Abstract
Declaring “replication” from results of genome wide association (GWA) studies is straightforward when major gene effects provide genome-wide significance for association of the same allele of the same SNP in each of multiple independent samples. However, such unambiguous replication may be unlikely when phenotypes display polygenic genetic architecture, allelic heterogeneity, locus heterogeneity and when different samples display linkage disequilibria with different fine structures. We seek chromosomal regions that are tagged by clustered SNPs that display nominally-significant association in each of several independent samples. This approach provides one “nontemplate” approach to identifying overall replication of groups of GWA results in the face of difficult genetic architectures. We apply this strategy to 1M SNP Affymetrix and Illumina GWA results for dependence on illegal substances. This approach provides high confidence in rejecting the null hypothesis that chance alone accounts for the extent to which clustered, nominally-significant SNPs from samples of the same racial/ethnic background identify the same chromosomal regions. There is more modest confidence in: a) identification of individual chromosomal regions and genes and b) overlap between results from samples of different racial/ethnic backgrounds. The strong overlap identified among the samples with similar racial/ethnic backgrounds, together with prior work that identified overlapping results in samples of different racial/ethnic backgrounds, support contributions to individual differences in vulnerability to addictions that come from both relatively older allelic variants that are common in many current human populations and newer allelic variants that are common in fewer current human populations.
Keywords: substance dependence, microarray
Introduction
Genome wide association (GWA) is a method of choice for identifying genes whose variants influence vulnerability to complex disorders. Declaring “replication” of individual results of genome wide association studies is straightforward when major gene effects provide associations between marker and phenotype that display the same phase and “genome wide” levels of significance (p ca 10−8) in each of several independent samples. However, such “template” replication for individual markers is unlikely to be achieved in many otherwise-reasonable samples for many phenotypes. Phenotypes and samples that display polygenic genetic architecture, allelic heterogeneity, locus heterogeneity and sample to sample differences in fine structure of linkage disequilibrium can provide especial difficulties for this “template” approach. These difficulties can be exacerbated when data comes from different genotyping platforms that do not assess allele frequencies for identical sets of SNPs. Much current genome wide association and linkage data suggests that we may have identified many or even most of the loci at which we might expect “template” analyses to identify reproducible genome wide significance in reasonably sized samples (see references below). Much of the risk attributable to genetic influences on common phenotypes may well arise from polygenic influences whose properties are likely to provide many false negative results in searches for replicated “genome wide” significance in multiple independent samples, using “template” criteria for replication.
Vulnerability to heavy use and development of dependence on an illegal substance (“addiction vulnerability”) appears to be such a trait. The substantial genetic influences on addiction vulnerability are documented by data from family, adoption and twin studies (Karkowski and others 2000; True and others 1999; Tsuang and others 1998; Uhl and others 1995). Twin studies also document shared heritable influences on vulnerability to dependence on addictive substances from different pharmacological classes (eg opiates and stimulants) (Karkowski and others 2000; Kendler and others 2000; Tsuang and others 1998). Combined data from linkage and initial GWA studies (Bierut and others 2007; Johnson and others 2006; Johnson and others 2008; Liu and others 2006; Liu and others 2005; Thorgeirsson and others 2008; Treutlein and others 2009; Uhl and others 2008b; Uhl and others 2008c; Uhl and others 2001) suggest that much of the genetic influence on vulnerability to substance dependence is likely to be polygenic. These prior studies have identified a number of genetic influences that appear to be shared among individuals from different racial/ethnic backgrounds. However, no prior work of which we are aware provides dense genome-wide data with which we can seek variants that may be more likely to replicate in independent samples of individuals from the same racial/ethnic group than they might be to replicate in samples of individuals from different racial/ethnic groups.
We have developed a “nontemplate” strategy that identifies overall replication of sets of genome wide association (GWA) results in the face of difficulties with genetic architectures, samples and genotyping methods (Drgon and others 2010; Johnson and others 2009; Liu and others 2006; Uhl and others 2008b). Such an approach can complement meta-analyses that seek to combine data from single markers whose significance in single samples does not achieve genome wide significance.
We now report application of this nontemplate strategy to identify overall replication of groups of results from GWA studies of four samples of individuals with dependence on illegal substances and matched controls (Drgon and others 2010), (http://www.ncbi.nlm.nih.gov/gap). Two of these samples display European-American and two display African-American genetic backgrounds. These data come from individual genotyping and multiple-pool genotyping approaches that use 1M SNP Illumina and Affymetrix platforms, respectively. The results focus attention on chromosomal regions that are identified by clusters of SNPs for which case vs control differences achieve nominal statistical significance in multiple samples from the same racial/ethnic group. We describe the high confidence with which this approach rejects the null hypothesis that nominally-significant SNPs from each sample from the same racial/ethnic group identify the same chromosomal regions with frequencies expected by chance. We note the more modest levels of confidence that this approach provides for identification of individual SNPs, individual chromosomal regions, individual genes and the overlap between data from samples of the two racial/ethnic groups studied. We discuss this work in light of its technical and analytic limitations and in its similarities and differences with “template” GWA analyses and meta-analyses that seek reproducible associations of striking levels of significance at single SNP markers. The current “nontemplate” replication of sets of results may be useful in other settings in which the underlying properties of the disorder and of the samples create difficulties for searches for individual SNPs with replicated genome wide significance.
Materials and Methods
Subjects, genotyping and assignment of nominal significance of dependent vs control allele frequencies in each sample
1) NIDA/MNB
European-American and African-American research volunteers, largely non treatment seeking, came to the NIDA research facility in Baltimore, Maryland between 1990 and 2007 in response to advertisements and referrals from other research volunteers provided informed consents, self-reported ethnicity data, drug use histories via the Drug Use Survey and DSMIII-R or IV diagnoses (Diagnostic and Statistical Manual) and were reimbursed for their time as previously described (Drgon and others 2010; Persico and others 1996; Smith and others 1992; Uhl and others 2001). Genotypes were assessed in DNA pools using Affymetrix 6.0 arrays and methods that we have extensively validated, as previously described (Drgon and others 2010; Johnson and others 2006; Liu and others 2006; Liu and others 2005; Uhl and others 2001). Pooling 1) provided us with the maximal ability to protect the genetic confidentiality of subjects who volunteered for study of genetics of illegal behaviors, 2) allowed us to utilize DNAs from individuals who consented to participation in this study during time periods when consents did not explicitly describe studies using high densities of DNA markers, 3) allowed us to use methods that we have developed and validated in this and in previous work and 4) reduced costs. Many of these subjects would thus not have been available for studies that assessed substantial numbers of polymorphisms using individual genotyping, though many of the most recently-consented subjects (who constituted virtually all of the subjects in four DNA pools) consented to allow their DNAs to be used for unlimited genotyping. Nominal p values for each SNP were determined based on t tests that compared data from multiple abuser vs control pools that contained DNAs from 680 European-American and 940 African-American individuals who had mean ages of 32.8 and 34.0 and were 69.5 and 58.8% male, respectively, as described (Drgon and others 2010). In addition, to validate pooling results, we performed individual genotyping for the 155 African American research volunteers who constituted virtually all of the members of 8 DNA pools and who had consented to unlimited individual genotyping, using Affymetrix 6.0 arrays whose results all passed Affymetrix quality control standards and resulted in at least 98% call rates and employing Pearson correlation coefficients.
2) dbGAP samples from the family study of cocaine dependence, COGA and COGEND studies
Unrelated subjects who met DSM criteria for cocaine dependence and control subjects with no evidence for dependence on any addictive substance and who reported smoking fewer than 100 cigarettes in their lives were assembled from three studies and deposited in dbGAP. Family study of cocaine dependence subjects were recruited from treatment centers close to St. Louis. Mo; 55% of contacted subjects participated (Bierut and others 2008). Community-based comparison subjects were recruited through driver’s license records from the Missouri Family Registry and were matched to cocaine dependent subjects based on date of birth, ethnicity, gender, and zip code. Eighty percent of screened and eligible comparison subjects participated. Other participants came from individuals who participated in the Collaborative Study on the Genetics of Alcoholism (Nurnberger and others 2004) and the Collaborative Study on the Genetics of Nicotine Dependence (Bierut and others 2007). Dependent individuals displayed DSM (Diagnostic and Statistical Manual IV) dependence on cocaine as reflected in the dbGAP variable phv00066444.v1.p1. Controls displayed no DSM dependence on cocaine, nicotine, alcohol, marijuana, opioids or other drugs. We eliminated individuals who smoked more than 100 cigarettes in their lives from the control group. We identified 481 dependent and 1053 control unrelated European-American subjects and 516 dependent and 409 control unrelated African-American subjects for this analysis who averaged 40.2 and 37.2 years old and were 47.1 and 46.95 male, respectively. The numbers of cocaine-dependent African-American subjects who were also dependent on nicotine, cannabis, opioids, alcohol or other substances were: 383, 210, 86, 426 and 115, respectively; corresponding numbers for European-American subjects were: 366, 288, 141, 453 and 246.
Genotyping for these samples was performed using Illumina 1M SNP arrays at the Center for Inherited Disease Research (CIDR), with quality controls and principal components analysis (PCA) controls for racial/ethnic background available at the CIDR website (www.cidr.jhmi.edu). Genotypes from dependent and control individuals were selected from dbGAP files.
Primary p values for each SNP were based on χ2 tests. In addition, to compare analyses with those for NIDA-MNB subjects, we also calculated mean allele frequencies for each SNP for each “pseudopool” that contained data for 20 individuals. We than performed t tests using mean and variance information from the multiple pseudopools that represented the entire sample in ways that paralleled the t tests for NIDA-MNB data noted above.
3) Identification of chromosomal regions containing clusters of SNPs with nominally-significant case vs control differences in single or multiple samples
We performed analyses based on previously-defined criteria using datasets of approximately 1million SNPs (Drgon and others 2010). We identified chromosomal regions of interest in individual samples by seeking regions in which at least 4 clustered SNPs displayed case vs control differences with nominal, p < 0.05 levels of statistical significance. We defined clustering based on separation of each clustered SNP from the nearest nominally-significant SNP by ≤ 10kb. We identified similarities between the results obtained from multiple samples by identifying the chromosomal regions that were tagged by such clustered, nominally positive SNPs in each of the samples of individuals from the same racial/ethnic group. We identified genes for which these chromosomal intervals lay within the exons of the gene and/or in 10kb of 5’ or 3’ flanking sequence.
4) Monte Carlo methods for assignment of levels of significance of: a) the extent of clustering in each sample and b) the degree to which clustered nominally-positive SNPs from multiple independent samples identify the same chromosomal regions
For each Monte Carlo trial, we randomly selected a number of “pseudo positive” SNPs from each dataset that matched the number that achieved nominal significance in the bona fide dataset. Thus, we constructed a list of autosomal SNPs assayed in each sample and assigned a number to each SNP that corresponded to its position on the list. To select the pseudo-positive SNPs for each trial of the European-American datasets, we selected 75,413 random numbers for the NIDA (see below) and 45,108 random numbers for the dbGAP datasets. For each trial, the SNPs identified by the positions on the list that corresponded to these randomly assigned numbers were then queried for the extent to which their results equaled or exceeded the results obtained for the actual dataset. In 10,000 such trials for each sample, we compared results concerning the extent of chromosomal clustering from these sets of pseudo-positive SNPs to those for the true positive SNPs. These empirical Monte Carlo p values thus addressed the null hypothesis that the true positive SNPs from single samples were randomly arrayed on the chromosomes. This Monte Carlo method thus samples from the actual datasets, providing a good method to address this null hypothesis in light of differences in the linkage disequilibrium structure that might occur from sample to sample.
In 10,000 trials from pairs of samples, we compared the results from pseudo-positive SNPs to those for true positive SNPs. We compared the extent of overlap between chromosomal clusters identified in each independent sample. This analysis addressed the null hypothesis that all of the clustering in each sample was based only on linkage disequilibrium between sets of SNPs that was not related to phenotype. Put another way, the Monte Carlo p values that derive from these 10,000 trials addressed the null hypothesis that the clusters of SNPs found in the true data identified the same chromosomal regions in independent samples as often as anticipated by chance.
Secondary analysis of dbGAP data used permutation approaches as implemented in PLINK (v1.06) (http://pngu.mgh.harvard.edu/purcell/plink/) (Purcell and others 2007). We randomized assignment of the phenotypes to data derived from the current SNPs and analyzed the data from 1,000 permutation trials.
To assess the power of our current approach we used current sample sizes and standard deviations, power calculator PS v2.1.31 (Dupont and Plummer 1990; Dupont and Plummer 1998) and α = 0.05.
5) Individual SNP genotyping to follow up SNPs with apparent genome wide significance in dbGAP samples
Several SNPs appeared to achieve “genome wide” levels of significance in dbGAP datasets, including SNPs rs3817222 and rs12734338 in the PPP1R12B gene. We sought individual genotypes for these SNPs and the additional SNP in this gene rs12741415 in NIDA samples using Sequenom MALDITOF assays and the oligonucleotide primers TGGATGAGCAGTCCTCTAAGA, CCACTTACATCCTTTGTCCAG and TCCATCCGAGAGAGGAGG for rs381722; CTGTGGTTACTGGAGTCTGG, TTAGTGCTATAGAACACTAGAAC and TCTGTTAACCAACCTCTGACT for rs12734338 and GGCTCGATTGCCTAATATGGT, AGCTCACCTACCATGTCTTTAA and GTATTTCCCAGACAAGATTGC for rs12741415.
Results
As noted elsewhere (Drgon and others 2010), variation among the allele frequency estimates between pools from individuals of the same phenotype for each racial/ethnic group from the NIDA/MNB samples was +/− 0.02 (standard error of the mean SEM). This represented 0.028 and 0.032 of the mean hybridization density measure for data from African- and European-American samples, respectively. “Pseudopools” constructed from groups of 20 of the dbGAP samples displayed standard error measurements that represented larger, 0.060 and 0.059, corresponding proportions of mean values.
European-American samples
For the NIDA/MNB European-American samples, 75,413 of the autosomal Affymetrix 6.0 SNPs displayed t values with p < 0.05 in comparisons between data from substance dependent vs control samples (Drgon and others 2010). For the dbGAP data from European-Americans, χ2 tests displayed p < 0.05 for 45,108 autosomal Illumina SNPs. The more modest pool-to-pool variance in the NIDA datasets, as noted above, provided more nominally-positive results from these European-American samples than the dbGAP datasets. Use of t testing also appeared to contribute to the greater number of nominally-positive results in the NIDA datasets. When the pseudopool data from the dbGAP individuals was analyzed using t testing, 49,141 SNPs displayed nominal statistical significance.
Searches for genome wide significance in each European-American sample
We identified case vs control p values for t test results from NIDA/MNB samples and for χ2 results from dbGAP samples from unrelated individuals. Permutation testing for the dbGAP European-American samples revealed p < 0.001 for the number of SNPs with nominal case vs control p values < 0.05. However, only a few of the p values reached the 10−8 level deemed necessary for genome wide significance (see below).
Searches for clustering of SNPs with nominally-significant case vs control differences in each European-American sample
We identified 2931 clusters of SNPs that displayed nominally significant, p < 0.05 case vs control differences for p values from t test results from NIDA/MNB samples and 2783 clusters for χ2 results from dbGAP samples.
Searches for chromosomal regions identified by clustered SNPs with nominally-significant case vs control differences in both European-American samples
One hundred sixty two chromosomal regions contained clusters of nominally-positive SNPs from each of the two European-American samples.
None of 10,000 Monte Carlo simulation trials that each began with random sets of SNPs selected from each of the datasets identified as many overlapping regions as found in the true dataset. The overall Monte Carlo p < 0.0001 for the overlap noted in the true data thus provides very high levels of confidence that these independently-derived sets of results do not identify the same set of chromosomal regions by chance alone.
The chromosomal regions identified by data from both European-American samples and the genes that they contain are listed in Table 1A. The fraction of the genome occupied by these results is about 1.7 × that expected by chance, based on the fraction of the genome occupied by clustered nominally positive results from each of the two European-American samples (data not shown).
Table 1.
Chromosomal regions and genes identified by clusters of SNPs that provide nominally-significant differences between individuals dependent on at least one illegal substance (MNB) or cocaine (dbGAP) and controls of European-American (Table 1A) or African-American (Table 1B) heritage.. Columns list: chromosome, chromosomal region identified by clustered nominally-significant associations in MNB samples, number of nominally-positive SNPs in the corresponding region in MNB samples, chromosomal region identified by clustered nominally-significant associations in dbGAP samples, number of nominally-positive SNPs in the corresponding region in dbGAP samples, and gene(s) identified by the overlapping clusters from the two datasets.
Table 1A: | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
chr | Region from MNB SNPs (bp start and end) |
#MNB SNP |
MNB SNP with pmin in region |
pmin for MNB SNP |
region from dbGAP SNPs (bp start and end) |
#dbGAP SNP |
dbGAP SNP with pmin in region |
pmin for dbGAP SNP |
gene(s) | ||
1 | 22,403,721 | 22,436,892 | 13 | rs926435 | 0.00425140 | 22,411,184 | 22,419,939 | 4 | rs10917176 | 0.01722000 | |
1 | 68,372,474 | 68,386,908 | 9 | rs2820487 | 0.00122278 | 68,354,587 | 68,404,090 | 19 | rs3736934 | 0.00050580 | GPR177 |
1 | 119,001,219 | 119,007,078 | 4 | rs1742848 | 0.00673615 | 119,001,219 | 119,005,312 | 4 | rs12143914 | 0.00572800 | |
1 | 168,537,794 | 168,553,713 | 4 | rs6691470 | 0.00224958 | 168,545,502 | 168,609,913 | 16 | rs10919359 | 0.00007620 | |
1 | 170,608,822 | 170,631,953 | 7 | rs9425586 | 0.00114059 | 170,613,171 | 170,633,685 | 7 | rs9425592 | 0.00145600 | DNM3 |
1 | 199,383,345 | 199,385,625 | 4 | rs7525970 | 0.00023529 | 199,376,607 | 199,388,146 | 4 | rs8158 | 0.03283000 | TMEM9 |
1 | 208,048,696 | 208,072,444 | 7 | rs6540560 | 0.00695414 | 208,063,165 | 208,073,226 | 7 | rs660975 | 0.00953700 | C1orf107 |
1 | 215,665,471 | 215,672,528 | 5 | rs12128013 | 0.00002786 | 215,661,970 | 215,670,747 | 6 | rs1048126 | 0.01201000 | GPATC2 |
1 | 218,996,115 | 219,012,012 | 5 | rs447861 | 0.01636257 | 218,987,480 | 219,005,191 | 9 | rs12407624 | 0.02106000 | MOSC2 |
1 | 219,037,105 | 219,039,825 | 4 | rs12033808 | 0.00116666 | 219,022,635 | 219,052,523 | 8 | rs1389742 | 0.00440100 | MOSC1,MOSC2 |
1 | 238,293,444 | 238,295,582 | 4 | rs10926111 | 0.00440053 | 238,292,083 | 238,297,349 | 4 | rs10495457 | 0.02269000 | |
1 | 244,061,674 | 244,078,022 | 4 | rs12066580 | 0.01505649 | 244,056,488 | 244,072,421 | 14 | rs7514038 | 0.00265400 | SMYD3 |
1 | 246,129,313 | 246,145,669 | 5 | rs11204548 | 0.00006478 | 246,127,164 | 246,142,892 | 6 | rs11204553 | 0.00367600 | OR2W3,OR2T8 |
2 | 624,905 | 633,303 | 4 | rs13393304 | 0.01855858 | 628,144 | 649,958 | 8 | rs17042288 | 0.01080000 | |
2 | 15,803,805 | 15,813,722 | 4 | rs10460289 | 0.00507678 | 15,811,342 | 15,819,589 | 4 | rs6705499 | 0.01577000 | |
2 | 18,998,937 | 19,027,306 | 5 | rs16985758 | 0.00097855 | 19,015,666 | 19,040,397 | 5 | rs16985798 | 0.01087000 | FLJ41481 |
2 | 33,939,112 | 33,956,488 | 4 | rs17014226 | 0.00100593 | 33,947,740 | 33,960,168 | 4 | rs13432090 | 0.01374000 | |
2 | 35,937,831 | 35,952,465 | 4 | rs7585354 | 0.00217892 | 35,930,844 | 35,974,162 | 11 | rs1607614 | 0.00392200 | |
2 | 37,942,034 | 37,952,362 | 5 | rs2565637 | 0.00073196 | 37,909,907 | 37,949,126 | 11 | rs10206788 | 0.00332400 | |
2 | 105,611,105 | 105,631,845 | 6 | rs4851821 | 0.00435854 | 105,622,619 | 105,633,659 | 5 | rs4851823 | 0.00958500 | |
2 | 129,575,592 | 129,583,075 | 4 | rs17049088 | 0.01424614 | 129,574,528 | 129,580,602 | 4 | rs13428187 | 0.00148200 | |
2 | 166,871,909 | 166,879,436 | 5 | rs6760472 | 0.00023737 | 166,876,339 | 166,885,605 | 4 | rs6432901 | 0.01052000 | SCN9A |
2 | 192,527,275 | 192,541,198 | 4 | rs6756470 | 0.00665492 | 192,529,143 | 192,540,026 | 6 | rs12612396 | 0.01866000 | TMEFF2 |
2 | 233,429,928 | 233,444,593 | 5 | rs6731064 | 0.00006057 | 233,441,388 | 233,445,376 | 8 | rs2592116 | 0.00107300 | NGEF,UNQ830 |
3 | 3,069,450 | 3,079,303 | 4 | rs163577 | 0.00731718 | 3,055,509 | 3,074,174 | 9 | rs17600202 | 0.00508400 | CNTN4 |
3 | 7,166,923 | 7,178,151 | 5 | rs1499142 | 0.00760806 | 7,142,701 | 7,170,141 | 11 | rs17824908 | 0.00137500 | GRM7 |
3 | 15,274,441 | 15,283,244 | 4 | rs9310472 | 0.00752533 | 15,270,368 | 15,283,244 | 4 | rs1318937 | 0.00000016 | CAP \N7 |
3 | 16,139,950 | 16,151,784 | 6 | rs17041907 | 0.00016140 | 16,139,124 | 16,159,346 | 8 | rs17041904 | 0.00165300 | |
3 | 54,887,170 | 54,902,941 | 4 | rs17054513 | 0.00000519 | 54,860,213 | 54,893,478 | 7 | rs9849795 | 0.00611900 | CACNA2D3 |
3 | 58,533,096 | 58,550,150 | 4 | rs17059410 | 0.00232310 | 58,533,809 | 58,541,032 | 6 | rs13088795 | 0.00396600 | FAM107A |
3 | 76,530,357 | 76,553,163 | 8 | rs3907672 | 0.01018072 | 76,553,163 | 76,556,830 | 4 | rs6796472 | 0.00712900 | |
3 | 100,527,865 | 100,546,621 | 9 | rs900060 | 0.01172881 | 100,534,837 | 100,543,049 | 4 | rs9861463 | 0.00109800 | |
3 | 127,698,906 | 127,707,980 | 4 | rs4679251 | 0.00634307 | 127,698,963 | 127,749,921 | 15 | rs1799388 | 0.00260500 | UROC1 |
3 | 144,099,828 | 144,129,850 | 12 | rs4683702 | 0.00045855 | 144,112,552 | 144,120,180 | 4 | rs2608077 | 0.00203800 | |
3 | 147,738,756 | 147,759,347 | 7 | rs13074501 | 0.00733947 | 147,725,651 | 147,754,452 | 6 | rs2587014 | 0.04025000 | PLSCR1 |
4 | 6,083,725 | 6,111,793 | 10 | rs11935825 | 0.00073626 | 6,093,633 | 6,110,127 | 5 | rs6850751 | 0.01339000 | JAKMIP1 |
4 | 12,271,875 | 12,280,659 | 4 | rs13151462 | 0.00941052 | 12,263,493 | 12,291,255 | 8 | rs7668124 | 0.00169800 | |
4 | 15,553,008 | 15,603,859 | 13 | rs6824333 | 0.00002604 | 15,567,988 | 15,583,320 | 5 | rs12504895 | 0.00084520 | FGFBP2,PROM1 |
4 | 37,801,804 | 37,821,864 | 4 | rs6854169 | 0.00021616 | 37,815,419 | 37,822,024 | 4 | rs17580037 | 0.01405000 | TBC1D1 |
4 | 75,904,150 | 75,914,700 | 5 | rs6840306 | 0.00032172 | 75,900,241 | 75,906,167 | 4 | rs4859417 | 0.02485000 | BTC |
4 | 110,831,986 | 110,848,343 | 5 | rs5030551 | 0.00042129 | 110,823,233 | 110,831,986 | 4 | rs3212153 | 0.00425400 | CASP6,CCDC109B |
4 | 178,492,904 | 178,512,644 | 5 | rs2048077 | 0.00004146 | 178,482,163 | 178,494,510 | 4 | rs987467 | 0.02244000 | NEIL3 |
4 | 185,570,737 | 185,604,825 | 11 | rs17585389 | 0.00531928 | 185,568,339 | 185,593,905 | 5 | rs793810 | 0.00999800 | IRF2 |
5 | 3,481,925 | 3,490,919 | 4 | rs42742 | 0.00099008 | 3,481,015 | 3,488,141 | 4 | rs251444 | 0.00439900 | |
5 | 4,691,885 | 4,705,364 | 5 | rs11134064 | 0.00000493 | 4,698,455 | 4,716,347 | 4 | rs2077369 | 0.01712000 | |
5 | 30,841,081 | 30,853,465 | 4 | rs2330607 | 0.00947254 | 30,821,797 | 30,850,626 | 7 | rs1547531 | 0.00737700 | |
5 | 36,474,931 | 36,496,015 | 5 | rs13359394 | 0.00045106 | 36,495,832 | 36,508,747 | 4 | rs2455280 | 0.00375300 | |
5 | 54,288,790 | 54,318,552 | 9 | rs1508891 | 0.00088078 | 54,286,951 | 54,293,569 | 4 | rs10491370 | 0.00253100 | |
5 | 71,293,156 | 71,303,134 | 4 | rs16873479 | 0.00086331 | 71,281,566 | 71,293,943 | 4 | rs1217745 | 0.00135500 | |
5 | 83,255,824 | 83,262,289 | 4 | rs7725359 | 0.00416153 | 83,236,774 | 83,258,315 | 10 | rs9791160 | 0.00188800 | |
5 | 110,754,871 | 110,761,539 | 4 | rs17133238 | 0.01132727 | 110,758,456 | 110,770,337 | 4 | rs523389 | 0.03461000 | CAMK4 |
5 | 122,633,795 | 122,637,938 | 5 | rs255628 | 0.00206409 | 122,634,922 | 122,668,972 | 7 | rs2897737 | 0.00300600 | |
5 | 159,208,420 | 159,222,284 | 7 | rs31689 | 0.00025307 | 159,195,539 | 159,215,437 | 8 | rs12656957 | 0.00354100 | |
5 | 178,580,524 | 178,586,436 | 4 | rs17079221 | 0.00575412 | 178,586,200 | 178,600,590 | 5 | rs469568 | 0.00530800 | ADAMTS2 |
6 | 17,170,349 | 17,175,020 | 6 | rs9383242 | 0.00149357 | 17,156,118 | 17,175,124 | 5 | rs13213842 | 0.00446300 | |
6 | 22,666,801 | 22,689,492 | 4 | rs9460760 | 0.00550320 | 22,671,616 | 22,678,043 | 6 | rs12193939 | 0.00339600 | HDGFL1 |
6 | 39,396,068 | 39,414,041 | 4 | rs16891904 | 0.00129830 | 39,409,896 | 39,446,413 | 17 | rs3818308 | 0.00219800 | KIF6 |
6 | 81,087,550 | 81,099,967 | 8 | rs10455370 | 0.00019544 | 81,098,304 | 81,117,440 | 14 | rs4502885 | 0.01434000 | BCKDHB |
6 | 167,007,415 | 167,038,192 | 6 | rs874277 | 0.00350443 | 167,026,084 | 167,052,518 | 10 | rs874277 | 0.00465700 | RPS6KA2 |
6 | 167,600,547 | 167,619,694 | 6 | rs1209349 | 0.00045238 | 167,603,059 | 167,638,386 | 15 | rs3010558 | 0.00111400 | UNC93A |
7 | 7,867,768 | 7,876,393 | 4 | rs6979457 | 0.01230576 | 7,866,490 | 7,874,760 | 5 | rs7812102 | 0.02089000 | |
7 | 11,479,011 | 11,498,204 | 6 | rs7793532 | 0.00747784 | 11,485,443 | 11,502,428 | 8 | rs12539888 | 0.00184400 | THSD7A |
7 | 36,067,711 | 36,083,854 | 14 | rs1986698 | 0.00005694 | 36,083,063 | 36,091,506 | 6 | rs11975227 | 0.00947300 | |
7 | 50,491,702 | 50,517,353 | 11 | rs11575492 | 0.00006757 | 50,468,934 | 50,501,033 | 9 | rs10243511 | 0.00533400 | DDC,FIGNL1 |
7 | 88,998,520 | 89,002,076 | 4 | rs11972083 | 0.00028462 | 88,999,065 | 89,006,198 | 4 | rs7787163 | 0.01037000 | |
8 | 3,035,516 | 3,042,040 | 4 | rs17079607 | 0.00825003 | 3,023,076 | 3,040,999 | 4 | rs2730048 | 0.03149000 | CSMD1 |
8 | 4,100,361 | 4,118,520 | 5 | rs9693235 | 0.00648816 | 4,092,064 | 4,112,474 | 6 | rs779107 | 0.00463900 | CSMD1 |
8 | 4,166,391 | 4,177,720 | 7 | rs10094349 | 0.00710516 | 4,173,158 | 4,177,630 | 5 | rs1847570 | 0.00817800 | CSMD1 |
8 | 4,397,922 | 4,404,404 | 4 | rs1526335 | 0.01126980 | 4,362,810 | 4,427,218 | 24 | rs6996668 | 0.00002430 | CSMD1 |
8 | 6,789,365 | 6,804,222 | 7 | rs11137078 | 0.00017713 | 6,802,066 | 6,816,223 | 5 | rs2702910 | 0.04002000 | DEFA8P |
8 | 10,415,815 | 10,426,892 | 4 | rs6601481 | 0.01062660 | 10,426,159 | 10,433,464 | 5 | rs6601483 | 0.01067000 | UNQ9391 |
8 | 15,702,024 | 15,712,594 | 4 | rs13438987 | 0.00708685 | 15,681,763 | 15,708,708 | 8 | rs2721207 | 0.00929400 | |
8 | 18,433,022 | 18,440,225 | 4 | rs7812358 | 0.01422666 | 18,425,515 | 18,440,225 | 4 | rs3739396 | 0.00293000 | PSD3 |
8 | 18,562,990 | 18,573,060 | 4 | rs17695641 | 0.00001669 | 18,558,380 | 18,601,178 | 9 | rs17695724 | 0.00165400 | PSD3 |
8 | 18,586,760 | 18,599,333 | 4 | rs974053 | 0.00317847 | 18,558,380 | 18,601,178 | 9 | PSD3 | ||
8 | 58,003,214 | 58,007,822 | 5 | rs6474089 | 0.00104125 | 57,967,108 | 58,003,814 | 9 | rs17791051 | 0.00237600 | |
8 | 58,258,438 | 58,267,213 | 6 | rs7843659 | 0.01294892 | 58,260,868 | 58,277,297 | 6 | rs7463453 | 0.00925100 | |
8 | 62,639,801 | 62,651,888 | 4 | rs16927574 | 0.00027789 | 62,623,810 | 62,642,488 | 6 | rs4291265 | 0.00046220 | ASPH |
8 | 124,991,629 | 125,041,794 | 12 | rs16893054 | 0.00135755 | 124,996,892 | 125,008,826 | 4 | rs16899060 | 0.00966600 | C8ORFK23 |
8 | 127,648,673 | 127,671,391 | 5 | rs2385684 | 0.00032344 | 127,670,209 | 127,680,387 | 4 | rs2385694 | 0.00962100 | |
8 | 129,218,353 | 129,290,287 | 17 | rs2608038 | 0.00176380 | 129,222,462 | 129,239,290 | 6 | rs16893188 | 0.00388300 | |
8 | 129,218,353 | 129,290,287 | 17 | 129,285,356 | 129,299,376 | 8 | rs1967314 | 0.00212200 | |||
8 | 138,940,511 | 138,960,987 | 8 | rs1511849 | 0.00935925 | 138,933,078 | 138,955,125 | 5 | rs7003495 | 0.00996600 | FLJ45872 |
9 | 2,896,806 | 2,920,875 | 5 | rs12345926 | 0.00136364 | 2,890,843 | 2,921,341 | 6 | rs4013197 | 0.00141900 | |
9 | 28,661,665 | 28,685,002 | 4 | rs10968712 | 0.01706234 | 28,683,005 | 28,709,213 | 5 | rs1438478 | 0.01349000 | LINGO2 |
9 | 35,986,553 | 35,993,037 | 5 | rs12006109 | 0.00025560 | 35,981,104 | 36,008,310 | 8 | rs16932816 | 0.00029160 | OR13C6P,OR13C7P |
9 | 71,067,399 | 71,087,631 | 6 | rs1538579 | 0.01729508 | 71,063,582 | 71,073,044 | 4 | rs2039785 | 0.00812000 | TJP2 |
9 | 77,493,107 | 77,503,221 | 5 | rs4745437 | 0.00007905 | 77,492,323 | 77,497,877 | 4 | rs10512050 | 0.00148800 | |
9 | 100,853,373 | 100,876,607 | 6 | rs16918220 | 0.00087819 | 100,869,363 | 100,901,588 | 7 | rs7034462 | 0.00865900 | COL15A1 |
9 | 109,450,466 | 109,466,790 | 6 | rs7870632 | 0.00375761 | 109,426,897 | 109,450,709 | 5 | rs11794132 | 0.00394600 | |
9 | 113,010,883 | 113,038,389 | 14 | rs700137 | 0.00003992 | 113,016,564 | 113,037,708 | 5 | rs700131 | 0.02814000 | |
10 | 10,290,510 | 10,304,657 | 4 | rs12240935 | 0.00011649 | 10,296,338 | 10,301,536 | 4 | rs11256534 | 0.02209000 | |
10 | 12,619,610 | 12,632,973 | 4 | rs10458806 | 0.00032252 | 12,631,718 | 12,635,821 | 5 | rs6602595 | 0.00384600 | CAMK1D |
10 | 61,585,961 | 61,607,010 | 6 | rs12355908 | 0.00022946 | 61,607,010 | 61,624,482 | 5 | rs11814752 | 0.00431800 | ANK3 |
10 | 72,174,962 | 72,201,406 | 6 | rs16927943 | 0.00355390 | 72,153,016 | 72,184,617 | 9 | rs1816002 | 0.00148600 | ADAMTS14 |
10 | 72,174,962 | 72,201,406 | 6 | 72,197,459 | 72,210,540 | 7 | rs10999530 | 0.02139000 | C10orf27 | ||
10 | 72,997,579 | 73,016,551 | 4 | rs7093128 | 0.01759826 | 72,979,535 | 73,002,134 | 5 | rs12247922 | 0.00699100 | CDH23 |
10 | 79,015,837 | 79,028,608 | 4 | rs2673402 | 0.00120627 | 79,010,407 | 79,018,741 | 4 | rs11002212 | 0.00323600 | KCNMA1 |
10 | 79,793,193 | 79,801,448 | 4 | rs7910471 | 0.00758798 | 79,801,448 | 79,818,231 | 5 | rs10490996 | 0.00218800 | |
10 | 125,273,355 | 125,279,327 | 4 | rs705172 | 0.00064930 | 125,278,294 | 125,284,639 | 4 | rs2486032 | 0.01196000 | |
10 | 127,471,364 | 127,485,187 | 4 | rs11244653 | 0.00333472 | 127,474,643 | 127,494,379 | 7 | rs11244664 | 0.02392000 | UROS |
10 | 131,563,111 | 131,590,700 | 6 | rs7916096 | 0.00196947 | 131,574,091 | 131,584,075 | 4 | rs1334011 | 0.00135800 | EBF3 |
11 | 7,234,696 | 7,239,825 | 4 | rs10839752 | 0.00449163 | 7,216,630 | 7,280,083 | 23 | rs12799959 | 0.00003960 | SYT9 |
11 | 13,185,312 | 13,209,685 | 6 | rs7117211 | 0.01818414 | 13,189,314 | 13,196,135 | 5 | rs7112005 | 0.00175100 | |
11 | 75,658,173 | 75,686,316 | 7 | rs11236682 | 0.01030137 | 75,638,688 | 75,663,802 | 13 | rs4945056 | 0.00021350 | |
11 | 99,351,517 | 99,368,357 | 6 | rs7103510 | 0.00140884 | 99,348,596 | 99,387,874 | 15 | rs6590484 | 0.00329900 | CNTN5 |
12 | 6,196,175 | 6,218,155 | 5 | rs10849423 | 0.01440004 | 6,210,649 | 6,223,258 | 5 | rs3181301 | 0.00436100 | CD9 |
12 | 50,447,681 | 50,471,244 | 6 | rs11834760 | 0.00446783 | 50,455,698 | 50,480,150 | 5 | rs2099715 | 0.02340000 | SCN8A |
12 | 51,229,951 | 51,235,142 | 4 | rs681387 | 0.01722113 | 51,224,587 | 51,246,335 | 5 | rs584436 | 0.02329000 | KRT71 |
12 | 68,341,365 | 68,357,160 | 5 | rs11609972 | 0.00050239 | 68,311,754 | 68,381,836 | 16 | rs796538 | 0.00462900 | BEST3 |
12 | 74,665,362 | 74,685,000 | 6 | rs2117047 | 0.00209276 | 74,665,362 | 74,685,878 | 5 | rs2367446 | 0.01159000 | |
12 | 106,948,693 | 106,971,387 | 4 | rs11113696 | 0.00003020 | 106,960,526 | 106,983,174 | 7 | rs2374951 | 0.00852500 | |
12 | 110,118,664 | 110,143,457 | 6 | rs1034603 | 0.00545252 | 110,129,183 | 110,133,727 | 6 | rs1034603 | 0.01413000 | CUX2 |
12 | 111,761,119 | 111,804,161 | 11 | rs2384069 | 0.00273499 | 111,803,488 | 111,811,401 | 4 | rs7958347 | 0.03495000 | RPH3A |
12 | 114,518,824 | 114,574,647 | 11 | rs7308283 | 0.00241900 | 114,495,519 | 114,519,550 | 7 | rs10735088 | 0.00349200 | |
12 | 114,518,824 | 114,574,647 | 11 | 1.00000000 | 114,532,466 | 114,546,369 | 6 | rs12813282 | 0.00510100 | ||
13 | 19,936,904 | 19,941,804 | 5 | rs9315602 | 0.00179960 | 19,934,921 | 19,948,575 | 8 | rs9509234 | 0.00052670 | CRYL1 |
13 | 35,830,527 | 35,860,622 | 6 | rs7989684 | 0.00052489 | 35,853,261 | 35,868,927 | 5 | rs1935099 | 0.00946500 | |
13 | 91,720,738 | 91,727,447 | 7 | rs7996483 | 0.01455500 | 91,672,385 | 91,722,251 | 15 | rs16947178 | 0.00012110 | GPC5 |
13 | 91,850,009 | 91,865,378 | 7 | rs16947570 | 0.00046885 | 91,865,189 | 91,895,126 | 8 | rs2149065 | 0.00165600 | GPC5 |
14 | 22,047,062 | 22,069,382 | 5 | rs11157651 | 0.00001672 | 22,048,615 | 22,091,560 | 11 | rs412790 | 0.00585100 | TRAJ… |
14 | 32,295,737 | 32,307,125 | 4 | rs17091659 | 0.00007471 | 32,287,714 | 32,298,632 | 5 | rs2383376 | 0.01102000 | AKAP6 |
14 | 52,852,578 | 52,907,458 | 27 | rs10138849 | 0.00212501 | 52,883,170 | 52,889,727 | 5 | rs1255309 | 0.02097000 | |
14 | 59,408,099 | 59,431,061 | 5 | rs2882302 | 0.00108852 | 59,408,099 | 59,432,923 | 5 | rs1951366 | 0.03292000 | RTN1 |
14 | 72,786,527 | 72,791,053 | 7 | rs17126352 | 0.00053080 | 72,786,268 | 72,798,710 | 5 | rs8017937 | 0.02057000 | PAPLN |
15 | 29,128,656 | 29,135,666 | 5 | rs10162727 | 0.00155011 | 29,117,572 | 29,130,036 | 5 | rs2288242 | 0.00519000 | TRPM1 |
15 | 78,392,966 | 78,407,553 | 5 | rs17312725 | 0.00253795 | 78,389,782 | 78,394,769 | 4 | rs3858961 | 0.00133600 | |
15 | 84,164,168 | 84,168,771 | 4 | rs12907270 | 0.01138496 | 84,147,059 | 84,173,904 | 5 | rs7170181 | 0.01905000 | |
15 | 91,532,308 | 91,561,784 | 6 | rs6416582 | 0.00633997 | 91,537,949 | 91,553,882 | 8 | rs1872052 | 0.00022430 | UNQ9370 |
15 | 95,882,705 | 95,892,369 | 4 | rs12912857 | 0.01286705 | 95,889,109 | 95,897,046 | 5 | rs1500652 | 0.01793000 | |
15 | 99,375,750 | 99,389,091 | 4 | rs4965764 | 0.00344841 | 99,363,718 | 99,378,615 | 4 | rs2412004 | 0.01233000 | LRRK1 |
16 | 13,664,264 | 13,673,793 | 4 | rs7195434 | 0.02469525 | 13,672,242 | 13,714,719 | 13 | rs12927885 | 0.00027820 | |
16 | 13,697,788 | 13,705,700 | 4 | rs12927885 | 0.02151072 | 13,672,242 | 13,714,719 | 13 | |||
16 | 17,900,348 | 17,902,894 | 5 | rs9937528 | 0.00030556 | 17,891,568 | 17,942,205 | 10 | rs1019807 | 0.00607700 | |
16 | 22,903,047 | 22,932,189 | 5 | rs16975343 | 0.02802412 | 22,916,103 | 22,931,597 | 9 | rs6497620 | 0.00180000 | |
16 | 26,545,758 | 26,553,480 | 4 | rs7192315 | 0.00008835 | 26,540,129 | 26,553,877 | 7 | rs237131 | 0.00995300 | |
16 | 72,303,484 | 72,331,638 | 9 | rs8053939 | 0.00021607 | 72,282,721 | 72,326,475 | 14 | rs2639311 | 0.00090850 | |
16 | 76,385,234 | 76,399,547 | 5 | rs8055855 | 0.00064341 | 76,356,809 | 76,389,690 | 9 | rs2914451 | 0.00206500 | KIAA1576 |
16 | 76,665,475 | 76,674,781 | 6 | rs16947096 | 0.00347086 | 76,669,764 | 76,681,421 | 5 | rs12927327 | 0.00547300 | |
16 | 77,117,528 | 77,144,008 | 5 | rs16948054 | 0.00272614 | 77,121,333 | 77,130,526 | 4 | rs11645676 | 0.02285000 | |
16 | 79,205,051 | 79,220,547 | 4 | rs13334600 | 0.01441691 | 79,194,516 | 79,210,822 | 6 | rs8063688 | 0.01390000 | CDYL2 |
16 | 79,474,356 | 79,491,623 | 4 | rs889519 | 0.00138143 | 79,485,938 | 79,501,696 | 4 | rs12446361 | 0.00359000 | |
16 | 81,717,335 | 81,727,918 | 4 | rs8054536 | 0.00213238 | 81,722,037 | 81,731,139 | 5 | rs7200240 | 0.01624000 | CDH13 |
16 | 82,523,937 | 82,537,492 | 4 | rs12051537 | 0.01162385 | 82,535,061 | 82,543,753 | 6 | rs4782866 | 0.00462500 | OSGIN1 |
16 | 85,297,250 | 85,308,696 | 5 | rs177267 | 0.00390403 | 85,257,186 | 85,302,819 | 14 | rs12919935 | 0.00038490 | |
17 | 6,102,642 | 6,106,744 | 4 | rs12452660 | 0.02094881 | 6,087,870 | 6,102,642 | 4 | rs3888640 | 0.00428800 | |
17 | 28,827,828 | 28,847,221 | 6 | rs7221633 | 0.00013241 | 28,828,513 | 28,835,589 | 4 | rs7214958 | 0.00799600 | |
17 | 51,729,141 | 51,736,750 | 5 | rs17820092 | 0.00220747 | 51,710,283 | 51,729,610 | 4 | rs7211966 | 0.00563400 | ANKFN1 |
18 | 8,777,857 | 8,784,092 | 4 | rs588397 | 0.00047409 | 8,778,967 | 8,810,886 | 9 | rs8084760 | 0.00620500 | KIAA0802 |
18 | 10,727,373 | 10,755,792 | 7 | rs10775415 | 0.01410891 | 10,731,011 | 10,749,961 | 6 | rs8086878 | 0.02024000 | |
18 | 43,270,667 | 43,289,577 | 4 | rs7234972 | 0.00191113 | 43,268,749 | 43,287,484 | 7 | rs4939797 | 0.00268300 | |
18 | 53,153,236 | 53,176,313 | 5 | rs626109 | 0.00012961 | 53,164,072 | 53,225,658 | 20 | rs221877 | 0.00598900 | ST8SIA3 |
18 | 53,871,218 | 53,879,450 | 4 | rs1573390 | 0.00994035 | 53,871,218 | 53,883,190 | 4 | rs8097619 | 0.00254900 | NEDD4L |
18 | 55,287,091 | 55,289,937 | 5 | rs12961264 | 0.01295180 | 55,285,808 | 55,298,331 | 9 | rs644856 | 0.01538000 | CCBE1 |
18 | 73,028,757 | 73,048,874 | 4 | rs1562774 | 0.00005443 | 73,021,843 | 73,050,153 | 6 | rs7243404 | 0.00078880 | |
19 | 56,057,764 | 56,066,404 | 4 | rs2739460 | 0.01128400 | 56,056,192 | 56,068,842 | 4 | rs8104556 | 0.01320000 | KLK2,KLK3 |
19 | 58,276,230 | 58,293,226 | 6 | rs10406237 | 0.00461575 | 58,272,963 | 58,286,618 | 4 | rs3745178 | 0.00844300 | ZNF160 |
20 | 8,381,577 | 8,388,510 | 4 | rs2022452 | 0.00049261 | 8,356,544 | 8,386,789 | 5 | rs2719795 | 0.00295100 | PLCB1 |
20 | 38,902,918 | 38,913,320 | 4 | rs16989370 | 0.00258077 | 38,904,841 | 38,930,253 | 8 | rs875627 | 0.00209200 | |
20 | 42,459,198 | 42,465,058 | 4 | rs6031586 | 0.00758220 | 42,464,223 | 42,475,778 | 4 | rs8116574 | 0.02672000 | HNF4A |
21 | 40,057,196 | 40,094,216 | 12 | rs2837220 | 0.00609864 | 40,058,582 | 40,064,802 | 6 | rs8128850 | 0.00162400 | |
22 | 24,505,564 | 24,516,610 | 4 | rs5752205 | 0.00144446 | 24,498,558 | 24,524,941 | 10 | rs9624894 | 0.00597400 | MYO18B |
22 | 25,576,070 | 25,584,122 | 6 | rs136544 | 0.02053141 | 25,557,454 | 25,581,367 | 8 | rs136535 | 0.00244300 | |
22 | 35,035,050 | 35,049,131 | 4 | rs5995281 | 0.00064906 | 35,034,987 | 35,063,884 | 14 | rs8136069 | 0.00066410 | MYH9 |
22 | 36,387,284 | 36,391,396 | 4 | rs732857 | 0.00244107 | 36,384,208 | 36,404,380 | 8 | rs12167604 | 0.01628000 | PDXP |
Table 1B: | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
chr | region from MNB SNPs (bp start and end) |
#MNB SNP |
MNB SNP with pmin in region |
pmin for MNB SNP |
region from dbGAP SNPs (bp start and end) |
#dbGAP SNP |
dbGAP SNP with pmin in region |
pmin for dbGAP SNP |
gene(s) | ||
1 | 38,673,230 | 38,682,775 | 8 | rs7530323 | 0.00182633 | 38,672,323 | 38,678,275 | 4 | rs7530233 | 0.02940000 | |
1 | 55,521,558 | 55,522,237 | 4 | rs4927218 | 0.00711474 | 55,515,425 | 55,530,395 | 5 | rs10888912 | 0.00051570 | |
1 | 56,899,238 | 56,905,582 | 5 | rs2796528 | 0.01599004 | 56,880,731 | 56,899,297 | 7 | rs2746349 | 0.00682100 | PRKAA2 |
1 | 77,588,107 | 77,602,693 | 6 | rs10493604 | 0.00123856 | 77,582,434 | 77,612,068 | 5 | rs17100475 | 0.00249000 | AK5 |
1 | 87,386,807 | 87,404,362 | 5 | rs6675309 | 0.00250523 | 87,385,946 | 87,399,117 | 4 | rs7553864 | 0.00711000 | |
1 | 91,692,105 | 91,695,992 | 4 | rs17131455 | 0.03028641 | 91,690,036 | 91,695,842 | 4 | rs7526795 | 0.00921700 | |
1 | 100,960,780 | 100,995,245 | 8 | rs6660837 | 0.00214425 | 100,948,239 | 100,968,247 | 5 | rs6680254 | 0.01082000 | VCAM1 |
1 | 107,639,114 | 107,669,240 | 7 | rs17509160 | 0.00006763 | 107,638,682 | 107,647,621 | 4 | rs10494068 | 0.01202000 | NTNG1 |
1 | 119,812,845 | 119,825,411 | 5 | rs17023786 | 0.01083654 | 119,795,007 | 119,815,398 | 7 | rs6672903 | 0.00002160 | |
1 | 164,558,365 | 164,576,468 | 11 | rs1343295 | 0.00086242 | 164,557,685 | 164,570,256 | 4 | rs10918425 | 0.01093000 | |
1 | 175,396,660 | 175,414,782 | 5 | rs12092285 | 0.00071788 | 175,395,853 | 175,409,818 | 4 | rs2014384 | 0.00334800 | ASTN,FAM5B |
1 | 198,455,928 | 198,466,594 | 5 | rs6427809 | 0.01928194 | 198,429,293 | 198,467,479 | 11 | rs10919843 | 0.00173300 | FAM58B |
1 | 207,161,387 | 207,180,891 | 6 | rs1320539 | 0.00120812 | 207,161,387 | 207,186,803 | 6 | rs4129434 | 0.00355800 | |
1 | 225,031,957 | 225,044,955 | 4 | rs12071493 | 0.00299142 | 225,018,938 | 225,037,652 | 5 | rs12729579 | 0.00415200 | |
1 | 236,819,330 | 236,842,448 | 5 | rs16837190 | 0.00236112 | 236,824,948 | 236,830,118 | 4 | rs6698914 | 0.02440000 | |
2 | 42,969,929 | 43,006,396 | 9 | rs1078100 | 0.00022284 | 42,974,788 | 42,989,296 | 4 | rs4953675 | 0.00376700 | |
2 | 70,790,369 | 70,823,291 | 7 | rs12621522 | 0.01574612 | 70,796,646 | 70,805,231 | 4 | rs7597992 | 0.01524000 | ADD2 |
2 | 105,953,667 | 105,963,513 | 4 | rs2377342 | 0.00175814 | 105,951,160 | 105,955,449 | 4 | rs6729693 | 0.02341000 | |
2 | 111,507,275 | 111,521,726 | 5 | rs17482961 | 0.00299924 | 111,506,262 | 111,514,272 | 4 | rs13012948 | 0.00210900 | ACOXL |
2 | 142,088,710 | 142,106,899 | 4 | rs10928113 | 0.02228862 | 142,093,010 | 142,108,825 | 6 | rs10190730 | 0.00300700 | LRP1B |
2 | 166,123,345 | 166,130,256 | 4 | rs16850914 | 0.00340857 | 166,129,660 | 166,148,793 | 6 | rs10803799 | 0.00017960 | TAIP-2,TTC21B |
2 | 205,043,875 | 205,056,347 | 5 | rs1550910 | 0.00392530 | 205,054,912 | 205,058,894 | 4 | rs4673300 | 0.00962500 | |
2 | 221,797,760 | 221,813,151 | 4 | rs1368064 | 0.00352961 | 221,789,700 | 221,797,772 | 6 | rs1368065 | 0.01004000 | |
3 | 8,976,672 | 8,987,903 | 5 | rs369651 | 0.01369019 | 8,950,538 | 8,978,124 | 6 | rs11922749 | 0.00581300 | RAD18 |
3 | 14,470,459 | 14,486,703 | 5 | rs17237132 | 0.00177376 | 14,469,859 | 14,481,638 | 7 | rs3773176 | 0.01040000 | SLC6A6 |
3 | 22,569,059 | 22,615,321 | 9 | rs7429763 | 0.00481766 | 22,565,692 | 22,575,090 | 7 | rs17011585 | 0.00002090 | |
3 | 22,569,059 | 22,615,321 | 9 | 22,589,646 | 22,601,911 | 4 | rs12633771 | 0.01866000 | |||
3 | 23,156,363 | 23,160,166 | 4 | rs13317243 | 0.00052540 | 23,153,852 | 23,192,342 | 12 | rs4858060 | 0.00003840 | |
3 | 24,598,884 | 24,631,782 | 7 | rs17014858 | 0.00050497 | 24,618,577 | 24,632,679 | 5 | rs11706529 | 0.00018420 | |
3 | 41,447,475 | 41,465,298 | 7 | rs12054014 | 0.00247884 | 41,451,566 | 41,467,634 | 7 | rs13059459 | 0.00078210 | ULK4 |
3 | 111,451,489 | 111,462,018 | 4 | rs7641787 | 0.00190343 | 111,431,424 | 111,451,489 | 4 | rs9883805 | 0.00167000 | |
3 | 141,266,707 | 141,276,065 | 7 | rs2350488 | 0.00021976 | 141,261,763 | 141,270,836 | 4 | rs12496538 | 0.00396500 | CLSTN2 |
4 | 28,246,176 | 28,269,954 | 5 | rs9996729 | 0.00006797 | 28,268,952 | 28,274,987 | 4 | rs6815271 | 0.00229500 | |
4 | 35,681,896 | 35,691,621 | 4 | rs1510653 | 0.01976209 | 35,681,615 | 35,691,365 | 6 | rs10010285 | 0.01869000 | |
4 | 93,684,320 | 93,699,740 | 9 | rs17319672 | 0.00000872 | 93,698,298 | 93,730,844 | 10 | rs7663835 | 0.00026950 | GRID2 |
4 | 93,719,692 | 93,727,390 | 4 | rs17019608 | 0.00344821 | 93,698,298 | 93,730,844 | 10 | GRID2 | ||
4 | 96,544,322 | 96,580,176 | 7 | rs3912477 | 0.00399730 | 96,567,001 | 96,598,044 | 12 | rs265045 | 0.00686000 | UNC5C |
5 | 10,316,582 | 10,320,090 | 5 | rs699113 | 0.01235791 | 10,302,790 | 10,320,915 | 9 | rs544 | 0.02792000 | CCT5 |
5 | 11,553,608 | 11,561,023 | 4 | rs17218080 | 0.00061574 | 11,560,368 | 11,572,347 | 4 | rs10038337 | 0.00494500 | CTNND2 |
5 | 15,285,308 | 15,298,402 | 4 | rs10513204 | 0.01908972 | 15,278,383 | 15,302,785 | 6 | rs11133806 | 0.00498100 | |
5 | 36,484,652 | 36,492,055 | 4 | rs2468513 | 0.00006454 | 36,471,233 | 36,495,341 | 5 | rs17358298 | 0.00228500 | |
5 | 41,575,032 | 41,617,270 | 10 | rs620876 | 0.00499106 | 41,608,479 | 41,624,460 | 4 | rs583442 | 0.03793000 | TCP1L2 |
5 | 112,511,503 | 112,544,608 | 10 | rs6594693 | 0.00058306 | 112,510,548 | 112,529,682 | 5 | rs10068491 | 0.00426800 | MCC |
5 | 117,910,641 | 117,923,508 | 4 | rs7719858 | 0.01062481 | 117,922,957 | 117,949,143 | 7 | rs10213999 | 0.00230800 | |
5 | 117,956,000 | 117,975,073 | 7 | rs2043052 | 0.00763322 | 117,971,158 | 117,979,439 | 4 | rs10519559 | 0.00978400 | |
5 | 147,243,171 | 147,266,206 | 8 | rs6889356 | 0.00306666 | 147,265,906 | 147,266,651 | 4 | rs2250145 | 0.00579900 | MGC23985 |
5 | 151,176,039 | 151,185,293 | 4 | rs10051560 | 0.01270991 | 151,176,325 | 151,189,083 | 4 | rs7733241 | 0.02632000 | GLRA1 |
5 | 167,361,210 | 167,387,579 | 5 | rs17069636 | 0.00029339 | 167,355,762 | 167,377,584 | 4 | rs17069578 | 0.01187000 | ODZ2 |
5 | 169,470,056 | 169,484,203 | 6 | rs4867917 | 0.01444342 | 169,462,713 | 169,474,921 | 5 | rs10063424 | 0.00185200 | FOXI1 |
6 | 12,225,541 | 12,239,453 | 4 | rs2327514 | 0.00235903 | 12,228,076 | 12,247,206 | 5 | rs2228211 | 0.00802000 | HIVEP1 |
6 | 37,617,436 | 37,635,955 | 5 | rs914351 | 0.00424126 | 37,609,945 | 37,618,910 | 4 | rs2797777 | 0.02426000 | FLJ45825 |
6 | 45,987,042 | 46,031,767 | 10 | rs9367228 | 0.00911977 | 46,016,169 | 46,032,945 | 6 | rs4714892 | 0.00042350 | CLIC5 |
6 | 93,036,860 | 93,065,931 | 10 | rs1020320 | 0.00070018 | 93,022,018 | 93,040,012 | 5 | rs1822589 | 0.00492300 | |
6 | 129,838,313 | 129,856,864 | 6 | rs17057464 | 0.00275262 | 129,830,351 | 129,838,730 | 5 | rs6569603 | 0.00056950 | LAMA2 |
6 | 148,822,606 | 148,840,150 | 5 | rs6927662 | 0.00352616 | 148,823,419 | 148,848,906 | 9 | rs11961740 | 0.00091830 | SASH1 |
6 | 152,505,595 | 152,510,136 | 4 | rs2747665 | 0.01626437 | 152,498,721 | 152,511,829 | 6 | rs17082180 | 0.00073110 | SYNE1 |
6 | 164,339,429 | 164,354,618 | 5 | rs11962696 | 0.00054469 | 164,346,155 | 164,372,273 | 12 | rs206003 | 0.00017690 | |
7 | 37,754,332 | 37,786,876 | 6 | rs2709114 | 0.00351750 | 37,749,625 | 37,757,420 | 4 | rs7780507 | 0.00972500 | GPR141 |
7 | 103,559,035 | 103,584,883 | 7 | rs194846 | 0.00030790 | 103,568,301 | 103,599,077 | 8 | rs3808008 | 0.00947200 | ORC5L |
8 | 1,468,372 | 1,478,011 | 4 | rs17681530 | 0.00386125 | 1,455,744 | 1,477,325 | 6 | rs10503166 | 0.00219000 | DLGAP2 |
8 | 3,438,465 | 3,449,401 | 6 | rs2469359 | 0.00114465 | 3,419,770 | 3,440,295 | 6 | rs7013570 | 0.00021750 | CSMD1 |
8 | 3,537,344 | 3,551,589 | 6 | rs17067079 | 0.00000979 | 3,543,065 | 3,557,725 | 10 | rs17326670 | 0.00081010 | CSMD1 |
8 | 5,152,475 | 5,160,210 | 4 | rs7824050 | 0.00064229 | 5,137,521 | 5,156,796 | 4 | rs1420838 | 0.00613500 | |
8 | 6,891,919 | 6,909,100 | 4 | rs4448295 | 0.01744268 | 6,887,848 | 6,897,291 | 7 | rs10867025 | 0.00608900 | DEFA5 |
8 | 15,030,038 | 15,044,483 | 8 | rs17575278 | 0.00019902 | 15,044,188 | 15,054,249 | 4 | rs6530838 | 0.01897000 | SGCZ |
8 | 17,281,156 | 17,285,461 | 4 | rs7460082 | 0.02311621 | 17,281,979 | 17,290,933 | 5 | rs10088485 | 0.00482200 | MTMR7 |
8 | 54,323,669 | 54,330,478 | 4 | rs12675595 | 0.00008797 | 54,319,624 | 54,323,911 | 5 | rs2303432 | 0.02985000 | OPRK1 |
8 | 72,351,592 | 72,382,875 | 8 | rs6989867 | 0.00080278 | 72,336,968 | 72,357,946 | 6 | rs11991562 | 0.00544100 | EYA1 |
8 | 91,022,384 | 91,031,953 | 4 | rs13312938 | 0.00179646 | 91,027,598 | 91,040,111 | 5 | rs3026268 | 0.00650500 | NBN |
8 | 139,358,561 | 139,377,143 | 7 | rs1512407 | 0.00007918 | 139,357,180 | 139,371,129 | 5 | rs1512406 | 0.00687200 | |
9 | 1,439,084 | 1,458,895 | 5 | rs4142436 | 0.00055175 | 1,443,731 | 1,495,268 | 15 | rs10124818 | 0.00233700 | |
9 | 1,785,030 | 1,788,805 | 4 | rs3847228 | 0.00403372 | 1,786,760 | 1,789,943 | 4 | rs16934200 | 0.02853000 | |
9 | 5,186,695 | 5,200,671 | 5 | rs10491650 | 0.00008609 | 5,164,638 | 5,208,524 | 11 | rs7850294 | 0.00437700 | |
9 | 7,050,854 | 7,088,109 | 9 | rs2381545 | 0.00308869 | 7,050,825 | 7,081,794 | 9 | rs17449018 | 0.00786400 | JMJD2C |
9 | 7,435,627 | 7,453,978 | 4 | rs2997549 | 0.01025102 | 7,438,612 | 7,456,024 | 5 | rs2997554 | 0.00175800 | |
9 | 8,351,136 | 8,362,565 | 7 | rs10976994 | 0.00580524 | 8,360,763 | 8,376,063 | 6 | rs1392521 | 0.00694000 | PTPRD |
9 | 9,196,006 | 9,222,484 | 6 | rs12685122 | 0.00604373 | 9,218,094 | 9,228,929 | 4 | rs4742571 | 0.01041000 | |
9 | 32,867,013 | 32,879,615 | 4 | rs16918758 | 0.00489732 | 32,860,406 | 32,879,200 | 6 | rs10813887 | 0.01238000 | |
9 | 86,974,347 | 86,985,714 | 5 | rs1387929 | 0.00466538 | 86,959,005 | 86,974,347 | 5 | rs1931101 | 0.00116700 | |
9 | 121,591,631 | 121,603,681 | 4 | rs11792644 | 0.00838348 | 121,588,889 | 121,606,307 | 4 | rs2151641 | 0.00389900 | |
10 | 74,540,513 | 74,570,789 | 9 | rs6480671 | 0.00122779 | 74,519,332 | 74,553,223 | 7 | rs6480668 | 0.02900000 | |
10 | 79,882,845 | 79,900,632 | 9 | rs12763437 | 0.00333686 | 79,891,113 | 79,894,711 | 4 | rs10740479 | 0.00584900 | |
10 | 100,128,973 | 100,149,126 | 5 | rs4345897 | 0.00444606 | 100,134,191 | 100,145,953 | 5 | rs942812 | 0.00917700 | C10orf33 |
10 | 123,963,836 | 123,982,634 | 4 | rs3752956 | 0.00474689 | 123,965,713 | 123,974,432 | 4 | rs6585804 | 0.01989000 | TACC2 |
10 | 130,305,059 | 130,314,680 | 4 | rs10829448 | 0.00336612 | 130,300,924 | 130,319,479 | 4 | rs12359499 | 0.01333000 | |
11 | 4,550,165 | 4,583,486 | 8 | rs11033006 | 0.00029178 | 4,548,315 | 4,553,041 | 4 | rs10768096 | 0.01066000 | C11orf40 |
11 | 11,346,019 | 11,359,653 | 6 | rs7131111 | 0.00464535 | 11,344,701 | 11,360,293 | 4 | rs4533032 | 0.03032000 | GALNTL4 |
11 | 12,241,317 | 12,255,138 | 5 | rs11022270 | 0.00932509 | 12,253,357 | 12,271,091 | 6 | rs7106205 | 0.00324200 | |
11 | 20,586,739 | 20,621,980 | 7 | rs2298826 | 0.00015712 | 20,620,979 | 20,646,062 | 6 | rs4923627 | 0.00240900 | SLC6A5 |
11 | 94,021,355 | 94,035,517 | 4 | rs12786215 | 0.00130691 | 94,014,342 | 94,039,094 | 7 | rs4127396 | 0.00314200 | |
11 | 101,965,568 | 101,975,466 | 4 | rs1711433 | 0.00007807 | 101,965,987 | 101,990,940 | 9 | rs1711410 | 0.00134600 | MMP20 |
11 | 112,578,791 | 112,596,098 | 4 | rs17115160 | 0.00347427 | 112,573,849 | 112,582,751 | 5 | rs965560 | 0.01576000 | NCAM1 |
11 | 121,528,137 | 121,554,885 | 6 | rs588354 | 0.00713157 | 121,539,353 | 121,556,909 | 4 | rs11218544 | 0.00146200 | |
11 | 122,121,115 | 122,128,473 | 4 | rs1540113 | 0.00129108 | 122,111,119 | 122,128,603 | 4 | rs4935804 | 0.00053370 | STS-1 |
12 | 2,345,664 | 2,357,262 | 4 | rs10774040 | 0.00073318 | 2,343,619 | 2,359,651 | 4 | rs880342 | 0.00695700 | CACNA1C |
12 | 4,970,508 | 4,982,410 | 4 | rs11063455 | 0.01164648 | 4,968,898 | 4,977,809 | 5 | rs1014665 | 0.00953200 | |
12 | 21,479,435 | 21,491,311 | 4 | rs11046048 | 0.01997843 | 21,470,347 | 21,512,686 | 14 | rs2110165 | 0.01154000 | FLJ22028 |
12 | 51,572,319 | 51,601,000 | 5 | rs2131161 | 0.00335765 | 51,571,353 | 51,585,102 | 6 | rs7964223 | 0.00493500 | KRT8 |
12 | 52,379,381 | 52,408,340 | 11 | rs2277370 | 0.00220727 | 52,380,303 | 52,390,299 | 5 | rs12309211 | 0.00025460 | CALCOCO1 |
12 | 72,409,569 | 72,423,208 | 4 | rs17112634 | 0.00560549 | 72,381,821 | 72,409,569 | 5 | rs7299914 | 0.00909200 | |
12 | 96,551,822 | 96,563,336 | 4 | rs1376345 | 0.00525089 | 96,541,866 | 96,555,782 | 5 | rs1901242 | 0.00821800 | |
12 | 126,937,406 | 126,963,369 | 12 | rs2699066 | 0.00032841 | 126,958,313 | 126,965,920 | 4 | rs4034627 | 0.00940100 | |
13 | 94,684,855 | 94,734,859 | 13 | rs9590213 | 0.00015363 | 94,691,883 | 94,714,801 | 15 | rs4258481 | 0.00091180 | ABCC4 |
13 | 101,624,080 | 101,636,884 | 4 | rs9554852 | 0.00779793 | 101,633,794 | 101,650,449 | 5 | rs554393 | 0.00467000 | FGF14 |
13 | 102,832,714 | 102,855,295 | 4 | rs9519026 | 0.00288507 | 102,824,275 | 102,834,437 | 4 | rs1929081 | 0.00935600 | |
14 | 33,479,907 | 33,484,416 | 5 | rs1680692 | 0.00181106 | 33,465,851 | 33,480,643 | 4 | rs996347 | 0.03129000 | EGLN3 |
14 | 83,250,117 | 83,258,316 | 6 | rs12587990 | 0.00010936 | 83,245,454 | 83,261,998 | 5 | rs10873350 | 0.04028000 | |
14 | 85,086,089 | 85,096,268 | 4 | rs985620 | 0.00019794 | 85,074,724 | 85,098,018 | 8 | rs1955418 | 0.00203300 | FLRT2 |
14 | 85,481,243 | 85,490,806 | 4 | rs10130564 | 0.00214065 | 85,474,713 | 85,484,033 | 5 | rs2373038 | 0.00057550 | |
14 | 93,382,087 | 93,405,207 | 6 | rs10139611 | 0.00062020 | 93,401,569 | 93,408,486 | 5 | rs11622652 | 0.00555300 | |
15 | 24,761,221 | 24,768,832 | 4 | rs4887529 | 0.00107167 | 24,768,052 | 24,784,705 | 4 | rs28551016 | 0.01719000 | GABRA5 |
15 | 55,531,225 | 55,551,279 | 6 | rs1280420 | 0.00475293 | 55,542,924 | 55,555,274 | 4 | rs2922219 | 0.02513000 | CGNL1 |
15 | 59,264,774 | 59,293,721 | 6 | rs7167741 | 0.01522023 | 59,272,657 | 59,278,332 | 4 | rs7177846 | 0.01107000 | RORA |
15 | 93,113,075 | 93,120,231 | 5 | rs11073388 | 0.00419825 | 93,114,079 | 93,123,625 | 5 | rs2199734 | 0.01306000 | |
16 | 6,695,415 | 6,722,737 | 6 | rs17141047 | 0.00299366 | 6,689,567 | 6,710,879 | 9 | rs7195574 | 0.00045400 | A2BP1 |
16 | 27,283,288 | 27,304,096 | 6 | rs13337730 | 0.00011074 | 27,267,076 | 27,284,411 | 7 | rs3024613 | 0.00581200 | IL4R |
16 | 47,988,550 | 47,995,541 | 4 | rs27807 | 0.03801566 | 47,990,325 | 48,002,247 | 4 | rs2278026 | 0.01335000 | MGC33367 |
16 | 76,126,270 | 76,134,741 | 5 | rs17771897 | 0.00296411 | 76,127,850 | 76,134,916 | 4 | rs277522 | 0.02168000 | |
17 | 3,154,169 | 3,161,666 | 4 | rs9898721 | 0.02078650 | 3,159,529 | 3,161,064 | 4 | rs9911226 | 0.00701900 | OR3A4 |
17 | 44,633,488 | 44,638,057 | 5 | rs8077444 | 0.02228124 | 44,628,966 | 44,651,340 | 7 | rs658979 | 0.00361600 | ABI3,GNGT2 |
17 | 65,826,027 | 65,840,454 | 5 | rs16975551 | 0.00019999 | 65,802,728 | 65,826,901 | 7 | rs11868369 | 0.00023950 | |
17 | 66,739,190 | 66,753,642 | 4 | rs16976490 | 0.01802707 | 66,747,639 | 66,750,867 | 4 | rs16976482 | 0.03162000 | |
18 | 7,013,371 | 7,026,249 | 7 | rs7231835 | 0.00226836 | 7,013,371 | 7,022,221 | 5 | rs475598 | 0.00450600 | LAMA1 |
18 | 42,478,970 | 42,518,576 | 9 | rs16960377 | 0.00085181 | 42,462,780 | 42,478,970 | 4 | rs16939868 | 0.01107000 | |
18 | 52,007,225 | 52,023,316 | 4 | rs11151630 | 0.00068356 | 51,999,397 | 52,009,049 | 4 | rs12606608 | 0.00598200 | FLJ45743 |
18 | 63,645,346 | 63,664,475 | 6 | rs2318817 | 0.00452456 | 63,636,269 | 63,655,293 | 8 | rs3907154 | 0.00369900 | |
18 | 66,187,616 | 66,202,453 | 4 | rs17082526 | 0.00209317 | 66,183,437 | 66,201,794 | 10 | rs7244215 | 0.00008850 | |
18 | 71,933,136 | 71,949,147 | 4 | rs11664972 | 0.00461757 | 71,943,758 | 71,952,541 | 4 | rs12954572 | 0.00895100 | |
18 | 74,015,536 | 74,017,210 | 4 | rs10468814 | 0.00433017 | 73,987,910 | 74,019,487 | 7 | rs4798896 | 0.00310900 | |
20 | 19,997,088 | 20,023,996 | 5 | rs9808594 | 0.00056896 | 20,001,676 | 20,012,701 | 4 | rs6046605 | 0.00938500 | C20orf26 |
20 | 48,912,167 | 48,930,175 | 4 | rs6096138 | 0.00545080 | 48,907,768 | 48,921,399 | 5 | rs6020802 | 0.00131400 | BCAS4 |
20 | 58,212,371 | 58,233,187 | 7 | rs11696896 | 0.00256062 | 58,223,398 | 58,242,908 | 4 | rs11907714 | 0.02357000 | |
21 | 26,419,368 | 26,465,912 | 12 | rs9984764 | 0.00160837 | 26,422,204 | 26,447,377 | 6 | rs2830072 | 0.00922800 | APP |
21 | 30,117,231 | 30,124,346 | 7 | rs388700 | 0.01343888 | 30,118,644 | 30,167,132 | 13 | rs468879 | 0.00006290 | GRIK1 |
21 | 40,446,501 | 40,459,247 | 6 | rs11911749 | 0.00032530 | 40,435,728 | 40,454,200 | 4 | rs8130732 | 0.00670100 | DSCAM |
22 | 15,689,881 | 15,706,432 | 5 | rs2075120 | 0.00016219 | 15,695,102 | 15,706,432 | 4 | rs165611 | 0.00015620 | CECR8 |
22 | 29,169,346 | 29,215,980 | 10 | rs5753152 | 0.00021758 | 29,185,674 | 29,194,610 | 4 | rs5753158 | 0.00046370 | SEC14L3 |
22 | 35,733,248 | 35,738,081 | 5 | rs130598 | 0.00785100 | 35,727,496 | 35,747,143 | 7 | rs916213 | 0.01242000 | MGC35206,MPST,TST |
African American samples
For the NIDA/MNB African-American samples, 83,330 SNPs displayed “nominally positive” t values with p < 0.05. 46,433 SNPs displayed nominally-significant case vs control χ2 differences for dbGAP samples from individuals from this racial/ethnic group. The more modest pool-to-pool variance in the NIDA datasets, as noted above, provided more nominally-positive results from these African-American samples than the dbGAP datasets. Use of t testing also appeared to contribute to the greater number of nominally-positive results in the NIDA datasets. When the pseudopool data from the dbGAP individuals was analyzed using t testing, 48,811 SNPs displayed nominal statistical significance. Permutation testing for the dbGAP African-American samples revealed p = 0.25 for the number of SNPs with nominal case vs control p values < 0.05
Searches for genome wide significance in each African-American sample
We identified case vs control p values for t test results from NIDA/MNB pooled samples (Drgon and others 2010) and for χ2 results from dbGAP samples from unrelated individuals. Few of these p values approached the 10−8 level deemed necessary for genome wide significance, though more reached ca 10−6.
In NIDA samples for which there was adequate consent, we performed individual genotyping for the three PPP1R12B SNPs that appeared to achieve high levels of near-genome-wide significance in dbGAP samples. In these individually-genotyped NIDA samples, rs12734338 initially appeared to provide a highly-significant difference in genotype frequencies between dependent and control individuals in 439 NIDA African American samples (p = 0.15 × 10−11). Neither rs3817222 nor rs12741415 provided any individuals with minor alleles, consistent with data in dbSNP, but inconsistent with HapMap data that indicated that rs3817222 minor allele frequencies were 0.186–0.256. BLAST searches for the PPP1R12B DNA sequences that surround these SNPs revealed a chromosome Y pseudogene that displayed high homology to PPP1R12B DNA. There were sequence differences between this pseudogene and the authentic PPP1R12B gene that correspond to the annotated PPP1R12B “SNPs”. The suggestion, from this data, that the allelic polymorphism of this gene is based on differences between the PPP1R12B gene and the chromosome Y pseudogene was further reinforced by our analysis of gender association with the rs12734338 “SNP”: 98.6 % of males were “heterozygous” and 96.7 % of females were “homozygous” for this “pseudo-SNP” (data not shown).
Searches for clustering of SNPs with nominally-significant case vs control differences in each African-American sample
We identified clusters of SNPs that displayed nominally significant, p < 0.05 case vs control differences for p values from t test results from NIDA/MNB samples and for χ2 results from dbGAP samples (3383 and 2053 clusters, respectively).
Searches for chromosomal regions identified by clustered SNPs with nominally-significant case vs control differences in both African-American samples
One hundred thirty six chromosomal regions were identified by clustered nominally-positive results from both of the two African-American samples. None of 10,000 Monte Carlo simulation trials that each began with random sets of SNPs selected from each of the datasets identified as many overlapping regions as found in the true dataset; hence Monte Carlo p < 0.0001.
The chromosomal regions identified by data from both African-American samples are listed in Table 1B. The fraction of the genome occupied by these results is about 2 × that expected by chance, based on the fraction of the genome occupied by clustered nominally positive results from each of the African American samples (data not shown).
Searches for chromosomal regions identified by clustered SNPs with nominally-significant case vs control differences in all four samples
The clusters from both of the two African-American samples identified a single chromosome five region that is also identified by clusters from both of the two European-American samples. The 5’ aspects of one large gene, CSMD1, were identified by overlapping clusters from both African-American and from both European-American samples. However, slightly different portions of the 5’ end of this gene were identified in the data from the replicate European-American samples compared to the data from the replicate African-American samples. This relatively sparse overlap contrasts with the significant overall overlap between the Affymetrix datasets for the African-American vs European American NIDA/MNB samples (Drgon and others 2010) and the Illumina datasets for the African-American vs European American dbGAP samples. In the latter case, we can identify 150 chromosomal regions in which overlapping results between the two racial/ethnic groups are found in ways not found by chance in 10,000 Monte Carlo simulation trials (data not shown).
Validation of pooling vs individual genotyping for SNPs whose results provided the clusters
We compared individual vs pooled allele frequency estimations for the 12,184 SNPs that displayed minor allele frequencies > 0.1 and provided clustered, nominally positive results in data from the NIDA pooled samples. These correlations were more modest than those identified in validating studies for pooling that used larger ranges of expected allele frequencies (average 0.19 range for these genotypes vs 0.95 range for the SNPs used in validating studies). Nevertheless, the results from these SNPs displayed 0.68 Pearson correlation coefficients between data from pooled and individual genotyping.
Discussion
Genome-wide association data of increasing richness is available for a number of complex disorders. Several of these GWA datasets contain relatively robust results at “oligogenic” loci that can also be identified, in many cases, by linkage-based approaches (Hageman and others 2005; Haines and others 2005; Lambert and Amouyel 2007; McElroy and Oksenberg 2008). Even moderately secure GWA identification of “polygenic” influences on disease, however, is likely to require replicated data from multiple independent samples.
“Template” analyses seek SNPs that provide “genome wide significance” with the same phase of association in data from each of multiple independent samples. However, there have been no unanimous criteria for declaring replication of sets of data in circumstances in which no SNP achieves this level of statistical significance in each of multiple samples.
We have focused on identification of statistical significance for sets of chromosomal regions that are each identified by sets of nominally-significant SNPs from each of several independent samples. This approach identifies chromosomal regions and genes that are very likely, as a group, to display bona fide association with individual differences in vulnerability to develop dependence on addictive substances. This overall confidence derives from approaches that address distinct sets of null and/or alternative hypotheses to explain the results obtained. First, seeking chromosomal regions in each sample that are identified by at least 4 closely-spaced nominally-positive SNPs addresses the null hypothesis that the results obtained are randomly distributed across chromosomes. This initial process also addresses the alternative hypothesis that the nominally-positive SNPs are identified based on technical problems that result in misassignment of allele frequency differences to case vs control sample comparisons (or misassignment of variances in these values). The second way in which we seek replication identifies many of the same chromosomal regions based on their content of clustered, nominally positive results from each of several independent samples. This comparison addresses the null hypothesis that the clustering observed in each sample derives from stochastic case vs control differences in haplotype frequencies rather than case vs control differences that are truly related to differences in phenotypes. Thus, if clusters of nominally-positive SNPs lay near each other simply because a haplotype was overrepresented in one “case” or “control” group by chance, it is unlikely that this would also occur by chance in another independent sample nearly as often as we have observed. This comparison also provides additional support for the ability to reject the null and alternative hypotheses relating to assay noise.
There are a number of important limitations that come from these samples, these analyses, and from the application of this approach to these datasets. The NIDA/MNB samples, largely of individuals who were not seeking treatment, were recruited at a single site and compare dependent individuals with heavy levels of substance use to controls with modest or no substance use. These features might provide differences from the dbGAP samples which were recruited at a number of sites from largely treatment-seeking individuals (or from pedigrees with treatment-seeking probands). The dbGAP samples compare dependent individuals to controls whose levels of substance use do not produce dependence, but whose levels of use might be substantial. In addition, these samples provide greater “pseudopool-to-pseudopool” variance than the pool-to-pool variance from NIDA samples, possibly reflecting the substantial site-of-collection to site-of-collection variance noted in another recent report concerning analyses of data from an overlapping subset of these individuals (Bierut and others 2010). Based on statistical considerations, the present analyses are likely to provide many false negative results. The power of each of these samples to detect small, polygenic influences is modest to moderate. The requirement for convergent identification of the same chromosomal region by data from both samples provides a likelihood of even more false negative results. Case vs control allele frequency differences in the NIDA/MNB samples were genotyped using multiple DNA pools and an Affymetrix 6.0 platform, providing t tests that use information about both mean differences and variances and not allowing imputation of alleles of SNPs that were not genotyped for most samples. The modest number of individuals who have consented to unlimited genotyping provides additional support for the correlations between individual and pooled genotyping, but is too modest to contribute precise data for case vs control analyses in the NIDA samples. Case vs control differences in the dbGAP samples were assessed using Illumina platform genotyping of individual samples, yielding χ2 results without explicit assessment of variance. The requirement that at least 4 nominally-significant SNPs lie within 10kb of each other cannot be fulfilled in a number of chromosomal regions or in a number of genes in which the density of SNPs is too low to meet this stringent requirement (see Supplement of (Uhl and others 2008b) for list of the genes that cannot be assessed with these criteria using the Affymetrix platform). There are only about ¼ million autosomal SNPs that are shared between the ca. 900K and 1M autosomal SNPs evaluated by the Affymetrix and Illumina platforms, respectively, further exacerbating this problem in many genomic regions. Further, as noted in the follow up studies of SNPs in PPP1R12B, properties of the SNP assays, rather than of the SNPs themselves, may provide some of the more striking nominal levels of significance in these assays, whether they are genotyped in individual or pooled samples.
Despite these limitations, there is highly-significant overall convergence between two comparisons of NIDA/MNB and dbGAP GWA data that compare individuals who are dependent on at least one illegal substance to controls: one comparison in European-American subjects and another comparison in African-American subjects. For each of these comparisons, the degree to which clusters of nominally-positive SNPs identify the same chromosomal regions and genes is never found by chance in up to 10,000 Monte Carlo simulation trials.
This striking evidence for replication, defined in this fashion, also provides striking contrasts to results from attempts to identify replication (and/or generalization) in other ways. For example, results that seek to identify the extent to which the same SNPs display nominally-significant associations with the same phase in each of these replicate samples within each racial/ethnic group identify about as many SNPs with these properties as expected by chance (data not shown).
We have previously reported overlapping results from application of similar analyses to data from replicate samples of methamphetamine-dependent and control Asian samples (Uhl and others 2008c). None of the chromosomal regions identified by these results is labeled by each of the four samples on which we focus in the present report. However, the data from the dbGAP samples identify 15 chromosomal regions in which clustered, nominally-positive SNPs from these two Asian stimulant dependence samples are found along with clustered SNPs from at least one of the dbGAP samples (data not shown); six of these regions are shared by the African-American dbGAP subsample, and nine by the European-American subsample.
We have previously reported the apparent success of “nontemplate” analyses that are similar to those used herein when applied to data from four independent case vs control samples for bipolar disorder (Johnson and others 2009). None of these bipolar vs control samples, individually, provided results with genome wide significance. These samples combined data from individual and pooled genotyping using different genotyping platforms. Despite these difficulties, the results of nontemplate analyses provided much more frequent identification of the same genomic regions and genes by clustered, nominally positive SNPs from multiple independent samples in bipolar disorder than we would anticipate by chance.
Studies that focus on identifying same-phase association with genome wide levels of significance in multiple independent samples appear most likely to succeed when oligogenic genetic architecture confers large association signals in each independent sample, when the same SNP sets are studied in each, when the disease exhibits little allelic or locus heterogeneity and when there are good matches between the fine patterns of linkage disequilibrium of the samples being studied. Apparent replication “failures” using this approach could thus relate to a number of features that include associations of modest magnitude, sample-to-sample differences in fine patterns of linkage disequilibrium, different amounts of information provided by markers that display population-specific differences in allele frequencies, allelic heterogeneity and locus heterogeneity.
Monte Carlo methods allow us to test the probabilities of chance clustering of nominally-positive SNPs and the chance of convergence between clusters identified in one sample with clusters identified in other samples. Our Monte Carlo approaches deploy an empirical method that uses the existing dataset as a source for “randomly selected” SNPs for each Monte Carlo trial. The results of these simulations provide strong overall confidence that these results are not due to chance. By contrast, these approaches provide absolutely unequivocal identification of few individual SNPs or genes. This lack of unequivocal identification of individual SNPs is consistent with polygenic/allelic heterogeneity current working models for the genetic architecture of vulnerability to substance abuse (Uhl and others 2008a; Uhl and others 2008b).
Previous analyses that have compared the MNB/NIDA European-American to African-American results have identified genomic regions that are labeled by clustered, nominally-positive SNPs from both samples, supporting roles for some allelic variants that are likely to have arisen relatively long ago in human history (Drgon and others 2010; Liu and others 2006; Liu and others 2005; Uhl and others 2001). Such identification of SNP markers whose allelic frequencies distinguish controls from addicts of different ethnicities supports “common disease/common allele” genetic architecture (Lander and Schork 1994) for part of the genetics of addiction vulnerability. However, the substantially greater convergence, noted here, for data from the same racial/ethnic groups also points to possibly-substantial roles for variants that have been accumulated more recently in human populations that have been more separate until relatively recently.
Genes identified by this work include those in several classes. When we compare the list of genes identified by European-American samples to functional classes as annotated in Gene ontology (GO), we find the greatest (ca 10−4) statistical significance for underrepresentation of the genes whose products are involved with nucleobase, nucleoside, nucleotide and nucleic acid and biosynthetic processes. For African American samples, there is significant overrepresentation for “neuromuscular process”, “cyanate metabolic process”, “synaptic transmission”, “neurological process”, “transmission of nerve impulse”, “regulation of cell migration”, “nerve-nerve synaptic transmission”, “regulation of embryonic development and “regulation of cell motility” genes with nearly-significant corrected p values for “cell adhesion/neuron adhesion”.
The findings presented here promise to add to the ongoing consideration of methods for comparing GWA datasets as they enhance understanding of genetic underpinnings of human addiction. For addictions, as for many complex disorders, such data provides an increasingly rich basis for improved understanding and for personalized prevention and treatment strategies.
Acknowledgements
This research was supported financially by the NIH Intramural Research Program, NIDA, DHHS. We are grateful for dedicated help with clinical characterization of NIDA/MNB subjects from Dan Lipstein, Fely Carillo and other Johns Hopkins Bayview support staff. Support for dbGAP data came from the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004422)/ Gene Environment Association Studies (GENEVA) that received assistance with phenotype harmonization, genotype cleaning, and study coordination from the GENEVA Coordinating Center (U01 HG004446), assistance with data cleaning from the National Center for Biotechnology Information, and assistance with collection of datasets and samples by the Collaborative Study on the Genetics of Alcoholism (COGA; U10 AA008401), the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392) and the Family Study of Cocaine Dependence (FSCD; R01 DA013423). Support for genotyping dbGAP samples at the Johns Hopkins University Center for Inherited Disease Research came through (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease" (HHSN268200782096C). dbGaP datasets were obtained at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000092.v1.p1 through dbGaP accession number phs000092.v1.p.
Footnotes
Financial Disclosure
Authors report no biomedical financial interests or potential conflicts of interest
References
- Bierut LJ, Agrawal A, Bucholz KK, Doheny KF, Laurie C, Pugh E, Fisher S, Fox L, Howells W, Bertelsen S, et al. A genome-wide association study of alcohol dependence. Proc Natl Acad Sci U S A. 2010;107(11):5082–5087. doi: 10.1073/pnas.0911109107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF, Swan GE, Rutter J, Bertelsen S, Fox L, et al. Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. 2007;16(1):24–35. doi: 10.1093/hmg/ddl441. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bierut LJ, Strickland JR, Thompson JR, Afful SE, Cottler LB. Drug use and dependence in cocaine dependent subjects, community-based individuals, and their siblings. Drug Alcohol Depend. 2008;95(1–2):14–22. doi: 10.1016/j.drugalcdep.2007.11.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Drgon T, Zhang PW, Johnson C, Walther D, Hess J, Nino M, Uhl GR. Genome wide association for addiction: replicated results and comparisons of two analytic approaches. PLoS ONE. 2010 doi: 10.1371/journal.pone.0008832. (in press) [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dupont WD, Plummer WD., Jr Power and sample size calculations. A review and computer program. Control Clin Trials. 1990;11(2):116–128. doi: 10.1016/0197-2456(90)90005-m. [DOI] [PubMed] [Google Scholar]
- Dupont WD, Plummer WD., Jr Power and sample size calculations for studies involving linear regression. Control Clin Trials. 1998;19(6):589–601. doi: 10.1016/s0197-2456(98)00037-3. [DOI] [PubMed] [Google Scholar]
- Hageman GS, Anderson DH, Johnson LV, Hancox LS, Taiber AJ, Hardisty LI, Hageman JL, Stockman HA, Borchardt JD, Gehrs KM, et al. A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration. Proc Natl Acad Sci U S A. 2005;102(20):7227–7232. doi: 10.1073/pnas.0501536102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haines JL, Hauser MA, Schmidt S, Scott WK, Olson LM, Gallins P, Spencer KL, Kwan SY, Noureddine M, Gilbert JR, et al. Complement factor H variant increases the risk of age-related macular degeneration. Science. 2005;308(5720):419–421. doi: 10.1126/science.1110359. [DOI] [PubMed] [Google Scholar]
- Johnson C, Drgon T, Liu QR, Walther D, Edenberg H, Rice J, Foroud T, Uhl GR. Pooled association genome scanning for alcohol dependence using 104,268 SNPs: validation and use to identify alcoholism vulnerability loci in unrelated individuals from the collaborative study on the genetics of alcoholism. Am J Med Genet B Neuropsychiatr Genet. 2006;141(8):844–853. doi: 10.1002/ajmg.b.30346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Johnson C, Drgon T, Liu QR, Zhang PW, Walther D, Li CY, Anthony JC, Ding Y, Eaton WW, Uhl GR. Genome wide association for substance dependence: convergent results from epidemiologic and research volunteer samples. BMC Med Genet. 2008;9:113. doi: 10.1186/1471-2350-9-113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Johnson C, Drgon T, McMahon FJ, Uhl GR. Convergent genome wide association results for bipolar disorder and substance dependence. Am J Med Genet B Neuropsychiatr Genet. 2009;150B(2):182–190. doi: 10.1002/ajmg.b.30900. [DOI] [PubMed] [Google Scholar]
- Karkowski LM, Prescott CA, Kendler KS. Multivariate assessment of factors influencing illicit substance use in twins from female-female pairs. Am J Med Genet. 2000;96(5):665–670. [PubMed] [Google Scholar]
- Kendler KS, Karkowski LM, Neale MC, Prescott CA. Illicit psychoactive substance use, heavy use, abuse, and dependence in a US population-based sample of male twins. Arch Gen Psychiatry. 2000;57(3):261–269. doi: 10.1001/archpsyc.57.3.261. [DOI] [PubMed] [Google Scholar]
- Lambert JC, Amouyel P. Genetic heterogeneity of Alzheimer's disease: complexity and advances. Psychoneuroendocrinology. 2007;32 Suppl 1:S62–S70. doi: 10.1016/j.psyneuen.2007.05.015. [DOI] [PubMed] [Google Scholar]
- Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994;265(5181):2037–2048. doi: 10.1126/science.8091226. [DOI] [PubMed] [Google Scholar]
- Liu QR, Drgon T, Johnson C, Walther D, Hess J, Uhl GR. Addiction molecular genetics: 639,401 SNP whole genome association identifies many "cell adhesion" genes. Am J Med Genet B Neuropsychiatr Genet. 2006;141(8):918–925. doi: 10.1002/ajmg.b.30436. [DOI] [PubMed] [Google Scholar]
- Liu QR, Drgon T, Walther D, Johnson C, Poleskaya O, Hess J, Uhl GR. Pooled association genome scanning: validation and use to identify addiction vulnerability loci in two samples. Proc Natl Acad Sci U S A. 2005;102(33):11864–11869. doi: 10.1073/pnas.0500329102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McElroy JP, Oksenberg JR. Multiple sclerosis genetics. Curr Top Microbiol Immunol. 2008;318:45–72. doi: 10.1007/978-3-540-73677-6_3. [DOI] [PubMed] [Google Scholar]
- Nurnberger JI, Jr, Wiegand R, Bucholz K, O'Connor S, Meyer ET, Reich T, Rice J, Schuckit M, King L, Petti T, et al. A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands. Arch Gen Psychiatry. 2004;61(12):1246–1256. doi: 10.1001/archpsyc.61.12.1246. [DOI] [PubMed] [Google Scholar]
- Persico AM, Bird G, Gabbay FH, Uhl GR. D2 dopamine receptor gene TaqI A1 and B1 restriction fragment length polymorphisms: enhanced frequencies in psychostimulant-preferring polysubstance abusers. Biol Psychiatry. 1996;40(8):776–784. doi: 10.1016/0006-3223(95)00483-1. [DOI] [PubMed] [Google Scholar]
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Smith SS, O'Hara BF, Persico AM, Gorelick DA, Newlin DB, Vlahov D, Solomon L, Pickens R, Uhl GR. Genetic vulnerability to drug abuse. The D2 dopamine receptor Taq I B1 restriction fragment length polymorphism appears more frequently in polysubstance abusers. Arch Gen Psychiatry. 1992;49(9):723–727. doi: 10.1001/archpsyc.1992.01820090051009. [DOI] [PubMed] [Google Scholar]
- Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, Manolescu A, Thorleifsson G, Stefansson H, Ingason A, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452(7187):638–642. doi: 10.1038/nature06846. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Treutlein J, Cichon S, Ridinger M, Wodarz N, Soyka M, Zill P, Maier W, Moessner R, Gaebel W, Dahmen N, et al. Genome-wide association study of alcohol dependence. Arch Gen Psychiatry. 2009;66(7):773–784. doi: 10.1001/archgenpsychiatry.2009.83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- True WR, Heath AC, Scherrer JF, Xian H, Lin N, Eisen SA, Lyons MJ, Goldberg J, Tsuang MT. Interrelationship of genetic and environmental influences on conduct disorder and alcohol and marijuana dependence symptoms. Am J Med Genet. 1999;88(4):391–397. doi: 10.1002/(sici)1096-8628(19990820)88:4<391::aid-ajmg17>3.0.co;2-l. [DOI] [PubMed] [Google Scholar]
- Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, True W, Lin N, Toomey R, Eaves L. Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry. 1998;55(11):967–972. doi: 10.1001/archpsyc.55.11.967. [DOI] [PubMed] [Google Scholar]
- Uhl GR, Drgon T, Johnson C, Fatusin OO, Liu QR, Contoreggi C, Li CY, Buck K, Crabbe J. "Higher order" addiction molecular genetics: convergent data from genome-wide association in humans and mice. Biochem Pharmacol. 2008a;75(1):98–111. doi: 10.1016/j.bcp.2007.06.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Uhl GR, Drgon T, Johnson C, Li CY, Contoreggi C, Hess J, Naiman D, Liu QR. Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects. Ann N Y Acad Sci. 2008b;1141:318–381. doi: 10.1196/annals.1441.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Uhl GR, Drgon T, Liu QR, Johnson C, Walther D, Komiyama T, Harano M, Sekine Y, Inada T, Ozaki N. Genome-wide association for methamphetamine dependence: convergent results from 2 samples. Arch Gen Psychiatry. 2008c;65(3):345–355. doi: 10.1001/archpsyc.65.3.345. [DOI] [PubMed] [Google Scholar]
- Uhl GR, Elmer GI, Labuda MC, Pickens RW. Genetic influences in drug abuse. In: Gloom FE, Kupfer DJ, editors. Psychopharmacology: The Fourth Generation of Progress. New York: Raven Press; 1995. pp. 1793–2783. [Google Scholar]
- Uhl GR, Liu QR, Walther D, Hess J, Naiman D. Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms. Am J Hum Genet. 2001;69(6):1290–1300. doi: 10.1086/324467. [DOI] [PMC free article] [PubMed] [Google Scholar]