AGT	IgA nephropathy	161950	24	.55	8	.11	40	.96	16	.41
ENSG00000159640	ACE	IgA nephropathy	161950	29	.59	29	.55	17	.77	9	.23
ENSG00000007908	SELE	IgA nephropathy	161950	50	1.00	9	.10	50	1.00	1	.04
ENSG00000133256	PDE6B	Night blindness, congenital stationary; CSNB3	163500	50	1.00	44	.95	7	.05	27	.38
ENSG00000114349	GNAT1	Night blindness, congenital stationary; CSNB3	163500	42	.69	8	.14	36	.37	16	.21
ENSG00000163914	RHO	Night blindness, congenital stationary; CSNB3	163500	50	1.00	40	.85	48	.86	34	.68
ENSG00000108576	SLC6A4	Obsessive-compulsive disorder 1; OCD1	164230	3	.02	10	.30	48	.94	26	.68
ENSG00000102468	HTR2A	Obsessive-compulsive disorder 1; OCD1	164230	40	.41	26	.64	44	.81	34	.73
ENSG00000176697	BDNF	Obsessive-compulsive disorder 1; OCD1	164230	50	1.00	30	.58	23	.25	48	.95
ENSG00000004948	CALCR	Osteoporosis, involutional	166710	50	1.00	17	.28	32	.54	20	.26
ENSG00000136244	IL6	Osteoporosis, involutional	166710	7	.10	15	.26	27	.52	25	.48
ENSG00000162337	LRP5	Osteoporosis, involutional	166710	13	.11	8	.15	11	.17	3	.02
ENSG00000108821	COL1A1	Osteoporosis, involutional	166710	2	.02	3	.02	1	.00	15	.14
ENSG00000111424	VDR	Osteoporosis, involutional	166710	32	.38	19	.47	30	.47	14	.13
ENSG00000145335	SNCA	Parkinson disease; PD	168600	11	.20	20	.23	22	.59	1	.00
ENSG00000154277	UCHL1	Parkinson disease; PD	168600	9	.11	33	.57	12	.25	2	.02
ENSG00000185345	PARK2	Parkinson disease; PD	168600	23	.52	34	.42	16	.54	1	.01
ENSG00000064692	SNCAIP	Parkinson disease; PD	168600	48	.79	50	1.00	41	.88	1	.00
ENSG00000081248	CACNA1S	Hypokalemic periodic paralysis; HOKPP	170400	50	1.00	36	.82	45	.95	2	.05
ENSG00000175538	KCNE3	Hypokalemic periodic paralysis; HOKPP	170400	50	1.00	7	.14	1	.17	2	.11
ENSG00000007314	SCN4A	Hypokalemic periodic paralysis; HOKPP	170400	37	.74	13	.40	19	.67	8	.38
ENSG00000165731	RET	Pheochromocytoma	171300	44	.70	12	.26	20	.38	17	.52
ENSG00000117118	SDHB	Pheochromocytoma	171300	35	.76	30	.68	10	.23	16	.39
ENSG00000196712	NF1	Pheochromocytoma	171300	27	.68	48	.95	36	.74	11	.26
ENSG00000183765	CHEK2	Prostate cancer	176807	6	.08	20	.24	39	.75	38	.58
ENSG00000085117	CD82	Prostate cancer	176807	11	.09	50	1.00	17	.32	20	.29
ENSG00000067082	KLF6	Prostate cancer	176807	22	.21	39	.63	11	.17	12	.11
ENSG00000133216	EPHB2	Prostate cancer	176807	26	.30	16	.16	45	.92	26	.38
ENSG00000135828	RNASEL	Prostate cancer	176807	50	1.00	14	.14	34	.56	37	.60
ENSG00000038945	MSR1	Prostate cancer	176807	50	1.00	10	.08	12	.10	50	.96
ENSG00000171862	PTEN_HUMAN	Prostate cancer	176807	3	.02	28	.23	14	.22	23	.20
ENSG00000006744	RNZ2_HUMAN	Prostate cancer	176807	9	.10	3	.03	11	.11	35	.47
ENSG00000002822	MAD1L1	Prostate cancer	176807	47	.60	27	.37	25	.42	21	.28
ENSG00000159339	PADI4	Rheumatoid arthritis	180300	50	1.00	37	.64	21	.22	39	.84
ENSG00000168593	NFKBIL1	Rheumatoid arthritis	180300	50	1.00	44	.79	13	.37	37	.77
ENSG00000159216	RUNX1	Rheumatoid arthritis	180300	30	.32	18	.21	33	.54	49	.95
ENSG00000134242	PTPN22	Rheumatoid arthritis	180300	27	.27	38	.64	38	.77	10	.17
ENSG00000197208	SLC22A4	Rheumatoid arthritis	180300	24	.20	19	.28	21	.25	2	.03
ENSG00000138293	NCOA4	Thyroid carcinoma, papillary	188550	13	.09	41	.97	21	.37	49	.99
ENSG00000108946	PRKAR1A	Thyroid carcinoma, papillary	188550	2	.01	46	.96	21	.38	17	.25
ENSG00000198400	NTRK1	Thyroid carcinoma, papillary	188550	50	1.00	31	.50	27	.30	22	.28
ENSG00000114354	TFG	Thyroid carcinoma, papillary	188550	42	.53	18	.37	10	.16	22	.32
ENSG00000047410	TPR	Thyroid carcinoma, papillary	188550	12	.13	4	.10	5	.03	22	.35
ENSG00000129991	TNNI3	Cardiomyopathy, familial hypertrophic; CMH	192600	50	1.00	21	.36	39	.67	17	.17
ENSG00000160808	MYL3	Cardiomyopathy, familial hypertrophic; CMH	192600	44	.35	1	.00	1	.00	1	.00
ENSG00000140416	TPM1	Cardiomyopathy, familial hypertrophic; CMH	192600	44	.41	1	.00	1	.02	2	.01
ENSG00000155657	NP_59687.1	Cardiomyopathy, familial hypertrophic; CMH	192600	35	.23	1	.00	4	.02	13	.12
ENSG00000092054	MYH7	Cardiomyopathy, familial hypertrophic; CMH	192600	39	.23	1	.00	24	.37	48	.79
ENSG00000111245	MYL2	Cardiomyopathy, familial hypertrophic; CMH	192600	49	.46	1	.00	1	.00	3	.04
ENSG00000118194	TNNT2	Cardiomyopathy, familial hypertrophic; CMH	192600	15	.05	2	.01	46	.63	2	.01
ENSG00000134571	MYBPC3	Cardiomyopathy, familial hypertrophic; CMH	192600	50	1.00	1	.00	4	.03	7	.05
ENSG00000101306	MYLK2	Cardiomyopathy, familial hypertrophic; CMH	192600	50	1.00	41	.51	36	.58	11	.14
ENSG00000183785	PEX26	Adrenoleukodystrophy, autosomal neonatal form	202370	46	.69	1	.00	1	.01	1	.01
ENSG00000162928	PEX13	Adrenoleukodystrophy, autosomal neonatal form	202370	46	.79	1	.00	38	.66	1	.02
ENSG00000127980	PEX1	Adrenoleukodystrophy, autosomal neonatal form	202370	40	.41	7	.20	29	.53	1	.01
ENSG00000157911	PEX10	Adrenoleukodystrophy, autosomal neonatal form	202370	25	.11	1	.00	44	.63	1	.01
ENSG00000139197	PEX5	Adrenoleukodystrophy, autosomal neonatal form	202370	31	.38	1	.00	1	.01	1	.00
ENSG00000047579	DTNBP1	Hermansky-Pudlak syndrome; HPS	203300	25	.16	15	.04	7	.07	42	.81
ENSG00000107521	HPS1	Hermansky-Pudlak syndrome; HPS	203300	37	.35	3	.03	32	.54	11	.12
ENSG00000132842	AP3B1	Hermansky-Pudlak syndrome; HPS	203300	27	.24	50	1.00	19	.21	7	.12
ENSG00000110756	HPS5	Hermansky-Pudlak syndrome; HPS	203300	49	.62	50	1.00	45	.70	50	.93
ENSG00000100099	HPS4	Hermansky-Pudlak syndrome; HPS	203300	11	.11	36	.63	46	.86	8	.08
ENSG00000163755	HPS3	Hermansky-Pudlak syndrome; HPS	203300	50	1.00	50	1.00	50	1.00	50	1.00
ENSG00000092200	RPGRIP1	Leber congenital amaurosis, type I; LCA1	204000	45	.52	1	.00	4	.03	8	.07
ENSG00000132518	GUCY2D	Leber congenital amaurosis, type I; LCA1	204000	50	1.00	45	.63	27	.28	11	.13
ENSG00000116745	RPE65	Leber congenital amaurosis, type I; LCA1	204000	45	.48	35	.47	23	.23	21	.29
ENSG00000139988	RDH12_HUMAN	Leber congenital amaurosis, type I; LCA1	204000	37	.56	41	.51	5	.04	26	.28
ENSG00000134376	CRB1	Leber congenital amaurosis, type I; LCA1	204000	50	1.00	10	.09	18	.15	2	.00
ENSG00000105392	CRX	Leber congenital amaurosis, type I; LCA1	204000	42	.36	21	.15	16	.11	29	.32
ENSG00000112041	TULP1	Leber congenital amaurosis, type I; LCA1	204000	7	.03	2	.00	29	.35	29	.48
ENSG00000165731	RET	Autonomic control, congenital failure of	209880	11	.09	41	.67	12	.19	23	.34
ENSG00000124205	EDN3	Autonomic control, congenital failure of	209880	50	1.00	9	.16	4	.10	43	.81
ENSG00000109132	PHOX2B	Autonomic control, congenital failure of	209880	50	.87	14	.18	1	.01	1	.02
ENSG00000176697	BDNF	Autonomic control, congenital failure of	209880	50	1.00	34	.44	49	.92	37	.61
ENSG00000168621	GDNF	Autonomic control, congenital failure of	209880	50	1.00	10	.10	34	.68	3	.04
ENSG00000165533	TTC8	Bardet-Biedl syndrome; BBS	209900	50	1.00	5	.05	1	.01	1	.00
ENSG00000174483	DPP3	Bardet-Biedl syndrome; BBS	209900	38	.37	50	.94	13	.26	11	.19
ENSG00000138686	BBS7_HUMAN	Bardet-Biedl syndrome; BBS	209900	35	.29	1	.00	21	.29	16	.29
ENSG00000125124	BBS2	Bardet-Biedl syndrome; BBS	209900	50	1.00	1	.00	1	.00	1	.00
ENSG00000125863	MKKS	Bardet-Biedl syndrome; BBS	209900	46	.53	33	.36	43	.78	32	.52
ENSG00000140463	BBS4	Bardet-Biedl syndrome; BBS	209900	19	.13	9	.05	2	.01	1	.01
ENSG00000113966	ARL6	Bardet-Biedl syndrome; BBS	209900	50	1.00	32	.56	33	.45	21	.29
ENSG00000163093	BBS5	Bardet-Biedl syndrome; BBS	209900	2	.02	50	1.00	10	.23	13	.19
ENSG00000133111	RFXAP	Bare lymphocyte syndrome, type II	209920	29	.46	20	.36	20	.48	1	.00
ENSG00000179583	MHC2TA	Bare lymphocyte syndrome, type II	209920	50	1.00	5	.06	5	.05	1	.00
ENSG00000064490	RFXANK	Bare lymphocyte syndrome, type II	209920	20	.30	24	.27	18	.28	1	.00
ENSG00000143390	RFX5	Bare lymphocyte syndrome, type II	209920	30	.57	26	.34	21	.29	1	.00
ENSG00000159128	IFNGR2	Atypical mycobacteriosis, familial	209950	6	.07	5	.01	3	.01	2	.00
ENSG00000096996	IL12RB1	Atypical mycobacteriosis, familial	209950	50	1.00	5	.03	18	.12	1	.00
ENSG00000027697	IFNGR1	Atypical mycobacteriosis, familial	209950	2	.03	1	.00	3	.02	1	.00
ENSG00000113302	IL12B	Atypical mycobacteriosis, familial	209950	50	1.00	5	.04	38	.38	1	.00
ENSG00000115415	STAT1	Atypical mycobacteriosis, familial	209950	26	.19	23	.32	6	.03	15	.16
ENSG00000073734	ABCB11	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	50	1.00	37	.78	40	.88	19	.33
ENSG00000005471	ABCB4	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	50	1.00	32	.69	14	.36	23	.43
ENSG00000081923	ATP8B1	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	24	.63	5	.14	33	.71	28	.74
ENSG00000099377	HSD3B7	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	17	.11	1	.02	1	.02	11	.29
ENSG00000133703	RASK_HUMAN	Lung cancer	211980	40	.39	44	.64	5	.04	19	.24
ENSG00000157764	BRAF	Lung cancer	211980	6	.02	30	.53	2	.02	1	.01
ENSG00000141510	TP53	Lung cancer	211980	1	.00	13	.22	39	.66	49	.97
ENSG00000146648	EGFR	Lung cancer	211980	25	.26	13	.27	5	.09	15	.24
ENSG00000139197	PEX5	Zellweger syndrome; ZS	214100	7	.03	6	.03	1	.00	1	.00
ENSG00000142655	PEX14	Zellweger syndrome; ZS	214100	14	.06	9	.10	3	.01	2	.00
ENSG00000127980	PEX1	Zellweger syndrome; ZS	214100	14	.06	1	.03	1	.00	3	.01
ENSG00000124587	PEX6	Zellweger syndrome; ZS	214100	3	.01	2	.03	2	.01	1	.02
ENSG00000108733	PEX12	Zellweger syndrome; ZS	214100	19	.12	18	.31	42	.62	1	.00
ENSG00000164751	PXMP3	Zellweger syndrome; ZS	214100	24	.14	35	.57	34	.41	1	.00
ENSG00000183785	PEX26	Zellweger syndrome; ZS	214100	9	.04	2	.03	4	.04	1	.00
ENSG00000034693	PEX3	Zellweger syndrome; ZS	214100	1	.00	1	.00	14	.11	1	.00
ENSG00000172660	TAF15	Chondrosarcoma	215300	33	.34	44	.72	45	.66	9	.13
ENSG00000182197	EXT1	Chondrosarcoma	215300	15	.05	7	.10	30	.52	2	.01
ENSG00000151348	EXT2	Chondrosarcoma	215300	2	.01	8	.12	17	.31	1	.02
ENSG00000182944	EWSR1	Chondrosarcoma	215300	27	.23	41	.74	10	.18	15	.33
ENSG00000134183	GNAT2	Achromatopsia 2; ACHM2	216900	42	.71	25	.53	34	.79	36	.79
ENSG00000144191	CNGA3	Achromatopsia 2; ACHM2	216900	44	.57	5	.16	2	.03	35	.87
ENSG00000170289	CNGB3	Achromatopsia 2; ACHM2	216900	50	1.00	21	.67	16	.33	11	.33
ENSG00000065618	COHA1_HUMAN	Epidermolysis bullosa junctionalis, disentis type	226650	16	.41	34	.41	12	.31	12	.19
ENSG00000132470	ITGB4	Epidermolysis bullosa junctionalis, disentis type	226650	50	1.00	38	.78	9	.16	32	.59
ENSG00000196878	LAMB3	Epidermolysis bullosa junctionalis, disentis type	226650	50	1.00	3	.04	1	.04	9	.15
ENSG00000053747	LAMA3	Epidermolysis bullosa junctionalis, disentis type	226650	31	.65	20	.37	41	.77	15	.21
ENSG00000165281	FANCG	Fanconi anemia; FA	227650	12	.03	36	.34	33	.49	1	.00
ENSG00000144554	FANCD2	Fanconi anemia; FA	227650	50	1.00	2	.01	1	.01	1	.00
ENSG00000183161	FANCF	Fanconi anemia; FA	227650	19	.11	2	.01	1	.02	1	.00
ENSG00000158169	FANCC	Fanconi anemia; FA	227650	21	.10	2	.02	22	.23	1	.00
ENSG00000115392	FANCL	Fanconi anemia; FA	227650	40	.45	26	.36	19	.31	1	.00
ENSG00000112039	FANCE	Fanconi anemia; FA	227650	2	.01	3	.01	15	.25	1	.00
ENSG00000105379	ETFB	Glutaricaciduria IIA	231680	1	.01	1	.06	1	.00	1	.00
ENSG00000140374	ETFA	Glutaricaciduria IIA	231680	2	.01	3	.06	1	.00	1	.00
ENSG00000171503	ETFDH	Glutaricaciduria IIA	231680	1	.00	10	.19	1	.00	1	.00
ENSG00000188690	UROS	Hydrops fetalis, idiopathic	236750	26	.20	50	.96	8	.11	28	.54
ENSG00000169919	GUSB	Hydrops fetalis, idiopathic	236750	28	.41	21	.54	46	.91	42	.92
ENSG00000177628	GBA	Hydrops fetalis, idiopathic	236750	46	.67	40	.93	46	.88	27	.64
ENSG00000188536	HBA1	Hydrops fetalis, idiopathic	236750	12	.06	43	.93	16	.46	6	.14
ENSG00000179477	ALOX12B	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	50	1.00	21	.41	6	.14	4	.06
ENSG00000011198	ABHD5	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	7	.10	11	.19	28	.51	31	.62
ENSG00000092295	TGM1	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	12	.23	2	.04	4	.04	36	.60
ENSG00000122735	DNAI1	Kartagener syndrome	244400	12	.12	50	1.00	6	.14	16	.53
ENSG00000039139	DNAH5	Kartagener syndrome	244400	50	1.00	1	.01	1	.04	1	.04
ENSG00000105877	DNAH11	Kartagener syndrome	244400	50	1.00	1	.02	5	.07	1	.04
ENSG00000112964	GHR	Laron syndrome, type II	245590	50	1.00	11	.15	30	.66	2	.01
ENSG00000189162	GH1	Laron syndrome, type II	245590	50	1.00	4	.03	3	.06	3	.03
ENSG00000173757	STAT5B	Laron syndrome, type II	245590	17	.45	31	.63	39	.88	12	.21
ENSG00000137992	DBT	Maple syrup urine disease, type IA	248600	29	.45	21	.27	41	.54	2	.00
ENSG00000091140	DLD	Maple syrup urine disease, type IA	248600	22	.19	38	.52	3	.04	1	.01
ENSG00000142046	BCKDHA	Maple syrup urine disease, type IA	248600	4	.05	10	.13	1	.02	1	.00
ENSG00000083123	BCKDHB	Maple syrup urine disease, type IA	248600	31	.40	5	.03	30	.44	2	.00
ENSG00000167792	NDUFV1	Complex I, mitochondrial respiratory chain, deficiency of	252010	37	.34	1	.00	3	.01	3	.00
ENSG00000115286	NDUFS7	Complex I, mitochondrial respiratory chain, deficiency of	252010	42	.62	1	.01	4	.01	3	.01
ENSG00000164258	NDUFS4	Complex I, mitochondrial respiratory chain, deficiency of	252010	27	.35	1	.01	1	.00	1	.00
ENSG00000023228	NDUFS1	Complex I, mitochondrial respiratory chain, deficiency of	252010	42	.61	1	.00	1	.00	1	.00
ENSG00000158864	NDUFS2	Complex I, mitochondrial respiratory chain, deficiency of	252010	12	.06	1	.00	1	.00	1	.00
ENSG00000110092	CCND1	Myeloma, multiple	254500	2	.02	50	.92	44	.67	44	.85
ENSG00000130076	IGHG3	Myeloma, multiple	254500	41	.68	10	.20	44	.84	35	.70
ENSG00000068078	FGFR3	Myeloma, multiple	254500	21	.29	3	.03	15	.34	4	.03
ENSG00000137265	IRF4	Myeloma, multiple	254500	30	.41	19	.29	22	.40	15	.21
ENSG00000115286	NDUFS7	Leigh syndrome; LS	256000	9	.02	2	.03	1	.00	1	.00
ENSG00000074582	BCS1L	Leigh syndrome; LS	256000	12	.07	23	.32	3	.03	9	.05
ENSG00000073578	SDHA	Leigh syndrome; LS	256000	9	.04	32	.53	8	.08	6	.06
ENSG00000110536	NDUFS3	Leigh syndrome; LS	256000	8	.04	1	.02	1	.00	1	.01
ENSG00000091140	DLD	Leigh syndrome; LS	256000	39	.23	46	.79	25	.29	26	.38
ENSG00000110717	NDUFS8	Leigh syndrome; LS	256000	5	.00	5	.03	3	.00	1	.00
ENSG00000006071	ABCC8	Nesidioblastosis of pancreas	256450	37	.48	50	.99	44	.89	36	.81
ENSG00000106633	GCK	Nesidioblastosis of pancreas	256450	46	.71	19	.26	2	.10	30	.63
ENSG00000148672	GLUD1	Nesidioblastosis of pancreas	256450	13	.07	18	.30	7	.23	29	.50
ENSG00000141655	TNFRSF11A	Osteogenic sarcoma	259500	50	1.00	2	.12	29	.67	4	.11
ENSG00000139687	RB1	Osteogenic sarcoma	259500	23	.25	31	.58	39	.62	24	.50
ENSG00000183765	CHEK2	Osteogenic sarcoma	259500	49	.92	44	.90	46	.88	25	.57
ENSG00000081087	OSTM1	Osteopetrosis, autosomal recessive	259700	7	.31	50	1.00	12	.35	26	.66
ENSG00000103249	CLCN7	Osteopetrosis, autosomal recessive	259700	40	.71	30	.75	33	.79	30	.68
ENSG00000110719	TCIRG1	Osteopetrosis, autosomal recessive	259700	46	.82	30	.70	6	.08	4	.27
ENSG00000133703	RASK_HUMAN	Pancreatic carcinoma	260350	50	.62	24	.19	7	.06	17	.25
ENSG00000141510	TP53	Pancreatic carcinoma	260350	12	.12	30	.31	21	.29	18	.20
ENSG00000145050	ARMET	Pancreatic carcinoma	260350	38	.43	50	1.00	41	.41	41	.58
ENSG00000147889	CDKN2A	Pancreatic carcinoma	260350	50	1.00	20	.25	48	.51	45	.55
ENSG00000141646	SMAD4	Pancreatic carcinoma	260350	1	.01	34	.34	28	.26	38	.65
ENSG00000139618	BRCA2	Pancreatic carcinoma	260350	45	.75	33	.40	42	.50	15	.18
ENSG00000144191	CNGA3	Achromatopsia 3; ACHM3	262300	42	.58	9	.20	1	.02	34	.85
ENSG00000134183	GNAT2	Achromatopsia 3; ACHM3	262300	36	.68	25	.54	36	.79	38	.83
ENSG00000170289	CNGB3	Achromatopsia 3; ACHM3	262300	50	1.00	21	.68	15	.33	10	.33
ENSG00000107187	LHX3	Pituitary dwarfism III	262600	30	.40	41	.86	31	.46	41	.90
ENSG00000163666	HESX1	Pituitary dwarfism III	262600	50	1.00	36	.79	46	.91	6	.02
ENSG00000175325	PROP1	Pituitary dwarfism III	262600	50	1.00	37	.78	37	.76	2	.01
ENSG00000111319	SCNN1A	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	25	.69	1	.00	2	.05	2	.10
ENSG00000168447	SCNN1B	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	18	.31	1	.00	6	.07	5	.07
ENSG00000151623	NR3C2	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	39	.72	30	.69	40	.97	24	.58
ENSG00000168878	SFTPB	Pulmonary alveolar proteinosis	265120	50	1.00	2	.01	18	.60	7	.31
ENSG00000100368	CSF2RB	Pulmonary alveolar proteinosis	265120	50	1.00	33	.64	46	.99	50	.99
ENSG00000168484	SFTPC	Pulmonary alveolar proteinosis	265120	50	1.00	2	.00	1	.01	15	.43
ENSG00000164751	PXMP3	Refsum disease, infantile form	266510	47	.87	5	.15	27	.65	35	.90
ENSG00000127980	PEX1	Refsum disease, infantile form	266510	3	.05	37	.78	16	.50	1	.00
ENSG00000183785	PEX26	Refsum disease, infantile form	266510	29	.58	1	.02	3	.01	1	.00
ENSG00000197375	SLC22A5	Inflammatory bowel disease 1; IBD1	266600	6	.05	47	.96	24	.58	48	.97
ENSG00000151208	DLG5	Inflammatory bowel disease 1; IBD1	266600	38	.74	20	.37	9	.27	25	.55
ENSG00000167207	CARD15	Inflammatory bowel disease 1; IBD1	266600	50	1.00	45	.93	7	.24	22	.62
ENSG00000085563	ABCB1	Inflammatory bowel disease 1; IBD1	266600	50	1.00	14	.24	42	.89	36	.79
ENSG00000092929	UNC13D	Reticulosis, familial histiocytic	267700	50	1.00	49	.97	50	1.00	5	.16
ENSG00000166349	RAG1	Reticulosis, familial histiocytic	267700	51	1.00	12	.29	50	.90	39	.92
ENSG00000180644	PRF1	Reticulosis, familial histiocytic	267700	21	.24	44	.95	8	.12	36	.56
ENSG00000135903	PAX3	Rhabdomyosarcoma 2; RMS2	268220	19	.39	12	.31	23	.45	36	.86
ENSG00000150907	FOXO1A	Rhabdomyosarcoma 2; RMS2	268220	48	.84	39	.83	10	.25	35	.97
ENSG00000009709	PAX7	Rhabdomyosarcoma 2; RMS2	268220	3	.06	19	.41	9	.30	18	.40
ENSG00000108576	SLC6A4	Sudden infant death syndrome	272120	33	.67	23	.51	12	.41	29	.71
ENSG00000183873	SCN5A	Sudden infant death syndrome	272120	40	.75	5	.06	4	.04	39	.85
ENSG00000053918	KCNQ1	Sudden infant death syndrome	272120	50	1.00	42	.89	46	.65	39	.64
ENSG00000163599	CTLA4	Graves disease	275000	50	1.00	2	.03	50	1.00	25	.81
ENSG00000145321	GC	Graves disease	275000	5	.08	21	.51	25	.43	2	.04
ENSG00000111424	VDR	Graves disease	275000	8	.07	38	.87	28	.56	15	.37
ENSG00000134982	APC	Turcot syndrome	276300	2	.00	41	.72	40	.68	37	.86
ENSG00000076242	MLH1	Turcot syndrome	276300	32	.71	2	.06	12	.40	27	.61
ENSG00000122512	PMS2	Turcot syndrome	276300	4	.07	2	.07	2	.03	2	.07
ENSG00000100146	SOX10	Waardenburg-Shah syndrome	277580	34	.57	3	.06	46	.85	27	.73
ENSG00000124205	EDN3	Waardenburg-Shah syndrome	277580	50	1.00	46	.97	23	.56	2	.04
ENSG00000136160	EDNRB	Waardenburg-Shah syndrome	277580	50	1.00	6	.09	1	.01	5	.13
ENSG00000112357	PEX7	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	18	.29	40	.79	27	.73	43	.97
ENSG00000116906	GNPAT	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	26	.35	20	.44	40	.76	4	.04
ENSG00000018510	AGPS	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	13	.23	1	.05	40	.75	2	.01
ENSG00000170175	CHRNB1	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	5	.06	35	.69	2	.09	47	.95
ENSG00000135902	CHRND	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	15	.11	2	.00	1	.00	7	.14
ENSG00000138435	CHRNA1	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	50	1.00	1	.00	1	.00	2	.02
ENSG00000175426	PCSK1	Obesity	601665	48	.62	47	.84	28	.31	20	.27
ENSG00000115138	POMC	Obesity	601665	24	.14	17	.22	35	.30	6	.03
ENSG00000116678	LEPR	Obesity	601665	1	.00	45	.86	14	.15	2	.00
ENSG00000166603	MC4R	Obesity	601665	50	1.00	18	.22	41	.65	4	.02
ENSG00000174483	DPP3	Obesity	601665	18	.11	23	.37	1	.02	3	.01
ENSG00000174697	LEP	Obesity	601665	50	1.00	26	.30	37	.42	4	.01
ENSG00000130203	APOE	Obesity	601665	34	.24	11	.24	14	.16	38	.33
ENSG00000142156	COL6A1	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	50	1.00	24	.48	13	.32	15	.17
ENSG00000112112	COL11A2	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	32	.57	5	.18	5	.21	1	.06
ENSG00000197594	ENPP1	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	50	1.00	24	.43	41	.71	20	.42
ENSG00000115211	EIF2B4	Leukoencephalopathy with vanishing white matter; VWM	603896	2	.00	1	.00	1	.00	1	.00
ENSG00000070785	EIF2B3	Leukoencephalopathy with vanishing white matter; VWM	603896	1	.00	1	.00	2	.00	1	.00
ENSG00000111361	EIF2B1	Leukoencephalopathy with vanishing white matter; VWM	603896	1	.00	1	.00	1	.00	1	.00
ENSG00000119718	EIF2B2	Leukoencephalopathy with vanishing white matter; VWM	603896	1	.00	1	.00	1	.00	1	.00
ENSG00000145191	EIF2B5	Leukoencephalopathy with vanishing white matter; VWM	603896	1	.00	2	.00	1	.00	1	.00
ENSG00000138061	CYP1B1	Peters anomaly	604229	21	.35	33	.48	9	.11	23	.31
ENSG00000054598	FOXC1	Peters anomaly	604229	15	.21	27	.36	47	.79	50	.99
ENSG00000164093	PITX2	Peters anomaly	604229	50	1.00	24	.37	2	.03	41	.80
ENSG00000007372	PAX6	Peters anomaly	604229	40	.77	18	.32	28	.51	15	.23
ENSG00000144285	SCN1A	Generalized epilepsy with febrile seizures plus; GEFS+	604233	33	.55	2	.02	4	.01	8	.04
ENSG00000113327	GABRG2	Generalized epilepsy with febrile seizures plus; GEFS+	604233	40	.63	4	.20	25	.49	23	.64
ENSG00000105711	SCN1B	Generalized epilepsy with febrile seizures plus; GEFS+	604233	11	.12	1	.02	1	.01	31	.70
ENSG00000157764	BRAF	Lymphoma, non-Hodgkin, familial	605027	41	.53	1	.03	10	.15	13	.30
ENSG00000003400	CASP10	Lymphoma, non-Hodgkin, familial	605027	15	.16	6	.06	4	.03	27	.32
ENSG00000149311	ATM	Lymphoma, non-Hodgkin, familial	605027	35	.30	36	.37	35	.50	32	.61
ENSG00000085999	RAD54L	Lymphoma, non-Hodgkin, familial	605027	37	.35	24	.41	25	.29	1	.00
ENSG00000143294	PRCC	Renal cell carcinoma, papillary	605074	38	.75	50	1.00	31	.74	29	.59
ENSG00000068323	TFE3	Renal cell carcinoma, papillary	605074	8	.08	19	.57	43	.82	2	.02
ENSG00000105976	MET	Renal cell carcinoma, papillary	605074	31	.46	4	.36	14	.37	36	.74
ENSG00000102245	TNFL5_HUMAN	Immunodeficiency with hyper-IgM, type 2	605258	50	1.00	32	.62	43	.85	3	.11
ENSG00000101017	CD40	Immunodeficiency with hyper-IgM, type 2	605258	3	.08	6	.17	21	.29	16	.37
ENSG00000111732	AICDA	Immunodeficiency with hyper-IgM, type 2	605258	50	1.00	29	.46	33	.68	18	.50
ENSG00000139515	IPF1	Maturity-onset diabetes of the young; MODY	606391	50	1.00	40	.66	32	.54	1	.00
ENSG00000135100	TCF1	Maturity-onset diabetes of the young; MODY	606391	50	1.00	33	.43	26	.56	1	.01
ENSG00000101076	HNF4A	Maturity-onset diabetes of the young; MODY	606391	9	.08	40	.64	20	.18	8	.08
ENSG00000106633	GCK	Maturity-onset diabetes of the young; MODY	606391	28	.30	47	.72	25	.36	24	.25
ENSG00000162992	NEUROD1	Maturity-onset diabetes of the young; MODY	606391	9	.07	3	.03	40	.57	4	.01
ENSG00000102245	TNFL5_HUMAN	Immunodeficiency with hyper-IgM, type 3	606843	50	1.00	31	.60	45	.88	3	.12
ENSG00000101017	CD40	Immunodeficiency with hyper-IgM, type 3	606843	4	.09	6	.16	20	.31	17	.38
ENSG00000111732	AICDA	Immunodeficiency with hyper-IgM, type 3	606843	50	1.00	29	.51	30	.66	16	.50
ENSG00000022355	GABRA1	Myoclonic epilepsy, juvenile; JME	606904	36	.30	26	.25	37	.49	32	.51
ENSG00000096093	EFHC1	Myoclonic epilepsy, juvenile; JME	606904	50	1.00	50	1.00	50	1.00	6	.07
ENSG00000182389	CACNB4	Myoclonic epilepsy, juvenile; JME	606904	45	.77	37	.50	47	.67	23	.44
ENSG00000114859	CLCN2	Myoclonic epilepsy, juvenile; JME	606904	50	1.00	11	.18	41	.47	8	.14
ENSG00000179295	PTPN11	Juvenile myelomonocytic leukemia	607785	32	.41	8	.08	36	.65	5	.12
ENSG00000196712	NF1	Juvenile myelomonocytic leukemia	607785	44	.64	3	.02	2	.05	1	.00
ENSG00000133703	RASK_HUMAN	Juvenile myelomonocytic leukemia	607785	36	.39	2	.04	4	.01	5	.04
ENSG00000168638	NRAS	Juvenile myelomonocytic leukemia	607785	4	.01	14	.25	3	.01	5	.06
ENSG00000197499	HLA-A	Mycobacterium tuberculosis, susceptibility to infection by	607948	49	.91	38	.70	42	.93	34	.53
ENSG00000165471	MBL2	Mycobacterium tuberculosis, susceptibility to infection by	607948	50	1.00	32	.88	49	.95	13	.34
ENSG00000111424	VDR	Mycobacterium tuberculosis, susceptibility to infection by	607948	35	.82	31	.69	28	.57	14	.37
ENSG00000108556	CHRNE	Myasthenic syndrome, congenital, fast-channel	608930	50	1.00	36	.69	44	.61	1	.00
ENSG00000138435	CHRNA1	Myasthenic syndrome, congenital, fast-channel	608930	50	1.00	1	.00	1	.00	1	.00
ENSG00000135902	CHRND	Myasthenic syndrome, congenital, fast-channel	608930	12	.12	1	.00	1	.00	1	.00
100 Genes:
ENSG00000091513	TF	Alzheimer disease; AD	104300	70	.29	80	.69	86	.74	46	.39
ENSG00000175899	A2M	Alzheimer disease; AD	104300	72	.37	69	.69	94	.92	2	.02
ENSG00000143801	PSEN2	Alzheimer disease; AD	104300	31	.08	10	.08	57	.45	92	.86
ENSG00000123384	LRP1	Alzheimer disease; AD	104300	100	1.00	44	.31	39	.32	33	.30
ENSG00000142192	APP	Alzheimer disease; AD	104300	71	.38	72	.67	76	.60	30	.20
ENSG00000130203	APOE	Alzheimer disease; AD	104300	34	.08	76	.63	68	.56	5	.02
ENSG00000010704	HFE	Alzheimer disease; AD	104300	52	.12	34	.70	88	.61	1	.00
ENSG00000080815	PSEN1	Alzheimer disease; AD	104300	26	.07	11	.06	92	.78	98	.98
ENSG00000101439	CST3	Amyloidosis VI	105150	10	.22	96	.98	96	.98	59	.71
ENSG00000165029	ABCA1	Amyloidosis VI	105150	100	1.00	80	.93	70	.81	3	.07
ENSG00000136156	ITM2B	Amyloidosis VI	105150	46	.62	6	.16	18	.35	92	.99
ENSG00000171560	FGA	Amyloidosis, familial visceral	105200	100	1.00	88	.98	87	.85	71	.81
ENSG00000090382	LYZ	Amyloidosis, familial visceral	105200	66	.73	34	.26	76	.80	72	.80
ENSG00000118137	APOA1	Amyloidosis, familial visceral	105200	14	.19	59	.54	28	.30	58	.79
ENSG00000124164	VAPB	Amyotrophic lateral sclerosis 1; ALS1	105400	12	.05	22	.15	61	.58	23	.17
ENSG00000003393	ALS2	Amyotrophic lateral sclerosis 1; ALS1	105400	100	1.00	38	.32	92	.83	68	.60
ENSG00000142168	SOD1	Amyotrophic lateral sclerosis 1; ALS1	105400	32	.10	2	.00	26	.11	92	.93
ENSG00000100285	NEFH	Amyotrophic lateral sclerosis 1; ALS1	105400	62	.36	19	.12	75	.52	84	.76
ENSG00000135406	PRPH	Amyotrophic lateral sclerosis 1; ALS1	105400	63	.25	100	1.00	22	.15	75	.60
ENSG00000112112	COL11A2	Stickler syndrome, type I; STL1	108300	83	.89	26	.29	13	.20	9	.16
ENSG00000139219	COL2A1	Stickler syndrome, type I; STL1	108300	100	1.00	35	.36	35	.38	64	.69
ENSG00000060718	COL11A1	Stickler syndrome, type I; STL1	108300	100	1.00	26	.29	14	.19	10	.16
ENSG00000139687	RB1	Bladder cancer	109800	37	.48	78	.89	53	.59	54	.71
ENSG00000068078	FGFR3	Bladder cancer	109800	48	.54	22	.38	66	.76	39	.62
ENSG00000174775	HRAS	Bladder cancer	109800	39	.48	17	.17	88	.82	95	1.00
ENSG00000139618	BRCA2	Breast cancer	114480	62	.17	23	.06	16	.02	21	.04
ENSG00000138376	BARD1	Breast cancer	114480	31	.06	48	.22	6	.01	1	.00
ENSG00000124151	NCOA3	Breast cancer	114480	74	.31	14	.04	21	.08	26	.05
ENSG00000121879	PIK3CA	Breast cancer	114480	37	.07	75	.50	1	.00	78	.40
ENSG00000012048	BRCA1	Breast cancer	114480	2	.00	26	.14	29	.10	9	.02
ENSG00000170836	PPM1D	Breast cancer	114480	70	.31	14	.04	73	.47	47	.19
ENSG00000141510	TP53	Breast cancer	114480	57	.15	43	.20	3	.00	17	.04
ENSG00000023287	RB1CC1	Breast cancer	114480	59	.15	65	.40	99	.84	78	.40
ENSG00000183765	CHEK2	Breast cancer	114480	10	.02	8	.02	9	.04	24	.07
ENSG00000169083	AR	Breast cancer	114480	100	1.00	69	.38	72	.33	72	.38
ENSG00000105976	MET	Hepatocellular carcinoma	114550	79	.85	41	.48	17	.31	4	.03
ENSG00000168036	CTNNB1	Hepatocellular carcinoma	114550	42	.52	90	.98	32	.53	19	.23
ENSG00000141510	TP53	Hepatocellular carcinoma	114550	72	.77	52	.70	87	.92	96	1.00
ENSG00000138109	CYP2C9	Coumarin resistance	122700	61	.62	88	.93	28	.42	41	.44
ENSG00000167397	VKOR1_HUMAN	Coumarin resistance	122700	39	.15	63	.73	89	.79	10	.10
ENSG00000198470	CYP2A6	Coumarin resistance	122700	95	.98	27	.24	50	.70	62	.72
ENSG00000101981	F9	Coumarin resistance	122700	100	1.00	36	.30	44	.70	29	.39
ENSG00000162992	NEUROD1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	6	.00	16	.04	8	.02	73	.60
ENSG00000121653	MAPK8IP1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	82	.32	40	.30	38	.22	13	.05
ENSG00000163581	SLC2A2	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	86	.41	16	.03	5	.01	50	.25
ENSG00000181856	SLC2A4	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	63	.15	11	.03	9	.02	3	.00
ENSG00000105221	AKT2	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	15	.01	80	.71	47	.30	38	.11
ENSG00000101076	HNF4A	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	9	.01	77	.52	78	.58	57	.22
ENSG00000142330	CAPN10	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	100	1.00	44	.32	20	.09	22	.07
ENSG00000135100	TCF1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	100	1.00	80	.61	81	.61	56	.37
ENSG00000104918	RETN	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	95	.46	82	.61	100	1.00	15	.06
ENSG00000171867	PRNP	Dementia, Lewy body; DLB	127750	23	.24	100	1.00	37	.44	87	.95
ENSG00000145335	SNCA	Dementia, Lewy body; DLB	127750	8	.11	90	.98	50	.69	73	.90
ENSG00000130203	APOE	Dementia, Lewy body; DLB	127750	86	.93	18	.40	87	.90	90	.94
ENSG00000070182	SPTB	Elliptocytosis, Rhesus-unlinked type	130600	100	1.00	7	.09	37	.63	1	.00
ENSG00000163554	SPTA1	Elliptocytosis, Rhesus-unlinked type	130600	59	.45	12	.17	23	.26	2	.02
ENSG00000004939	SLC4A1	Elliptocytosis, Rhesus-unlinked type	130600	39	.37	25	.36	18	.16	9	.09
ENSG00000186847	KRT14	Epidermolysis bullosa of hands and feet	131800	100	1.00	13	.04	1	.01	13	.01
ENSG00000186081	KRT5	Epidermolysis bullosa of hands and feet	131800	100	1.00	8	.04	8	.03	2	.00
ENSG00000132470	ITGB4	Epidermolysis bullosa of hands and feet	131800	100	1.00	60	.56	47	.40	25	.21
ENSG00000114270	COL7A1	Epidermolysis bullosa of hands and feet	131800	21	.10	14	.10	39	.26	63	.59
ENSG00000127870	RNF6	Esophageal cancer	133239	27	.21	69	.53	14	.10	8	.04
ENSG00000134982	APC	Esophageal cancer	133239	4	.01	92	.91	11	.11	14	.08
ENSG00000147889	CDKN2A	Esophageal cancer	133239	100	1.00	75	.57	36	.31	23	.17
ENSG00000008226	DLEC1	Esophageal cancer	133239	100	1.00	21	.16	3	.03	4	.03
ENSG00000061337	LZTS1	Esophageal cancer	133239	100	1.00	84	.78	37	.29	17	.09
ENSG00000141510	TP53	Esophageal cancer	133239	67	.56	69	.61	77	.81	1	.02
ENSG00000187323	DCC	Esophageal cancer	133239	100	1.00	35	.23	22	.20	3	.03
ENSG00000186153	NP_570606.1	Esophageal cancer	133239	32	.19	69	.57	35	.29	1	.00
ENSG00000140522	RLBP1	Fundus albipunctatus	136880	93	.77	65	.72	91	.90	3	.02
ENSG00000112619	RDS	Fundus albipunctatus	136880	100	1.00	37	.44	83	.82	69	.71
ENSG00000135437	RDH5	Fundus albipunctatus	136880	100	1.00	55	.55	39	.47	3	.03
ENSG00000130203	APOE	Fundus albipunctatus	136880	96	.91	7	.02	93	.92	6	.05
ENSG00000063169	GLTSCR1	Glioma of brain, familial	137800	64	.36	100	1.00	100	1.00	100	1.00
ENSG00000111087	GLI1_HUMAN	Glioma of brain, familial	137800	17	.07	31	.17	6	.03	25	.14
ENSG00000108231	LGI1	Glioma of brain, familial	137800	45	.17	100	1.00	100	1.00	100	1.00
ENSG00000147889	CDKN2A	Glioma of brain, familial	137800	100	1.00	97	.96	27	.20	62	.45
ENSG00000146648	EGFR	Glioma of brain, familial	137800	2	.00	3	.00	50	.42	32	.19
ENSG00000132170	PPARG	Glioma of brain, familial	137800	50	.22	65	.64	22	.14	97	.90
ENSG00000117298	ECE1	Hirschsprung disease	142623	38	.18	87	.72	47	.37	56	.50
ENSG00000136160	EDNRB	Hirschsprung disease	142623	100	1.00	44	.34	64	.46	29	.14
ENSG00000169554	ZFHX1B	Hirschsprung disease	142623	9	.02	70	.54	13	.09	5	.08
ENSG00000165731	RET	Hirschsprung disease	142623	43	.21	56	.54	47	.33	38	.29
ENSG00000124205	EDN3	Hirschsprung disease	142623	100	1.00	27	.21	41	.31	12	.10
ENSG00000168621	GDNF	Hirschsprung disease	142623	100	1.00	70	.54	39	.23	44	.47
ENSG00000130164	LDLR	Hypercholesterolemia, familial	143890	23	.22	3	.04	5	.04	8	.04
ENSG00000055955	ITIH4	Hypercholesterolemia, familial	143890	46	.39	25	.27	50	.44	99	.99
ENSG00000158874	APOA2	Hypercholesterolemia, familial	143890	100	1.00	13	.14	9	.08	24	.20
ENSG00000169174	PCSK9	Hypercholesterolemia, familial	143890	11	.09	40	.44	49	.46	44	.35
ENSG00000120915	EPHX2	Hypercholesterolemia, familial	143890	54	.31	80	.82	91	.84	26	.18
ENSG00000084674	APOB	Hypercholesterolemia, familial	143890	50	.61	1	.01	2	.05	25	.23
ENSG00000111664	GNB3	Hypertension, essential	145500	2	.00	35	.24	55	.26	29	.13
ENSG00000124212	PTGIS	Hypertension, essential	145500	68	.39	64	.56	69	.52	36	.28
ENSG00000087274	NP_789771.1	Hypertension, essential	145500	28	.06	75	.58	65	.55	95	.90
ENSG00000106258	CYP3A5	Hypertension, essential	145500	74	.46	35	.29	81	.63	47	.32
ENSG00000028137	TNFRSF1B	Hypertension, essential	145500	36	.10	47	.33	71	.54	23	.12
ENSG00000144891	AGTR1	Hypertension, essential	145500	40	.15	2	.01	28	.16	65	.46
ENSG00000135744	AGT	Hypertension, essential	145500	43	.19	19	.13	48	.29	58	.47
ENSG00000105227	PRX	Hypertrophic neuropathy of Dejerine-Sottas	145900	100	1.00	1	.01	4	.03	59	.64
ENSG00000109099	PMP22	Hypertrophic neuropathy of Dejerine-Sottas	145900	92	.92	46	.41	72	.71	8	.08
ENSG00000122877	EGR2	Hypertrophic neuropathy of Dejerine-Sottas	145900	26	.20	63	.75	93	.97	48	.58
ENSG00000158887	MPZ	Hypertrophic neuropathy of Dejerine-Sottas	145900	100	1.00	7	.07	3	.04	1	.02
ENSG00000169083	AR	Hypospadias	146450	100	1.00	87	.97	65	.66	51	.58
ENSG00000101871	MID1	Hypospadias	146450	100	1.00	17	.08	64	.58	7	.04
ENSG00000049319	SRD5A2	Hypospadias	146450	32	.39	13	.09	84	.83	41	.46
ENSG00000005471	ABCB4	Cholestasis, intrahepatic, of pregnancy; ICP	147480	100	1.00	31	.43	64	.53	63	.66
ENSG00000073734	ABCB11	Cholestasis, intrahepatic, of pregnancy; ICP	147480	100	1.00	94	.97	91	.95	37	.41
ENSG00000081923	ATP8B1	Cholestasis, intrahepatic, of pregnancy; ICP	147480	8	.03	18	.26	68	.59	72	.78
ENSG00000167768	KRT1	Keratosis palmoplantaris striata I	148700	79	.85	42	.46	95	1.00	4	.01
ENSG00000134760	DSG1	Keratosis palmoplantaris striata I	148700	100	1.00	13	.27	30	.37	4	.01
ENSG00000096696	DSP	Keratosis palmoplantaris striata I	148700	34	.23	2	.03	1	.01	1	.01
ENSG00000111275	ALDH2	Leiomyoma, uterine	150699	83	.78	33	.37	10	.10	37	.45
ENSG00000143196	DPT	Leiomyoma, uterine	150699	71	.48	100	1.00	100	1.00	12	.05
ENSG00000182185	RAD51L1	Leiomyoma, uterine	150699	100	1.00	81	.88	11	.11	59	.64
ENSG00000164919	COX6C	Leiomyoma, uterine	150699	28	.11	30	.24	13	.08	19	.24
ENSG00000087237	CETP	Longevity	152430	47	.28	65	.71	32	.26	49	.55
ENSG00000108599	AKAP10	Longevity	152430	100	.97	27	.22	19	.13	26	.29
ENSG00000136869	TLR4	Longevity	152430	29	.15	15	.17	29	.23	42	.53
ENSG00000005421	PON1	Longevity	152430	100	1.00	7	.10	11	.04	45	.42
ENSG00000126594	DNASE1	Lupus erythematosus, systemic; SLE	152700	87	.74	55	.52	88	.95	77	.81
ENSG00000163599	CTLA4	Lupus erythematosus, systemic; SLE	152700	100	1.00	61	.65	100	1.00	51	.59
ENSG00000134242	PTPN22	Lupus erythematosus, systemic; SLE	152700	72	.56	44	.41	38	.33	39	.27
ENSG00000143226	FCGR2A	Lupus erythematosus, systemic; SLE	152700	22	.11	11	.07	23	.11	59	.44
ENSG00000168036	CTNNB1	Medulloblastoma	155255	46	.42	13	.11	4	.05	5	.09
ENSG00000134982	APC	Medulloblastoma	155255	3	.01	87	.93	1	.01	2	.02
ENSG00000117425	PTCH2	Medulloblastoma	155255	100	1.00	71	.70	50	.54	54	.50
ENSG00000107882	SUFU	Medulloblastoma	155255	43	.32	64	.72	29	.36	59	.63
ENSG00000107815	PEO1	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	11	.10	101	1.00	91	1.00	73	.88
ENSG00000151729	SLC25A4	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	7	.02	71	.79	78	.82	36	.62
ENSG00000140521	POLG	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	77	.92	9	.05	36	.56	90	.97
ENSG00000136827	TOR1A	Myoclonic dystonia	159900	25	.21	61	.72	19	.22	67	.87
ENSG00000127990	SGCE	Myoclonic dystonia	159900	26	.35	21	.18	73	.86	69	.86
ENSG00000149295	DRD2	Myoclonic dystonia	159900	18	.27	40	.34	49	.42	1	.01
ENSG00000143632	ACTA1	Nemaline myopathy 1, autosomal dominant; NEM1	161800	72	.83	59	.68	18	.25	12	.15
ENSG00000143549	TPM3	Nemaline myopathy 1, autosomal dominant; NEM1	161800	11	.06	55	.75	22	.38	13	.17
ENSG00000198467	TPM2	Nemaline myopathy 1, autosomal dominant; NEM1	161800	67	.61	4	.03	4	.04	11	.15
ENSG00000179142	CYP11B2	IgA nephropathy	161950	100	1.00	23	.32	95	1.00	38	.53
ENSG00000135744	AGT	IgA nephropathy	161950	84	.89	19	.18	27	.67	67	.80
ENSG00000159640	ACE	IgA nephropathy	161950	2	.00	61	.68	75	.91	50	.65
ENSG00000007908	SELE	IgA nephropathy	161950	100	1.00	24	.18	20	.24	1	.01
ENSG00000133256	PDE6B	Night blindness, congenital stationary; CSNB3	163500	100	1.00	74	.90	9	.09	69	.76
ENSG00000114349	GNAT1	Night blindness, congenital stationary; CSNB3	163500	97	.96	30	.27	34	.26	57	.60
ENSG00000163914	RHO	Night blindness, congenital stationary; CSNB3	163500	100	1.00	82	.86	79	.83	83	.94
ENSG00000108576	SLC6A4	Obsessive-compulsive disorder 1; OCD1	164230	12	.10	9	.08	15	.26	88	.97
ENSG00000102468	HTR2A	Obsessive-compulsive disorder 1; OCD1	164230	84	.73	51	.68	89	.96	45	.55
ENSG00000176697	BDNF	Obsessive-compulsive disorder 1; OCD1	164230	100	1.00	20	.26	28	.22	46	.67
ENSG00000004948	CALCR	Osteoporosis, involutional	166710	100	1.00	14	.11	60	.62	55	.53
ENSG00000136244	IL6	Osteoporosis, involutional	166710	19	.10	60	.62	74	.79	21	.17
ENSG00000162337	LRP5	Osteoporosis, involutional	166710	30	.11	13	.10	47	.45	15	.09
ENSG00000108821	COL1A1	Osteoporosis, involutional	166710	12	.07	4	.01	1	.01	73	.50
ENSG00000111424	VDR	Osteoporosis, involutional	166710	93	.72	87	.89	36	.31	60	.52
ENSG00000145335	SNCA	Parkinson disease; PD	168600	51	.37	42	.40	47	.56	1	.00
ENSG00000154277	UCHL1	Parkinson disease; PD	168600	37	.24	82	.83	31	.33	4	.06
ENSG00000185345	PARK2	Parkinson disease; PD	168600	92	.88	38	.30	16	.12	1	.01
ENSG00000064692	SNCAIP	Parkinson disease; PD	168600	25	.08	100	1.00	81	.63	1	.00
ENSG00000081248	CACNA1S	Hypokalemic periodic paralysis; HOKPP	170400	100	1.00	70	.82	59	.76	5	.12
ENSG00000175538	KCNE3	Hypokalemic periodic paralysis; HOKPP	170400	100	1.00	33	.27	14	.26	7	.06
ENSG00000007314	SCN4A	Hypokalemic periodic paralysis; HOKPP	170400	47	.57	51	.58	33	.72	54	.77
ENSG00000165731	RET	Pheochromocytoma	171300	91	.92	54	.54	12	.07	64	.88
ENSG00000117118	SDHB	Pheochromocytoma	171300	59	.70	51	.64	44	.51	50	.70
ENSG00000196712	NF1	Pheochromocytoma	171300	88	.92	84	.93	60	.56	53	.62
ENSG00000183765	CHEK2	Prostate cancer	176807	25	.21	52	.48	31	.41	14	.08
ENSG00000085117	CD82	Prostate cancer	176807	22	.09	100	1.00	57	.61	76	.78
ENSG00000067082	KLF6	Prostate cancer	176807	53	.28	94	.88	78	.69	27	.22
ENSG00000133216	EPHB2	Prostate cancer	176807	66	.47	35	.27	100	1.00	53	.43
ENSG00000135828	RNASEL	Prostate cancer	176807	100	1.00	60	.35	66	.66	25	.15
ENSG00000038945	MSR1	Prostate cancer	176807	100	1.00	24	.16	38	.28	94	.83
ENSG00000171862	PTEN_HUMAN	Prostate cancer	176807	6	.01	20	.07	6	.02	73	.57
ENSG00000006744	RNZ2_HUMAN	Prostate cancer	176807	58	.36	21	.10	66	.63	60	.53
ENSG00000002822	MAD1L1	Prostate cancer	176807	76	.61	59	.55	47	.51	89	.78
ENSG00000159339	PADI4	Rheumatoid arthritis	180300	100	1.00	77	.92	33	.29	55	.58
ENSG00000168593	NFKBIL1	Rheumatoid arthritis	180300	100	1.00	58	.41	64	.60	72	.74
ENSG00000159216	RUNX1	Rheumatoid arthritis	180300	43	.29	80	.72	85	.85	85	.89
ENSG00000134242	PTPN22	Rheumatoid arthritis	180300	57	.36	62	.59	89	.91	62	.57
ENSG00000197208	SLC22A4	Rheumatoid arthritis	180300	35	.17	40	.47	52	.44	17	.14
ENSG00000138293	NCOA4	Thyroid carcinoma, papillary	188550	7	.04	72	.91	70	.82	100	1.00
ENSG00000108946	PRKAR1A	Thyroid carcinoma, papillary	188550	7	.03	47	.45	31	.40	49	.73
ENSG00000198400	NTRK1	Thyroid carcinoma, papillary	188550	100	1.00	38	.41	31	.29	56	.42
ENSG00000114354	TFG	Thyroid carcinoma, papillary	188550	70	.51	24	.37	27	.38	30	.30
ENSG00000047410	TPR	Thyroid carcinoma, papillary	188550	16	.06	48	.36	11	.09	21	.21
ENSG00000129991	TNNI3	Cardiomyopathy, familial hypertrophic; CMH	192600	100	1.00	67	.63	70	.72	18	.05
ENSG00000160808	MYL3	Cardiomyopathy, familial hypertrophic; CMH	192600	17	.02	1	.00	1	.00	1	.01
ENSG00000140416	TPM1	Cardiomyopathy, familial hypertrophic; CMH	192600	47	.21	1	.00	5	.05	2	.01
ENSG00000155657	NP_59687.1	Cardiomyopathy, familial hypertrophic; CMH	192600	35	.08	1	.01	12	.08	41	.29
ENSG00000092054	MYH7	Cardiomyopathy, familial hypertrophic; CMH	192600	52	.14	5	.03	34	.23	42	.32
ENSG00000111245	MYL2	Cardiomyopathy, familial hypertrophic; CMH	192600	94	.58	1	.00	1	.00	3	.03
ENSG00000118194	TNNT2	Cardiomyopathy, familial hypertrophic; CMH	192600	45	.10	4	.03	93	.78	1	.00
ENSG00000134571	MYBPC3	Cardiomyopathy, familial hypertrophic; CMH	192600	101	1.00	1	.00	7	.06	11	.14
ENSG00000101306	MYLK2	Cardiomyopathy, familial hypertrophic; CMH	192600	100	1.00	47	.40	37	.44	42	.38
ENSG00000183785	PEX26	Adrenoleukodystrophy, autosomal neonatal form	202370	75	.46	1	.00	5	.01	14	.06
ENSG00000162928	PEX13	Adrenoleukodystrophy, autosomal neonatal form	202370	97	.94	1	.00	86	.67	1	.01
ENSG00000127980	PEX1	Adrenoleukodystrophy, autosomal neonatal form	202370	91	.64	33	.37	74	.50	8	.04
ENSG00000157911	PEX10	Adrenoleukodystrophy, autosomal neonatal form	202370	82	.46	2	.00	79	.61	10	.03
ENSG00000139197	PEX5	Adrenoleukodystrophy, autosomal neonatal form	202370	62	.27	2	.00	1	.00	4	.01
ENSG00000047579	DTNBP1	Hermansky-Pudlak syndrome; HPS	203300	78	.51	21	.14	44	.27	93	.89
ENSG00000107521	HPS1	Hermansky-Pudlak syndrome; HPS	203300	87	.83	8	.07	25	.21	29	.24
ENSG00000132842	AP3B1	Hermansky-Pudlak syndrome; HPS	203300	33	.14	100	1.00	52	.41	34	.42
ENSG00000110756	HPS5	Hermansky-Pudlak syndrome; HPS	203300	96	.94	100	1.00	44	.43	50	.46
ENSG00000100099	HPS4	Hermansky-Pudlak syndrome; HPS	203300	67	.50	84	.83	87	.83	13	.14
ENSG00000163755	HPS3	Hermansky-Pudlak syndrome; HPS	203300	100	1.00	100	1.00	100	1.00	100	1.00
ENSG00000092200	RPGRIP1	Leber congenital amaurosis, type I; LCA1	204000	30	.15	1	.02	4	.03	16	.07
ENSG00000132518	GUCY2D	Leber congenital amaurosis, type I; LCA1	204000	100	1.00	37	.24	49	.26	71	.68
ENSG00000116745	RPE65	Leber congenital amaurosis, type I; LCA1	204000	45	.15	35	.23	46	.27	64	.56
ENSG00000139988	RDH12_HUMAN	Leber congenital amaurosis, type I; LCA1	204000	40	.20	31	.26	14	.08	73	.51
ENSG00000134376	CRB1	Leber congenital amaurosis, type I; LCA1	204000	101	1.00	5	.01	39	.19	1	.00
ENSG00000105392	CRX	Leber congenital amaurosis, type I; LCA1	204000	56	.21	53	.36	7	.05	22	.09
ENSG00000112041	TULP1	Leber congenital amaurosis, type I; LCA1	204000	13	.04	5	.02	50	.40	59	.49
ENSG00000165731	RET	Autonomic control, congenital failure of	209880	38	.13	81	.84	42	.32	12	.12
ENSG00000124205	EDN3	Autonomic control, congenital failure of	209880	100	1.00	23	.30	20	.17	71	.77
ENSG00000109132	PHOX2B	Autonomic control, congenital failure of	209880	79	.45	69	.60	3	.02	3	.02
ENSG00000176697	BDNF	Autonomic control, congenital failure of	209880	100	1.00	86	.85	84	.71	35	.39
ENSG00000168621	GDNF	Autonomic control, congenital failure of	209880	100	1.00	30	.18	80	.56	13	.12
ENSG00000165533	TTC8	Bardet-Biedl syndrome; BBS	209900	100	1.00	15	.10	12	.05	9	.02
ENSG00000174483	DPP3	Bardet-Biedl syndrome; BBS	209900	93	.69	37	.19	51	.34	31	.22
ENSG00000138686	BBS7_HUMAN	Bardet-Biedl syndrome; BBS	209900	33	.12	1	.01	73	.52	86	.82
ENSG00000125124	BBS2	Bardet-Biedl syndrome; BBS	209900	100	1.00	2	.01	1	.00	2	.02
ENSG00000125863	MKKS	Bardet-Biedl syndrome; BBS	209900	57	.23	80	.63	87	.67	73	.59
ENSG00000140463	BBS4	Bardet-Biedl syndrome; BBS	209900	31	.15	23	.10	9	.05	5	.03
ENSG00000113966	ARL6	Bardet-Biedl syndrome; BBS	209900	100	1.00	30	.21	45	.39	35	.28
ENSG00000163093	BBS5	Bardet-Biedl syndrome; BBS	209900	12	.04	100	1.00	11	.05	14	.05
ENSG00000133111	RFXAP	Bare lymphocyte syndrome, type II	209920	9	.09	71	.77	48	.52	1	.00
ENSG00000179583	MHC2TA	Bare lymphocyte syndrome, type II	209920	100	1.00	17	.22	18	.09	1	.00
ENSG00000064490	RFXANK	Bare lymphocyte syndrome, type II	209920	36	.25	76	.71	46	.26	1	.00
ENSG00000143390	RFX5	Bare lymphocyte syndrome, type II	209920	73	.65	68	.75	4	.03	1	.00
ENSG00000159128	IFNGR2	Atypical mycobacteriosis, familial	209950	3	.01	8	.05	6	.03	3	.02
ENSG00000096996	IL12RB1	Atypical mycobacteriosis, familial	209950	100	1.00	7	.08	20	.17	6	.03
ENSG00000027697	IFNGR1	Atypical mycobacteriosis, familial	209950	29	.18	7	.05	6	.06	2	.02
ENSG00000113302	IL12B	Atypical mycobacteriosis, familial	209950	100	1.00	9	.10	50	.44	3	.01
ENSG00000115415	STAT1	Atypical mycobacteriosis, familial	209950	76	.63	58	.60	25	.13	49	.45
ENSG00000073734	ABCB11	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	100	1.00	95	.97	85	.95	33	.31
ENSG00000005471	ABCB4	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	100	1.00	51	.55	74	.68	78	.78
ENSG00000081923	ATP8B1	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	4	.03	13	.24	84	.81	78	.82
ENSG00000099377	HSD3B7	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	41	.14	5	.06	5	.03	36	.31
ENSG00000133703	RASK_HUMAN	Lung cancer	211980	49	.40	63	.71	12	.16	15	.14
ENSG00000157764	BRAF	Lung cancer	211980	22	.08	15	.17	3	.04	3	.02
ENSG00000141510	TP53	Lung cancer	211980	2	.02	58	.61	43	.60	100	1.00
ENSG00000146648	EGFR	Lung cancer	211980	65	.60	43	.42	17	.18	10	.13
ENSG00000139197	PEX5	Zellweger syndrome; ZS	214100	21	.02	18	.04	2	.01	1	.00
ENSG00000142655	PEX14	Zellweger syndrome; ZS	214100	21	.04	5	.01	2	.01	2	.00
ENSG00000127980	PEX1	Zellweger syndrome; ZS	214100	53	.18	10	.09	2	.01	4	.03
ENSG00000124587	PEX6	Zellweger syndrome; ZS	214100	39	.12	6	.07	1	.02	11	.11
ENSG00000108733	PEX12	Zellweger syndrome; ZS	214100	81	.44	54	.46	57	.40	1	.00
ENSG00000164751	PXMP3	Zellweger syndrome; ZS	214100	53	.19	75	.59	86	.67	1	.00
ENSG00000183785	PEX26	Zellweger syndrome; ZS	214100	48	.18	11	.06	12	.06	5	.03
ENSG00000034693	PEX3	Zellweger syndrome; ZS	214100	1	.00	1	.00	41	.19	1	.00
ENSG00000172660	TAF15	Chondrosarcoma	215300	80	.75	72	.74	80	.69	35	.35
ENSG00000182197	EXT1	Chondrosarcoma	215300	35	.17	16	.17	89	.86	2	.03
ENSG00000151348	EXT2	Chondrosarcoma	215300	4	.02	10	.15	72	.66	4	.07
ENSG00000182944	EWSR1	Chondrosarcoma	215300	16	.06	91	.95	14	.12	15	.17
ENSG00000134183	GNAT2	Achromatopsia 2; ACHM2	216900	50	.55	68	.86	90	.98	88	.97
ENSG00000144191	CNGA3	Achromatopsia 2; ACHM2	216900	91	.90	34	.32	6	.06	74	.83
ENSG00000170289	CNGB3	Achromatopsia 2; ACHM2	216900	100	1.00	53	.76	20	.28	38	.58
ENSG00000065618	COHA1_HUMAN	Epidermolysis bullosa junctionalis, disentis type	226650	8	.08	53	.48	36	.36	47	.38
ENSG00000132470	ITGB4	Epidermolysis bullosa junctionalis, disentis type	226650	100	1.00	52	.58	25	.27	85	.93
ENSG00000196878	LAMB3	Epidermolysis bullosa junctionalis, disentis type	226650	100	1.00	13	.11	4	.07	36	.41
ENSG00000053747	LAMA3	Epidermolysis bullosa junctionalis, disentis type	226650	95	.94	50	.50	60	.62	18	.17
ENSG00000165281	FANCG	Fanconi anemia; FA	227650	59	.21	73	.57	29	.13	1	.00
ENSG00000144554	FANCD2	Fanconi anemia; FA	227650	100	1.00	17	.09	19	.10	1	.00
ENSG00000183161	FANCF	Fanconi anemia; FA	227650	25	.09	14	.10	21	.12	1	.00
ENSG00000158169	FANCC	Fanconi anemia; FA	227650	42	.24	18	.09	43	.33	1	.00
ENSG00000115392	FANCL	Fanconi anemia; FA	227650	80	.69	70	.56	9	.06	4	.01
ENSG00000112039	FANCE	Fanconi anemia; FA	227650	1	.00	20	.09	21	.16	1	.00
ENSG00000105379	ETFB	Glutaricaciduria IIA	231680	1	.01	5	.09	1	.00	1	.00
ENSG00000140374	ETFA	Glutaricaciduria IIA	231680	2	.00	1	.07	1	.01	1	.00
ENSG00000171503	ETFDH	Glutaricaciduria IIA	231680	1	.00	22	.33	1	.00	1	.00
ENSG00000188690	UROS	Hydrops fetalis, idiopathic	236750	29	.17	91	.94	2	.03	28	.25
ENSG00000169919	GUSB	Hydrops fetalis, idiopathic	236750	91	.84	75	.85	43	.36	81	.93
ENSG00000177628	GBA	Hydrops fetalis, idiopathic	236750	81	.79	74	.89	58	.64	52	.65
ENSG00000188536	HBA1	Hydrops fetalis, idiopathic	236750	20	.12	82	.96	54	.50	42	.37
ENSG00000179477	ALOX12B	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	100	1.00	52	.74	17	.17	14	.17
ENSG00000011198	ABHD5	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	31	.22	3	.04	1	.01	2	.02
ENSG00000092295	TGM1	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	48	.55	4	.05	4	.06	85	.90
ENSG00000122735	DNAI1	Kartagener syndrome	244400	3	.03	100	1.00	13	.22	24	.22
ENSG00000039139	DNAH5	Kartagener syndrome	244400	100	1.00	2	.03	1	.01	14	.11
ENSG00000105877	DNAH11	Kartagener syndrome	244400	100	1.00	4	.03	2	.02	5	.08
ENSG00000112964	GHR	Laron syndrome, type II	245590	100	1.00	42	.35	13	.12	4	.04
ENSG00000189162	GH1	Laron syndrome, type II	245590	100	1.00	6	.05	12	.12	6	.10
ENSG00000173757	STAT5B	Laron syndrome, type II	245590	32	.36	74	.91	78	.93	33	.37
ENSG00000137992	DBT	Maple syrup urine disease, type IA	248600	87	.81	54	.47	76	.68	3	.01
ENSG00000091140	DLD	Maple syrup urine disease, type IA	248600	33	.18	78	.83	42	.27	1	.02
ENSG00000142046	BCKDHA	Maple syrup urine disease, type IA	248600	4	.03	23	.27	8	.09	1	.00
ENSG00000083123	BCKDHB	Maple syrup urine disease, type IA	248600	53	.40	9	.06	63	.49	1	.00
ENSG00000167792	NDUFV1	Complex I, mitochondrial respiratory chain, deficiency of	252010	65	.40	6	.03	5	.01	7	.02
ENSG00000115286	NDUFS7	Complex I, mitochondrial respiratory chain, deficiency of	252010	63	.35	2	.01	12	.04	9	.05
ENSG00000164258	NDUFS4	Complex I, mitochondrial respiratory chain, deficiency of	252010	68	.56	1	.01	1	.01	1	.00
ENSG00000023228	NDUFS1	Complex I, mitochondrial respiratory chain, deficiency of	252010	74	.53	6	.01	1	.00	2	.00
ENSG00000158864	NDUFS2	Complex I, mitochondrial respiratory chain, deficiency of	252010	37	.20	5	.01	2	.00	1	.00
ENSG00000110092	CCND1	Myeloma, multiple	254500	2	.00	86	.83	93	.95	24	.22
ENSG00000130076	IGHG3	Myeloma, multiple	254500	18	.13	38	.44	90	.91	94	.97
ENSG00000068078	FGFR3	Myeloma, multiple	254500	65	.66	2	.04	61	.62	22	.18
ENSG00000137265	IRF4	Myeloma, multiple	254500	54	.44	53	.66	46	.37	47	.48
ENSG00000115286	NDUFS7	Leigh syndrome; LS	256000	19	.02	2	.01	1	.00	1	.00
ENSG00000074582	BCS1L	Leigh syndrome; LS	256000	20	.05	60	.52	36	.27	53	.41
ENSG00000073578	SDHA	Leigh syndrome; LS	256000	5	.01	17	.11	47	.39	26	.16
ENSG00000110536	NDUFS3	Leigh syndrome; LS	256000	6	.01	2	.01	1	.01	1	.02
ENSG00000091140	DLD	Leigh syndrome; LS	256000	96	.65	4	.04	65	.49	36	.31
ENSG00000110717	NDUFS8	Leigh syndrome; LS	256000	4	.01	2	.00	2	.01	2	.01
ENSG00000006071	ABCC8	Nesidioblastosis of pancreas	256450	86	.84	75	.95	59	.84	86	.97
ENSG00000106633	GCK	Nesidioblastosis of pancreas	256450	93	.89	34	.39	31	.28	62	.77
ENSG00000148672	GLUD1	Nesidioblastosis of pancreas	256450	35	.16	7	.09	36	.58	66	.73
ENSG00000141655	TNFRSF11A	Osteogenic sarcoma	259500	100	1.00	17	.18	20	.25	12	.26
ENSG00000139687	RB1	Osteogenic sarcoma	259500	56	.50	82	.91	53	.52	21	.24
ENSG00000183765	CHEK2	Osteogenic sarcoma	259500	95	.94	27	.39	85	.89	72	.87
ENSG00000081087	OSTM1	Osteopetrosis, autosomal recessive	259700	10	.16	100	1.00	36	.48	35	.46
ENSG00000103249	CLCN7	Osteopetrosis, autosomal recessive	259700	91	.93	80	.84	70	.76	74	.92
ENSG00000110719	TCIRG1	Osteopetrosis, autosomal recessive	259700	29	.45	37	.35	30	.29	44	.58
ENSG00000133703	RASK_HUMAN	Pancreatic carcinoma	260350	30	.11	11	.09	6	.03	16	.11
ENSG00000141510	TP53	Pancreatic carcinoma	260350	33	.21	83	.80	36	.32	67	.61
ENSG00000145050	ARMET	Pancreatic carcinoma	260350	43	.30	100	1.00	92	.89	83	.75
ENSG00000147889	CDKN2A	Pancreatic carcinoma	260350	100	1.00	8	.09	97	.92	96	.93
ENSG00000141646	SMAD4	Pancreatic carcinoma	260350	3	.03	85	.83	72	.73	70	.65
ENSG00000139618	BRCA2	Pancreatic carcinoma	260350	83	.74	90	.85	84	.83	25	.15
ENSG00000144191	CNGA3	Achromatopsia 3; ACHM3	262300	91	.89	31	.28	8	.07	72	.81
ENSG00000134183	GNAT2	Achromatopsia 3; ACHM3	262300	52	.59	69	.85	92	.97	82	.96
ENSG00000170289	CNGB3	Achromatopsia 3; ACHM3	262300	100	1.00	51	.70	20	.28	39	.58
ENSG00000107187	LHX3	Pituitary dwarfism III	262600	40	.31	90	.98	73	.77	88	.99
ENSG00000163666	HESX1	Pituitary dwarfism III	262600	101	1.00	93	.98	44	.68	6	.04
ENSG00000175325	PROP1	Pituitary dwarfism III	262600	100	1.00	74	.81	17	.21	4	.03
ENSG00000111319	SCNN1A	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	11	.17	1	.01	3	.03	7	.22
ENSG00000168447	SCNN1B	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	44	.62	2	.01	1	.00	17	.17
ENSG00000151623	NR3C2	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	56	.56	86	.95	92	.97	26	.37
ENSG00000168878	SFTPB	Pulmonary alveolar proteinosis	265120	100	1.00	2	.05	11	.15	29	.59
ENSG00000100368	CSF2RB	Pulmonary alveolar proteinosis	265120	100	1.00	75	.91	67	.99	65	.89
ENSG00000168484	SFTPC	Pulmonary alveolar proteinosis	265120	100	1.00	2	.02	1	.02	56	.77
ENSG00000164751	PXMP3	Refsum disease, infantile form	266510	62	.62	37	.32	48	.51	92	.98
ENSG00000127980	PEX1	Refsum disease, infantile form	266510	10	.12	88	.97	67	.79	1	.00
ENSG00000183785	PEX26	Refsum disease, infantile form	266510	44	.42	2	.03	3	.02	1	.00
ENSG00000197375	SLC22A5	Inflammatory bowel disease 1; IBD1	266600	18	.12	58	.76	9	.06	5	.04
ENSG00000151208	DLG5	Inflammatory bowel disease 1; IBD1	266600	99	.97	66	.61	15	.21	74	.83
ENSG00000167207	CARD15	Inflammatory bowel disease 1; IBD1	266600	100	1.00	78	.89	35	.36	17	.15
ENSG00000085563	ABCB1	Inflammatory bowel disease 1; IBD1	266600	100	1.00	39	.50	23	.20	15	.20
ENSG00000092929	UNC13D	Reticulosis, familial histiocytic	267700	100	1.00	16	.21	13	.19	28	.40
ENSG00000166349	RAG1	Reticulosis, familial histiocytic	267700	100	1.00	50	.61	77	.88	97	1.00
ENSG00000180644	PRF1	Reticulosis, familial histiocytic	267700	14	.12	100	1.00	25	.27	83	.92
ENSG00000135903	PAX3	Rhabdomyosarcoma 2; RMS2	268220	37	.25	64	.72	59	.72	93	.99
ENSG00000150907	FOXO1A	Rhabdomyosarcoma 2; RMS2	268220	53	.31	80	.91	49	.53	78	.95
ENSG00000009709	PAX7	Rhabdomyosarcoma 2; RMS2	268220	17	.13	78	.80	65	.57	63	.61
ENSG00000108576	SLC6A4	Sudden infant death syndrome	272120	56	.61	29	.41	16	.27	86	.97
ENSG00000183873	SCN5A	Sudden infant death syndrome	272120	91	.95	6	.10	7	.07	11	.17
ENSG00000053918	KCNQ1	Sudden infant death syndrome	272120	100	1.00	9	.09	5	.12	31	.41
ENSG00000163599	CTLA4	Graves disease	275000	100	1.00	10	.11	100	1.00	79	.97
ENSG00000145321	GC	Graves disease	275000	18	.21	69	.78	58	.45	22	.14
ENSG00000111424	VDR	Graves disease	275000	35	.24	78	.93	12	.09	4	.08
ENSG00000134982	APC	Turcot syndrome	276300	2	.01	88	.96	3	.03	14	.15
ENSG00000076242	MLH1	Turcot syndrome	276300	57	.64	1	.00	50	.72	14	.18
ENSG00000122512	PMS2	Turcot syndrome	276300	2	.03	1	.00	7	.10	2	.04
ENSG00000100146	SOX10	Waardenburg-Shah syndrome	277580	45	.47	28	.17	93	.98	79	.93
ENSG00000124205	EDN3	Waardenburg-Shah syndrome	277580	100	1.00	85	.96	68	.84	4	.09
ENSG00000136160	EDNRB	Waardenburg-Shah syndrome	277580	100	1.00	1	.01	1	.01	35	.35
ENSG00000112357	PEX7	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	70	.56	17	.22	59	.70	78	.90
ENSG00000116906	GNPAT	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	67	.72	65	.73	66	.76	9	.06
ENSG00000018510	AGPS	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	50	.57	6	.03	62	.69	2	.02
ENSG00000170175	CHRNB1	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	14	.13	30	.29	18	.25	67	.79
ENSG00000135902	CHRND	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	45	.27	1	.00	1	.00	33	.32
ENSG00000138435	CHRNA1	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	100	1.00	1	.00	1	.00	3	.02
ENSG00000175426	PCSK1	Obesity	601665	13	.03	42	.28	20	.12	20	.09
ENSG00000115138	POMC	Obesity	601665	72	.45	57	.55	73	.62	2	.00
ENSG00000116678	LEPR	Obesity	601665	5	.00	63	.60	28	.24	2	.00
ENSG00000166603	MC4R	Obesity	601665	100	1.00	5	.03	68	.57	4	.04
ENSG00000174483	DPP3	Obesity	601665	14	.07	56	.49	2	.02	4	.02
ENSG00000174697	LEP	Obesity	601665	100	1.00	87	.82	92	.81	20	.12
ENSG00000130203	APOE	Obesity	601665	25	.14	15	.18	63	.40	43	.23
ENSG00000142156	COL6A1	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	100	1.00	27	.29	21	.20	43	.47
ENSG00000112112	COL11A2	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	94	.90	23	.32	15	.26	7	.17
ENSG00000197594	ENPP1	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	100	1.00	35	.40	51	.64	26	.43
ENSG00000115211	EIF2B4	Leukoencephalopathy with vanishing white matter; VWM	603896	3	.00	1	.00	1	.00	2	.01
ENSG00000070785	EIF2B3	Leukoencephalopathy with vanishing white matter; VWM	603896	4	.00	1	.00	2	.00	5	.01
ENSG00000111361	EIF2B1	Leukoencephalopathy with vanishing white matter; VWM	603896	3	.00	3	.00	1	.00	2	.00
ENSG00000119718	EIF2B2	Leukoencephalopathy with vanishing white matter; VWM	603896	4	.00	1	.00	1	.00	1	.00
ENSG00000145191	EIF2B5	Leukoencephalopathy with vanishing white matter; VWM	603896	1	.00	2	.00	1	.00	3	.01
ENSG00000138061	CYP1B1	Peters anomaly	604229	17	.14	40	.32	32	.38	9	.07
ENSG00000054598	FOXC1	Peters anomaly	604229	16	.04	63	.74	94	.88	97	.97
ENSG00000164093	PITX2	Peters anomaly	604229	100	1.00	80	.77	8	.08	68	.81
ENSG00000007372	PAX6	Peters anomaly	604229	90	.84	64	.71	38	.40	4	.04
ENSG00000144285	SCN1A	Generalized epilepsy with febrile seizures plus; GEFS+	604233	1	.01	9	.06	3	.01	9	.06
ENSG00000113327	GABRG2	Generalized epilepsy with febrile seizures plus; GEFS+	604233	8	.04	25	.36	81	.84	65	.86
ENSG00000105711	SCN1B	Generalized epilepsy with febrile seizures plus; GEFS+	604233	28	.29	6	.05	4	.02	91	.92
ENSG00000157764	BRAF	Lymphoma, non-Hodgkin, familial	605027	80	.66	10	.09	48	.43	68	.69
ENSG00000003400	CASP10	Lymphoma, non-Hodgkin, familial	605027	42	.29	1	.01	6	.05	3	.05
ENSG00000149311	ATM	Lymphoma, non-Hodgkin, familial	605027	59	.30	88	.84	23	.14	89	.96
ENSG00000085999	RAD54L	Lymphoma, non-Hodgkin, familial	605027	92	.69	33	.31	7	.07	4	.01
ENSG00000143294	PRCC	Renal cell carcinoma, papillary	605074	58	.65	100	1.00	14	.20	25	.37
ENSG00000068323	TFE3	Renal cell carcinoma, papillary	605074	27	.30	58	.90	92	.98	7	.07
ENSG00000105976	MET	Renal cell carcinoma, papillary	605074	5	.02	25	.45	50	.68	82	.98
ENSG00000102245	TNFL5_HUMAN	Immunodeficiency with hyper-IgM, type 2	605258	100	1.00	6	.08	29	.32	2	.04
ENSG00000101017	CD40	Immunodeficiency with hyper-IgM, type 2	605258	25	.24	20	.12	55	.66	4	.09
ENSG00000111732	AICDA	Immunodeficiency with hyper-IgM, type 2	605258	100	1.00	33	.26	73	.65	19	.32
ENSG00000139515	IPF1	Maturity-onset diabetes of the young; MODY	606391	100	1.00	78	.71	86	.74	1	.00
ENSG00000135100	TCF1	Maturity-onset diabetes of the young; MODY	606391	100	1.00	89	.81	61	.53	2	.05
ENSG00000101076	HNF4A	Maturity-onset diabetes of the young; MODY	606391	32	.14	78	.75	65	.47	19	.10
ENSG00000106633	GCK	Maturity-onset diabetes of the young; MODY	606391	75	.41	62	.50	60	.52	15	.10
ENSG00000162992	NEUROD1	Maturity-onset diabetes of the young; MODY	606391	4	.01	29	.16	2	.00	10	.07
ENSG00000102245	TNFL5_HUMAN	Immunodeficiency with hyper-IgM, type 3	606843	100	1.00	5	.07	28	.30	4	.09
ENSG00000101017	CD40	Immunodeficiency with hyper-IgM, type 3	606843	26	.26	10	.09	53	.63	5	.11
ENSG00000111732	AICDA	Immunodeficiency with hyper-IgM, type 3	606843	100	1.00	34	.25	72	.68	32	.34
ENSG00000022355	GABRA1	Myoclonic epilepsy, juvenile; JME	606904	54	.31	47	.41	56	.42	9	.06
ENSG00000096093	EFHC1	Myoclonic epilepsy, juvenile; JME	606904	100	1.00	100	1.00	100	1.00	25	.30
ENSG00000182389	CACNB4	Myoclonic epilepsy, juvenile; JME	606904	95	.81	71	.71	43	.34	74	.76
ENSG00000114859	CLCN2	Myoclonic epilepsy, juvenile; JME	606904	100	1.00	2	.00	17	.09	21	.17
ENSG00000179295	PTPN11	Juvenile myelomonocytic leukemia	607785	79	.77	5	.06	60	.75	24	.35
ENSG00000196712	NF1	Juvenile myelomonocytic leukemia	607785	42	.26	5	.06	13	.12	1	.00
ENSG00000133703	RASK_HUMAN	Juvenile myelomonocytic leukemia	607785	73	.45	9	.09	8	.05	13	.12
ENSG00000168638	NRAS	Juvenile myelomonocytic leukemia	607785	24	.05	56	.57	4	.02	19	.16
ENSG00000197499	HLA-A	Mycobacterium tuberculosis, susceptibility to infection by	607948	46	.60	27	.33	50	.76	65	.80
ENSG00000165471	MBL2	Mycobacterium tuberculosis, susceptibility to infection by	607948	100	1.00	27	.33	11	.22	40	.61
ENSG00000111424	VDR	Mycobacterium tuberculosis, susceptibility to infection by	607948	92	.98	83	.96	83	.90	45	.68
ENSG00000108556	CHRNE	Myasthenic syndrome, congenital, fast-channel	608930	100	1.00	36	.30	93	.92	1	.00
ENSG00000138435	CHRNA1	Myasthenic syndrome, congenital, fast-channel	608930	100	1.00	1	.00	1	.00	1	.00
ENSG00000135902	CHRND	Myasthenic syndrome, congenital, fast-channel	608930	39	.27	2	.00	2	.01	3	.01
150 Genes:
ENSG00000091513	TF	Alzheimer disease; AD	104300	121	.53	116	.81	135	.90	27	.15
ENSG00000175899	A2M	Alzheimer disease; AD	104300	98	.31	109	.71	144	.96	10	.06
ENSG00000143801	PSEN2	Alzheimer disease; AD	104300	78	.17	32	.09	109	.61	146	.97
ENSG00000123384	LRP1	Alzheimer disease; AD	104300	150	1.00	76	.44	72	.45	45	.25
ENSG00000142192	APP	Alzheimer disease; AD	104300	61	.27	97	.83	115	.72	51	.22
ENSG00000130203	APOE	Alzheimer disease; AD	104300	63	.15	8	.02	1	.00	25	.10
ENSG00000010704	HFE	Alzheimer disease; AD	104300	48	.11	117	.76	138	.76	2	.01
ENSG00000080815	PSEN1	Alzheimer disease; AD	104300	60	.14	21	.07	143	.94	120	.79
ENSG00000101439	CST3	Amyloidosis VI	105150	48	.39	141	1.00	93	.74	115	.91
ENSG00000165029	ABCA1	Amyloidosis VI	105150	150	1.00	128	.98	29	.29	6	.10
ENSG00000136156	ITM2B	Amyloidosis VI	105150	99	.76	25	.29	49	.50	53	.53
ENSG00000171560	FGA	Amyloidosis, familial visceral	105200	151	1.00	150	1.00	136	.95	120	.93
ENSG00000090382	LYZ	Amyloidosis, familial visceral	105200	108	.89	26	.20	125	.94	55	.48
ENSG00000118137	APOA1	Amyloidosis, familial visceral	105200	46	.30	35	.22	49	.31	110	.90
ENSG00000124164	VAPB	Amyotrophic lateral sclerosis 1; ALS1	105400	44	.10	54	.26	79	.44	78	.43
ENSG00000003393	ALS2	Amyotrophic lateral sclerosis 1; ALS1	105400	150	1.00	77	.38	104	.58	103	.67
ENSG00000142168	SOD1	Amyotrophic lateral sclerosis 1; ALS1	105400	60	.23	6	.02	66	.32	137	.98
ENSG00000100285	NEFH	Amyotrophic lateral sclerosis 1; ALS1	105400	109	.47	24	.13	127	.76	140	.92
ENSG00000135406	PRPH	Amyotrophic lateral sclerosis 1; ALS1	105400	107	.40	150	1.00	54	.28	129	.81
ENSG00000112112	COL11A2	Stickler syndrome, type I; STL1	108300	142	.98	29	.36	30	.27	23	.28
ENSG00000139219	COL2A1	Stickler syndrome, type I; STL1	108300	150	1.00	62	.52	66	.44	110	.87
ENSG00000060718	COL11A1	Stickler syndrome, type I; STL1	108300	151	1.00	56	.41	32	.25	33	.33
ENSG00000139687	RB1	Bladder cancer	109800	24	.16	124	.98	2	.02	23	.33
ENSG00000068078	FGFR3	Bladder cancer	109800	34	.21	58	.58	110	.90	24	.36
ENSG00000174775	HRAS	Bladder cancer	109800	85	.66	56	.30	144	.98	143	1.00
ENSG00000139618	BRCA2	Breast cancer	114480	117	.37	50	.14	42	.09	34	.08
ENSG00000138376	BARD1	Breast cancer	114480	88	.19	110	.56	21	.07	7	.02
ENSG00000124151	NCOA3	Breast cancer	114480	128	.49	40	.10	37	.10	34	.05
ENSG00000121879	PIK3CA	Breast cancer	114480	106	.28	123	.69	1	.00	65	.20
ENSG00000012048	BRCA1	Breast cancer	114480	5	.00	86	.38	42	.10	50	.14
ENSG00000170836	PPM1D	Breast cancer	114480	110	.46	14	.02	124	.66	96	.42
ENSG00000141510	TP53	Breast cancer	114480	28	.05	114	.56	2	.01	83	.24
ENSG00000023287	RB1CC1	Breast cancer	114480	64	.10	86	.38	52	.14	36	.07
ENSG00000183765	CHEK2	Breast cancer	114480	18	.02	25	.07	62	.23	66	.23
ENSG00000169083	AR	Breast cancer	114480	150	1.00	124	.72	132	.74	76	.21
ENSG00000105976	MET	Hepatocellular carcinoma	114550	135	.94	88	.70	65	.47	11	.07
ENSG00000168036	CTNNB1	Hepatocellular carcinoma	114550	2	.02	83	.65	85	.70	36	.35
ENSG00000141510	TP53	Hepatocellular carcinoma	114550	85	.48	111	.89	95	.69	128	.92
ENSG00000138109	CYP2C9	Coumarin resistance	122700	125	.82	129	.93	76	.56	96	.67
ENSG00000167397	VKOR1_HUMAN	Coumarin resistance	122700	60	.24	113	.92	58	.30	36	.20
ENSG00000198470	CYP2A6	Coumarin resistance	122700	79	.48	45	.30	124	.86	83	.60
ENSG00000101981	F9	Coumarin resistance	122700	150	1.00	73	.44	54	.36	23	.20
ENSG00000162992	NEUROD1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	5	.00	41	.13	17	.02	79	.26
ENSG00000121653	MAPK8IP1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	138	.53	85	.43	4	.02	2	.01
ENSG00000163581	SLC2A2	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	130	.45	29	.06	8	.01	112	.50
ENSG00000181856	SLC2A4	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	122	.29	15	.08	17	.06	2	.00
ENSG00000105221	AKT2	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	63	.05	109	.79	72	.30	49	.16
ENSG00000101076	HNF4A	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	27	.02	127	.85	141	.87	100	.48
ENSG00000142330	CAPN10	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	113	1.00	76	.46	37	.13	56	.19
ENSG00000135100	TCF1	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	150	1.00	130	.88	118	.60	126	.66
ENSG00000104918	RETN	Diabetes mellitus, non–insulin-dependent; NIDDM	125853	104	.22	105	.53	150	1.00	59	.24
ENSG00000171867	PRNP	Dementia, Lewy body; DLB	127750	57	.40	150	1.00	80	.55	137	.99
ENSG00000145335	SNCA	Dementia, Lewy body; DLB	127750	26	.17	43	.32	93	.87	128	.97
ENSG00000130203	APOE	Dementia, Lewy body; DLB	127750	145	.99	48	.59	137	.98	94	.80
ENSG00000070182	SPTB	Elliptocytosis, Rhesus-unlinked type	130600	150	1.00	7	.11	82	.82	1	.01
ENSG00000163554	SPTA1	Elliptocytosis, Rhesus-unlinked type	130600	101	.58	26	.26	67	.45	2	.02
ENSG00000004939	SLC4A1	Elliptocytosis, Rhesus-unlinked type	130600	97	.61	45	.50	26	.20	21	.15
ENSG00000186847	KRT14	Epidermolysis bullosa of hands and feet	131800	150	1.00	24	.07	5	.02	19	.03
ENSG00000186081	KRT5	Epidermolysis bullosa of hands and feet	131800	150	1.00	15	.08	14	.05	3	.00
ENSG00000132470	ITGB4	Epidermolysis bullosa of hands and feet	131800	150	1.00	47	.23	93	.62	51	.35
ENSG00000114270	COL7A1	Epidermolysis bullosa of hands and feet	131800	59	.25	32	.15	72	.39	64	.37
ENSG00000127870	RNF6	Esophageal cancer	133239	48	.17	107	.68	15	.08	30	.15
ENSG00000134982	APC	Esophageal cancer	133239	2	.00	136	.97	30	.15	28	.13
ENSG00000147889	CDKN2A	Esophageal cancer	133239	150	1.00	111	.73	98	.53	61	.31
ENSG00000008226	DLEC1	Esophageal cancer	133239	150	1.00	19	.11	11	.04	9	.05
ENSG00000061337	LZTS1	Esophageal cancer	133239	150	1.00	141	.96	51	.26	57	.25
ENSG00000141510	TP53	Esophageal cancer	133239	143	.85	97	.64	133	.87	14	.07
ENSG00000187323	DCC	Esophageal cancer	133239	145	1.00	51	.28	44	.30	26	.09
ENSG00000186153	NP_570606.1	Esophageal cancer	133239	29	.11	111	.76	98	.50	2	.00
ENSG00000140522	RLBP1	Fundus albipunctatus	136880	139	.90	43	.27	124	.86	6	.03
ENSG00000112619	RDS	Fundus albipunctatus	136880	150	1.00	78	.56	116	.81	141	.96
ENSG00000135437	RDH5	Fundus albipunctatus	136880	150	1.00	81	.64	101	.71	5	.06
ENSG00000130203	APOE	Fundus albipunctatus	136880	147	1.00	1	.00	10	.04	23	.11
ENSG00000063169	GLTSCR1	Glioma of brain, familial	137800	48	.16	150	1.00	151	1.00	151	1.00
ENSG00000111087	GLI1_HUMAN	Glioma of brain, familial	137800	5	.01	17	.06	16	.06	33	.15
ENSG00000108231	LGI1	Glioma of brain, familial	137800	77	.27	150	1.00	150	1.00	150	1.00
ENSG00000147889	CDKN2A	Glioma of brain, familial	137800	150	1.00	145	.99	69	.38	77	.34
ENSG00000146648	EGFR	Glioma of brain, familial	137800	2	.00	4	.02	109	.65	27	.08
ENSG00000132170	PPARG	Glioma of brain, familial	137800	106	.45	107	.84	18	.11	110	.75
ENSG00000117298	ECE1	Hirschsprung disease	142623	87	.32	87	.48	93	.53	117	.79
ENSG00000136160	EDNRB	Hirschsprung disease	142623	150	1.00	96	.58	121	.68	59	.28
ENSG00000169554	ZFHX1B	Hirschsprung disease	142623	1	.00	125	.87	24	.10	29	.19
ENSG00000165731	RET	Hirschsprung disease	142623	92	.34	57	.27	23	.09	82	.44
ENSG00000124205	EDN3	Hirschsprung disease	142623	150	1.00	42	.23	32	.15	10	.08
ENSG00000168621	GDNF	Hirschsprung disease	142623	150	1.00	113	.62	85	.37	95	.70
ENSG00000130164	LDLR	Hypercholesterolemia, familial	143890	43	.15	15	.06	7	.07	4	.02
ENSG00000055955	ITIH4	Hypercholesterolemia, familial	143890	92	.37	63	.38	83	.62	135	.94
ENSG00000158874	APOA2	Hypercholesterolemia, familial	143890	150	1.00	65	.30	27	.17	31	.25
ENSG00000169174	PCSK9	Hypercholesterolemia, familial	143890	49	.20	50	.28	93	.72	90	.66
ENSG00000120915	EPHX2	Hypercholesterolemia, familial	143890	114	.47	59	.32	136	.95	91	.47
ENSG00000084674	APOB	Hypercholesterolemia, familial	143890	105	.55	7	.03	9	.05	23	.18
ENSG00000111664	GNB3	Hypertension, essential	145500	18	.04	70	.30	17	.05	79	.33
ENSG00000124212	PTGIS	Hypertension, essential	145500	132	.66	114	.86	117	.71	69	.49
ENSG00000087274	NP_789771.1	Hypertension, essential	145500	63	.16	119	.77	124	.80	147	.98
ENSG00000106258	CYP3A5	Hypertension, essential	145500	90	.34	62	.43	100	.64	73	.39
ENSG00000028137	TNFRSF1B	Hypertension, essential	145500	30	.09	87	.48	133	.82	81	.42
ENSG00000144891	AGTR1	Hypertension, essential	145500	68	.19	11	.04	32	.21	61	.33
ENSG00000135744	AGT	Hypertension, essential	145500	93	.34	31	.11	77	.42	31	.18
ENSG00000105227	PRX	Hypertrophic neuropathy of Dejerine-Sottas	145900	150	1.00	2	.03	4	.02	19	.11
ENSG00000109099	PMP22	Hypertrophic neuropathy of Dejerine-Sottas	145900	7	.02	77	.59	112	.85	12	.19
ENSG00000122877	EGR2	Hypertrophic neuropathy of Dejerine-Sottas	145900	79	.38	112	.93	139	.98	102	.78
ENSG00000158887	MPZ	Hypertrophic neuropathy of Dejerine-Sottas	145900	150	1.00	15	.12	13	.08	4	.05
ENSG00000169083	AR	Hypospadias	146450	150	1.00	112	.92	118	.90	25	.20
ENSG00000101871	MID1	Hypospadias	146450	103	1.00	20	.11	110	.81	10	.06
ENSG00000049319	SRD5A2	Hypospadias	146450	56	.48	21	.11	127	.93	70	.63
ENSG00000005471	ABCB4	Cholestasis, intrahepatic, of pregnancy; ICP	147480	150	1.00	73	.61	106	.71	112	.74
ENSG00000073734	ABCB11	Cholestasis, intrahepatic, of pregnancy; ICP	147480	150	1.00	139	.99	139	.98	85	.58
ENSG00000081923	ATP8B1	Cholestasis, intrahepatic, of pregnancy; ICP	147480	23	.07	41	.39	29	.17	121	.92
ENSG00000167768	KRT1	Keratosis palmoplantaris striata I	148700	71	.66	66	.61	67	.54	6	.05
ENSG00000134760	DSG1	Keratosis palmoplantaris striata I	148700	145	1.00	35	.44	53	.56	6	.03
ENSG00000096696	DSP	Keratosis palmoplantaris striata I	148700	60	.28	3	.04	1	.03	2	.02
ENSG00000111275	ALDH2	Leiomyoma, uterine	150699	87	.52	74	.53	25	.22	75	.64
ENSG00000143196	DPT	Leiomyoma, uterine	150699	70	.29	150	1.00	150	1.00	20	.11
ENSG00000182185	RAD51L1	Leiomyoma, uterine	150699	150	1.00	127	.97	29	.19	51	.34
ENSG00000164919	COX6C	Leiomyoma, uterine	150699	59	.23	60	.41	31	.17	30	.22
ENSG00000087237	CETP	Longevity	152430	93	.49	104	.91	52	.28	60	.52
ENSG00000108599	AKAP10	Longevity	152430	147	.99	29	.14	45	.22	63	.45
ENSG00000136869	TLR4	Longevity	152430	5	.02	34	.27	53	.31	85	.68
ENSG00000005421	PON1	Longevity	152430	150	1.00	26	.20	12	.04	91	.68
ENSG00000126594	DNASE1	Lupus erythematosus, systemic; SLE	152700	143	.92	89	.67	65	.52	121	.93
ENSG00000163599	CTLA4	Lupus erythematosus, systemic; SLE	152700	150	1.00	97	.82	150	1.00	106	.84
ENSG00000134242	PTPN22	Lupus erythematosus, systemic; SLE	152700	67	.32	37	.25	75	.54	66	.48
ENSG00000143226	FCGR2A	Lupus erythematosus, systemic; SLE	152700	41	.18	11	.10	30	.16	123	.75
ENSG00000168036	CTNNB1	Medulloblastoma	155255	97	.65	35	.20	15	.10	21	.21
ENSG00000134982	APC	Medulloblastoma	155255	7	.02	130	.98	2	.01	12	.06
ENSG00000117425	PTCH2	Medulloblastoma	155255	150	1.00	116	.88	82	.57	105	.68
ENSG00000107882	SUFU	Medulloblastoma	155255	91	.50	120	.87	87	.52	63	.38
ENSG00000107815	PEO1	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	19	.16	150	1.00	79	.73	69	.66
ENSG00000151729	SLC25A4	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	11	.03	111	.95	65	.52	96	.78
ENSG00000140521	POLG	Progressive external ophthalmoplegia with mtDNA deletions; PEO	157640	139	.98	14	.07	86	.75	136	.99
ENSG00000136827	TOR1A	Myoclonic dystonia	159900	59	.34	93	.81	61	.39	130	.97
ENSG00000127990	SGCE	Myoclonic dystonia	159900	67	.51	36	.25	57	.48	62	.64
ENSG00000149295	DRD2	Myoclonic dystonia	159900	54	.41	63	.48	81	.51	3	.02
ENSG00000143632	ACTA1	Nemaline myopathy 1, autosomal dominant; NEM1	161800	75	.66	97	.79	47	.37	25	.27
ENSG00000143549	TPM3	Nemaline myopathy 1, autosomal dominant; NEM1	161800	22	.13	62	.53	47	.51	23	.27
ENSG00000198467	TPM2	Nemaline myopathy 1, autosomal dominant; NEM1	161800	107	.77	3	.02	10	.08	22	.24
ENSG00000179142	CYP11B2	IgA nephropathy	161950	150	1.00	40	.43	73	.64	47	.43
ENSG00000135744	AGT	IgA nephropathy	161950	118	.74	32	.23	92	.68	132	.95
ENSG00000159640	ACE	IgA nephropathy	161950	1	.00	114	.88	102	.84	113	.83
ENSG00000007908	SELE	IgA nephropathy	161950	150	1.00	34	.26	32	.28	3	.02
ENSG00000133256	PDE6B	Night blindness, congenital stationary; CSNB3	163500	150	1.00	97	.67	25	.17	71	.55
ENSG00000114349	GNAT1	Night blindness, congenital stationary; CSNB3	163500	109	.75	66	.35	71	.42	91	.78
ENSG00000163914	RHO	Night blindness, congenital stationary; CSNB3	163500	150	1.00	126	.95	137	.96	131	.97
ENSG00000108576	SLC6A4	Obsessive-compulsive disorder 1; OCD1	164230	28	.13	23	.19	57	.39	150	1.00
ENSG00000102468	HTR2A	Obsessive-compulsive disorder 1; OCD1	164230	141	.89	86	.82	105	.83	95	.76
ENSG00000176697	BDNF	Obsessive-compulsive disorder 1; OCD1	164230	150	1.00	49	.41	49	.33	89	.81
ENSG00000004948	CALCR	Osteoporosis, involutional	166710	150	1.00	6	.05	102	.68	9	.07
ENSG00000136244	IL6	Osteoporosis, involutional	166710	37	.15	112	.86	80	.50	30	.16
ENSG00000162337	LRP5	Osteoporosis, involutional	166710	57	.24	6	.03	79	.49	10	.05
ENSG00000108821	COL1A1	Osteoporosis, involutional	166710	29	.08	5	.02	6	.02	125	.71
ENSG00000111424	VDR	Osteoporosis, involutional	166710	127	.74	139	.98	34	.14	114	.75
ENSG00000145335	SNCA	Parkinson disease; PD	168600	67	.36	93	.63	105	.76	1	.01
ENSG00000154277	UCHL1	Parkinson disease; PD	168600	59	.36	72	.37	61	.45	16	.12
ENSG00000185345	PARK2	Parkinson disease; PD	168600	85	.54	8	.04	35	.19	2	.03
ENSG00000064692	SNCAIP	Parkinson disease; PD	168600	41	.12	150	1.00	71	.43	2	.00
ENSG00000081248	CACNA1S	Hypokalemic periodic paralysis; HOKPP	170400	150	1.00	92	.80	71	.64	24	.23
ENSG00000175538	KCNE3	Hypokalemic periodic paralysis; HOKPP	170400	150	1.00	59	.42	38	.41	16	.13
ENSG00000007314	SCN4A	Hypokalemic periodic paralysis; HOKPP	170400	16	.16	41	.39	48	.58	114	.92
ENSG00000165731	RET	Pheochromocytoma	171300	143	.98	87	.77	30	.14	118	.94
ENSG00000117118	SDHB	Pheochromocytoma	171300	36	.26	90	.86	88	.77	100	.88
ENSG00000196712	NF1	Pheochromocytoma	171300	139	.97	94	.83	43	.36	113	.87
ENSG00000183765	CHEK2	Prostate cancer	176807	35	.08	89	.53	56	.42	47	.22
ENSG00000085117	CD82	Prostate cancer	176807	24	.03	150	1.00	38	.47	115	.88
ENSG00000067082	KLF6	Prostate cancer	176807	13	.01	145	.98	83	.58	61	.40
ENSG00000133216	EPHB2	Prostate cancer	176807	93	.33	64	.34	47	.41	81	.46
ENSG00000135828	RNASEL	Prostate cancer	176807	150	1.00	119	.67	103	.71	25	.09
ENSG00000038945	MSR1	Prostate cancer	176807	150	1.00	41	.22	64	.40	49	.22
ENSG00000171862	PTEN_HUMAN	Prostate cancer	176807	47	.08	25	.07	13	.04	96	.50
ENSG00000006744	RNZ2_HUMAN	Prostate cancer	176807	67	.17	12	.04	119	.87	42	.21
ENSG00000002822	MAD1L1	Prostate cancer	176807	114	.50	104	.70	107	.84	115	.81
ENSG00000159339	PADI4	Rheumatoid arthritis	180300	150	1.00	56	.38	83	.54	72	.44
ENSG00000168593	NFKBIL1	Rheumatoid arthritis	180300	150	1.00	89	.60	118	.80	117	.86
ENSG00000159216	RUNX1	Rheumatoid arthritis	180300	66	.49	108	.90	40	.22	132	.96
ENSG00000134242	PTPN22	Rheumatoid arthritis	180300	97	.57	64	.41	120	.78	59	.39
ENSG00000197208	SLC22A4	Rheumatoid arthritis	180300	63	.31	85	.58	89	.53	5	.02
ENSG00000138293	NCOA4	Thyroid carcinoma, papillary	188550	27	.11	123	.98	113	.84	138	.97
ENSG00000108946	PRKAR1A	Thyroid carcinoma, papillary	188550	23	.08	31	.18	59	.34	135	.97
ENSG00000198400	NTRK1	Thyroid carcinoma, papillary	188550	150	1.00	79	.49	67	.46	102	.67
ENSG00000114354	TFG	Thyroid carcinoma, papillary	188550	116	.66	36	.20	53	.45	70	.49
ENSG00000047410	TPR	Thyroid carcinoma, papillary	188550	10	.02	100	.63	20	.10	23	.15
ENSG00000129991	TNNI3	Cardiomyopathy, familial hypertrophic; CMH	192600	150	1.00	108	.83	135	.87	51	.17
ENSG00000160808	MYL3	Cardiomyopathy, familial hypertrophic; CMH	192600	52	.07	1	.00	1	.01	2	.02
ENSG00000140416	TPM1	Cardiomyopathy, familial hypertrophic; CMH	192600	117	.43	2	.00	16	.07	1	.00
ENSG00000155657	NP_59687.1	Cardiomyopathy, familial hypertrophic; CMH	192600	93	.29	1	.00	22	.08	98	.51
ENSG00000092054	MYH7	Cardiomyopathy, familial hypertrophic; CMH	192600	80	.20	1	.00	57	.31	82	.43
ENSG00000111245	MYL2	Cardiomyopathy, familial hypertrophic; CMH	192600	129	.52	2	.00	1	.00	13	.09
ENSG00000118194	TNNT2	Cardiomyopathy, familial hypertrophic; CMH	192600	84	.17	3	.01	137	.91	2	.00
ENSG00000134571	MYBPC3	Cardiomyopathy, familial hypertrophic; CMH	192600	150	1.00	1	.00	1	.00	33	.31
ENSG00000101306	MYLK2	Cardiomyopathy, familial hypertrophic; CMH	192600	150	1.00	97	.72	123	.74	95	.59
ENSG00000183785	PEX26	Adrenoleukodystrophy, autosomal neonatal form	202370	124	.54	1	.00	10	.02	33	.15
ENSG00000162928	PEX13	Adrenoleukodystrophy, autosomal neonatal form	202370	141	.81	1	.00	83	.45	1	.00
ENSG00000127980	PEX1	Adrenoleukodystrophy, autosomal neonatal form	202370	61	.17	74	.46	85	.45	25	.15
ENSG00000157911	PEX10	Adrenoleukodystrophy, autosomal neonatal form	202370	88	.24	1	.00	108	.62	9	.02
ENSG00000139197	PEX5	Adrenoleukodystrophy, autosomal neonatal form	202370	69	.19	1	.00	1	.00	4	.01
ENSG00000047579	DTNBP1	Hermansky-Pudlak syndrome; HPS	203300	131	.80	27	.20	59	.42	145	.98
ENSG00000107521	HPS1	Hermansky-Pudlak syndrome; HPS	203300	118	.59	30	.15	37	.33	52	.41
ENSG00000132842	AP3B1	Hermansky-Pudlak syndrome; HPS	203300	74	.30	150	1.00	88	.62	84	.62
ENSG00000110756	HPS5	Hermansky-Pudlak syndrome; HPS	203300	141	.92	150	1.00	77	.61	101	.76
ENSG00000100099	HPS4	Hermansky-Pudlak syndrome; HPS	203300	50	.21	70	.37	100	.76	24	.16
ENSG00000163755	HPS3	Hermansky-Pudlak syndrome; HPS	203300	150	1.00	150	1.00	150	1.00	150	1.00
ENSG00000092200	RPGRIP1	Leber congenital amaurosis, type I; LCA1	204000	56	.18	21	.11	5	.04	46	.25
ENSG00000132518	GUCY2D	Leber congenital amaurosis, type I; LCA1	204000	150	1.00	69	.34	34	.14	117	.70
ENSG00000116745	RPE65	Leber congenital amaurosis, type I; LCA1	204000	103	.40	28	.09	90	.46	100	.59
ENSG00000139988	RDH12_HUMAN	Leber congenital amaurosis, type I; LCA1	204000	64	.34	59	.34	34	.16	70	.35
ENSG00000134376	CRB1	Leber congenital amaurosis, type I; LCA1	204000	150	1.00	16	.06	52	.24	4	.00
ENSG00000105392	CRX	Leber congenital amaurosis, type I; LCA1	204000	83	.24	116	.67	34	.14	7	.02
ENSG00000112041	TULP1	Leber congenital amaurosis, type I; LCA1	204000	30	.09	17	.08	62	.35	68	.42
ENSG00000165731	RET	Autonomic control, congenital failure of	209880	76	.26	113	.89	31	.16	28	.22
ENSG00000124205	EDN3	Autonomic control, congenital failure of	209880	150	1.00	39	.37	44	.21	73	.49
ENSG00000109132	PHOX2B	Autonomic control, congenital failure of	209880	54	.15	122	.86	8	.05	4	.02
ENSG00000176697	BDNF	Autonomic control, congenital failure of	209880	150	1.00	132	.94	139	.88	89	.64
ENSG00000168621	GDNF	Autonomic control, congenital failure of	209880	150	1.00	53	.24	131	.75	48	.26
ENSG00000165533	TTC8	Bardet-Biedl syndrome; BBS	209900	150	1.00	51	.24	17	.06	13	.02
ENSG00000174483	DPP3	Bardet-Biedl syndrome; BBS	209900	147	.93	57	.22	97	.52	90	.46
ENSG00000138686	BBS7_HUMAN	Bardet-Biedl syndrome; BBS	209900	49	.14	13	.05	65	.33	88	.50
ENSG00000125124	BBS2	Bardet-Biedl syndrome; BBS	209900	150	1.00	6	.05	8	.05	17	.05
ENSG00000125863	MKKS	Bardet-Biedl syndrome; BBS	209900	104	.37	126	.78	103	.53	54	.27
ENSG00000140463	BBS4	Bardet-Biedl syndrome; BBS	209900	71	.24	49	.21	23	.08	9	.03
ENSG00000113966	ARL6	Bardet-Biedl syndrome; BBS	209900	150	1.00	42	.22	48	.30	106	.61
ENSG00000163093	BBS5	Bardet-Biedl syndrome; BBS	209900	44	.08	150	1.00	31	.13	29	.09
ENSG00000133111	RFXAP	Bare lymphocyte syndrome, type II	209920	33	.23	123	.94	110	.76	2	.00
ENSG00000179583	MHC2TA	Bare lymphocyte syndrome, type II	209920	150	1.00	51	.41	10	.04	1	.00
ENSG00000064490	RFXANK	Bare lymphocyte syndrome, type II	209920	79	.50	122	.90	96	.49	1	.00
ENSG00000143390	RFX5	Bare lymphocyte syndrome, type II	209920	120	.81	116	.94	15	.07	1	.00
ENSG00000159128	IFNGR2	Atypical mycobacteriosis, familial	209950	10	.01	15	.07	11	.06	12	.06
ENSG00000096996	IL12RB1	Atypical mycobacteriosis, familial	209950	150	1.00	33	.15	45	.27	22	.09
ENSG00000027697	IFNGR1	Atypical mycobacteriosis, familial	209950	85	.31	13	.05	23	.13	13	.07
ENSG00000113302	IL12B	Atypical mycobacteriosis, familial	209950	150	1.00	10	.09	66	.47	4	.03
ENSG00000115415	STAT1	Atypical mycobacteriosis, familial	209950	114	.43	109	.84	50	.28	101	.63
ENSG00000073734	ABCB11	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	150	1.00	141	1.00	144	.99	78	.53
ENSG00000005471	ABCB4	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	150	1.00	102	.80	120	.82	133	.91
ENSG00000081923	ATP8B1	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	23	.08	31	.28	38	.24	123	.92
ENSG00000099377	HSD3B7	Cholestasis, progressive familial intrahepatic 1; PFIC1	211600	94	.35	8	.11	8	.05	4	.03
ENSG00000133703	RASK_HUMAN	Lung cancer	211980	94	.62	103	.77	22	.10	61	.30
ENSG00000157764	BRAF	Lung cancer	211980	40	.19	44	.28	9	.02	14	.06
ENSG00000141510	TP53	Lung cancer	211980	12	.03	113	.82	40	.38	150	1.00
ENSG00000146648	EGFR	Lung cancer	211980	113	.76	85	.60	40	.27	33	.24
ENSG00000139197	PEX5	Zellweger syndrome; ZS	214100	26	.02	18	.04	6	.01	1	.00
ENSG00000142655	PEX14	Zellweger syndrome; ZS	214100	60	.11	15	.05	4	.01	2	.00
ENSG00000127980	PEX1	Zellweger syndrome; ZS	214100	49	.09	8	.03	7	.01	12	.04
ENSG00000124587	PEX6	Zellweger syndrome; ZS	214100	56	.09	14	.04	9	.02	32	.15
ENSG00000108733	PEX12	Zellweger syndrome; ZS	214100	118	.39	79	.58	131	.69	1	.00
ENSG00000164751	PXMP3	Zellweger syndrome; ZS	214100	117	.41	3	.01	133	.71	1	.00
ENSG00000183785	PEX26	Zellweger syndrome; ZS	214100	76	.23	17	.04	35	.14	14	.05
ENSG00000034693	PEX3	Zellweger syndrome; ZS	214100	3	.00	1	.00	74	.30	4	.00
ENSG00000172660	TAF15	Chondrosarcoma	215300	83	.40	114	.96	139	.91	18	.15
ENSG00000182197	EXT1	Chondrosarcoma	215300	77	.28	40	.28	31	.23	4	.07
ENSG00000151348	EXT2	Chondrosarcoma	215300	19	.06	24	.30	27	.19	19	.17
ENSG00000182944	EWSR1	Chondrosarcoma	215300	28	.10	84	.59	36	.26	38	.33
ENSG00000134183	GNAT2	Achromatopsia 2; ACHM2	216900	40	.29	121	.97	142	.99	143	1.00
ENSG00000144191	CNGA3	Achromatopsia 2; ACHM2	216900	143	.97	59	.44	15	.08	120	.92
ENSG00000170289	CNGB3	Achromatopsia 2; ACHM2	216900	150	1.00	91	.87	49	.47	70	.77
ENSG00000065618	COHA1_HUMAN	Epidermolysis bullosa junctionalis, disentis type	226650	19	.08	94	.61	74	.47	102	.59
ENSG00000132470	ITGB4	Epidermolysis bullosa junctionalis, disentis type	226650	150	1.00	96	.73	47	.35	140	.98
ENSG00000196878	LAMB3	Epidermolysis bullosa junctionalis, disentis type	226650	150	1.00	23	.14	12	.09	98	.66
ENSG00000053747	LAMA3	Epidermolysis bullosa junctionalis, disentis type	226650	143	.99	93	.68	108	.74	55	.33
ENSG00000165281	FANCG	Fanconi anemia; FA	227650	34	.07	118	.82	15	.08	2	.00
ENSG00000144554	FANCD2	Fanconi anemia; FA	227650	150	1.00	53	.24	52	.26	2	.00
ENSG00000183161	FANCF	Fanconi anemia; FA	227650	43	.12	45	.26	47	.26	2	.00
ENSG00000158169	FANCC	Fanconi anemia; FA	227650	26	.17	49	.28	53	.34	1	.00
ENSG00000115392	FANCL	Fanconi anemia; FA	227650	56	.21	108	.74	30	.17	4	.01
ENSG00000112039	FANCE	Fanconi anemia; FA	227650	6	.01	40	.25	32	.18	1	.00
ENSG00000105379	ETFB	Glutaricaciduria IIA	231680	7	.03	11	.07	1	.00	1	.00
ENSG00000140374	ETFA	Glutaricaciduria IIA	231680	9	.03	3	.09	1	.01	1	.00
ENSG00000171503	ETFDH	Glutaricaciduria IIA	231680	1	.00	38	.48	1	.00	1	.00
ENSG00000188690	UROS	Hydrops fetalis, idiopathic	236750	67	.30	104	.91	6	.04	76	.52
ENSG00000169919	GUSB	Hydrops fetalis, idiopathic	236750	143	.96	120	.95	98	.55	112	.84
ENSG00000177628	GBA	Hydrops fetalis, idiopathic	236750	108	.69	118	.95	113	.79	115	.85
ENSG00000188536	HBA1	Hydrops fetalis, idiopathic	236750	42	.16	133	.99	112	.69	76	.53
ENSG00000179477	ALOX12B	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	150	1.00	111	.91	48	.33	33	.31
ENSG00000011198	ABHD5	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	70	.35	4	.03	2	.02	3	.02
ENSG00000092295	TGM1	Ichthyosiform erythroderma, congenital, nonbullous, 1; NCIE1	242100	28	.16	12	.09	14	.08	31	.19
ENSG00000122735	DNAI1	Kartagener syndrome	244400	14	.06	150	1.00	35	.34	60	.39
ENSG00000039139	DNAH5	Kartagener syndrome	244400	150	1.00	3	.06	6	.03	17	.09
ENSG00000105877	DNAH11	Kartagener syndrome	244400	150	1.00	7	.05	6	.03	7	.08
ENSG00000112964	GHR	Laron syndrome, type II	245590	150	1.00	76	.57	19	.15	9	.09
ENSG00000189162	GH1	Laron syndrome, type II	245590	150	1.00	13	.08	24	.17	19	.20
ENSG00000173757	STAT5B	Laron syndrome, type II	245590	77	.50	119	.98	90	.71	79	.54
ENSG00000137992	DBT	Maple syrup urine disease, type IA	248600	54	.22	103	.71	47	.18	12	.04
ENSG00000091140	DLD	Maple syrup urine disease, type IA	248600	78	.37	115	.94	76	.43	3	.04
ENSG00000142046	BCKDHA	Maple syrup urine disease, type IA	248600	44	.06	51	.35	34	.19	1	.00
ENSG00000083123	BCKDHB	Maple syrup urine disease, type IA	248600	114	.66	19	.12	112	.69	1	.01
ENSG00000167792	NDUFV1	Complex I, mitochondrial respiratory chain, deficiency of	252010	45	.11	12	.05	13	.05	17	.07
ENSG00000115286	NDUFS7	Complex I, mitochondrial respiratory chain, deficiency of	252010	77	.20	7	.04	21	.13	35	.17
ENSG00000164258	NDUFS4	Complex I, mitochondrial respiratory chain, deficiency of	252010	99	.42	6	.03	4	.02	2	.02
ENSG00000023228	NDUFS1	Complex I, mitochondrial respiratory chain, deficiency of	252010	139	.79	10	.04	1	.00	1	.00
ENSG00000158864	NDUFS2	Complex I, mitochondrial respiratory chain, deficiency of	252010	22	.04	11	.05	2	.00	2	.01
ENSG00000110092	CCND1	Myeloma, multiple	254500	4	.02	126	.94	142	.99	7	.06
ENSG00000130076	IGHG3	Myeloma, multiple	254500	34	.21	78	.60	143	.99	150	1.00
ENSG00000068078	FGFR3	Myeloma, multiple	254500	105	.83	16	.14	41	.28	51	.41
ENSG00000137265	IRF4	Myeloma, multiple	254500	31	.14	125	.76	34	.16	102	.72
ENSG00000115286	NDUFS7	Leigh syndrome; LS	256000	31	.02	5	.01	9	.02	9	.02
ENSG00000074582	BCS1L	Leigh syndrome; LS	256000	63	.16	110	.77	98	.61	115	.73
ENSG00000073578	SDHA	Leigh syndrome; LS	256000	15	.03	31	.21	38	.17	80	.36
ENSG00000110536	NDUFS3	Leigh syndrome; LS	256000	33	.05	3	.02	1	.01	1	.01
ENSG00000091140	DLD	Leigh syndrome; LS	256000	113	.39	5	.02	132	.80	98	.55
ENSG00000110717	NDUFS8	Leigh syndrome; LS	256000	6	.00	5	.02	1	.00	1	.00
ENSG00000006071	ABCC8	Nesidioblastosis of pancreas	256450	136	.96	139	1.00	76	.81	57	.67
ENSG00000106633	GCK	Nesidioblastosis of pancreas	256450	98	.72	3	.04	3	.02	53	.50
ENSG00000148672	GLUD1	Nesidioblastosis of pancreas	256450	67	.29	15	.15	79	.75	56	.42
ENSG00000141655	TNFRSF11A	Osteogenic sarcoma	259500	145	1.00	38	.31	38	.32	5	.08
ENSG00000139687	RB1	Osteogenic sarcoma	259500	104	.65	131	.98	100	.75	40	.37
ENSG00000183765	CHEK2	Osteogenic sarcoma	259500	147	.98	81	.60	104	.74	120	.96
ENSG00000081087	OSTM1	Osteopetrosis, autosomal recessive	259700	45	.33	150	1.00	37	.24	81	.65
ENSG00000103249	CLCN7	Osteopetrosis, autosomal recessive	259700	144	.99	118	.95	135	.93	90	.67
ENSG00000110719	TCIRG1	Osteopetrosis, autosomal recessive	259700	69	.61	77	.53	71	.48	81	.74
ENSG00000133703	RASK_HUMAN	Pancreatic carcinoma	260350	4	.01	28	.14	10	.05	56	.26
ENSG00000141510	TP53	Pancreatic carcinoma	260350	108	.53	111	.71	84	.43	102	.54
ENSG00000145050	ARMET	Pancreatic carcinoma	260350	106	.61	150	1.00	100	.61	91	.51
ENSG00000147889	CDKN2A	Pancreatic carcinoma	260350	150	1.00	27	.14	28	.15	107	.61
ENSG00000141646	SMAD4	Pancreatic carcinoma	260350	33	.14	130	.96	82	.62	122	.84
ENSG00000139618	BRCA2	Pancreatic carcinoma	260350	122	.74	140	.96	96	.64	50	.25
ENSG00000144191	CNGA3	Achromatopsia 3; ACHM3	262300	144	.98	54	.45	15	.08	119	.92
ENSG00000134183	GNAT2	Achromatopsia 3; ACHM3	262300	37	.25	115	.96	134	.99	150	1.00
ENSG00000170289	CNGB3	Achromatopsia 3; ACHM3	262300	150	1.00	91	.86	47	.47	72	.76
ENSG00000107187	LHX3	Pituitary dwarfism III	262600	81	.46	112	.94	125	.91	104	.92
ENSG00000163666	HESX1	Pituitary dwarfism III	262600	150	1.00	123	.94	33	.18	23	.08
ENSG00000175325	PROP1	Pituitary dwarfism III	262600	150	1.00	120	.93	39	.33	4	.03
ENSG00000111319	SCNN1A	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	35	.30	2	.02	6	.04	33	.36
ENSG00000168447	SCNN1B	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	104	.82	3	.03	1	.00	24	.26
ENSG00000151623	NR3C2	Pseudohypoaldosteronism, type I, autosomal recessive; PHA1	264350	51	.40	133	.99	150	1.00	62	.57
ENSG00000168878	SFTPB	Pulmonary alveolar proteinosis	265120	150	1.00	9	.09	16	.20	67	.68
ENSG00000100368	CSF2RB	Pulmonary alveolar proteinosis	265120	150	1.00	19	.18	13	.08	121	.99
ENSG00000168484	SFTPC	Pulmonary alveolar proteinosis	265120	150	1.00	4	.03	3	.04	114	.88
ENSG00000164751	PXMP3	Refsum disease, infantile form	266510	118	.82	76	.53	32	.22	27	.18
ENSG00000127980	PEX1	Refsum disease, infantile form	266510	33	.17	29	.24	86	.59	1	.01
ENSG00000183785	PEX26	Refsum disease, infantile form	266510	94	.63	6	.04	8	.03	2	.02
ENSG00000197375	SLC22A5	Inflammatory bowel disease 1; IBD1	266600	35	.18	95	.78	16	.12	10	.06
ENSG00000151208	DLG5	Inflammatory bowel disease 1; IBD1	266600	45	.22	114	.81	47	.36	94	.64
ENSG00000167207	CARD15	Inflammatory bowel disease 1; IBD1	266600	150	1.00	102	.80	54	.32	47	.30
ENSG00000085563	ABCB1	Inflammatory bowel disease 1; IBD1	266600	150	1.00	29	.22	35	.21	43	.29
ENSG00000092929	UNC13D	Reticulosis, familial histiocytic	267700	150	1.00	36	.30	30	.34	52	.53
ENSG00000166349	RAG1	Reticulosis, familial histiocytic	267700	150	1.00	92	.82	92	.75	16	.22
ENSG00000180644	PRF1	Reticulosis, familial histiocytic	267700	57	.23	53	.36	43	.31	138	.99
ENSG00000135903	PAX3	Rhabdomyosarcoma 2; RMS2	268220	70	.43	114	.89	100	.82	69	.60
ENSG00000150907	FOXO1A	Rhabdomyosarcoma 2; RMS2	268220	86	.49	128	.97	110	.73	135	.98
ENSG00000009709	PAX7	Rhabdomyosarcoma 2; RMS2	268220	37	.26	122	.95	98	.71	99	.77
ENSG00000108576	SLC6A4	Sudden infant death syndrome	272120	120	.80	50	.55	39	.36	150	1.00
ENSG00000183873	SCN5A	Sudden infant death syndrome	272120	145	.99	17	.20	23	.10	33	.31
ENSG00000053918	KCNQ1	Sudden infant death syndrome	272120	150	1.00	2	.01	7	.10	58	.53
ENSG00000163599	CTLA4	Graves disease	275000	150	1.00	34	.19	150	1.00	94	.90
ENSG00000145321	GC	Graves disease	275000	50	.37	88	.76	91	.61	51	.30
ENSG00000111424	VDR	Graves disease	275000	67	.33	124	.99	20	.15	14	.17
ENSG00000134982	APC	Turcot syndrome	276300	1	.00	134	.99	4	.02	35	.26
ENSG00000076242	MLH1	Turcot syndrome	276300	115	.83	1	.01	9	.10	1	.03
ENSG00000122512	PMS2	Turcot syndrome	276300	7	.06	1	.01	2	.01	1	.01
ENSG00000100146	SOX10	Waardenburg-Shah syndrome	277580	53	.32	8	.05	123	.93	82	.68
ENSG00000124205	EDN3	Waardenburg-Shah syndrome	277580	150	1.00	70	.50	140	.99	14	.13
ENSG00000136160	EDNRB	Waardenburg-Shah syndrome	277580	150	1.00	1	.01	5	.02	72	.56
ENSG00000112357	PEX7	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	112	.80	26	.25	111	.85	125	.97
ENSG00000116906	GNPAT	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	130	.88	116	.91	121	.90	16	.14
ENSG00000018510	AGPS	Rhizomelic chondrodysplasia punctata, type 3; RCDP3	600121	98	.71	8	.08	93	.87	5	.06
ENSG00000170175	CHRNB1	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	36	.20	58	.43	40	.34	115	.90
ENSG00000135902	CHRND	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	56	.33	1	.00	2	.00	6	.01
ENSG00000138435	CHRNA1	Myasthenic syndrome, slow-channel congenital; SCCMS	601462	132	1.00	1	.01	2	.01	1	.00
ENSG00000175426	PCSK1	Obesity	601665	10	.01	63	.34	46	.18	29	.13
ENSG00000115138	POMC	Obesity	601665	66	.17	105	.68	93	.49	7	.02
ENSG00000116678	LEPR	Obesity	601665	8	.02	94	.72	62	.32	1	.00
ENSG00000166603	MC4R	Obesity	601665	145	1.00	17	.10	108	.67	19	.09
ENSG00000174483	DPP3	Obesity	601665	66	.21	95	.64	10	.04	4	.01
ENSG00000174697	LEP	Obesity	601665	150	1.00	130	.94	133	.86	52	.31
ENSG00000130203	APOE	Obesity	601665	58	.22	46	.16	43	.22	52	.27
ENSG00000142156	COL6A1	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	112	1.00	36	.38	40	.28	77	.70
ENSG00000112112	COL11A2	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	138	.95	33	.41	31	.36	23	.27
ENSG00000197594	ENPP1	Ossification of the posterior longitudinal ligament of spine; OPLL	602475	150	1.00	29	.16	69	.52	59	.60
ENSG00000115211	EIF2B4	Leukoencephalopathy with vanishing white matter; VWM	603896	4	.00	2	.01	2	.00	9	.02
ENSG00000070785	EIF2B3	Leukoencephalopathy with vanishing white matter; VWM	603896	4	.00	3	.01	2	.00	10	.02
ENSG00000111361	EIF2B1	Leukoencephalopathy with vanishing white matter; VWM	603896	2	.00	4	.01	1	.00	11	.03
ENSG00000119718	EIF2B2	Leukoencephalopathy with vanishing white matter; VWM	603896	1	.00	2	.00	4	.00	7	.02
ENSG00000145191	EIF2B5	Leukoencephalopathy with vanishing white matter; VWM	603896	2	.00	1	.00	1	.00	5	.01
ENSG00000138061	CYP1B1	Peters anomaly	604229	49	.29	58	.40	87	.60	28	.17
ENSG00000054598	FOXC1	Peters anomaly	604229	38	.09	133	.96	145	.98	99	.58
ENSG00000164093	PITX2	Peters anomaly	604229	150	1.00	130	.95	21	.17	133	.95
ENSG00000007372	PAX6	Peters anomaly	604229	142	.96	118	.90	35	.26	11	.11
ENSG00000144285	SCN1A	Generalized epilepsy with febrile seizures plus; GEFS+	604233	5	.01	11	.09	10	.04	12	.09
ENSG00000113327	GABRG2	Generalized epilepsy with febrile seizures plus; GEFS+	604233	17	.04	68	.47	130	.97	123	.97
ENSG00000105711	SCN1B	Generalized epilepsy with febrile seizures plus; GEFS+	604233	64	.41	13	.08	12	.05	105	.70
ENSG00000157764	BRAF	Lymphoma, non-Hodgkin, familial	605027	139	.84	17	.13	65	.30	122	.92
ENSG00000003400	CASP10	Lymphoma, non-Hodgkin, familial	605027	77	.44	1	.00	29	.11	7	.06
ENSG00000149311	ATM	Lymphoma, non-Hodgkin, familial	605027	36	.13	130	.95	61	.33	137	.98
ENSG00000085999	RAD54L	Lymphoma, non-Hodgkin, familial	605027	141	.87	18	.11	3	.02	2	.01
ENSG00000143294	PRCC	Renal cell carcinoma, papillary	605074	122	.83	150	1.00	32	.28	48	.47
ENSG00000068323	TFE3	Renal cell carcinoma, papillary	605074	66	.45	115	.98	121	.94	7	.09
ENSG00000105976	MET	Renal cell carcinoma, papillary	605074	10	.04	71	.68	42	.42	50	.45
ENSG00000102245	TNFL5_HUMAN	Immunodeficiency with hyper-IgM, type 2	605258	150	1.00	8	.11	46	.45	8	.08
ENSG00000101017	CD40	Immunodeficiency with hyper-IgM, type 2	605258	67	.43	22	.21	99	.79	23	.21
ENSG00000111732	AICDA	Immunodeficiency with hyper-IgM, type 2	605258	150	1.00	62	.39	117	.86	44	.48
ENSG00000139515	IPF1	Maturity-onset diabetes of the young; MODY	606391	150	1.00	124	.88	115	.62	12	.04
ENSG00000135100	TCF1	Maturity-onset diabetes of the young; MODY	606391	150	1.00	135	.93	97	.52	12	.09
ENSG00000101076	HNF4A	Maturity-onset diabetes of the young; MODY	606391	76	.33	124	.90	125	.74	37	.17
ENSG00000106633	GCK	Maturity-onset diabetes of the young; MODY	606391	133	.64	102	.66	78	.46	19	.09
ENSG00000162992	NEUROD1	Maturity-onset diabetes of the young; MODY	606391	7	.02	30	.14	3	.01	31	.14
ENSG00000102245	TNFL5_HUMAN	Immunodeficiency with hyper-IgM, type 3	606843	150	1.00	10	.12	44	.43	7	.07
ENSG00000101017	CD40	Immunodeficiency with hyper-IgM, type 3	606843	70	.48	22	.19	100	.81	20	.19
ENSG00000111732	AICDA	Immunodeficiency with hyper-IgM, type 3	606843	150	1.00	67	.45	115	.84	62	.55
ENSG00000022355	GABRA1	Myoclonic epilepsy, juvenile; JME	606904	14	.03	37	.13	104	.63	28	.16
ENSG00000096093	EFHC1	Myoclonic epilepsy, juvenile; JME	606904	150	1.00	150	1.00	150	1.00	27	.14
ENSG00000182389	CACNB4	Myoclonic epilepsy, juvenile; JME	606904	49	.17	118	.86	68	.39	28	.18
ENSG00000114859	CLCN2	Myoclonic epilepsy, juvenile; JME	606904	150	1.00	6	.01	42	.18	40	.26
ENSG00000179295	PTPN11	Juvenile myelomonocytic leukemia	607785	125	.91	20	.13	133	.95	52	.46
ENSG00000196712	NF1	Juvenile myelomonocytic leukemia	607785	74	.42	5	.06	30	.20	2	.01
ENSG00000133703	RASK_HUMAN	Juvenile myelomonocytic leukemia	607785	118	.66	17	.14	18	.10	38	.30
ENSG00000168638	NRAS	Juvenile myelomonocytic leukemia	607785	51	.13	104	.81	6	.03	44	.30
ENSG00000197499	HLA-A	Mycobacterium tuberculosis, susceptibility to infection by	607948	102	.81	41	.52	100	.88	115	.90
ENSG00000165471	MBL2	Mycobacterium tuberculosis, susceptibility to infection by	607948	150	1.00	33	.41	14	.15	87	.79
ENSG00000111424	VDR	Mycobacterium tuberculosis, susceptibility to infection by	607948	141	1.00	119	.99	125	.98	98	.84
ENSG00000108556	CHRNE	Myasthenic syndrome, congenital, fast-channel	608930	150	1.00	64	.45	141	.96	1	.00
ENSG00000138435	CHRNA1	Myasthenic syndrome, congenital, fast-channel	608930	150	1.00	1	.00	1	.00	1	.00
ENSG00000135902	CHRND	Myasthenic syndrome, congenital, fast-channel	608930	56	.36	1	.00	1	.00	6	.02

Correction for Bias in Gold Standard

Once the likelihood ratios of the microarray coexpression data sets were determined, it became evident that the likelihood ratio of the gene pairs composed of genes that were present on the microarrays was >1, whereas that of gene pairs composed of genes not represented on the arrays was <1. We had not expected any difference in likelihood ratios between genes represented on the array and genes not on the array, but, in fact, the observed difference was pronounced (fig. A1).

Figure A1.

Difference in likelihood ratios between genes that were represented on the microarrays and genes that were not.

It turned out that only a small subset of different genes (7,197 of the 55,606 genes) made up the 55,606 true-positive, gold standard gene pairs. After we had determined the subset of genes that made up the initial, true-negative, gold standard gene pairs and had compared this with the 7,197 genes making up the true-positive gene pairs, we found that the overlap for individual genes was smaller than expected. A large number of the genes in the true-negative gene pairs were never part of a true-positive gene pair. In addition, the genes in the true-negative pairs were generally less-well annotated. If we subsequently determine the likelihood ratio of the bins in a data set—where one bin contains many gene pairs composed of only a limited number of genes, and the other bins contain hardly any gene pairs formed with one of those genes—the inclusion or exclusion of any of those genes will bias the likelihood ratio, as observed in the microarray coexpression data sets.

To overcome this bias, we tried to come up with a gold standard in which every gene forms both a true-positive gene pair (together with another gene) and also a true-negative gene pair (in conjunction with another, different gene). Only 5,105 genes met our criterion of forming a true-positive gene pair at least three times, and these genes were then allowed to form gene pairs within the true-negative, gold standard reference. This restriction confined the true-negative, gold standard reference list to 801,108 gene pairs, from which 500 gene pairs could be removed because they were already known to be true-positive gene pairs.

Bayesian Integration and Graph-Theoretical Distance Measure Calculation

To generate the four networks, the various data sets were combined in a Bayesian manner. First, two measures, derived from the GO Biological Process data set, were combined in a naive way. This method was also applied to the two GO Molecular Function data sets. Subsequently, the overall GO Biological Process data set and the overall GO Molecular Function data set were combined in a fully connected way; for each gene pair, we determined the combination of the two GO bins to which they belonged. Once all the gene pairs had been assessed, the bin combinations contained a large number of gene pairs, which permitted determination of the likelihood ratio. Once the overall GO data set had been generated in a fully connected way, all remaining data sets were combined in a naive way, as shown in figure 2.

To determine the overall likelihood ratio when combining n data sets (f₁…f_n), defined as

we assumed conditional independence between the data sets and used the simplified, naive Bayes formula to compute the likelihood ratio by taking the product of the likelihood ratios (L) for each independent feature:

To calculate the eventual microarray coexpression plus protein-protein interaction likelihood ratios, the previously determined likelihood ratios from the microarray coexpression data set and the protein-protein interaction data set were multiplied. The likelihood ratios of the overall GO data set and the overall microarray plus protein-protein interaction data set were multiplied to generate the combined GO, microarray coexpression, and protein-protein interaction network.

Usually, after the likelihood ratios have been determined, we want to calculate a posterior probability of interaction by multiplying the likelihood ratio with the prior probability of interaction, defined as

However, since it is difficult to estimate the number of existing human gene-gene interactions, no specific prior probability for interaction was assumed. To facilitate successful learning of the classifier, a uniform (ignorant) prior was used for both the training and the test set, which allowed us to adjust the computed posterior probability, to take into account any plausible prior probability of interaction.

In addition, it was decided that the gene pairs would not be discretized into interacting or noninteracting pairs, but, instead, a continuous graph-theoretical distance measure that could be employed in the graph network would be used, while taking into account that evidence for interaction could vary. First, all gene pairs were ranked on the basis of the computed likelihood ratio. Subsequently, this ranking was used to define a distance measure that ranged from 1 (highly likely gene-gene interaction) to 255 (highly unlikely gene-gene interaction) and followed a cumulative distribution function (CDF):

Degree Distribution of the Four Networks

Degree distributions were determined to assess whether the reconstructed networks followed a scale-free, power-law distribution (fig. A2). The number of interacting genes per gene k was determined, assuming a conservative prior of 0.005. The proportion p of genes having k interactions was plotted against k.

Figure A2.

Degree distributions for the four networks. The MA+Y2H network has a topology that most closely follows a scale-free, power-law distribution, compared with the other three networks.