Genome-wide Association Studies Catalog Research Articles

Germline Genetic Variants and Lung Cancer Survival in African Americans

Background: African Americans have the highest lung cancer mortality in the United States. Genome-wide association studies (GWASs) of germline variants influencing lung cancer survival have not yet been conducted with African Americans. We examined five previously reported GWAS catalog variants and explored additional genome-wide associations among African American lung cancer cases.Methods: Incident non-small cell lung cancer cases (N = 286) in the Southern Community Cohort Study were genotyped on the Illumina HumanExome BeadChip. We used Cox proportional hazards models to estimate HRs and 95% confidence intervals (CIs) for overall mortality. Two independent African American studies (N = 316 and 298) were used for replication.Results: One previously reported variant, rs1878022 on 12q23.3, was significantly associated with mortality (HR = 0.70; 95% CI: 0.54-0.92). Replication findings were in the same direction, although attenuated (HR = 0.87 and 0.94). Meta-analysis had a HR of 0.83 (95% CI, 0.71-0.97). Analysis of common variants identified an association between chromosome 6q21.33 and mortality (HR = 0.46; 95% CI, 0.33-0.66).Conclusions: We identified an association between rs1878022 in CMKLR1 and lung cancer survival. However, our results in African Americans have a different direction of effect compared with a prior study in European Americans, suggesting a different genetic architecture or presence of gene-environment interactions. We also identified variants on chromosome 6 within the gene-rich HLA region, which has been previously implicated in lung cancer risk and survival.Impact: We found evidence that inherited genetic risk factors influence lung cancer survival in African Americans. Replication in additional populations is necessary to confirm potential genetic differences in lung cancer survival across populations. Cancer Epidemiol Biomarkers Prev; 26(8); 1288-95. ©2017 AACR.

Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis.

Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was confirmed. In addition, significant genetic overlaps were also found among phenotype pairs that were previously unidentified by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations.

BMI prediction within a Korean population.

BackgroundBody Mass Index (BMI) is widely regarded as an important clinical trait for obesity and other diseases such as Type 2 diabetes, coronary heart disease, and osteoarthritis.MethodsThis study uses 6,011 samples of genotype data from ethnic Korean subjects. The data was retrieved from the Korea Association Resource. To identify the BMI-related markers within the Korean population, we collected genome-wide association study (GWAS) markers using a GWAS catalog and also obtained other markers from nearby regions. Of the total 6,011 samples, 5,410 subjects were used as part of a single nucleotide polymorphism (SNP) selection set in order to identify the overlapping BMI-associated SNPs within a 10-fold cross validation.ResultsWe selected nine SNPs (rs12566985 (FPGT-TNNI3K), rs6545809 (ADCY3), rs2943634 (located near LOC646736), rs734597 (located near TFAP2B), rs11030104 (BDNF), rs7988412 (GTF3A), rs2241423 (MAP2K5), rs7202116 (FTO), and rs6567160 (located near LOC105372152) to assist in BMI prediction. The calculated weighted genetic risk scores based on the selected 9 SNPs within the SNP selection set were applied to the final validation set consisting of 601 samples. Our results showed upward trends in the BMI values (P < 0.0001) within the 10-fold cross validation process for R2 > 0.22. These trends were also observed within the validation set for all subjects, as well as within the validation sets divided by gender (P < 0.0001, R2 > 0.46).DiscussionThe set of nine SNPs identified in this study may be useful for prospective predictions of BMI.

Are genetic polymorphisms in the renin-angiotensin-aldosterone system associated with essential hypertension? Evidence from genome-wide association studies.

In candidate gene era, dozens of single-nucleotide polymorphisms (SNPs) within renin-angiotensin-aldosterone system (RAAS) have been reported to be significantly associated with hypertension. However, the unbiased genome-wide association studies (GWAS) rarely identified the SNPs within RAAS were associated with hypertension or blood pressure (BP) traits. In order to figure out whether genetic polymorphisms of RAAS are really associated with hypertension, we systemically searched the GWAS Catalogue and identified all the known RAAS genes and relevant diseases/traits. After data processing, we found that polymorphisms within REN, AGT, ACE2, CYP11B2, ATP6AP2 and HSD11B2 were not associated with any disease or trait. SNPs within ACE, AGTR1, AGTR2, MAS1, RENBP and NR3C2 were associated with other diseases or traits, but showed no direct connection with hypertension. The only SNP associated with a BP trait, systolic BP was rs17367504. However, it is located in the intronic region of MTHFR near many plausible candidate genes, including CLCN6, NPPA, NPPB and AGTRAP. Therefore, the effect of RAAS polymorphisms may have been overestimated during the 'candidate gene era'. In the time of 'precision medicine', the power of RAAS variants needs to be reconsidered when evaluating one's susceptibility of hypertension.

THE ASSOCIATION OF HUMAN LEUKOCYTE ANTIGEN PHENOTYPES WITH ABDOMINAL AORTIC ANEURYSM EXPANSION

Background: Human leukocyte antigen (HLA) is a key regulator in immune response and implicated in abdominal aortic aneurysm (AAA) development. We assessed whether a HLA risk score (RS), derived from HLA-phenotypes in genome-wide association study catalog, was associated with AAA expansion. Methods

Integrated Comparison of GWAS, Transcriptome, and Proteomics Studies Highlights Similarities in the Biological Basis of Animal and Human Myopia.

To identify commonalities between the genes in close proximity to genome-wide association study (GWAS) refractive error and axial length loci, and the genes and proteins differentially expressed in animal models of optically induced refractive error. The GWAS catalog was searched for loci significantly (P ≤ 5*10-8) associated with refractive error or axial length. PubMed was searched for exploratory animal transcriptome and proteomics studies of optically induced refractive error. A total of 15 GWAS, 7 transcriptome, and 9 proteomics studies met inclusion criteria. Ensembl's BioMart was used to identify human orthologs for the differentially expressed genes and proteins from animal studies. These orthologs were then compared to the protein-coding genes within 1 megabase (Mb), 500 kilobases (kb), and 250 kb of human GWAS loci by using the GeneOverlap R package, and Benjamini-Hochberg-adjusted P values and odds ratios (ORs) were calculated for each intersection. The genes near human GWAS loci overlapped significantly with the genes downregulated during early myopia induction in animals (1Mb: OR = 1.56, P = 0.025; 500 kb: OR = 1.92, P = 0.010; 250 kb: OR = 2.33, P = 0.010). There was also significant overlap between the genes and proteins differentially expressed in late myopia (OR = 4.12, P = 0.018). When animal study results were segregated by methodologic parameters, GWAS candidate genes overlapped significantly with the genes differentially expressed at early (OR = 1.50, P = 0.010) but not late (OR = 1.04, P = 0.684) induction time-points. Gene and protein expression responses also appeared well conserved across model species, and there was no evidence of greater GWAS-transcriptome concordance in similar species to humans (e.g., primates or mammals). These findings suggest that genetic and environmental factors control ocular growth via similar biological pathways across species, and support the continued use of animal models for investigating the biological mechanisms underlying human myopia development.

Genome wide discovery of genetic variants affecting alternative splicing patterns in human using bioinformatics method

The alternative splicing pattern of transcription units can be influenced by the genotype of a neighboring locus, which is termed splicing quantitative trait locus (sQTL). Here we report a comprehensive catalog of sQTLs discovered from the public RNA-seq and matched genotype datasets of three European ancestries. Each pair of RNA-seq and genotype dataset was analyzed with IVAS, a locally developed R/Bioconductor package for sQTL discovery. A meta-analysis was applied to the three result sets to reach a consensus of 2525 sQTLs (FDR < 0.05). Among them, nine independent sQTLs overlapped the known signals in the catalog of genome-wide association studies. Interestingly, six of these sQTLs are associated with the alternative exons, whose absence would hamper the protein function by omitting a critical/conserved domain. Altogether, we report the list of candidate sQTLs, and it might be useful for the explanations of trait-associated polymorphisms.

Genome-wide association study in takotsubo syndrome — Preliminary results and future directions

BackgroundTakotsubo syndrome (TS) is an acute non-ischemic cardiomyopathy characterized by transient regional systolic dysfunction of the left and/or right ventricle with still unknown etiology. The aim of the current study was to conduct for the first time a genome-wide association study (GWAS) in a cohort of TS patients to identify potential genetic risk variants. MethodsThis single-center study was conducted at the University Heart Center Lübeck from 2008 to 2016. DNA isolation was done according to standard protocols. Imputation of genotypes were performed at the Michigan Imputation Server (https://imputationserver.sph.umich.edu) using the 1000G Phase 3 v5 reference panel. ResultsThe study population consisted of 96 TS patients (91 females, 5 males) with a mean age of 71.9±10.4years and 475 healthy controls (268 males, 207 females). The results of GWAS analysis showed several promising candidate loci (68 loci after applying threshold of p<5∗10−4 and MAF>5%). Of these 68 loci, 18 loci contained top single nucleotide polymorphisms (SNPs) that were supported by SNPs in high Linkage Disequilibrium (r2>0.8) with p<10−3. Two out of the 18 loci contained SNP with hits in the GWAS catalog (traits: blood pressure, thyroid stimulating hormone). ConclusionThis first GWAS analysis in a larger cohort of patients with TS showed promising preliminary results. Further intensive research efforts of international collaborators are now necessary to enable deep-phenotyping of TS patients to ultimately assess a potential genetic cause of TS.

Genetic risk variants for autoimmune diseases that influence gene expression in thymus.

Genome-wide association studies (GWAS) have boosted our knowledge of genetic risk variants in autoimmune diseases (AIDs). Most risk variants are located within or near genes with immunological functions, and the majority is found to be non-coding, pointing towards a regulatory role. In this study, we performed a cis expression quantitative trait locus (eQTL) screen restricted to 353 AID associated risk variants selected from the GWAS catalog to investigate whether these single nucleotide polymorphisms (SNPs) influence gene expression in thymus. Genotypes were obtained by Immunochip (Ichip) and tested against expression of surrounding genes (±1 Mb) in human thymic tissue (n = 42). We identified eight significant eQTLs located within seven genetic regions (FCRL3, RNASET2, C2orf74, NPIPB8, SIRPG, SYS1 and AJ006998.2) where the expression was associated with AID risk SNPs at a study-wide level of significance (P < 2.7 × 10-5). In NPIPB8 and AJ006998.2, the eQTL signals appeared to be thymus-specific. Furthermore, many AID risk SNPs from GWAS have been subsequently fine-mapped in recent Ichip projects, and fine-mapped AID SNPs overlapped with the thymic eQTLs within RNASET2 and SIRPG Finally, in all the eQTL regions, except C2orf74, SNPs underlying the thymic eQTLs were predicted to interfere with transcription factors important in T cell development. Our study therefore reveals autoimmune risk variants that act as eQTLs in thymus, and suggest that thymic gene regulation may play a functional role at some AID risk loci.

Colorectal cancer risk genes are functionally enriched in regulatory pathways.

Colorectal cancer (CRC) is a common complex disease caused by the combination of genetic variants and environmental factors. Genome-wide association studies (GWAS) have been performed and reported some novel CRC susceptibility variants. However, the potential genetic mechanisms for newly identified CRC susceptibility variants are still unclear. Here, we selected 85 CRC susceptibility variants with suggestive association P < 1.00E-05 from the National Human Genome Research Institute GWAS catalog. To investigate the underlying genetic pathways where these newly identified CRC susceptibility genes are significantly enriched, we conducted a functional annotation. Using two kinds of SNP to gene mapping methods including the nearest upstream and downstream gene method and the ProxyGeneLD, we got 128 unique CRC susceptibility genes. We then conducted a pathway analysis in GO database using the corresponding 128 genes. We identified 44 GO categories, 17 of which are regulatory pathways. We believe that our results may provide further insight into the underlying genetic mechanisms for these newly identified CRC susceptibility variants.

Studying the Genetics of Complex Disease With Ancestry‐Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations

ABSTRACTGenome‐wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry‐specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P‐value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all‐inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry‐specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry‐specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry‐specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine.

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

Linking short tandem repeat polymorphisms with cytosine modifications in human lymphoblastoid cell lines.

Inter-individual variation in cytosine modifications has been linked to complex traits in humans. Cytosine modification variation is partially controlled by single nucleotide polymorphisms (SNPs), known as modified cytosine quantitative trait loci (mQTL). However, little is known about the role of short tandem repeat polymorphisms (STRPs), a class of structural genetic variants, in regulating cytosine modifications. Utilizing the published data on the International HapMap Project lymphoblastoid cell lines (LCLs), we assessed the relationships between 721 STRPs and the modification levels of 283,540 autosomal CpG sites. Our findings suggest that, in contrast to the predominant cis-acting mode for SNP-based mQTL, STRPs are associated with cytosine modification levels in both cis-acting (local) and trans-acting (distant) modes. In local scans within the ±1 Mb windows of target CpGs, 21, 9, and 21 cis-acting STRP-based mQTL were detected in CEU (Caucasian residents from Utah, USA), YRI (Yoruba people from Ibadan, Nigeria), and the combined samples, respectively. In contrast, 139,420, 76,817, and 121,866 trans-acting STRP-based mQTL were identified in CEU, YRI, and the combined samples, respectively. A substantial proportion of CpG sites detected with local STRP-based mQTL were not associated with SNP-based mQTL, suggesting that STRPs represent an independent class of mQTL. Functionally, genetic variants neighboring CpG-associated STRPs are enriched with genome-wide association study (GWAS) loci for a variety of complex traits and diseases, including cancers, based on the National Human Genome Research Institute (NHGRI) GWAS Catalog. Therefore, elucidating these STRP-based mQTL in addition to SNP-based mQTL can provide novel insights into the genetic architectures of complex traits.

Database: A New Article Type in CPT: Pharmacometrics & Systems Pharmacology.

Database: A New Article Type in CPT: Pharmacometrics & Systems Pharmacology.

Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease

Background: There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease? Results: Here we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed. Conclusions: The high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation.

Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease.

BackgroundThere are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease?ResultsHere we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed.ConclusionsThe high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation.

Abstract 37: A Multi-locus Genetic Risk Score for Abdominal Aortic Aneurysm

Background: Genome-wide association studies (GWAS) have identified several common variants associated with abdominal aortic aneurysm (AAA). We investigated whether a multi-locus genetic risk score (GRS) for AAA was associated with presence and progression of AAA in a case-control study. Methods: The study comprised of 1098 patients with AAA (74±8 years, 83% men) and 6538 controls (67±10 years, 58% men) enrolled in the Mayo Vascular Disease Biorepository. AAA was defined as a transverse diameter of abdominal aorta ≥ 3.0 cm or history of AAA repair. Controls were participants without known AAA. We found five single nucleotide polymorphisms (SNPs) previously shown to be associated with AAA at GWAS significance (P≤10^-8) in NHGRI GWAS catalog and PubMed. A GRS for AAA for each individual was calculated from four SNPs (rs2383107, rs7025486, rs599839, rs1466535) that were replicated in our cohort, by summing the number of risk alleles for each SNP weighted by their estimated effect sizes in GWAS catalog or published largest meta-analysis. Results: GRS was associated with presence of AAA: odds ratio (OR) (95% confidence interval): 1.06 (1.03-1.08). The association remained significant after adjustment for age, sex, cardiovascular risk factors, and atherosclerotic cardiovascular diseases: adjusted OR: 1.05 (1.03-1.08). Further adjustment for each SNP did not attenuate association of GRS with presence of AAA (All P&lt;0.001). GRS was not associated with family history of aortic aneurysm (P=0.4). Adding GRS to conventional risk factors improved net reclassification index by 16% (P&lt;0.001).In a subset of patients with AAA who had sequential imaging studies (n=628), GRS was associated with AAA growth rate ≥ 1.75 mm/year (median of the cohort) after adjustment for baseline AAA size: adjusted OR: 1.07 (1.00-1.14). No conventional risk factors were associated with AAA growth. Patients with GRS &gt; 5.24 (median of the cohort) had 1.31 times higher odds of having AAA and 1.64 times higher odds of having AAA growth rate ≥1.75 mm/year compared to those with GRS ≤ 5.24 (both P≤0.005). Conclusions: A multi-locus GRS was associated with presence of AAA and aneurysm growth, suggesting genetic predisposition to disease initiation and progression.

FORGE: A tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions

Genome Wide Association Studies (GWAS) provide an unbiased discovery mechanism for numerous human diseases. However, a frustration in the analysis of GWAS is that the majority of variants discovered do not directly alter protein-coding genes. We have developed a simple analysis approach that detects the tissue-specific regulatory component of a set of GWAS SNPs by identifying enrichment of overlap with DNase I hotspots from diverse tissue samples. Functional element Overlap analysis of the Results of GWAS Experiments (FORGE) is available as a web tool and as standalone software and provides tabular and graphical summaries of the enrichments. Conducting FORGE analysis on SNP sets for 260 phenotypes available from the GWAS catalogue reveals numerous overlap enrichments with tissue-specific components reflecting the known aetiology of the phenotypes as well as revealing other unforeseen tissue involvements that may lead to mechanistic insights for disease.

Abstract B14: Population genetics analysis of prostate cancer GWAS SNPs to evaluate West African genetic ancestry as a risk factor

Abstract Background: Prostate cancer (Pca) is the most common cancer among men in the U.S., and African American (AA) men have higher incidence compared to European American men. Environmental and sociocultural factors may play a role for increasing Pca risk among AA men, but the causal relationship of any environmental and social/cultural factors with Pca risk is not well demonstrated. On the other hand, there is strong evidence for genetic factors that explain increased incidence in AAs. Admixture mapping identified loci at 8q24 region showing association between African genetic ancestry and Pca risk, and a genome-wide association study (GWAS) in AAs identified a novel locus at 17q21 associated with Pca. However, replication of variants identified in GWAS among European and Asian populations has been difficult in AAs. Here, we performed population genetics analyses of Pca GWAS identified single nucleotide polymorphisms (SNPs) to evaluate whether these SNPs are informative of genetic ancestry and whether West African genetic Ancestry (WAA) is a Pca risk factor. Samples and Methods: We accessed National Human Genome Research Institute GWAS catalog in December, 2012, and there were 80 SNPs reported in 17 studies. We downloaded genotype data of 76 SNPs in 1000 Genomes (1KG) Project Phase I May 2011 and 62 SNPs in Human Genome Diversity Project (HGDP) from SPSmart. We performed principal component analysis using Golden Helix SVS 7. STRUCTURE was also used to estimate genetic ancestry and examine population clustering patterns. Replication study subjects, 470 AA men from Washington, DC (220 Pca cases and 250 controls) were genotyped as a part of GWAS in AAs with Illumina Infinium 1M. Logistic regression analysis was performed adjusting for age and 5 principal components (PCs). Results: Mean heterozygosity of 76 Pca GWAS SNPs in the 1KG Project dataset was lower in African populations than in European populations, possibly due to ascertainment bias. The majority of Pca GWAS SNPs had FST less than 0.20, but rs9623117 on chromosome 22 was highly differentiated (FST=0.63). In the 1KG Project dataset, PC1 separated African populations from non-African populations, and PC2 reflects variation in non-African populations separating East Asian from European populations. AA (ASW) individuals fell between the African and European clusters. Fewer Pca GWAS SNPs were genotyped in HGDP dataset, but they successfully captured global human genome variation forming African and non-African population clusters. In the STRUCTURE analysis with the 1KG project dataset, K=2 separated African from non-African populations, and K=3 separated European from East Asian populations. WAA estimated using 76 Pca GWAS SNPs was significantly correlated with WAA estimated using ancestry informative markers. Only four Pca GWAS SNPs were replicated and three of them were independently associated (P&amp;lt;0.05) in our replication samples. The number of risk alleles were positively associated with Pca risk (PTREND=0.002), and AAs carrying ≥5 risk alleles had 3.5 times greater odds of Pca (95% C.I.; 1.36-9.17; P=0.009) compared to AAs with ≤ 2 risk alleles. Risk allele frequencies of the three independently associated SNPs were higher in West Africans than in Europeans and was positively correlated with WAA estimates (PANOVA&amp;lt;0.001). Conclusion: Results of our analyses provide evidence suggesting that WAA is a risk factor for Pca. Population genetics analyses show that Pca GWAS SNPs can successfully capture global human genome variation and are informative of genetic ancestry. AAs with high WAA are likely to carry more Pca risk alleles than AAs with low WAA. Citation Format: Ken Batai, Ebony Shah, Rick A. Kittles. Population genetics analysis of prostate cancer GWAS SNPs to evaluate West African genetic ancestry as a risk factor. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr B14. doi:10.1158/1538-7755.DISP13-B14

A meta-analysis of gene expression quantitative trait loci in brain.

Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10−9), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.