Exome Chip Data Research Articles

A Human MSH6 Germline Variant Associated With Systemic Lupus Erythematosus Induces Lupus-like Disease in Mice.

ObjectiveTo determine if single‐nucleotide polymorphisms (SNPs) in DNA repair genes are enriched in individuals with systemic lupus erythematosus (SLE) and if they are sufficient to confer a disease phenotype in a mouse model.MethodsHuman exome chip data of 2499 patients with SLE and 1230 healthy controls were analyzed to determine if variants in 10 different mismatch repair genes (MSH4, EXO1, MSH2, MSH6, MLH1, MSH3, POLH, PMS2, ML3, and APEX2) were enriched in individuals with SLE. A mouse model of the MSH6 SNP, which was found to be enriched in individuals with SLE, was created using CRISPR/Cas9 gene targeting. Wildtype mice and mice heterozygous and homozygous for the MSH6 variant were then monitored for 2 years for the development of autoimmune phenotypes, including the presence of high levels of antinuclear antibodies (ANA). Additionally, somatic hypermutation frequencies and spectra of the intronic region downstream of the VHJ558‐rearranged JH4 immunoglobulin gene was characterized from Peyer's patches.ResultsBased on the human exome chip data, the MSH6 variant (rs63750897, p.Ser503Cys) is enriched among patients with SLE versus controls after we corrected for ancestry (odds ratio = 8.39, P = 0.0398). Mice homozygous for the MSH6 variant (Msh6 S502C/S502C ) harbor significantly increased levels of ANA. Additionally, the Msh6 S502C/S502C mice display a significant increase in the infiltration of CD68+ cells (a marker for monocytes and macrophages) into the lung alveolar space as well as apoptotic cells. Furthermore, characterization of somatic hypermutation in these mice reveals an increase in the DNA polymerase η mutational signature.ConclusionAn MSH6 mutation that is enriched in humans diagnosed with lupus was identified. Mice harboring this Msh6 mutation develop increased autoantibodies and an inflammatory lung disease. These results suggest that the human MSH6 variant is linked to the development of SLE.

Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study

A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.

A Genome-Wide Association Study of a Korean Population Identifies Genetic Susceptibility to Hypertension Based on Sex-Specific Differences.

Genome-wide association studies have expanded our understanding of the genetic variation of hypertension. Hypertension and blood pressure are influenced by sex-specific differences; therefore, genetic variants may have sex-specific effects on phenotype. To identify the genetic factors influencing the sex-specific differences concerning hypertension, we conducted a heterogeneity analysis of a genome-wide association study (GWAS) on 13,926 samples from a Korean population. Using the Illumina exome chip data of the population, we performed GWASs of the male and female population independently and applied a statistical test that identified heterogeneous effects of the variants between the two groups. To gain information about the biological implication of the genetic heterogeneity, we used gene set enrichment analysis with GWAS catalog and pathway gene sets. The heterogeneity analysis revealed that the rs11066015 of ACAD10 was a significant locus that had sex-specific genetic effects on the development of hypertension. The rs2074356 of HECTD4 also showed significant genetic heterogeneity in systolic blood pressure. The enrichment analysis showed significant results that are consistent with the pathophysiology of hypertension. These results indicate a sex-specific genetic susceptibility to hypertension that should be considered in future genetic studies of hypertension.

Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson's Disease Risk.

Given that improved imputation software and high-coverage whole genome sequence (WGS)-based haplotype reference panels now enable inexpensive approximation of WGS genotype data, we hypothesised that WGS-based imputation and analysis of existing ExomeChip-based genome-wide association (GWA) data will identify novel intronic and intergenic single nucleotide polymorphism (SNP) effects associated with complex disease risk. In this study, we reanalysed a Parkinson’s disease (PD) dataset comprising 5540 cases and 5862 controls genotyped using the ExomeChip-based NeuroX array. After genotype imputation and extensive quality control, GWA analysis was performed using PLINK and a recently developed machine learning approach (GenEpi), to identify novel, conditional and joint genetic effects associated with PD. In addition to improved validation of previously reported loci, we identified five novel genome-wide significant loci associated with PD: three (rs137887044, rs78837976 and rs117672332) with 0.01 < MAF < 0.05, and two (rs187989831 and rs12100172) with MAF < 0.01. Conditional analysis within genome-wide significant loci revealed four loci (p < 1 × 10−5) with multiple independent risk variants, while GenEpi analysis identified SNP–SNP interactions in seven genes. In addition to identifying novel risk loci for PD, these results demonstrate that WGS-based imputation and analysis of existing exome genotype data can identify novel intronic and intergenic SNP effects associated with complex disease risk.

The copy number variation and stroke (CaNVAS) risk and outcome study.

The role of copy number variation (CNV) variation in stroke susceptibility and outcome has yet to be explored. The Copy Number Variation and Stroke (CaNVAS) Risk and Outcome study addresses this knowledge gap. Over 24,500 well-phenotyped IS cases, including IS subtypes, and over 43,500 controls have been identified, all with readily available genotyping on GWAS and exome arrays, with case measures of stroke outcome. To evaluate CNV-associated stroke risk and stroke outcome it is planned to: 1) perform Risk Discovery using several analytic approaches to identify CNVs that are associated with the risk of IS and its subtypes, across the age-, sex- and ethnicity-spectrums; 2) perform Risk Replication and Extension to determine whether the identified stroke-associated CNVs replicate in other ethnically diverse datasets and use biomarker data (e.g. methylation, proteomic, RNA, miRNA, etc.) to evaluate how the identified CNVs exert their effects on stroke risk, and lastly; 3) perform outcome-based Replication and Extension analyses of recent findings demonstrating an inverse relationship between CNV burden and stroke outcome at 3 months (mRS), and then determine the key CNV drivers responsible for these associations using existing biomarker data. The results of an initial CNV evaluation of 50 samples from each participating dataset are presented demonstrating that the existing GWAS and exome chip data are excellent for the planned CNV analyses. Further, some samples will require additional considerations for analysis, however such samples can readily be identified, as demonstrated by a sample demonstrating clonal mosaicism. The CaNVAS study will cost-effectively leverage the numerous advantages of using existing case-control data sets, exploring the relationships between CNV and IS and its subtypes, and outcome at 3 months, in both men and women, in those of African and European-Caucasian descent, this, across the entire adult-age spectrum.

Trans‐ethnic meta‐analysis of interactions between genetics and early life socioeconomic status on memory performance and decline in older Americans

AbstractBackgroundLater life cognitive function is influenced both by genetics as well as early‐ and later‐life socioeconomic context, and these factors may also interact with each other. Previous research has demonstrated interactions between adult socioeconomic status and several gene regions that influence memory performance and decline in older adulthood, but few studies have examined whether genetics also interacts with childhood socioeconomic factors.MethodsWe used growth curve modeling to estimate episodic memory performance at age 65 (trajectory intercept) and decline thereafter (trajectory slope) in European ancestry (EA, N = 10,468) and African ancestry (AA, N = 2,252) participants from the Health and Retirement Study. Early‐life socioeconomic context included measures of parental education and financial strain. We selected 39 gene regions previously associated with cognitive performance, dementia, and related traits from GWAS. We tested whether all variants (1000 Genomes imputed data) and/or rare exomic variants (exome chip data) in these gene regions interacted with childhood socioeconomic context to influence memory using gene‐based tests (Interaction Sequence Kernel Association Test (iSKAT) or iSKAT Optimal Unified Test (iSKAT‐O)). For iSKAT analyses, we conducted a trans‐ethnic meta‐analysis.ResultsOf the 39 genes, 22 in EA and 19 in AA had nominally significant interactions (p &lt; 0.05) with at least one childhood socioeconomic measure on memory performance and/or decline; however, all but one (father’s education by SLC24A4 gene region in AA) were not significant after multiple testing correction (FDR &lt;0.1). In trans‐ethnic meta‐analysis, two gene regions interacted with childhood socioeconomic context (FDR &lt;0.05). Both interactions were robust to adjustment for respondent’s own educational attainment and APOE ε4. For both interactions (mother’s education by MS4A4A on memory performance (meta‐analysis p = 0.0005), and father’s education by SLC24A4 on memory decline (meta‐analysis p = 0.0009)) in both EA and AA, the genetic effect was stronger in participants with low parental education.ConclusionsExamination of common and rare variants in genes discovered through GWAS shows that childhood socioeconomic context may interact with a few key gene regions to jointly impact memory function and decline in later life. Genetic effects may be more salient for those with lower childhood socioeconomic status.

Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation.

The P-wave duration (PWD) is an electrocardiographic measurement that represents cardiac conduction in the atria. Shortened or prolonged PWD is associated with atrial fibrillation (AF). We used exome-chip data to examine the associations between common and rare variants with PWD. Fifteen studies comprising 64 440 individuals (56 943 European, 5681 African, 1186 Hispanic, 630 Asian) and ≈230 000 variants were used to examine associations with maximum PWD across the 12-lead ECG. Meta-analyses summarized association results for common variants; gene-based burden and sequence kernel association tests examined low-frequency variant-PWD associations. Additionally, we examined the associations between PWD loci and AF using previous AF genome-wide association studies. We identified 21 common and low-frequency genetic loci (14 novel) associated with maximum PWD, including several AF loci (TTN, CAND2, SCN10A, PITX2, CAV1, SYNPO2L, SOX5, TBX5, MYH6, RPL3L). The top variants at known sarcomere genes (TTN, MYH6) were associated with longer PWD and increased AF risk. However, top variants at other loci (eg, PITX2 and SCN10A) were associated with longer PWD but lower AF risk. Our results highlight multiple novel genetic loci associated with PWD, and underscore the shared mechanisms of atrial conduction and AF. Prolonged PWD may be an endophenotype for several different genetic mechanisms of AF.

A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis.

As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called "aGE" tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. Using extensive simulations, we show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. We demonstrate the performance of the proposed aGE tests using Pancreatic Cancer Case-Control Consortium Exome Chip data. An R package "aGE" is available at http://github.com/ytzhong/projects/.

Identification of ADHD risk genes in extended pedigrees by combining linkage analysis and whole-exome sequencing

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with a complex genetic background, hampering identification of underlying genetic risk factors. We hypothesized that combining linkage analysis and whole-exome sequencing (WES) in multi-generation pedigrees with multiple affected individuals can point toward novel ADHD genes. Three families with multiple ADHD-affected members (Ntotal = 70) and apparent dominant inheritance pattern were included in this study. Genotyping was performed in 37 family members, and WES was additionally carried out in 10 of those. Linkage analysis was performed using multi-point analysis in Superlink Online SNP 1.1. From prioritized linkage regions with a LOD score ≥ 2, a total of 24 genes harboring rare variants were selected. Those genes were taken forward and were jointly analyzed in gene-set analyses of exome-chip data using the MAGMA software in an independent sample of patients with persistent ADHD and healthy controls (N = 9365). The gene-set including all 24 genes together, and particularly the gene-set from one of the three families (12 genes), were significantly associated with persistent ADHD in this sample. Among the latter, gene-wide analysis for the AAED1 gene reached significance. A rare variant (rs151326868) within AAED1 segregated with ADHD in one of the families. The analytic strategy followed here is an effective approach for identifying novel ADHD risk genes. Additionally, this study suggests that both rare and more frequent variants in multiple genes act together in contributing to ADHD risk, even in individual multi-case families.

Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits.

Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant. Several studies collect phenotypic data over time and using such longitudinal data can further increase the power to detect genetic associations. Although rare-variant approaches exist for testing cross-phenotype effects at a single time point, there is no analogous method for performing such analyses using longitudinal outcomes. In order to fill this important gap, we propose an extension of Gene Association with Multiple Traits (GAMuT) test, a method for cross-phenotype analysis of rare variants using a framework based on the distance covariance. The approach allows for both binary and continuous phenotypes and can also adjust for covariates. Our simple adjustment to the GAMuT test allows it to handle longitudinal data and to gain power by exploiting temporal correlation. The approach is computationally efficient and applicable on a genome-wide scale due to the use of a closed-form test whose significance can be evaluated analytically. We use simulated data to demonstrate that our method has favorable power over competing approaches and also apply our approach to exome chip data from the Genetic Epidemiology Network of Arteriopathy.

Investigating the modulation of genetic effects on late AMD by age and sex: Lessons learned and two additional loci.

Late-stage age-related macular degeneration (AMD) is the leading cause of visual impairment in the elderly with a complex etiology. The most important non-modifiable risk factors for onset and progression of late AMD are age and genetic risk factors, however, little is known about the interplay between genetics and age or sex. Here, we conducted a large-scale age- and sex-stratified genome-wide association study (GWAS) using 1000 Genomes imputed genome-wide and ExomeChip data (>12 million variants). The data were established by the International Age-related Macular Degeneration Genomics Consortium (IAMDGC) from 16,144 late AMD cases and 17,832 controls. Our systematic search for interaction effects yielded significantly stronger effects among younger individuals at two known AMD loci (near CFH and ARMS2/HTRA1). Accounting for age and gene-age interaction using a joint test identified two additional AMD loci compared to the previous main effect scan. One of these two is a novel AMD GWAS locus, near the retinal clusterin-like protein (CLUL1) gene, and the other, near the retinaldehyde binding protein 1 (RLBP1), was recently identified in a joint analysis of nuclear and mitochondrial variants. Despite considerable power in our data, neither sex-dependent effects nor effects with opposite directions between younger and older individuals were observed. This is the first genome-wide interaction study to incorporate age, sex and their interaction with genetic effects for late AMD. Results diminish the potential for a role of sex in the etiology of late AMD yet highlight the importance and existence of age-dependent genetic effects.

Whole exome analyses to examine the impact of rare variants on left ventricular traits in African American participants from the HyperGEN and GENOA studies.

Left ventricular (LV) hypertrophy, highest in prevalence among African Americans, is an established risk factor heart failure. Several genome wide association studies have identified common variants associated with LV-related quantitative-traits in African Americans. To date, however, the effect of rare variants on these traits has not been extensively studied, especially in minority groups. We therefore investigated the association between rare variants and LV traits among 1,934 African Americans using exome chip data from the Hypertension Genetic Epidemiology Network (HyperGEN) study, with replication in 1,090 African American from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We used single-variant analyses and gene-based tests to investigate the association between 86,927 variants and six structural and functional LV traits including LV mass, LV internal dimension-diastole, relative wall thickness, left atrial dimension (LAD), fractional shortening (FS), and the ratio of LV early-to-late transmitral velocity (E/A ratio). Only rare variants (MAF <1% and <5%) were considered in gene-based analyses. In gene-based analyses, we found a statistically significant association between potassium voltage-gated channel subfamily H member 4 (KCNH4) and E/A ratio (P=8.7*10-8 using a burden test). Endonuclease G (ENDOG) was associated with LAD using the Madsen Browning weighted burden (MB) test (P=1.4*10-7). Neither gene result was replicated in GENOA, but the direction of effect of single variants in common was comparable. G protein-coupled receptor 55 (GPR55) was marginally associated with LAD in HyperGEN (P=3.2*10-5 using the MB test) and E/A ratio in GENOA, but with opposing directions of association for variants in common (P=0.03 for the MB test). No single variant was statistically significantly associated with any trait after correcting for multiple testing. The findings in this study highlight the potential cumulative contributions of rare variants to LV traits which, if validated, could improve our understanding of heart failure in African Americans.

Genetic Influences on Evening Preference Overlap with Those for Bipolar Disorder in a Sample of Mexican Americans and American Indians.

Diurnal preference (e.g., being an owl or lark) has been associated with several psychiatric disorders including bipolar disorder (BP), major depressive disorder, and substance use disorders. Previous large-scale genome-wide association studies (GWAS) aimed at identifying genetic influences on diurnal preference have exclusively included subjects of European ancestry. This study examined the genetic architecture of diurnal preference in two minority samples: a young adult sample of Mexican Americans (MAs), and a family-based sample of American Indians (AIs). Typed or imputed variants from exome chip data from the MA sample and low pass whole-genome sequencing from the AI cohort were analyzed using a mixed linear model approach for association with being an owl, as defined by a usual bedtime after 23:00 hrs. Genetic risk score (GRS) profiling detected shared genetic risk between evening preference and related disorders. Four variants in KIAA1549 like (KIAA1549L), a gene previously associated with attempted suicide in bipolar patients, were suggestively associated with being an owl at p < 1.82E-05; post hoc analyses showed the top variant trending in both the MA and AI cohorts at p = 2.50E-05 and p = .030, respectively. Variants associated with BP at p < .03 from the Psychiatric Genomics Consortium nominally predicted being an owl in the MA/AI cohort at p = .012. This study provides some additional evidence that genetic risk factors for BP also confer risk for being an owl in MAs/AIs and that evening preference may be a useful endophenotype for future studies of BP.

Exome Variant Analysis of Chronic Periodontitis in 2 Large Cohort Studies

Periodontitis is characterized by inflammation of the gingival tissue. The main risk factors are socioeconomic factors, sex, age, smoking, and diabetes, but periodontal disease has also a genetic background. Previous genome-wide association studies failed to reveal genome-wide significant associations of single common single-nucleotide polymorphisms with chronic periodontitis. Using the Illumina ExomeChip data of 6,576 participants of the German population-based cohort studies Study of Health in Pomerania (SHIP) and SHIP-Trend, the authors performed single variant and also gene-based association studies of rare and common exonic variations on different periodontal case definitions. Although our study comprised the largest sample size to date to assess genetic predisposition for chronic periodontitis, the authors found no significant association. This study emphasizes that for chronic periodontitis, large sample sizes will be necessary to find genetic associations, even when examining rare genetic variants.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

Abstract MP84: Trans-ethnic Meta-analysis of Exome Chip Data Reveals Novel Low-frequency Variants Contributing to Central Adiposity

Central adiposity is a leading risk factor for cardiovascular disease, and genetic factors contribute both to fat distribution, measured as waist-to-hip ratio adjusted for BMI (WHRa), and to differences in central adiposity prevalence. To date, 49 loci have been associated with WHRa, based on studies of common [minor allele frequency (MAF) ≥5%] single nucleotide variants (SNVs), primarily in European descent populations. Our aim was to identify low frequency (LFV: MAF &lt;5%) and rare (RV: MAF &lt;1%) coding variants associated with WHRa using Exome-Chip data from 344,369 individuals of European (84%), South Asian (8%), African (5%), East Asian(2%), and Hispanic/Latino (1%) ancestry. We performed fixed effects meta-analyses of study-specific WHRa associations stratified by sex and ancestry and then combined across strata for both SNV and gene-based results. We used a strict definition of variants annotated as damaging by 5 algorithms to perform gene-based analyses using the sequence kernel association test (SKAT). Analyses included up to 284,499 SNVs (218,195 with MAF&lt;5%), and 15,063 genes with at least one SNV that met our inclusion criteria. Five LFVs reached chip-wide significance (CWS: P&lt;2.5E-7) in our all ancestry sex-combined analyses, including one novel non-synonymous LFV in RAPGEF3 [MAF=0.01, β (SE) = -0.09 (0.012), P=1.28E-13]. In addition, one novel RV reached CWS in men for UGGT2 [MAF&lt;0.01, β (SE) = -0.142 (0.025), P=9.71E-9], and one RV reached CWS in women for ACVR1C [MAF&lt;0.01, β (SE) = -0.09 (0.018), P=1.09E-7]. Gene-based analyses identified RAPGEF3 (P=1.18E-11) as significantly associated with WHRa in the all ancestry sex combined analyses after correction for multiple tests (P&lt;2.5E-6), though conditional analysis revealed that this result is driven by the top SV identified in this region. RAPGEF3 also shows a significant association (p=4.68E-12) in all ancestry, sex combined gene-based analysis of BMI. RAPGEF3 is expressed in subcutaneous and visceral adipose tissue, and has been implicated in insulin regulation. RAPGEF3 plays a role in the GLP1 pathway, which controls insulin secretion in response to blood glucose concentration. Our results highlight the importance of large-scale genomic studies for identifying LFV and RV influencing central fat distribution. Understanding these genetic effects may provide insights into the progression of central adiposity and highlight potential population-specific variants that increase susceptibility.

Abstract MP83: Exomechip Meta-analysis of 526,508 Individuals From Five Ancestries Identifies Coding Variation in MC4R and KSR2 Associated with Body Mass Index

Increased body mass index (BMI) increases risk of chronic diseases including heart disease, stroke, and type 2 diabetes (T2D). Through genome-wide association studies (GWAS), 97 genetic variants, mostly common (MAF&gt;5%) and non-coding, have been identified for BMI. To investigate the role of coding variants with putative functional effects (missense, nonsense, splicing, stop gain), we meta-analyzed ExomeChip data of 526,508 individuals from over 110 studies, predominantly of European ancestry. Individual studies performed association analyses using Rvtest or RareMetalWorker; results were subsequently combined in single variant meta-analyses and gene-based tests (SKAT, VT) stratified by ancestry using RareMETALS. Gene-based tests were grouped based on functional annotation and in silico predicted impact on proteins for variants with a MAF&lt;5%. Among the 245,497 variants analyzed, of the 93 loci that reached array-wide significance (P&lt;2E-07); 56 were novel or independent of previously identified BMI loci. This includes low frequency variants in ACHE (rs1799805, p.H353N, p=8.01E-10, EAF=4%, β(SE)=0.032(0.005) SD/allele), HIP1R (rs34149579, p.V67F, p=1.88E-08, EAF=4%, β(SE)=-0.027(0.005) SD/allele) and MC4R (rs13447324, p.Y35X, p=2.37e-11, EAF=0.01%, β(SE)=0.64(0.09) SD/allele). Eight gene-based associations were significant (p&lt;2.5E-06), including GIPR (p=7.17E-09), whose effects were independent of the common GWAS proxy in the gene (rs1800437, p. E354Q, p=7.91E-30, MAF=20%, β(SE)=-0.029(0.003) SD/allele). GIPR is part of the GLP1 pathway responsible for the secretion of postprandial insulin. Other gene-based associations were largely driven by a single variant, such as for KSR2 (p=7.15E-09), RAPGEF3 (p=8.91E-15) and PRKAG1 (p=2.75E-12). Ksr2 knockout mice are obese. Multiple variants in KSR2 have been shown to associate with severe early onset obesity. Mutations in KSR2 disrupt the Raf-MEK-ERK pathway, resulting in impaired fatty acid oxidation and glucose oxidation. RAPGEF3 is involved in the GLP1 pathway and regulates glucose sensitivity of the K ATP channel, which regulates insulin secretion. PRKAG1 is involved in AMPK signaling and has previously been associated with T2D and weight gain on antipsychotic medication. Taken together, by interrogating coding variants in a large sample set, we have identified both additional genes and variants associated with BMI, some of which may be causal.

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data.

BackgroundRare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants.ResultsIn this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data.ConclusionsOur study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants.

Rare and low-frequency variants and their association with plasma levels of fibrinogen, FVII, FVIII, and vWF

AbstractFibrinogen, coagulation factor VII (FVII), and factor VIII (FVIII) and its carrier von Willebrand factor (vWF) play key roles in hemostasis. Previously identified common variants explain only a small fraction of the trait heritabilities, and additional variations may be explained by associations with rarer variants with larger effects. The aim of this study was to identify low-frequency (minor allele frequency [MAF] ≥0.01 and <0.05) and rare (MAF <0.01) variants that influence plasma concentrations of these 4 hemostatic factors by meta-analyzing exome chip data from up to 76 000 participants of 4 ancestries. We identified 12 novel associations of low-frequency (n = 2) and rare (n = 10) variants across the fibrinogen, FVII, FVIII, and vWF traits that were independent of previously identified associations. Novel loci were found within previously reported genes and had effect sizes much larger than and independent of previously identified common variants. In addition, associations at KCNT1, HID1, and KATNB1 identified new candidate genes related to hemostasis for follow-up replication and functional genomic analysis. Newly identified low-frequency and rare-variant associations accounted for modest amounts of trait variance and therefore are unlikely to increase predicted trait heritability but provide new information for understanding individual variation in hemostasis pathways.

Ancestry of the Timorese: age-related macular degeneration associated genotype and allele sharing among human populations from throughout the world.

We observed that the third leading cause of blindness in the world, age-related macular degeneration (AMD), occurs at a very low documented frequency in a population-based cohort from Timor-Leste. Thus, we determined a complete catalog of the ancestry of the Timorese by analysis of whole exome chip data and haplogroup analysis of SNP genotypes determined by sequencing the Hypervariable I and II regions of the mitochondrial genome and 17 genotyped YSTR markers obtained from 535 individuals. We genotyped 20 previously reported AMD-associated SNPs in the Timorese to examine their allele frequencies compared to and between previously documented AMD cohorts of varying ethnicities. For those without AMD (average age > 55 years), genotype and allele frequencies were similar for most SNPs with a few exceptions. The major risk allele of HTRA1 rs11200638 (10q26) was at a significantly higher frequency in the Timorese, as well as 3 of the 5 protective CFH (1q32) SNPs (rs800292, rs2284664, and rs12066959). Additionally, the most commonly associated AMD-risk SNP, CFH rs1061170 (Y402H), was also seen at a much lower frequency in the Korean and Timorese populations than in the assessed Caucasian populations (C ~7 vs. ~40%, respectively). The difference in allele frequencies between the Timorese population and the other genotyped populations, along with the haplogroup analysis, also highlight the genetic diversity of the Timorese. Specifically, the most common ancestry groupings were Oceanic (Melanesian and Papuan) and Eastern Asian (specifically Han Chinese). The low prevalence of AMD in the Timorese population (2 of 535 randomly selected participants) may be due to the enrichment of protective alleles in this population at the 1q32 locus.