Genome-wide Association Study Arrays Research Articles

Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients.

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3–4.2 million genotyped markers to approximately 11–43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

A Meta-Analysis of Genome-Wide Association Studies of Multiple Myeloma in Persons of African Ancestry [African-American Multiple Myeloma Study (AAMMS)] Identifies Suggestive Risk Variants

Background: Multiple myeloma (MM) is 2-3 times more common among persons of African ancestry (AA) compared to those of European ancestry (EA). The 2-3-fold increased risk among family members of cases suggests a genetic contribution to risk. Genome-wide association studies (GWAS) in EA populations have identified 16 novel risk loci. We previously showed that 5 of these replicated in populations of AA (Rand et al., CEBP, 2016). In order to increase the power to identify additional novel common genetic risk variants, we conducted a GWAS meta-analysis using two sets of AA MM patients recruited into the African American Multiple Myeloma Study (AAMMS) and the University of Arkansas for Medical Sciences (UAMS)) Myeloma Institute.Methods: The first GWAS included 1,305 incident AA MM cases enrolled in AAMMS, recruited from 10 clinical centers and population-based cancer registries in the NCI SEER Program from 2011 to 2013 and genotyped using the Illumina HumanCore GWAS array. Control data were obtained from 4425 male controls from the African Ancestry Prostate Cancer Consortium (consisting of 14 independent studies) and 2632 female controls from a breast cancer GWAS of African-American women (consisting of 9 independent studies) and genotyped using the Illumina 1M-Duo. The second GWAS included 95 additional AAMMS patients and 435 AA MM patients enrolled from UAMS, genotyped using the Illumina MegaBead Chip and compared to 2,390 AA controls from the Multiethnic Cohort. Both GWAS were imputed with the CAAPA Haplotype Reference panel on the University of Michigan Imputation Server http://www.caapa-project.org/. Single nucleotide polymorphisms (SNPs) with a minor allele frequency of less than 0.01 and an imputation score of less than 0.8 were excluded. Associations between SNPs and MM risk in each GWAS were calculated using unconditional logistic regression adjusted for sex, age at diagnosis (cases) or cohort sample collection (controls), and the first 10 genetic ancestry principal components. A meta-analysis of the two GWAS was conducted using a fixed effects model.Results: There were 8,715,278 overlapping SNPs in the meta-analysis with a lambda (inflation) = 1.066. Seven loci were associated with MM risk at p<10-6, but none reached genome-wide significance. We found several novel SNPs at the previously reported locus 6p21.33, with odds ratios (ORs) ranging from 1.20-1.28 and p-values ranging from 3.72-8.46 x 10-7 (Figure 1). We also identified six additional suggestive novel loci at chr4q32.1, chr9p24.3, chr12q12, chr12p13.3, chr16p12.2, chr18q12.1 with ORs ranging from 1.27-2.13 and p-values from 1.2x10-7-9.39x10-7.Of the 16 known risk loci in EA MM patients, eight replicated at p<0.05 in the AA MM GWAS meta-analysis. We previously reported better localized signals in AA patients for 7 of these known risk SNPs; five of these replicated at p< 0.05 in this meta-analysis. The highest p-value for the previously identified EA or AA SNPs was 1.90 x10-5.Conclusions: We have found suggestive loci associated with MM risk in AA's that appear distinct from risk loci identified in EA's, that may explain some of the disparity for MM risk in these two populations. Further follow-up with replication is warranted.Figure 1. A Manhattan plot showing results of a fixed effects meta-analysis of two genome wide association scans based on a total of 1,835 multiple myeloma patients and 9,468 controls of African ancestry, with genome-wide significance of p= 5x10-8 (red line). Y-axis= -Log10 of the p-value; X-axis= chromosomes; green dots= previously reported risk loci, excluding those with imputation scores<0.8 and a minor allele frequency of <1%. [Display omitted] DisclosuresAilawadhi:Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Pharmacyclics: Research Funding. Nooka:Amgen, Novartis, Spectrum, Adaptive: Consultancy. Anderson:Millenium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; MedImmune: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Other: scientific founder; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopep: Other: scientific founder. Vij:Amgen: Honoraria, Research Funding; Jazz: Honoraria; Konypharma: Honoraria; Bristol-Meyers-Squibb: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Celgene: Honoraria; Takeda: Honoraria, Research Funding. Zonder:Prothena: Consultancy; Pharmacyclics: Other: Data Safety Monitoring Committee; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Orlowski:BioTheryX: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Omics Approaches in Adipose Tissue and Skeletal Muscle Addressing the Role of Extracellular Matrix in Obesity and Metabolic Dysfunction.

Extracellular matrix (ECM) remodeling plays important roles in both white adipose tissue (WAT) and the skeletal muscle (SM) metabolism. Excessive adipocyte hypertrophy causes fibrosis, inflammation, and metabolic dysfunction in adipose tissue, as well as impaired adipogenesis. Similarly, disturbed ECM remodeling in SM has metabolic consequences such as decreased insulin sensitivity. Most of described ECM molecular alterations have been associated with DNA sequence variation, alterations in gene expression patterns, and epigenetic modifications. Among others, the most important epigenetic mechanism by which cells are able to modulate their gene expression is DNA methylation. Epigenome-Wide Association Studies (EWAS) have become a powerful approach to identify DNA methylation variation associated with biological traits in humans. Likewise, Genome-Wide Association Studies (GWAS) and gene expression microarrays have allowed the study of whole-genome genetics and transcriptomics patterns in obesity and metabolic diseases. The aim of this review is to explore the molecular basis of ECM in WAT and SM remodeling in obesity and the consequences of metabolic complications. For that purpose, we reviewed scientific literature including all omics approaches reporting genetic, epigenetic, and transcriptomic (GWAS, EWAS, and RNA-seq or cDNA arrays) ECM-related alterations in WAT and SM as associated with metabolic dysfunction and obesity.

Rare variants discovery by extensive whole-genome sequencing of the Han Chinese population in Taiwan: Applications to cardiovascular medicine

IntroductionA population-specific genomic reference is important for research and clinical practice, yet it remains unavailable for Han Chinese (HC) in Taiwan. ObjectivesWe report the first whole genome sequencing (WGS) database of HC (1000 Taiwanese genome (1KTW-WGS)) and demonstrate several applications to cardiovascular medicine. MethodsWhole genomes of 997 HC were sequenced to at least 30X depth. A total of 20,117 relatively healthy HC individuals were genotyped using a customized Axiom GWAS array. We performed a genome-wide genotype imputation technique using IMPUTE2. ResultsWe identified 26.7 million single-nucleotide variants (SNVs) and 4.2 million insertions-deletions. Of the SNVs, 16.1% were novel relative to dbSNP (build 152), and 34.2% were novel relative to gnomAD. A total of 18,450 healthy HC individuals were genotyped using a customized Genome-Wide Association Study (GWAS) array. We identified hypertension-associated variants and developed a hypertension prediction model based on the correlation between the WGS data and GWAS data (combined clinical and genetic models, AUC 0.887), and also identified 3 novel hyperlipidemia-associated variants. Each individual carried an average of 16.42 (SD = 3.72) disease-causing variants. Additionally, we established an online SCN5A (an important cardiac gene) database that can be used to explore racial differences. Finally, pharmacogenetics studies identified HC population-specific SNVs in genes (CYP2C9 and VKORC1) involved in drug metabolism and blood clotting. ConclusionThis research demonstrates the benefits of constructing a population-specific genomic reference database for precision medicine.

Investigating the missing heritability of fetal haemoglobin level in Africa.

Investigating the missing heritability of fetal haemoglobin level in Africa.

Genome-Wide Association for HbA1c in Malay Identified Deletion on SLC4A1 that Influences HbA1c Independent of Glycemia

Glycated hemoglobin A1c (HbA1c) level is used to screen and diagnose diabetes. Genetic determinants of HbA1c can vary across populations and many of the genetic variants influencing HbA1c level were specific to populations. To discover genetic variants associated with HbA1c level in nondiabetic Malay individuals. We conducted a genome-wide association study (GWAS) analysis for HbA1c using 2 Malay studies, the Singapore Malay Eye Study (SiMES, N = 1721 on GWAS array) and the Living Biobank study (N = 983 on GWAS array and whole-exome sequenced). We built a Malay-specific reference panel to impute ethnic-specific variants and validate the associations with HbA1c at ethnic-specific variants. Meta-analysis of the 1000 Genomes imputed array data identified 4 loci at genome-wide significance (P < 5 × 10-8). Of the 4 loci, 3 (ADAM15, LINC02226, JUP) were novel for HbA1c associations. At the previously reported HbA1c locus ATXN7L3-G6PC3, association analysis using the exome data fine-mapped the HbA1c associations to a 27-bp deletion (rs769664228) at SLC4A1 that reduced HbA1c by 0.38 ± 0.06% (P = 3.5 × 10-10). Further imputation of this variant in SiMES confirmed the association with HbA1c at SLC4A1. We also showed that these genetic variants influence HbA1c level independent of glucose level. We identified a deletion at SLC4A1 associated with HbA1c in Malay. The nonglycemic lowering of HbA1c at rs769664228 might cause individuals carrying this variant to be underdiagnosed for diabetes or prediabetes when HbA1c is used as the only diagnostic test for diabetes.

OP0047 GENOME-WIDE ASSOCIATION STUDY OF SJÖGREN’S SYNDROME IDENTIFIES TEN NEW RISK LOCI

Background:Sjögren’s syndrome (SS) is a complex autoimmune disease with exocrine gland dysfunction leading to substantial morbidity. There are 10 published genetic susceptibility loci.Objectives:Our genome-wide association study (GWAS) aimed to identify additional risk loci of genome-wide significance (GWS; p&lt;5E-08) in European-derived primary SS.Methods:A total of 3232 cases and 17481 controls genotyped on GWAS arrays and 619 cases and 6171 controls genotyped on ImmunoChip (IC) arrays were imputed after quality control. Logistic regression was calculated adjusting for ancestry using the first 4 principal components to identify SS-associated SNPs. GWAS and IC results were meta-analyzed using weighted Z-scores. Bayesian statistics were used to assign posterior probabilities and define credible SNP sets for each locus. Bioinformatic analyses were used to predict functionality.Results:Seven novel loci exceeded GWS in the GWAS analysis:NAB1,MIR146A-PTTG1,XKR6,MAPT-CRHR1,RPTOR-CHMP6-BAIAP2,TYK2andSYNGR1. Meta-analysis with IC data identified three more novel loci exceeding GWS:CD247,PRDM1-ATG5andTNFAIP3. Several additional loci with suggestive association (p&lt;1E-05) were also identified:ADAMTSL2,CGNL1andPHRF1.Several identified loci have reported functional implications in immune regulation and autoimmune disease. In lupus, rs2431697 correlated with rs2431098, which was shown to alterMIR146Aexpression, resulting in type I interferon pathway imbalance. Similarly,TYK2risk association reportedly drives interferon, IL10 and RET signaling pathways.PRDM1encodes Blimp-1, a master regulator of immune cell differentiation.CD247encodes the zeta subunit of the T cell receptor complex.XKR6is implicated in apoptotic cell ingestion.ATG5is also involved in apoptosis, as well as autophagy and antigen presentation.Additional bioinformatics analyses (Haploreg, Regulome DB, ENCODE, etc.) revealed immune-relevant functional implications for each risk locus. The SS-associated credible set included variants downstream ofTNFAIP3in a region reported to abolish looping between an enhancer and theTNFAIP3promoter in lupus and a coding variant that has been shown to alter NF-kB activity and neutrophil extra-cellular traps. The rs2293765 in the 5’ UTR ofNAB1showed evidence of enhancer/promoter activities. The rs2069235 in theSYNGR1locus showed enhancer and transcription start site activities in B and T cells. The rs7210219 in theMAPT-CRHR1locus showed enhancer/promotor activities in various tissues.Conclusion:We have identified ten novel genetic susceptibility loci associated with SS pathology. Our finding increases the current number of GWS regions in SS patients of European origin, from 10 to 20. Future work is needed to identify and characterize the functional variants in each region.Disclosure of Interests:Bhuwan Khatri: None declared, Tove Ragna Reksten: None declared, Kandice L Tessneer: None declared, Astrid Rasmussen Speakers bureau: Novartis, ThermoFischer, R Hal Scofield Grant/research support from: Pfizer, Simon J. Bowman Consultant of: Astrazeneca, Biogen, BMS, Celgene, Medimmune, MTPharma, Novartis, Ono, UCB, xtlbio, Glapagos, Speakers bureau: Novartis, Joel Guthridge Grant/research support from: Xencor, Bristol Myers Squibb, DXterity, Judith A. James Grant/research support from: Progentec Diagnostics, Inc, Consultant of: Abbvie, Novartis, Jannsen, Lars Ronnblom Grant/research support from: AZ, Speakers bureau: AZ, Blake M Warner: None declared, Xavier Mariette: None declared, Roald Omdal: None declared, Javier Martin Ibanez: None declared, Maria Teruel: None declared, Janicke Liaaen Jensen: None declared, Lara A Aqrawi: None declared, Øyvind Palm: None declared, Marie Wahren-Herlenius: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, A Darise Farris Speakers bureau: Biogen, Marta Alarcon-Riquelme: None declared, Wan-fai Ng: None declared, Kathy L Sivils: None declared, Gunnel Nordmark: None declared, Christopher Lessard: None declared

Refining genome-wide associated loci for serum uric acid in individuals with African ancestry.

Serum uric acid is the end-product of purine metabolism and at high levels is a risk factor for several human diseases including gout and cardiovascular disease. Heritability estimates range from 0.32 to 0.63. Genome-wide association studies (GWAS) provide an unbiased approach to identify loci influencing serum uric acid. Here, we performed the first GWAS for serum uric acid in continental Africans, with replication in African Americans. Africans (n = 4126) and African Americans (n = 5007) were genotyped on high-density GWAS arrays. Efficient mixed model association, a variance component approach, was used to perform association testing for a total of ~ 18 million autosomal genotyped and imputed variants. CAVIARBF was used to fine map significant regions. We identified two genome-wide significant loci: 4p16.1 (SLC2A9) and 11q13.1 (SLC22A12). At SLC2A9, the most strongly associated SNP was rs7683856 (P = 1.60 × 10-44). Conditional analysis revealed a second signal indexed by rs6838021 (P = 5.75 × 10-17). Gene expression and regulatory motif data prioritized a single-candidate causal variant for each signal. At SLC22A12, the most strongly associated SNP was rs147647315 (P = 6.65 × 10-25). Conditional analysis and functional annotation prioritized the missense variant rs147647315 (R (Arg) > H (His)) as the sole causal variant. Functional annotation of these three signals implicated processes in skeletal muscle, subcutaneous adipose tissue and the kidneys, respectively. This first GWAS of serum uric acid in continental Africans identified three associations at two loci, SLC2A9 and SLC22A12. The combination of weak linkage disequilibrium in Africans and functional annotation led to the identification of candidate causal SNPs for all three signals. Each candidate causal variant implicated a different cell type. Collectively, the three associations accounted for 4.3% of the variance of serum uric acid.

Unravelling the Roles of Susceptibility Loci for Autoimmune Diseases in the Post-GWAS Era

Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Apart from SNPs, it also comprises genetic variations such as insertions-deletions, copy number variations, and somatic mosaicism. Although previous studies suggest that common copy number variations do not play a major role in autoimmune disease risk, it is possible that certain rare genetic variations with large effect sizes are relevant to autoimmunity. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

Abstract 223: A meta-analysis of genome-wide association studies of multiple myeloma among African Americans

Abstract Multiple myeloma (MM) is twice as common in African Americans (AA) compared to European Americans (EA). The reported familial clustering and the elevated MM risk among first-degree relatives of cases implicate genetic susceptibility. Previous genome-wide association studies (GWAS) in EA have identified 16 novel risk loci. In this study, we tested the generalizability of the established risk alleles to AA and conducted a meta-GWAS analysis using two sets of AA to identify additional novel common MM risk variants. In the first study, we genotyped 1,305 incident AA MM cases from the African American Multiple Myeloma Study (AAMMS) using the Illumina HumanCore GWAS array and compared them to 7,078 AA controls from the African Ancestry Prostate Cancer Consortium (AAPC) and African Ancestry Breast Cancer Consortium (AABC) using the Illumina 1M-Duo. In the second study, 95 additional AAMMS cases and 435 AA MM cases from the University of Arkansas for Medical Sciences (UAMS) were genotyped using the Illumina MegaBead Chip and compared to 2,390 AA controls from the Multiethnic Cohort. The Haplotype Reference Consortium (HRC) was used to impute the overlapping typed SNPS from each GWAS case and control set together. Per-allele risk associations were tested for 8,715,278 overlapping genotyped and imputed variants with &amp;gt;1% frequency and &amp;gt;0.8 imputation score using unconditional logistic regression in both sets, and the combined effects were estimated using a fixed-effect meta-analysis. Of the 16 reported risk loci discovered in EA, directional consistency was present for 15 variants; eight of these replicated at nominal significance p&amp;lt;0.05, with the most statistically significant variant being rs4487645 at 7p15.3 (OR=1.38, p=3.56×10-6). AA individuals with polygenic risk scores from these 16 variants (PRS) in the top 10% stratum had a 1.44-fold increased MM risk compared to those with a PRS in the 25th -75th percentiles. Additionally, we identified three suggestive novel loci located at 12q12, 9p24.3 and 9p13.1 at p&amp;lt;1×10-6, with ORs ranging from 1.25-1.55, but none reached genome-wide significance. The variant at 9p24.3 is located in an intron in the KANK1 gene and a correlated SNP in EAs (r2=0.5) is strongly associated with gene expression in neoplastic plasma cells (unpublished, Weinhold and Morgan). Our study replicated most of the reported risk loci discovered among EA, demonstrated that a PRS constructed using the 16 reported risk alleles was associated with MM risk, and provides suggestive evidence for additional loci associated with MM risk in AAs. Citation Format: Zhaohui Du, Chi Song, Kristin Rand, Niels Weinhold, David Van Den Berg, Amie Hwang, Xin Sheng, Victor Hom, Sikander Ailawadhi, Ajay K. Nooka, Seema Singhal, Karen Pawlish, Edward S. Peters, Cathryn Bock, Ann Mohrbacher, Alexander Stram, Sonja I. Berndt, William Blot, John David Carpten, Antoinette Stroup, Andrew Olshan, Wei Zhang, African Ancestry Breast &amp; Prostate Consortium, Stephen Chanock, Jayesh Mehta, Graham A. Colditz, Jeffrey Wolf, Thomas G. Martin, Michael Tomasson, Mark A. Fiala, Howard Terebelo, Nalini Janakiraman, Laurence Kolonel, Loic LeMarchand, Elad Ziv, Daniel Stram, Ravi Vij, Leon Bernal-Mizrachi, Gareth J. Morgan, Jeffrey A. Zonder, Carol Ann Huff, Sagar Lonial, Robert Z. Orlowski, David V. Conti, Christopher A. Haiman, Wendy Cozen. A meta-analysis of genome-wide association studies of multiple myeloma among African Americans [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 223.

Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method

BackgroundPredicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn’s disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach.MethodsWe performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation.ResultsOn average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis.ConclusionsOur analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.

A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts.

The Nurses’ Health Study (NHS), Nurses’ Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)). We observed the strongest BMI association for the FTO SNP rs55872725 (β = 0.45, p = 3.48x10-22), and using a significance level of p = 0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR = 2.17, 95% CI: 1.79–2.63, p = 2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.

ABCG2 Polymorphism Is Associated with Hyperuricemia in a Study of a Community-Based Korean Cohort.

The purpose of the present study was to find novel loci associated with hyperuricemia using data from a genome-wide association study (GWAS) conducted on healthy Koreans. We conducted a GWAS using data from a community-based cohort study where 3,647 subjects aged 40–89 were recruited by the Korea National Institute of Health (KNIH). The community-based cohort consisted of subjects who did not suffer from any of 6 major diseases (hypertension, hyperlipidemia, diabetes, heart diseases, brain diseases, and cancers). Epidemiologic information includes 249 traits such as epidemiological surveys, physical examinations, and laboratory tests. A total of 3,647 participants, including 234 hyperuricemia cases (serum uric acid [SUA] level was 7 mg/dL or higher) and 3,413 controls, were genotyped by Illumina HumanOmni1-Quad BeadChip GWAS array at KNIH. In the multivariate regression analysis of clinical variables, significant variables associated with hyperuricemia were male gender (odds ratio [OR], 5.526; P = 3.2 × 10−10), old age (OR, 1.017; P = 0.040), high body mass index (BMI) (OR, 1.147; P = 5.4 × 10−7), current alcohol intake (OR, 2.413; P = 4.7 × 10−7), and high creatinine (OR, 1.647; P = 1.6 × 10−13). We identified a hyperuricemia susceptible loci (rs2054576 in ABCG2, OR, 1.883; P = 4.7 × 10−8) that passed a genome-wide significance threshold, adjusted by clinical variables (male, age, BMI, current alcohol, and creatinine). It was first identified that rs2054576 in ABCG2 is associated with hyperuricemia. Our results should be validated through replication studies among other Korean subjects or various ethnic groups.

Meta-GWAS and Meta-Analysis of Exome Array Studies Do Not Reveal Genetic Determinants of Serum Hepcidin.

Serum hepcidin concentration is regulated by iron status, inflammation, erythropoiesis and numerous other factors, but underlying processes are incompletely understood. We studied the association of common and rare single nucleotide variants (SNVs) with serum hepcidin in one Italian study and two large Dutch population-based studies. We genotyped common SNVs with genome-wide association study (GWAS) arrays and subsequently performed imputation using the 1000 Genomes reference panel. Cohort-specific GWAS were performed for log-transformed serum hepcidin, adjusted for age and gender, and results were combined in a fixed-effects meta-analysis (total N 6,096). Six top SNVs (p<5x10-6) were genotyped in 3,821 additional samples, but associations were not replicated. Furthermore, we meta-analyzed cohort-specific exome array association results of rare SNVs with serum hepcidin that were available for two of the three cohorts (total N 3,226), but no exome-wide significant signal (p<1.4x10-6) was identified. Gene-based meta-analyses revealed 19 genes that showed significant association with hepcidin. Our results suggest the absence of common SNVs and rare exonic SNVs explaining a large proportion of phenotypic variation in serum hepcidin. We recommend extension of our study once additional substantial cohorts with hepcidin measurements, GWAS and/or exome array data become available in order to increase power to identify variants that explain a smaller proportion of hepcidin variation. In addition, we encourage follow-up of the potentially interesting genes that resulted from the gene-based analysis of low-frequency and rare variants.

Large-Scale Genomic Analyses Link Reproductive Aging to Hypothalamic Signaling, Breast Cancer Susceptibility, and BRCA1-Mediated DNA Repair

ABSTRACT: Menopause timing has a major impact on infertility and risk of disease. Younger age at natural (nonsurgical) menopause (ANM) is associated with a higher risk of osteoporosis, cardiovascular disease, and type 2 diabetes and a lower risk of breast cancer. Late menopause is associated with a higher risk of breast cancer. It is well known that the age at which women go through menopause is partly determined by genes, but the underlying mechanisms are poorly understood. Genome-wide association studies have identified 18 common genetic variants associated with ANM. These variants explain less than 5% of the variation in ANM compared with the 21% explained by all common variants on genome-wide association study arrays. This genome-wide association study was the collaborative effort of researchers from 177 institutions worldwide. The study was designed to investigate genetic variants associated with timing of menopause among a population of approximately 70,000 women of European ancestry. A dual strategy was used to identify both common and, for the first time, low-frequency coding variants associated with ANM. The causal relationship between ANM and breast cancer was investigated using a Mendelian randomization approach. Combined analysis identified 1208 single-nucleotide polymorphisms (SNPs) of a total of approximately 2.6 million that reached the genome-wide significance threshold for association with ANM. Forty-four regions with common variants were identified; among these 44 loci were 2 rare low-frequency missense alleles of large effect. A majority of ANM SNPs were enriched in DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal relationship between delayed ANM and breast cancer risk; there was approximately 6% increase in risk per year; P = 3 × 10-14); increased risk with delayed menopause appeared to be mediated primarily by prolonged sex hormone exposure in a woman’s lifetime, not DDR mechanisms. This is the first study to confirm the link between early and late menopause and breast cancer risk using genetic information. Age at natural menopause genetic variants influence breast cancer risk primarily through variation in menopause timing. Although carrying higher numbers of ANM-increasing variants and enrichment in DDR genes are associated with a modest increase in breast cancer risk, the major mechanism for increased risk appears to be prolonged estrogen and/or progesterone exposure due to delayed menopause.

A large multiethnic genome-wide association study of prostate cancer identifies novel risk variants and substantial ethnic differences.

A genome-wide association study (GWAS) of prostate cancer in Kaiser Permanente health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) revealed a new independent risk indel rs4646284 at the previously identified locus 6q25.3 that replicated in PEGASUS (N = 7,539) and the Multiethnic Cohort (N = 4,679) with an overall P = 1.0 × 10(-19) (OR, 1.18). Across the 6q25.3 locus, rs4646284 exhibited the strongest association with expression of SLC22A1 (P = 1.3 × 10(-23)) and SLC22A3 (P = 3.2 × 10(-52)). At the known 19q13.33 locus, rs2659124 (P = 1.3 × 10(-13); OR, 1.18) nominally replicated in PEGASUS. A risk score of 105 known risk SNPs was strongly associated with prostate cancer (P < 1.0 × 10(-8)). Comparing the highest to lowest risk score deciles, the OR was 6.22 for non-Hispanic whites, 5.82 for Latinos, 3.77 for African-Americans, and 3.38 for East Asians. In non-Hispanic whites, the 105 risk SNPs explained approximately 7.6% of disease heritability. The entire GWAS array explained approximately 33.4% of heritability, with a 4.3-fold enrichment within DNaseI hypersensitivity sites (P = 0.004). Taken together, our findings of independent risk variants, ethnic variation in existing SNP replication, and remaining unexplained heritability have important implications for further clarifying the genetic risk of prostate cancer. Our findings also suggest that there may be much promise in evaluating understudied variation, such as indels and ethnically diverse populations.

Genetic Susceptibility Markers of Multiple Myeloma in African-Americans

Multiple myeloma (MM) is 2-3 times more common among African-Americans compared to non-Hispanic whites. The 2-3-fold increased risk among family members of cases suggests a genetic contribution to risk. Genome-wide association studies (GWAS) in populations of European ancestry have identified seven novel risk loci at 2p23.3 (rs6746082), 3q26.2 (rs10936599), 3p22.1 (rs1052501), 6p21.32 (rs2285803), 7p15.3 (rs4487645), 17p11.2 (rs4273077) and 22q13.1 (rs877529) (Broderick, et al. Nat Genet, 2011, Chubb, et al. Nat Genet, 2013), three of which were replicated in another European series (Martino et al., Br J Haematol, 2012). Here we examined the index signals and conducted fine-mapping for each locus in a case-control study of 1,049 multiple myeloma cases and 7,084 controls of African ancestry to identify better markers of risk and novel independent loci in seven previously reported regions in this high risk population.Incident cases were recruited from 10 clinical centers and SEER cancer registries from 2011 to 2013 and genotyped using the Illumina HumanCore GWAS array. Control data were obtained from previous genome-wide studies of breast and prostate cancer, genotyped using the Illumina 1M-Duo in 4425 male controls from the African Ancestry Prostate Cancer Consortium (consisting of 14 independent studies) and 2632 female controls from a breast cancer GWAS of African-American women (consisting of 9 independent studies). Imputation to 1000 Genomes (March 2012 release) was conducted for regions around six of the previously identified single nucleotide polymorphisms [SNPs] (the HLA region harboring rs2285803 is still being imputed, results will be presented). A case-control analysis of SNPs/indels >1% frequency within 250 kb of each index variant was conducted using unconditional multivariable logistic regression adjusting for age, sex and five leading principal components. Region-specific alpha levels were determined through permutation tests. The minimum alpha level across the six regions was α=0.002.All previously reported risk variants were common in African-Americans (minor allele frequency [MAF] >0.05). For five of the six SNPs, we had ≥94% power to detect the same effect observed in non-Hispanic whites, and 64% power for the less common variant rs10936599 (MAF=0.07). We observed directionally consistent effects (odds ratio [OR]>1) for the six risk variants tested, with three replicating at p≤0.05 (7p15.3, p=1.4x10-7; 17p11.2, p=0.05; 22q13.1, p=0.02). For three of the six regions, we observed better markers of risk in African-Americans that were correlated with the index SNP in Europeans (7p15.3, rs56333627, p=1.5x10-5, r2=0.89; 17p11.2, rs34562254, p=2.9x10-3, r2=0.90; 22q13.1, rs2092410, p=1.1x10-4 r2=.71). The missense variant identified in the 17p11.2 region (rs34562254, Pro251Leu) is located in TNFRSF13B, which encodes the protein TACI, a B cell surface receptor which plays a role in B cell maturation, apoptosis and antibody production by inducing activation of transcription factors including NFAT and NFκβ. In addition, there is evidence suggesting that TACI is involved in MM pathogenesis.Our results demonstrate that many of the risk loci for MM found in European ancestry populations are also risk loci in men and women of African ancestry and that by fine-mapping, we are able to identify variants that better capture risk in populations of African ancestry. DisclosuresTerebelo:Celgene Corp: Membership on an entity’s Board of Directors or advisory committees. Lonial:Millennium: The Takeda Oncology Company: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding.

Exome chip evaluation of genetic variants for association with uterine fibroids

Uterine fibroid(UFs) affect up to 77% of women by menopause with large racial disparities and account for $9.4 billion in yearly healthcare costs. Although UFs are heritable, genetic risk is poorly understood. The first genome-wide association study(GWAS) of UFs was performed in 2011 in a Japanese population, however, to-date few large-scale genetic studies have been performed in US populations.(1) The objective of this study is to conduct a whole exome association study of UF risk in European Americans(EA) and African Americans(AA). This is case-control genetic association study of UFs defined using pelvic imaging data obtained from an electronic medical record biorepository (BioVU DNA Repository). Logistic regression adjusted for ancestry and age was used in 728 EA and AA DNA samples (EA: 246 cases, 243 controls; AA: 121 cases, 118 controls) to evaluate SNPs for association, stratified by race. Meta-analyses of results with fixed effect models were performed to obtain combined evidence for associations across racial groups. Our strongest association within EAs was within t-complex 11, testis-specific like 1 (TCP11L1, rs1064005, OR 1.79, 95% CI 1.36-2.32, p 3.94x10-5) and within multiple SNPs within the HLA region (smallest p 9.28x10-5). It is of note that the exome chip has enhanced coverage of the HLA region compared to GWAS arrays. Among AAs strong associations were observed within glipican 4 (GC4, OR = 0.46, 95% CI 0.31-0.68, p = 8.73x10-5). Meta-analysis across EA and AA further strengthened the associations observed at TCP11L1 (smallest meta-p 4.09x-7, with effect sizes consistent across races). Prior gene expression studies of TCP11L have shown increased expression in tumor tissues compared to normal tissues across several different cancers and variants within HLA genes have been associated with UF risk in prior studies. These pilot data suggest common variants in exonic regions increase risk for UF in both EA and AA populations. However, further validation of our study findings is needed to confirm our results. We are currently genotyping >4,000 samples for GWAS and exome arrays to further evaluate the relationship between gene variants and UF risk.

A genome-wide association study of behavioral disinhibition.

We report results from a genome wide association study (GWAS) of five quantitative indicators of behavioral disinhibition: nicotine, alcohol consumption, alcohol dependence, illicit drugs, and non-substance related behavioral disinhibition. The sample, consisting of 7,188 Caucasian individuals clustered in 2,300 nuclear families, was genotyped on over 520,000 SNP markers from Illumina's Human 660W-Quad Array. Analysis of individual SNP associations revealed only one marker-component phenotype association, between rs1868152 and illicit drugs, with a p value below the standard genome-wide threshold of 5 × 10(-8). Because we had analyzed five separate phenotypes, we do not consider this single association to be significant. However, we report 13 SNPs that were associated at p < 10(-5) for one phenotype and p < 10(-3) for at least two other phenotypes, which are potential candidates for future investigations of variants associated with general behavioral disinhibition. Biometric analysis of the twin and family data yielded estimates of additive heritability for the component phenotypes ranging from 49 to 70%, GCTA estimates of heritability for the same phenotypes ranged from 8 to 37%. Consequently, even though the common variants genotyped on the GWAS array appear in aggregate to account for a sizable proportion of heritable effects in multiple indicators of behavioral disinhibition, our data suggest that most of the additive heritability remains "missing".

Detecting large copy number variants using exome genotyping arrays in a large Swedish schizophrenia sample

Although copy number variants (CNVs) are important in genomic medicine, CNVs have not been systematically assessed for many complex traits. Several large rare CNVs increase risk for schizophrenia (SCZ) and autism and often demonstrate pleiotropic effects; however, their frequencies in the general population and other complex traits are unknown. Genotyping large numbers of samples is essential for progress. Large cohorts from many different diseases are being genotyped using exome-focused arrays designed to detect uncommon or rare protein-altering sequence variation. Although these arrays were not designed for CNV detection, the hybridization intensity data generated in each experiment could, in principle, be used for gene-focused CNV analysis. Our goal was to evaluate the extent to which CNVs can be detected using data from one particular exome array (the Illumina Human Exome Bead Chip). We genotyped 9100 Swedish subjects (3962 cases with SCZ and 5138 controls) using both standard genome-wide association study (GWAS) and exome arrays. In comparison with CNVs detected using GWAS arrays, we observed high sensitivity and specificity for detecting genic CNVs 400 kb including known pathogenic CNVs along with replicating the literature finding that cases with SCZ had greater enrichment for genic CNVs. Our data confirm the association of SCZ with 16p11.2 duplications and 22q11.2 deletions, and suggest a novel association with deletions at 11q12.2. Our results suggest the utility of exome-focused arrays in surveying large genic CNVs in very large samples; and thereby open the door for new opportunities such as conducting well-powered CNV assessment and comparisons between different diseases. The use of a single platform also minimizes potential confounding factors that could impact accurate detection.