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. Author manuscript; available in PMC: 2012 Mar 1.
Published in final edited form as: Am J Med Genet B Neuropsychiatr Genet. 2010 Dec 16;156(2):125–138. doi: 10.1002/ajmg.b.31143

“Replicated” genome wide association for dependence on illegal substances: genomic regions identified by overlapping clusters of nominally positive SNPs

Tomas Drgon 1, Catherine Johnson 1, Michelle Nino 1, Jana Drgonova 1, Donna Walther 1, George R Uhl 1,*
PMCID: PMC3282182  NIHMSID: NIHMS258192  PMID: 21302341

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

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