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Every author has erroneously been assigned to the affiliation “62”. The affiliation 62 belongs to the author Graham Casey.

SummaryMetabolic dysregulation in multiple tissues alters glucose homeostasis and influences risk for type 2 diabetes (T2D). To identify pathways and tissues influencing T2D-relevant glycemic traits (fasting glucose [FG], fasting insulin [FI], two-hour glucose [2hGlu] and glycated hemoglobin [HbA1c]), we investigated associations of exome-array variants in up to 144,060 individuals without diabetes of multiple ancestries. Single-variant analyses identified novel associations at 21 coding variants in 18 novel loci, whilst gene-based tests revealed signals at two genes, TF (HbA1c) and G6PC (FG, FI). Pathway and tissue enrichment analyses of trait-associated transcripts confirmed the importance of liver and kidney for FI and pancreatic islets for FG regulation, implicated adipose tissue in FI and the gut in 2hGlu, and suggested a role for the non-endocrine pancreas in glucose homeostasis. Functional studies demonstrated that a novel FG/FI association at the liver-enriched G6PC transcript was driven by multiple rare loss-of-function variants. The FG/HbA1c-associated, islet-specific G6PC2 transcript also contained multiple rare functional variants, including two alleles within the same codon with divergent effects on glucose levels. Our findings highlight the value of integrating genomic and functional data to maximize biological inference.Highlights23 novel coding variant associations (single-point and gene-based) for glycemic traits51 effector transcripts highlighted different pathway/tissue signatures for each traitThe exocrine pancreas and gut influence fasting and 2h glucose, respectivelyMultiple variants in liver-enriched G6PC and islet-specific G6PC2 influence glycemia

Detecting and estimating DNA sample contamination are important steps to ensure high quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes.We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele-frequencies projected from reference genotypes onto principal component coordinates. We demonstrate this method robustly and accurately estimates contamination rates across different populations and contamination rates. We further demonstrate that in the presence contamination, quantitative estimates of genetic ancestry (e.g. principal component coordinates) can be substantially biased if contamination is ignored, and that our proposed method corrects for this bias. Our method is publicly available at http://github.com/Griffan/verifyBamID

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of ˜27M variants (˜21M with minor allele frequency [MAF]p−8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p−5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%≤MAF2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

Chronic Kidney Disease (CKD) is a growing health burden currently affecting 10-15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. Previous genome-wide association study (GWAS) meta-analyses of CKD and eGFR or related phenotypes have identified a number of variants associated with kidney function, but these only explain a fraction of the variability in kidney phenotypes attributed to genetic components. To extend these studies, we analyzed data from the Nord-Trøndelag Health Study (HUNT), which is more densely imputed than previous studies, and performed a GWAS meta-analysis of eGFR with publicly available summary statistics, more than doubling the sample size of previous meta-analyses. We identified 147 loci (53 novel loci) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Moreover, genes at these loci show enriched expression in urogenital tissues and highlight gene sets known to play a role in kidney function. In addition, sex-stratified analysis identified three regions (prioritized genes:PPM1J, MCL1, andSLC47A1) with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD but improve detection only modestly compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

Polygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog calledPRSwebthat provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.Author summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patient’s risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases’ shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

Protein-coding genetic variants that strongly affect disease risk can provide important clues into disease pathogenesis. Here we report an exome sequence analysis of 20,791 type 2 diabetes (T2D) cases and 24,440 controls from five ancestries. We identify rare (minor allele frequency30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to T2D drug targets (p=6.1×10−3) and candidate genes from knockout mice (p=5.2×10−3). Within our study, the strongest T2D rare variant gene-level signals explain at most 25% of the heritability of the strongest common single-variant signals, and the rare variant gene-level effect sizes we observe in established T2D drug targets will require 110K-180K sequenced cases to exceed exome-wide significance. To help prioritize genes using associations from current smaller sample sizes, we present a Bayesian framework to recalibrate association p-values as posterior probabilities of association, estimating that reaching ppwww.type2diabetesgenetics.org.

To identify genetic variation underlying atrial fibrillation, the most common cardiac arrhythmia, we performed a genome-wide association study of > 1,000,000 people, including 60,620 atrial fibrillation cases and 970,216 controls. We identified 142 independent risk variants at 111 loci and prioritized 151 functional candidate genes likely to be involved in atrial fibrillation. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans (GATA4, MYH6, NKX2–5, PITX2, TBX5)(1), or near genes important for striated muscle function and integrity (for example, CFL2 MYH7, PKP2, RBM20, SGCG, SSPN). Pathway and functional enrichment analyses also suggested that many of the putative atrial fibrillation genes act via cardiac structural remodeling, potentially in the form of an ‘atrial cardiomyopathy’(2), either during fetal heart development or as a response to stress in the adult heart.

Importance Patterns of preoperative opioid use are not well characterized across different surgical services, and studies in this patient population have lacked important self-reported data of pain and affect. Objectives To assess the prevalence of preoperative opioid use and the characteristics of these patients in a broadly representative surgical cohort. Design, Setting, and Participants Cross-sectional, observational study of patients undergoing surgery at a tertiary care academic medical center. Data were collected as a part of large prospective institutional research registries from March 1, 2010, through April 30, 2016. Exposures Preoperative patient and procedural characteristics, including prospectively assessed self-reported pain and functional measures. Main Outcomes and Measures Patient-reported opioid use before surgery. Results Of the total 34 186 patients recruited (54.2% women; mean [SD] age, 53.1 [16.1] years), preoperative opioid use was reported in 7894 (23.1%). The most common opioids used were hydrocodone bitartrate (4685 [59.4%]), tramadol hydrochloride (1677 [21.2%]), and oxycodone hydrochloride (1442 [18.3%]). Age of 31 to 40 years (adjusted odds ratio [aOR], 1.26; 95% CI, 1.10-1.45), tobacco use (former use aOR, 1.32 [95% CI, 1.22-1.42]; current use aOR, 1.62 [95% CI, 1.48-1.78]), illicit drug use (aOR, 1.74; 95% CI, 1.16-2.60), higher pain severity (aOR, 1.33; 95% CI, 1.31-1.35), depression (aOR, 1.22; 95% CI, 1.12-1.33), higher Fibromyalgia Survey scores (aOR, 1.06, 95% CI, 1.05-1.07), lower life satisfaction (aOR, 0.95, 95% CI, 0.93-0.96), and more medical comorbidities (American Society of Anesthesiology score aOR, 1.47 [95% CI, 1.37-1.58]; Charlson Comorbidity Index aOR, 1.29 [95% CI, 1.18-1.41]) were all independently associated with preoperative opioid use. Preoperative opioid use was most commonly reported by patients undergoing orthopedic (226 [65.1%]) and neurosurgical spinal (596 [55.1%]) procedures and least common among patients undergoing thoracic procedures (244 [15.7%]). After adjusting for patient characteristics, the patients undergoing lower extremity procedures were most likely to report preoperative opioid use (aOR, 3.61; 95% CI, 2.81-4.64), as well as those undergoing pelvic (excluding hip) (aOR, 3.09; 95% CI, 1.88-5.08), upper extremity (aOR, 3.07; 95% CI, 2.12-4.45), and spinal or spinal cord (aOR, 2.68; 95% CI, 2.15-3.32) procedures, with the group undergoing intrathoracic surgery as the reference group. Conclusions and Relevance In this large study of preoperative opioid use that includes patient-reported outcome measures, more than 1 in 4 patients presenting for surgery reported opioid use. These data provide important insights into this complicated patient population that would appear to help guide future preoperative optimization and perioperative opioid-weaning interventions.

Summary Estimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies. Large genetic datasets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD up to 2 orders of magnitude faster than current tools. Availability and implementation emeraLD is implemented in C++, and is open source under GPLv3. Source code and documentation are freely available at http://github.com/statgen/emeraLD. Supplementary information Supplementary data are available at Bioinformatics online.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants., Author summary It is of great interest to estimate the joint effects of multiple variants from large scale meta-analyses, in order to fine-map causal variants and understand the genetic architecture for complex traits. The summary association statistics from participating studies in a meta-analysis often contain missing values at some variant sites, as the imputation methods may not work well and the variants with low imputation quality will be filtered out. Missingness is especially likely when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing methods for conditional meta-analysis do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly inflated type-I errors for conditional analysis, which will result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

It is unclear whether insertions and deletions (indels) are more likely to influence complex traits than abundant single‐nucleotide polymorphisms (SNPs). We sought to understand which category of variation is more likely to impact health. Using the SardiNIA study as an exemplar, we characterized 478,876 common indels and 8,246,244 common SNPs in up to 5,949 well‐phenotyped individuals from an isolated valley in Sardinia. We assessed association between 120 traits, resulting in 89 nonoverlapping‐associated loci.We evaluated whether indels were enriched among credible sets of potential causal variants. These credible sets included 1,319 SNPs and 88 indels. We did not find indels to be significantly enriched. Indels were the most likely causal variant in seven loci, including one locus associated with monocyte count where an indel with causality and mechanism previously demonstrated (rs200748895:TGCTG/T) had a 0.999 posterior probability. Overall, our results show a very modest and nonsignificant enrichment for common indels in associated loci.

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether Polygenic Risk Scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide Association Study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRSs exhibited strong association for several cancer traits they were designed for including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of “exclusion PRS PheWAS” was introduced. This approach led to phenome-wide significant associations between a lower risk for hypothyroidism in patients with high thyroid cancer PRS and a higher risk for actinic keratosis in patients with high squamous cell carcinoma PRS after removing all cases of the primary cancer trait. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This is the first comprehensive PheWAS study using PRS instead of a single variant.

© 2018 The Author(s) In the version of this article initially published, the consortium authorship was not presented correctly. The 23andMe Research Team was listed as the last author, rather than the fourth, and a line directing readers to the Supplementary Note for a list of members did appear but was not directly associated with the consortium name. Also, the Supplementary Note description stated that both member names and affiliations were included; in fact, only names are given. Finally, the URL for S-PrediXcan was given in the Methods as https://github.com/hakyimlab/S-PrediXcan; the correct URL is https://github.com/hakyimlab/MetaXcan. The errors have been corrected in the HTML and PDF versions of the article.

SummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/emeraLDContactcorbinq@umich.eduSupplementary informationSupplementary data are available at Bioinformatics online.

BACKGROUND Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death.OBJECTIVES This meta-analysis sought to gain insights into the genetic determinants of myocardial mass.METHODS We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment.RESULTS We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p CONCLUSIONS Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets. (C) 2016 by the American College of Cardiology Foundation.

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

Psoriasis vulgaris (PsV) is a common inflammatory and hyperproliferative skin disease. Up to 30% of people with PsV eventually develop psoriatic arthritis (PsA), an inflammatory musculoskeletal condition. To discern differences in genetic risk factors for PsA and cutaneous-only psoriasis (PsC), we carried out a genome-wide association study (GWAS) of 1,430 PsA case subjects and 1,417 unaffected control subjects. Meta-analysis of this study with three other GWASs and two targeted genotyping studies, encompassing a total of 9,293 PsV case subjects, 3,061 PsA case subjects, 3,110 PsC case subjects, and 13,670 unaffected control subjects of European descent, detected 10 regions associated with PsA and 11 with PsC at genome-wide (GW) significance. Several of these association signals (IFNLR1, IFIH1, NFKBIA for PsA; TNFRSF9, LCE3C/B, TRAF3IP2, IL23A, NFKBIA for PsC) have not previously achieved GW significance. After replication, we also identified a PsV-associated SNP near CDKAL1 (rs4712528, odds ratio [OR] = 1.16, p = 8.4× 10(-11)). Among identified psoriasis risk variants, three were more strongly associated with PsC than PsA (rs12189871 near HLA-C, p = 5.0× 10(-19); rs4908742 near TNFRSF9, p = 0.00020; rs10888503 near LCE3A, p = 0.0014), and two were more strongly associated with PsA than PsC (rs12044149 near IL23R, p = 0.00018; rs9321623 near TNFAIP3, p = 0.00022). The PsA-specific variants were independent of previously identified psoriasis variants near IL23R and TNFAIP3. We also found multiple independent susceptibility variants in the IL12B, NOS2, and IFIH1 regions. These results provide insights into the pathogenetic similarities and differences between PsC and PsA.

We report genome-wide association study results for the levels of A1, A2 and fetal hemoglobins, analyzed for the first time concurrently. Integrating high-density array genotyping and whole-genome sequencing in a large general population cohort from Sardinia, we detected 23 associations at 10 loci. Five signals are due to variants at previously undetected loci: MPHOSPH9, PLTP-PCIF1, ZFPM1 (FOG1), NFIX and CCND3. Among the signals at known loci, ten are new lead variants and four are new independent signals. Half of all variants also showed pleiotropic associations with different hemoglobins, which further corroborated some of the detected associations and identified features of coordinated hemoglobin species production.

Accurate estimation of individual ancestry is important in genetic association studies, especially when a large number of samples are collected from multiple sources. However, existing approaches developed for genome-wide SNP data do not work well with modest amounts of genetic data, such as in targeted sequencing or exome chip genotyping experiments. We propose a statistical framework to estimate individual ancestry in a principal component ancestry map generated by a reference set of individuals. This framework extends and improves upon our previous method for estimating ancestry using low-coverage sequence reads (LASER 1.0) to analyze either genotyping or sequencing data. In particular, we introduce a projection Procrustes analysis approach that uses high-dimensional principal components to estimate ancestry in a low-dimensional reference space. Using extensive simulations and empirical data examples, we show that our new method (LASER 2.0), combined with genotype imputation on the reference individuals, can substantially outperform LASER 1.0 in estimating fine-scale genetic ancestry. Specifically, LASER 2.0 can accurately estimate fine-scale ancestry within Europe using either exome chip genotypes or targeted sequencing data with off-target coverage as low as 0.05×. Under the framework of LASER 2.0, we can estimate individual ancestry in a shared reference space for samples assayed at different loci or by different techniques. Therefore, our ancestry estimation method will accelerate discovery in disease association studies not only by helping model ancestry within individual studies but also by facilitating combined analysis of genetic data from multiple sources.

Multiple pterygium syndrome (MPS) is a phenotypically and genetically heterogeneous group of rare Mendelian conditions characterized by multiple pterygia, scoliosis, and congenital contractures of the limbs. MPS typically segregates as an autosomal-recessive disorder, but rare instances of autosomal-dominant transmission have been reported. Whereas several mutations causing recessive MPS have been identified, the genetic basis of dominant MPS remains unknown. We identified four families affected by dominantly transmitted MPS characterized by pterygia, camptodactyly of the hands, vertebral fusions, and scoliosis. Exome sequencing identified predicted protein-altering mutations in embryonic myosin heavy chain (MYH3) in three families. MYH3 mutations underlie distal arthrogryposis types 1, 2A, and 2B, but all mutations reported to date occur in the head and neck domains. In contrast, two of the mutations found to cause MPS in this study occurred in the tail domain. The phenotypic overlap among persons with MPS, coupled with physical findings distinct from other conditions caused by mutations in MYH3, suggests that the developmental mechanism underlying MPS differs from that of other conditions and/or that certain functions of embryonic myosin might be perturbed by disruption of specific residues and/or domains. Moreover, the vertebral fusions in persons with MPS, coupled with evidence of MYH3 expression in bone, suggest that embryonic myosin plays a role in skeletal development.

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.

Over forty-one genetic susceptibility loci of genome-wide significance (P < 5 × 10−8) are known for psoriasis, a common complex genetic disease characterized by epidermal hyperproliferation and cutaneous inflammation. The most strongly associated of these loci maps to the HLA-C gene in the major histocompatibility complex (MHC) on chromosome 6p21.3 (Tsoi et al., 2012; Zhang et al., 2009). Recent fine mapping studies have identified multiple independent association signals in the MHC, and the strongest signal is associated with HLA-C*06:02 (previous designations: HLA-Cw6, HLA-Cw*0602) or SNPs that are near-perfect surrogates for this allele (Das et al., 2014; Feng et al., 2009; Knight et al., 2012; Okada et al., 2014). HLA-C*06:02 has been known to be psoriasis-associated since the 1970s, and most world populations studied have shown strong evidence of association (Okada et al., 2014; Shaiq et al., 2013; Stuart et al., 2010; Zhang et al., 2009). Yet, it is difficult to perform a routine affordable laboratory test for the presence or absence of HLA-C* 06:02 because DNA-based testing is complicated both by the high degree of polymorphism of HLA-C (2375 alleles encoding 1677 protein variations; July 2014 release of IMGT/HLA database) (Robinson et al., 2013) and by the sequence similarity of HLA-C* 06:02 to other HLA-C as well as HLA-A and HLA-B alleles. We previously used a 7- SNP genotyping system to unambiguously define HLA-C*06:02 and several other HLA-C alleles (Nair et al., 2006). Others have used allele-specific amplification followed by electrophoresis (Bunce et al., 1995), PCR amplification followed by restriction enzyme digestion (Tazi Ahnini et al., 1999), a combination of four surrogate SNPs that are amenable to Taqman genotyping (Nikamo and Stahle, 2012), and a 3-SNP haplotype (rs1576-rs130076-rs2523619) known to be in strong linkage disequilibrium (LD) with HLA-C*06:02 (Huffmeier et al., 2009). Imputation of classical HLA alleles including HLA-C*06:02 is another widely-used approach (Jia et al., 2013; Leslie et al., 2008), but this requires high density genotyping of the sample for SNPs throughout the MHC region, as well as a reference panel drawn from the same population that has been typed for both classical HLA genes and a high density of MHC SNPs. During the course of our genome-wide association studies of psoriasis and fine mapping of MHC loci, we discovered that the A allele of SNP rs4406273, located 28.73 kb centromeric of HLA-C, is a near-perfect surrogate for HLA-C*06:02 allele. Notably, this SNP was the most strongly associated variant in the largest genome-wide association study of psoriasis to date (Tsoi et al., 2012). Since rs4406273 is only 3 bases away from another SNP, it is not amenable to the commonly-used Taqman (Applied Biosystems, Foster City, CA) genotyping assay design. However, it can be genotyped using single base extension methods. In this study, we examined the adequacy of genotyping rs4406273 as a single marker surrogate for HLA-C*06:02 in three different populations–5,009 European-ancestry samples collected in Michigan, 835 samples from Pakistan, and 307 samples from Thailand. All DNA samples used in this study were prepared from peripheral blood obtained following protocols approved by the ethics boards of participating institutions and adhering to the Declaration of Helsinki principles. Written informed consent was obtained from all study participants. Reference HLA-C*06:02 genotypes were generated by our previously reported 7-marker method (Nair et al., 2006), and rs4406273 was genotyped using the single base extension method implemented in the Applied Biosystems Snapshot Assay. Briefly, this assay involved (1) amplification of a 237 bp segment of DNA encompassing rs4406273 using PCR primers CTGGAAAGGGTGAGGAAACA and TGACCTCCCTACTGCAGCTT, (2) inactivation of unused primers and deoxynucleotide triphosphates by treatment with a mixture of shrimp alkaline phosphatase and exonuclease, (3) performing the primer extension reaction using an aliquot of the PCR product and probe GAGCCTCAGAAGAAATGCAGCTSTGAC that would be extended by one base corresponding to the allele(s) of rs4406273 (A and/or G), (4) inactivation of unused probe by treatment with alkaline phosphatase, and (5) identification of the added base by electrophoresis on an Applied Biosystems capillary electrophoresis apparatus using LIZ120 size standard ladder (Applied Biosystems, Foster City, CA). The utility of the rs4406273-A allele as a surrogate for HLA-C*06:02 was assessed using various metrics (Table 1). Allele frequencies were very similar in all three cohorts, with the A allele of rs4406273 slightly less abundant in the Michigan and Pakistani samples and slightly more abundant in the Thai sample than HLA-C*06:02. LD between the two variants, as measured by the D′ and r2 coefficients, was 0.95 or greater in all three samples. Genotype concordances were also uniformly high (0.984–0.996). The correspondence of cross-classified allele counts for the rs4406273 A and C alleles on the one hand and HLA-C* 06:02 and non-C:06:02 alleles on the other hand were also assessed by six different measures for the performance of a binary classification test—sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and the Matthews correlation coefficient (Matthews, 1975), which is the Pearson correlation coefficient between two binary classifications. Values for these performance measures were very high, ranging from 0.990–0.999 for the Michigan sample, 0.965–0.998 for the Pakistani sample, and 0.976–1.000 for the Thai sample. Table 1 Performance of the rs4406273-A allele as a surrogate for HLA-C*06:02. In Table 2A, we compare the results of a logistic regression test for association between psoriasis and dosage of the HLA-C*06:02 and rs4406273-A alleles. In all three cohorts, the odds ratio (OR) estimates and p-values for rs4406273 are slightly conservative compared to those of HLA-C*06:02, suggesting that this marker is unlikely to yield false positive results. Conditional haplotype testing of independent effects (Table 2B) shows that residual effects after conditioning are negligible compared to the association p-values. Table 2 We also assessed the performance of rs4406273 as a surrogate for 928 individuals from 13 different populations in phase 1 of the 1000 Genomes Project (Abecasis et al., 2012) for which sequence-based genotypes of HLA-C and rs4406273 were available (Gourraud et al., 2014); ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20140725_hla_genotypes/ and ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20110521/). LD was very strong between rs4406273 and HLA-C*06:02 in four populations of European descent from the United States, Finland, Great Britain and Italy (r2 = 0.984) and in three Asian populations from Japan and China (r2 = 1.000). LD values were somewhat lower but still strong in two African populations from Nigeria and Kenya (r2 = 0.929) and in four American populations from Puerto Rico, Colombia, and the United States (r2 = 0.909). The results presented here demonstrate that genotyping rs4406273 can be used as an excellent substitute for more elaborate and expensive genotyping of HLA-C*06:02 in people of European, Pakistani, Thai, Chinese, or Japanese ancestry. This SNP may also be a serviceable surrogate for HLA-C*06:02 in some African and American populations. Caution is warranted, however, when applying this method to genetic isolates or more distantly related populations. While not amenable to the popular Taqman assay due to the presence of proximal SNPs, any method employing primer extension can be used to genotype rs4406273. For high-throughput genotyping projects using commercial microarrays, this SNP can be added on as a custom marker if not already present as part of the standard set.

Thank you very much. I would first like to thank the Awards Committee, The Society, and all of you who are here this morning. For the past 15 years, I have treasured coming to this meeting, which is always full of energy, ideas, and brilliant scientists. To be honest, I find it hard to see myself in Aravinda Chakravarti’s and Michael Boehnke’s kind description of me, and in the next few minutes, I will probably disprove their claim that I am a good communicator. I stand before you trembling.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

SummaryTo understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. The pathogenesis of AF remains poorly understood, which contributes to the current lack of highly effective treatments. To understand the genetic variation and biology underlying AF, we undertook a genome-wide association study (GWAS) of 6,337 AF individuals and 61,607 AF-free individuals from Norway, including replication in an additional 30,679 AF individuals and 278,895 AF-free individuals. Through genotyping and dense imputation mapping from whole-genome sequencing, we tested almost nine million genetic variants across the genome and identified seven risk loci, including two novel loci. One novel locus (lead single-nucleotide variant [SNV] rs12614435; p = 6.76 × 10−18) comprised intronic and several highly correlated missense variants situated in the I-, A-, and M-bands of titin, which is the largest protein in humans and responsible for the passive elasticity of heart and skeletal muscle. The other novel locus (lead SNV rs56202902; p = 1.54 × 10−11) covered a large, gene-dense chromosome 1 region that has previously been linked to cardiac conduction. Pathway and functional enrichment analyses suggested that many AF-associated genetic variants act through a mechanism of impaired muscle cell differentiation and tissue formation during fetal heart development.

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future. NMRC (Natl Medical Research Council, S’pore) MOH (Min. of Health, S’pore)

Author(s): Sivakumaran, Theru A; Igo, Robert P; Kidd, Jeffrey M; Itsara, Andy; Kopplin, Laura J; Chen, Wei; Hagstrom, Stephanie A; Peachey, Neal S; Francis, Peter J; Klein, Michael L; Chew, Emily Y; Ramprasad, Vedam L; Tay, Wan-Ting; Mitchell, Paul; Seielstad, Mark; Stambolian, Dwight E; Edwards, Albert O; Lee, Kristine E; Leontiev, Dmitry V; Jun, Gyungah; Wang, Yang; Tian, Liping; Qiu, Feiyou; Henning, Alice K; LaFramboise, Thomas; Sen, Parveen; Aarthi, Manoharan; George, Ronnie; Raman, Rajiv; Das, Manmath Kumar; Vijaya, Lingam; Kumaramanickavel, Govindasamy; Wong, Tien Y; Swaroop, Anand; Abecasis, Goncalo R; Klein, Ronald; Klein, Barbara EK; Nickerson, Deborah A; Eichler, Evan E; Iyengar, Sudha K | Abstract: [This corrects the article DOI: 10.1371/journal.pone.0025598.].

Psoriatic arthritis (PsA) is a complex chronic musculoskeletal condition that occurs in ~30% of psoriasis patients. Currently, no systematic strategy is available that utilizes the differences in genetic architecture between PsA and cutaneous-only psoriasis (PsC) to assess PsA risk before symptoms appear. Here, we introduce a computational pipeline for predicting PsA among psoriasis patients using data from six cohorts with >7000 genotyped PsA and PsC patients. We identify 9 new loci for psoriasis or its subtypes and achieve 0.82 area under the receiver operator curve in distinguishing PsA vs. PsC when using 200 genetic markers. Among the top 5% of our PsA prediction we achieve >90% precision with 100% specificity and 16% recall for predicting PsA among psoriatic patients, using conditional inference forest or shrinkage discriminant analysis. Combining statistical and machine-learning techniques, we show that the underlying genetic differences between psoriasis subtypes can be used for individualized subtype risk assessment., Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic markers.

The controversy surrounding the use of genetic testing to guide the treatment of persons with age-related macular degeneration (AMD) continues. In 2001, the results of the Age-Related Eye Disease Study (AREDS), a placebo-controlled trial, demonstrated that oral supplementation with a combination of antioxidant vitamins and zinc reduced the risk of progression to late AMD by 25% in persons with intermediate AMD in at least one eye.1 Klein et al evaluated the influence of the genotypes complement factor H (CFH) (Y402H, rs1061170) and LOC387715/age-related maculopathy susceptibility 2 (ARMS2) (A69S, rs10490924) on the response to treatment with AREDS supplements (combination of antioxidants and zinc), zinc alone, or antioxidants alone in 876 AREDS participants who had available DNA and who were at high risk of developing advanced AMD.2 Although there was a possible interaction between CFH genotype and treatment, Klein et al concluded that the AREDS supplements were associated with a general reduction in the risk of developing late AMD in all genotype groups compared with placebo, and neither antioxidant alone nor zinc alone was superior to the antioxidant and zinc combination in any of the genetic groups examined. Awh and his colleagues created a genetic test to evaluate CFH and ARMS2 genes, and performed retrospective analyses of AREDS subgroups (n=989).3 They claimed that treatment with the AREDS supplements should be tailored according to the patient’s genotype, suggesting the need to genotype all patients taking the AREDS supplements. The AREDS investigators compared response to treatment in individuals with different genotype configurations in a larger group of AREDS participants (n=1,237) and failed to find statistically significant differences in response to treatment with AREDS supplement.4 In this current issue, Awh et al have further refined their genetic subgroups based on outcome, and furthered their claim that AREDS supplements can be harmful to individuals with certain genotypes.5 Are these findings by Awh et al true associations or are they the result of chance, selection bias, or some other confounder? Our request for the identification codes of the AREDS participants in their analyses was turned down. Since the DNA and data used by Awh et al. originated from our AREDS dataset, we have reconstructed their sample – which represents only a subset of AREDS participants for whom genetic information is available. Based on when and how the DNA were requested, we are confident of the identification codes for 893 (90%) of the 989 participants used in their analyses, which we verified by finding similar progression rates to late AMD and similar risk ratios for each of the supplements in each of their genetic risk groups. We agree with Awh et al, that the ultimate test of the validity of their study is a replication sample.5 Thus, it is fortuitous that Awh and colleagues had access to only a portion of the AREDS patients with available DNA. We were able to assemble a validation cohort from the remaining patients (n=526) and this cohort is referred here as the “residual cohort”. If the findings from Awh’s recent report are correct, the results of the analysis from this residual cohort will likely be in the same direction (either beneficial or harmful), and on average, of the same magnitude as those published by Awh et al, validating their analysis. However, if Awh’s results were generated by selection bias and not true associations, the results would be different, likely regressing to the overall mean differences observed in the AREDS primary study results.

Contains fulltext : 153372.pdf (Publisher’s version ) (Closed access) IMPORTANCE: Neuroticism is a pervasive risk factor for psychiatric conditions. It genetically overlaps with major depressive disorder (MDD) and is therefore an important phenotype for psychiatric genetics. The Genetics of Personality Consortium has created a resource for genome-wide association analyses of personality traits in more than 63,000 participants (including MDD cases). OBJECTIVES: To identify genetic variants associated with neuroticism by performing a meta-analysis of genome-wide association results based on 1000 Genomes imputation; to evaluate whether common genetic variants as assessed by single-nucleotide polymorphisms (SNPs) explain variation in neuroticism by estimating SNP-based heritability; and to examine whether SNPs that predict neuroticism also predict MDD. DESIGN, SETTING, AND PARTICIPANTS: Genome-wide association meta-analysis of 30 cohorts with genome-wide genotype, personality, and MDD data from the Genetics of Personality Consortium. The study included 63,661 participants from 29 discovery cohorts and 9786 participants from a replication cohort. Participants came from Europe, the United States, or Australia. Analyses were conducted between 2012 and 2014. MAIN OUTCOMES AND MEASURES: Neuroticism scores harmonized across all 29 discovery cohorts by item response theory analysis, and clinical MDD case-control status in 2 of the cohorts. RESULTS: A genome-wide significant SNP was found on 3p14 in MAGI1 (rs35855737; P = 9.26 x 10-9 in the discovery meta-analysis). This association was not replicated (P = .32), but the SNP was still genome-wide significant in the meta-analysis of all 30 cohorts (P = 2.38 x 10-8). Common genetic variants explain 15% of the variance in neuroticism. Polygenic scores based on the meta-analysis of neuroticism in 27 cohorts significantly predicted neuroticism (1.09 x 10-12 < P < .05) and MDD (4.02 x 10-9 < P < .05) in the 2 other cohorts. CONCLUSIONS AND RELEVANCE: This study identifies a novel locus for neuroticism. The variant is located in a known gene that has been associated with bipolar disorder and schizophrenia in previous studies. In addition, the study shows that neuroticism is influenced by many genetic variants of small effect that are either common or tagged by common variants. These genetic variants also influence MDD. Future studies should confirm the role of the MAGI1 locus for neuroticism and further investigate the association of MAGI1 and the polygenic association to a range of other psychiatric disorders that are phenotypically correlated with neuroticism.

Delay discounting (DD), the tendency to discount the value of delayed versus current rewards, is elevated in a constellation of diseases and behavioral conditions. We performed a genome-wide association study of DD using 23,127 research participants of European ancestry. The most significantly associated single-nucleotide polymorphism was rs6528024 (P = 2.40 × 10-8), which is located in an intron of the gene GPM6B. We also showed that 12% of the variance in DD was accounted for by genotype and that the genetic signature of DD overlapped with attention-deficit/hyperactivity disorder, schizophrenia, major depression, smoking, personality, cognition and body weight.

OBJECTIVE: To investigate the association of rare predicted loss-of-function (pLoF) variants within previously reported age-related macular degeneration (AMD) risk loci and sub-phenotypes characteristic of intermediate or advanced AMD, including geographic atrophy (GA), choroidal neovascularization (CNV), pseudoreticular drusen, calcified drusen, drusen area in the in the macula, and the Age-Related Eye Disease Study (AREDS) Extended AMD Severity Scale. DESIGN: Case-control study PARTICIPANTS: AREDS, AREDS2, and Michigan Genomics Initiative participants. METHODS: Whole genome sequencing data were analyzed for rare pLoF variants (frequency < 0.1% in the study population) in the regions of previously identified 52 independent risk variants known to be associated with AMD. Frequency of the rare pLoF variants in cases with intermediate or advanced AMD were compared with controls. Variants were also assigned to the complement, extracellular matrix (ECM), lipid, cell survival, immune system, metabolism, or unknown/other pathway. Associations of rare pLoF variant pathways with sub-phenotypes within the AMD population were analyzed using logistic and linear regression, and Cox proportional hazard regression models. MAIN OUTCOMES AND MEASURES: Differences in rare pLoF variant pathway burden and association of rare pLoF variant pathways with sub-phenotypes within the AMD population were evaluated. RESULTS: Rare pLoF variants were found in 298/1689 (17.6%) cases and 237/1518 (15.6%) controls (OR=1.11 [0.91, 1.36], p=0.310). An enrichment of rare pLoF variants in the complement pathway in cases versus controls (OR=2.94 [1.49, 5.79], p=0.002) was observed. Within cases, associations between all rare pLoF variants and CNV (OR=1.34 [1.04, 1.73], p=0.023), calcified drusen (OR=1.33 [1.04, 1.72], p=0.025), higher scores on the AREDS Extended AMD Severity Scale (Standardized Coefficient Beta (B)=0.346 [0.086, 0.605], p=0.009), and progression to advanced disease (HR=1.25 [1.01, 1.55], p=0.042) were observed. At the pathway level, there were associations between the complement pathway and GA (OR=2.17 [1.12, 4.24], p=0.023), the complement pathway and calcified drusen (OR=3.75 [1.79, 7.86], p

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values. In practice, genotype imputation is not always effective, e.g. when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Naïve extensions of existing methods either replace missing summary statistics with 0 or discard studies with missing data. These approaches can bias genetic effect estimates and lead to seriously inflated type-I or II errors in conditional analysis, which is a critical tool for identifying independently associated variants.To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amount of missing values. Based on this estimator, we propose a score statistic we call PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to analyze the CHRNA5-CHRNB4-CHRNA3 locus in a large-scale meta-analysis for cigarettes-per-day. Using the new method, we identified three novel variants, independent of known association signals, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants is .46%, improving that of previously reported associations by 17%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.AUTHOR SUMMARYIt is of great interest to estimate the joint and conditional effects of multiple correlated variants from large scale meta-analysis, in order to fine map causal variants and understand the genetic architecture for complex traits. The contributed summary statistics from participating studies in a meta-analysis often contain missing values, as the imputation methods are not often effective, especially when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing meta-analysis methods do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly biased estimates of joint effects and highly inflated type-I errors for conditional analysis, which will in turn result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics in the meta-analysis contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.

Motivation:There is great interest to understand the impact of rare variants in human diseases using large sequence datasets. In deep sequences datasets of >10,000 samples, ∼10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results.Results:We propose novel methods to encode multi-allelic sites, conduct single variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ∼18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single variant association tests, and enhanced gene-level tests over existing approaches.Availability:Software packages implementing these methods are available at (https://github.com/zhanxw/rvtestshttp://genome.sph.umich.edu/wiki/RareMETAL).Contact:xiaowei.zhan@utsouthwestem.edu; dajiang.liu@psu.edu

Meta-analysis is now an essential tool for genetic association studies, allowing these to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate why the standard meta-analysis methods lose power under unbalanced settings, and further propose a novel meta-analysis method that performs as efficiently as joint analysis under general settings. Our proposed method can accurately approximate the score statistics obtainable by joint analysis, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies (MAFs). In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard method. We further showed the power gain of our method in gene-level association studies with 26 unbalanced real studies of Age-related Macular Degeneration (AMD). In addition, we took the meta-analysis of three studies of type 2 diabetes (T2D) as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, we propose improved single-variant score statistics in meta-analysis, requiring “accurate” population-specific MAFs for multi-ethnic studies. These improved score statistics can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses.

BackgroundSmoking and alcohol use behaviors in humans have been associated with common genetic variants within multiple genomic loci. Investigation of rare variation within these loci holds promise for identifying causal variants impacting biological mechanisms in the etiology of disordered behavior. Microarrays have been designed to genotype rare nonsynonymous and putative loss of function variants. Such variants are expected to have greater deleterious consequences on gene function than other variants, and significantly contribute to disease risk.MethodsIn the present study, we analyzed ∼250,000 rare variants from 17 independent studies. Each variant was tested for association with five addiction-related phenotypes: cigarettes per day, pack years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted single variant tests of all variants, and gene-based burden tests of nonsynonymous or putative loss of function variants with minor allele frequency less than 1%.ResultsMeta-analytic sample sizes ranged from 70,847 to 164,142 individuals, depending on the phenotype. Known loci tagged by common variants replicated, but there was no robust evidence for individually associated rare variants, either in gene based or single variant tests. Using a modified method-of-moment approach, we found that all low frequency coding variants, in aggregate, contributed 1.7% to 3.6% of the phenotypic variation for the five traits (pConclusionsThe findings indicate that rare coding variants contribute to phenotypic variation, but that much larger samples and/or denser genotyping of rare variants will be required to successfully identify associations with these phenotypes, whether individual variants or gene‐ based associations.

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

Although genome-wide association studies (GWASs) have identified many risk loci for complex traits and common diseases, most of the identified associations reside in noncoding regions and have unknown biological functions. Recent genomic sequencing studies have produced a rich resource of annotations that help characterize the function of genetic variants. Integrative analysis that incorporates these functional annotations into GWAS can help elucidate the biological mechanisms underlying the identified associations and help prioritize causal-variants. Here, we develop a novel, flexible Bayesian variable selection model with efficient computational techniques for such integrative analysis. Different from previous approaches, our method models the effect-size distribution and probability of causality for variants with different annotations and jointly models genome-wide variants to account for linkage disequilibrium (LD), thus prioritizing associations based on the quantification of the annotations and allowing for multiple causal-variants per locus. Our efficient computational algorithm dramatically improves both computational speed and posterior sampling convergence by taking advantage of the block-wise LD structures of human genomes. With simulations, we show that our method accurately quantifies the functional enrichment and performs more powerful for identifying true causal-variants than several competing methods. The power gain brought up by our method is especially apparent in cases when multiple causal-variants in LD reside in the same locus. We also apply our method for an in-depth GWAS of age-related macular degeneration with 33,976 individuals and 9,857,286 variants. We find the strongest enrichment for causality among non-synonymous variants (54x more likely to be causal, 1.4x larger effect-sizes) and variants in active promoter (7.8x more likely, 1.4x larger effect-sizes), as well as identify 5 potentially novel loci in addition to the 32 known AMD risk loci. In conclusion, our method is shown to efficiently integrate functional information in GWASs, helping identify causal variants and underlying biology.Author summaryWe propose a novel Bayesian hierarchical model to account for linkage disequilibrium (LD) and multiple functional annotations in GWAS, paired with an expectation-maximization Markov chain Monte Carlo (EM-MCMC) computational algorithm to jointly analyze genome-wide variants. Our method improves the MCMC convergence property to ensure accurate Bayesian inference of the quantifications of the functional enrichment pattern and fine-mapped association results. By applying our method to the real GWAS of age-related macular degeneration (AMD) with various functional annotations (i.e., gene-based, regulatory, and chromatin states), we find that the variants of non-synonymous, coding, and active promoter annotations have the highest causal probability and the largest effect-sizes. In addition, our method produces fine-mapped association results in the identified risk loci, two of which are shown as examples (C2/CFB/SKIV2L and C3) with justifications by haplotype analysis, model comparison, and conditional analysis. Therefore, we believe our integrative method will be useful for quantifying the enrichment pattern of functional annotations in GWAS, and then prioritizing associations with respect to the learned functional enrichment pattern.

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project–based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7 , TNXB , LRP12 , LOC283335 , SEPT9 , and AKT2 , and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA . Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.

Motivation:Statistical power calculations are crucial in designing genetic association studies. They help guide tradeoffs between large sample sizes and detailed assessments of genotype and phenotype, help determine which studies are viable, and help interpret research findings. To facilitate widespread use of power analysis in the design and interpretation of genetic studies, it is important to enable users to calculate power and visualize the effect of different models and design choices in convenient, interactive tools that are easily accessible.Results:We developed the Genetic Association Study (GAS) Power Calculator to provide users with a simple interface that can be compute the power of genetic association studies in a convenient browser based interface.Availability:The GAS Power Calculator can be accessed from the web interface at http://csg.sph.umich.edu/abecasis/gas_power_calculator/. Source code is available at https://github.com/jenlij/GAS-power-calculator.

Delay discounting (DD), which is the tendency to discount the value of delayed versus current rewards, is elevated in a constellation of diseases and behavioral conditions. We performed a genome-wide association study of DD using 23,127 research participants of European ancestry. The most significantly associated SNP was rs6528024 (P = 2.40 × 10−8), which is located in an intron of the gene GPM6B. We also showed that 12% of the variance in DD was accounted for by genotype, and that the genetic signature of DD overlapped with attention-deficit/hyperactivity disorder, schizophrenia, major depression, smoking, personality, cognition, and body weight.

Rationale : Therapies that inhibit CETP (cholesteryl ester transfer protein) have failed to demonstrate a reduction in risk for coronary heart disease (CHD). Human DNA sequence variants that truncate the CETP gene may provide insight into the efficacy of CETP inhibition. Objective: To test whether protein-truncating variants (PTVs) at the CETP gene were associated with plasma lipid levels and CHD. Methods and Results: We sequenced the exons of the CETP gene in 58 469 participants from 12 case–control studies (18 817 CHD cases, 39 652 CHD-free controls). We defined PTV as those that lead to a premature stop, disrupt canonical splice sites, or lead to insertions/deletions that shift frame. We also genotyped 1 Japanese-specific PTV in 27561 participants from 3 case–control studies (14 286 CHD cases, 13 275 CHD-free controls). We tested association of CETP PTV carrier status with both plasma lipids and CHD. Among 58 469 participants with CETP gene-sequencing data available, average age was 51.5 years and 43% were women; 1 in 975 participants carried a PTV at the CETP gene. Compared with noncarriers, carriers of PTV at CETP had higher high-density lipoprotein cholesterol (effect size, 22.6 mg/dL; 95% confidence interval, 18–27; P −4 ), lower low-density lipoprotein cholesterol (−12.2 mg/dL; 95% confidence interval, −23 to −0.98; P =0.033), and lower triglycerides (−6.3%; 95% confidence interval, −12 to −0.22; P =0.043). CETP PTV carrier status was associated with reduced risk for CHD (summary odds ratio, 0.70; 95% confidence interval, 0.54–0.90; P =5.1×10 −3 ). Conclusions: Compared with noncarriers, carriers of PTV at CETP displayed higher high-density lipoprotein cholesterol, lower low-density lipoprotein cholesterol, lower triglycerides, and lower risk for CHD.

Genetic studies of complex traits have mainly identified associations with non-coding variants. To further determine the contribution of regulatory variation, we combined whole genome and transcriptome data for 624 individuals from Sardinia in order to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 novel QTLs. We identified high-frequency QTLs and evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z-score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

Sardinians are "outliers" in the European genetic landscape and, according to paleogenomic nuclear data, the closest to early European Neolithic farmers. To learn more about their genetic ancestry, we analyzed 3,491 modern and 21 ancient mitogenomes from Sardinia. We observed that 78.4% of modern mitogenomes cluster into 89 haplogroups that most likely arose in situ. For each Sardinian-specific haplogroup (SSH), we also identified the upstream node in the phylogeny, from which non-Sardinian mitogenomes radiate. This provided minimum and maximum time estimates for the presence of each SSH on the island. In agreement with demographic evidence, almost all SSHs coalesce in the post-Nuragic, Nuragic and Neolithic-Copper Age periods. For some rare SSHs, however, we could not dismiss the possibility that they might have been on the island prior to the Neolithic, a scenario that would be in agreement with archeological evidence of a Mesolithic occupation of Sardinia., This study received support from the University of Pavia strategic theme “Towards a governance model for international migration: an interdisciplinary and diachronic perspective” (MIGRAT-IN-G) (to A.O., A.A., O.S., A.T.), the British Academy Research Development Award n. 53097 (to R.S.), the Italian Ministry of Education, University and Research: Progetti Futuro in Ricerca 2012 (RBFR126B8I) (to A.O. and A.A.) and Progetti Ricerca Interesse Nazionale 2012 (to A.A., O.S. and A.T.). The sequencing data reported in the study were supported by the National Human Genome Research Institute grants HG005581, HG005552, HG006513, HG007022, and HG007089; the National Heart Lung and Blood Institute grant HL117626; the Intramural Research Program of the NIH, National Institute on Aging, with contracts N01-AG-1-2109 and HHSN271201100005C; the Sardinian Autonomous Region (L.R. no. 7/2009) grant cRP3-154. P.S. was supported by FCT, ESF and POPH through the FCT Investigator Programme (IF/01641/2013). P.S. and E.C.-S. acknowledge FCT IP and ERDF (COMPETE2020 – POCI) for the CBMA programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569). T.R. was supported by an FCT grant (SFRH/BPD/108126/2015). M.P., P.S. and M.B.R. acknowledge FCT support through project PTDC/EPH-ARQ/4164/2014 partially funded by FEDER funds (COMPETE 2020 project 016899). M.B.R. received support from a Leverhulme Doctoral Scholarship programme. C.P. was supported by the Baden Württemberg Foundation and the Max Planck Society. The sequence data for the modern (N = 2,136) and ancient (N = 21) Sardinian mitogenomes are available in GenBank with accession numbers KY408145−KY410236, KY399164−KY399207, and KY399143−KY399163, info:eu-repo/semantics/publishedVersion

Psoriasis is a complex disease of skin with a prevalence of about 2%. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for psoriasis to date, including data from eight different Caucasian cohorts, with a combined effective sample size >39,000 individuals. We identified 16 additional psoriasis susceptibility loci achieving genome-wide significance, increasing the number of identified loci to 63 for European-origin individuals. Functional analysis highlighted the roles of interferon signalling and the NFκB cascade, and we showed that the psoriasis signals are enriched in regulatory elements from different T cells (CD8+ T-cells and CD4+ T-cells including TH0, TH1 and TH17). The identified loci explain ∼28% of the genetic heritability and generate a discriminatory genetic risk score (AUC=0.76 in our sample) that is significantly correlated with age at onset (p=2 × 10−89). This study provides a comprehensive layout for the genetic architecture of common variants for psoriasis., Psoriasis is an immune-mediated skin disease with a complex genetic architecture. Here, Elder and colleagues identify 16 novel psoriasis susceptibility loci using GWAS meta-analysis with a combined effective sample size of over 39,000 individuals.

A primary goal of The C onsortium on A sthma among A frican-ancestry P opulations in the A mericas (CAAPA) is to develop an ‘African Diaspora Power Chip’ (ADPC), a genotyping array consisting of tagging SNPs, useful in comprehensively identifying African specific genetic variation. This array is designed based on the novel variation identified in 642 CAAPA samples of African ancestry with high coverage whole genome sequence data (~30× depth). This novel variation extends the pattern of variation catalogued in the 1000 Genomes and Exome Sequencing Projects to a spectrum of populations representing the wide range of West African genomic diversity. These individuals from CAAPA also comprise a large swath of the African Diaspora population and incorporate historical genetic diversity covering nearly the entire Atlantic coast of the Americas. Here we show the results of designing and producing such a microchip array. This novel array covers African specific variation far better than other commercially available arrays, and will enable better GWAS analyses for researchers with individuals of African descent in their study populations. A recent study cataloging variation in continental African populations suggests this type of African-specific genotyping array is both necessary and valuable for facilitating large-scale GWAS in populations of African ancestry.