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Although genome-wide association studies (GWAS) often collect data on multiple correlated traits for complex diseases, conventional gene-based analysis is usually univariate, and therefore, treating traits as uncorrelated. Multivariate analysis of multiple correlated traits can potentially increase the power to detect genes that affect some or all of these traits. In this study, we propose the multivariate hierarchically structured variable selection (HSVS-M) model, a flexible Bayesian model that tests the association of a gene with multiple correlated traits. With only summary statistics, HSVS-M can account for the correlations among genetic variants and among traits simultaneously and can also estimate the various directions and magnitudes of associations between a gene and multiple traits. Simulation studies show that HSVS-M substantially outperforms competing methods in various scenarios, particularly when variants in a gene are associated with a trait in similar directions and magnitudes. We applied HSVS-M to the summary statistics of a meta-analysis GWAS on four lipid traits from the Global Lipids Genetics Consortium and identified 15 genes that have also been confirmed as risk factors in previous studies.

BACKGROUND Rare coding mutations underlying deficiencies of antithrombin and proteins C and S contribute to familial venous thromboembolism (VTE). It is uncertain whether rare variants play a role in the etiology of VTE in the general population. OBJECTIVES We conducted a deep whole-exome sequencing (WES) study to investigate the associations between rare coding variants and the risk of VTE in two population-based prospective cohorts. PATIENTS/METHODS Whole-exome sequencing was performed in the Longitudinal Investigation of Thromboembolism Etiology (LITE), which combines the Atherosclerosis Risk in Communities (ARIC) study (316 incident VTE events among 3159 African Americans [AAs] and 458 incident VTEs among 7772 European Americans [EAs]) and the Cardiovascular Healthy Study (CHS; 60 incident VTEs among 1751 EAs). We performed gene-based tests of rare variants (allele frequency

While genome-wide association studies (GWASs) have been widely used to uncover associations between diseases and genetic variants, standard SNP-level GWASs often lack the power to identify SNPs that individually have a moderate effect size but jointly contribute to the disease. To overcome this problem, pathway-based GWASs methods have been developed as an alternative strategy that complements SNP-level approaches. We propose a Bayesian method that uses the generalized fused hierarchical structured variable selection prior to identify pathways associated with the disease using SNP-level summary statistics. Our prior has the flexibility to take in pathway structural information so that it can model the gene-level correlation based on prior biological knowledge, an important feature that makes it appealing compared to existing pathway-based methods. Using simulations, we show that our method outperforms competing methods in various scenarios, particularly when we have pathway structural information that involves complex gene-gene interactions. We apply our method to the Wellcome Trust Case Control Consortium Crohn’s disease GWAS data, demonstrating its practical application to real data.

SNP heritability of a trait is measured by the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms (SNPs). Linear mixed models are routinely used to estimate SNP heritability for many complex traits. The basic concept behind this approach is to model genetic contribution as a random effect, where the variance of this genetic contribution attributes to the heritability of the trait. This linear mixed model approach requires estimation of ‘relatedness’ among individuals in the sample, which is usually captured by estimating a genetic relationship matrix (GRM). Heritability is estimated by the restricted maximum likelihood (REML) or method of moments (MOM) approaches, and this estimation relies heavily on the GRM computed from the genetic data on individuals. Presence of population substructure in the data could significantly impact the GRM estimation and may introduce bias in heritability estimation. The common practice of accounting for such population substructure is to adjust for the top few principal components of the GRM as covariates in the linear mixed model. Here we propose an alternative way of estimating heritability in multi-ethnic studies. Our proposed approach is a MOM estimator derived from the Haseman-Elston regression and gives an asymptotically unbiased estimate of heritability in presence of population stratification. It introduces adjustments for the population stratification in a second-order estimating equation and allows for the total phenotypic variance vary by ethnicity. We study the performance of different MOM and REML approaches in presence of population stratification through extensive simulation studies. We estimate the heritability of height, weight and other anthropometric traits in the UK Biobank cohort to investigate the impact of subtle population substructure on SNP heritability estimation.

The single nucleotide polymorphism heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms. The existing approaches to estimate single nucleotide polymorphism heritability can be broadly classified into 2 categories. One set of approaches models the single nucleotide polymorphism effects as fixed effects and the other treats the single nucleotide polymorphism effects as random effects. These methods make certain assumptions about the dependency among individuals (familial relationship) as well as the dependency among markers (linkage disequilibrium) to provide consistent estimates of single nucleotide polymorphism heritability as the number of individuals increases. While various approaches have been proposed to account for such dependencies, it remains unclear which estimates reported in the literature are more robust against various model misspecifications. Here, we investigate the impact of different structures of linkage disequilibrium and familial relatedness on heritability estimation. We show that the performance of different methods for heritability estimation depends heavily on the structure of the underlying pattern of linkage disequilibrium and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the 2 method-of-moments estimators, one using a fixed-single nucleotide polymorphism-effects approach, and another using a random-single nucleotide polymorphism-effects approach.

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.

The kallikrein/kinin system, an intravascular biochemical pathway that includes several proteins involved in the contact activation system of coagulation, renin–angiotensin activation and inflammation, may or may not play a role in venous thromboembolism (VTE) occurrence. Within a large prospective population-based study in the United States, we conducted a nested case–cohort study to test the hypothesis that higher plasma levels of high molecular weight kininogen (HK) or prekallikrein are associated with greater VTE incidence. We related baseline enzyme-linked immunosorbent assay measures of HK and prekallikrein in 1993 to 1995 to incidence VTE of the lower extremity (n = 612) through 2015 (mean follow-up = 18 years). We found no evidence that plasma HK or prekallikrein was associated positively with incident VTE. HK, in fact, was associated inversely and significantly with VTE in most proportional hazards regression models. For example, the hazard ratio of VTE per standard deviation higher HK concentration was 0.88 (95% confidence interval = 0.81, 0.97), after adjustment for several VTE risk factors. Our findings suggest that plasma levels of these factors do not determine the risk of VTE in the general population.

Exogenous hormone treatments in women (oral contraceptives and hormone replacement therapy [HRT]) are established risk factors for venous thromboembolism (VTE), but less is known about associations between plasma levels of endogenous hormones and VTE risk. We examined the association of baseline dehydroepiandrosterone sulphate (DHEAS), testosterone and sex hormone-binding globulin (SHBG) with risk of future VTE in men and post-menopausal women in the Atherosclerosis Risk in Communities Study. Testosterone, DHEAS and SHBG were measured in plasma samples collected in 1996 to 1998. Cox proportional hazards models were used to estimate hazard ratios for incident VTE adjusting for age, race/ethnicity, body mass index, height, smoking, estimated glomerular filtration rate and C-reactive protein. All analyses were stratified by sex and by current HRT use in women. Among 3,051 non-HRT-using women, 1,414 HRT-using women and 3,925 men at risk at baseline, 184, 62 and 206 experienced incident VTE after a median follow-up of 17.6 years. Plasma hormones were not associated with incidence of VTE among men and non-HRT-using women, although lower plasma DHEAS, when modelled using quartiles or restricted cubic splines, was associated with higher risk of VTE among HRT-using women. This study does not support the existence of an important association between plasma concentrations of endogenous testosterone, DHEAS or SHBG with risk of VTE in middle-aged to older men or post-menopausal women not using HRT.

Essentials Inflammatory and cardiac diseases are associated with increased venous thromboembolism (VTE) risk. Our prospective study assessed rise in inflammatory or cardiac biomarkers and VTE risk. A greater 6-year rise in N-terminal natriuretic peptide is associated with increased VTE incidence. Volume overload or impending cardiac disease may contribute to VTE occurrence. SUMMARY Background We previously showed that participants in the population-based Atherosclerosis Risk in Communities (ARIC) cohort with elevated levels of blood biomarkers of inflammation or cardiac disease were at increased risk of venous thromboembolism (VTE). Objective We hypothesized that ARIC participants with larger 6-year increases in the levels of three biomarkers - C-reactive protein (CRP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and troponin T - would also have an increased subsequent risk of VTE. Methods We measured changes in the levels of these biomarkers in 9844 participants from 1990-1992 to 1996-1998, and then identified VTEs through 2015. Results A greater 6-year rise in the level of NT-proBNP, but not in that of CRP or troponin T, was significantly associated with increased VTE incidence over a median of 17.6 years of follow-up. After adjustment for other VTE risk factors, those whose NT-proBNP level rose from < 100 pg mL-1 to ≥ 100 pg mL-1 had a hazard ratio for VTE of 1.44 (95% confidence interval [CI] 1.15-1.80), as compared with the reference group with an NT-proBNP level of < 100 pg mL-1 at both times. This hazard ratio was slightly higher (1.66, 95% CI 1.19-2.31) during the first 10 years of follow-up, but was attenuated (1.24, 95% CI 0.99-1.56) after adjustment for prevalent and incident coronary heart disease, heart failure, and atrial fibrillation. Conclusions The two most likely explanations for our results are that: (i) an increasing NT-proBNP level reflects increasing subclinical volume overload and potentially increased venous stasis or subclinical PE that had gone unrecognized over time; or (ii) an increasing NT-proBNP level is a risk marker for impending cardiac disease that places patients at risk of VTE.

Although genome-wide association studies have been widely used to identify associations between complex diseases and genetic variants, standard single-variant analyses often have limited power when applied to rare variants. To overcome this problem, set-based methods have been developed with the aim of boosting power by borrowing strength from multiple rare variants. We propose the adaptive hierarchically structured variable selection (HSVS-A) before test for association of rare variants in a set with continuous or dichotomous phenotypes and to estimate the effect of individual rare variants simultaneously. HSVS-A has the flexibility to integrate a pairwise weighting scheme, which adaptively induces desirable correlations among variants of similar significance such that we can borrow information from potentially causal and noncausal rare variants to boost power. Simulation studies show that for both continuous and dichotomous phenotypes, HSVS-A is powerful when there are multiple causal rare variants, either in the same or opposite direction of effect, with the presence of a large number of noncausal variants. We also apply HSVS-A to the Wellcome Trust Case Control Consortium Crohn's disease data for testing the association of Crohn's disease with rare variants in pathways. HSVS-A identifies two pathways harboring novel protective rare variants for Crohn's disease.

The impact of an increased body mass index (BMI) on outcomes of neoadjuvant chemotherapy (NACT) in breast cancer remains controversial. The purpose of this study was to analyze the impact of BMI on pathological complete response (pCR) rates for operable breast cancer after NACT. We searched Medline, Embase, and Web of Science database for observational studies and randomized controlled trials that reported the association of BMI with pCR after NACT. We performed a meta-analysis to assess the impact of BMI on pCR rate. We identified 13 studies including a total of 18,702 women with operable breast cancer who underwent NACT. Two studies were pooled analyses of prospective clinical trials (10,669 patients); the rest were case–control studies (8033 patients). All studies provided data of two BMI groups (BMI

Background Relatively little is known about the long‐term consequences of venous thromboembolism (VTE) on physical functioning. We compared long‐term frailty status, physical function, and quality of life among survivors of VTE with survivors of coronary heart disease (CHD) and stroke, and with those without these diseases. Methods and Results Cases of VTE, CHD, and stroke were continuously identified since ARIC (Atherosclerosis Risk in Communities Study) recruitment during 1987 to 1989. Functional measures were objectively captured at ARIC clinic visits 5 (2011–2013) and 6 (2016–2017); quality of life was self‐reported. The 6161 participants at visit 5 were, on average, 75.7 (range, 66–90) years of age. By visit 5, 3.2% had had a VTE, 6.9% CHD, and 3.4% stroke. Compared with those without any of these conditions, VTE survivors were more likely to be frail (odds ratio [OR], 3.11; 95% CI, 1.80–5.36) and have low ( Conclusions VTE survivors had triple the odds of frailty and poorer physical function than those without the vascular diseases considered. Their function was somewhat worse than that of CHD survivors, but better than stroke survivors. These findings suggest that VTE patients may benefit from additional efforts to improve postevent physical functioning.

The development of a complex disease is an intricate interplay of genetic and environmental factors. "Heritability" is defined as the proportion of total trait variance due to genetic factors within a given population. Studies with monozygotic (MZ) and dizygotic (DZ) twins allow us to estimate heritability by fitting an "ACE" model which estimates the proportion of trait variance explained by additive genetic (A), common shared environment (C), and unique non-shared environmental (E) latent effects, thus helping us better understand disease risk and etiology. In this paper, we develop a flexible generalized estimating equations framework ("GEE2") for fitting twin ACE models that requires minimal distributional assumptions, rather only the first two moments need to be correctly specified. We prove that two commonly used methods for estimating heritability, the normal ACE model ("NACE") and Falconer's method, can both be fit within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot directly adjust for covariates, we show that the corresponding GEE2 version ("GEE2-Falconer") can incorporate covariate effects for both mean and variance-level parameters (e.g. let heritability vary by sex or age). Given non-normal data, we show that the GEE2 models attain significantly better coverage of the true heritability compared to the traditional NACE and Falconer's methods. Finally, we demonstrate an important scenario where the NACE model produces biased estimates of heritability while Falconer's method remains unbiased. Overall, we recommend using the robust and flexible GEE2-Falconer model for estimating heritability in twin studies.

Genome-wide association studies have identified many individual genetic loci associated with multiple complex traits and common diseases. There are, however, few examples where the molecular basis of such pleiotropy has been elucidated. To address this challenge, we describe an integrative approach, focusing on the p.Ser219Gly (rs867186 A>G) variant in thePROCRgene (encoding the endothelial protein C receptor, EPCR), which has been associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. In a phenome scan of 12 cardiometabolic diseases and 24 molecular factors, we found thatPROCR-219Gly associated with higher plasma levels of zymogenic and activated protein C as well as coagulation factor VII. Using statistical colocalization and Mendelian randomization analyses, we uncovered shared genetic etiology across activated protein C, factor VII, CAD and VTE, identifying p.S219G as the likely causal variant at the locus. In a recall-by-genotype study of 52 healthy volunteers stratified by p.S219G, we detected 2.5-fold higher soluble EPCR levels and 1.2-fold higher protein C levels in plasma per effect allele, suggesting the allele induces EPCR shedding from the membrane of endothelial cells. Finally, in cell adhesion assays, we found that increasing concentrations of activated protein C, but not soluble EPCR, reduced leukocyte–endothelial cell adhesion, a marker for vascular inflammation. These results support a role for protein C as a causal factor in arterial and venous diseases, suggesting thatPROCR-219Gly protects against CAD through anti-inflammatory mechanisms while it promotes VTE risk through pro-thrombotic mechanisms. Overall, our study illustrates a multi-modal approach that can help reveal molecular underpinnings of cross-disease associations.

Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.

Introduction Data from epidemiological studies and clinical trials suggest an influence of dietary and circulating polyunsaturated fatty acids (PUFAs) on the hemostasis profile. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) related to plasma PUFAs levels. We aimed to investigate whether the SNPs related to plasma PUFAs levels were also associated with plasma levels of hemostatic variables. Materials and methods We tested the associations between 9 PUFA-related SNPs and 6 hemostatic variables in 9035 European Americans (EAs) and 2702 African Americans (AAs) in the Atherosclerosis Risk in Communities (ARIC) Study. We then conducted a replication study by looking-up our novel observed associations in three published GWAS for hemostatic factors in different EA populations. Results We observed a novel linoleic acid-related locus at the JMJD1C region associated with factor VII activity (FVIIc): rs10740118 and rs1935, Beta (p) = −1.31 (1 × 10−3) and 1.37 (5 × 10−4) in EAs, respectively, and − 1.24 (5 × 10−4) and 1.28 (3 × 10−4) in meta-analysis of EAs and AAs of ARIC. This novel association was replicated in two of three independent EA populations (p = 0.01 and 0.03 in meta-analyses). We confirmed previously reported associations at the docosapentaenoic acid-related GCKR locus with protein C and FVIIc and at JMJD1C with fibrinogen. Adjustment for plasma PUFAs did not abolish the associations between these loci and hemostatic variables. Conclusions Our study identified a novel association for FVIIc at JMJD1C, a histone demethylase that plays a role in DNA repair and possibly transcription regulation and RNA processing.

Genome-wide association studies (GWASs) are a popular tool for detecting association between genetic variants or single nucleotide polymorphisms (SNPs) and complex traits. Family data introduce complexity due to the non-independence of the family members. Methods for non-independent data are well established, but when the GWAS contains distinct family types, explicit modeling of between-family-type differences in the dependence structure comes at the cost of significantly increased computational burden. The situation is exacerbated with binary traits. In this paper, we perform several simulation studies to compare multiple candidate methods to perform single SNP association analysis with binary traits. We consider generalized estimating equations (GEE), generalized linear mixed models (GLMMs), or generalized least square (GLS) approaches. We study the influence of different working correlation structures for GEE on the GWAS findings and also the performance of different analysis method(s) to conduct a GWAS with binary trait data in families. We discuss the merits of each approach with attention to their applicability in a GWAS. We also compare the performances of the methods on the alcoholism data from the Minnesota Center for Twin and Family Research (MCTFR) study.

In genome-wide association studies (GWAS), it has been increasingly recognized that, as a complementary approach to standard single SNP analyses, it may be beneficial to analyze a group of functionally related SNPs together. Among the existent population-based SNP-set association tests, two adaptive tests, the aSPU test and the aSPUpath test, offer a powerful and general approach at the gene- and pathway-levels by data-adaptively combining the results across multiple SNPs (and genes) such that high statistical power can be maintained across a wide range of scenarios. We extend the aSPU and the aSPUpath test to familial data under the framework of the generalized linear mixed models (GLMMs), which can take account of both subject relatedness and possible population structure. As in population-based GWAS, the proposed aSPU and aSPUpath tests require only fitting a single and common GLMM (under the null hypothesis) for all the SNPs, thus are computationally efficient and feasible for large GWAS data. We illustrate our approaches in identifying genes and pathways associated with alcohol dependence in the Minnesota Twin Family Study. The aSPU test detected a gene associated with the trait, in contrast to none by the standard single SNP analysis. Our aSPU test also controlled Type I errors satisfactorily in a small simulation study. We provide R code to conduct the aSPU and aSPUpath tests for familial and other correlated data.

Essentials Galectin-3, an inflammatory biomarker, is involved in murine thrombogenesis. Galectin-3–binding protein is up-regulated in microparticles from deep venous thrombosis patients compared to control patients. In this prospective epidemiological study, participants with plasma galectin-3 concentrations in the highest quintile had a greater risk of incident venous thromboembolism than did those in the lowest quintile. The association of galectin-3 and VTE was not replicated in a second prospective study, but the meta-analyzed hazard ratio still indicated a positive association. Galectin-3 is associated positively with risk of venous thromboembolism. Background The inflammatory biomarker galectin-3 contributes to pathologic conditions such as heart failure and stimulates murine thrombogenesis. Its association with venous thromboembolism (VTE) has been sparsely studied. Objectives To assess the prospective association of plasma galectin-3 and the LGALS3 rs4644 SNP with VTE incidence. Methods We measured plasma galectin-3 in 9916 participants in the Atherosclerosis Risk in Communities (ARIC) study cohort in 1996-1998 and identified VTEs through 2013. Using Cox regression, we estimated the hazard ratio associating galectin-3 with incident VTE over a median of 13.9 years. Replication was sought in the Cardiovascular Health Study (CHS). Results ARIC included 21.8% blacks and 56.2% females with mean baseline age of 62.7 years. The incidence rate of VTE (n=389 events) increased across quintiles of galectin-3, with hazard ratios (95% CI) of 1 (reference), 1.13 (0.80-1.61), 1.00 (0.70-1.43), 1.36 (0.96-1.91), and 1.55 (1.09-2.19) (P-trend=.005), adjusted for age, sex, race, body mass index, diabetes status, and renal function. Results did not replicate in the CHS (124 VTE), but meta-analysis of both studies yielded a pooled hazard ratio (95% CI) for 1 SD increment in log galectin-3 of 1.10 (1.00-1.22). In ARIC, the C allele of rs4644 in the LGALS3 gene was associated with higher galectin-3 level, and in whites, with an increased rate of VTE. Conclusion Galectin-3 levels were associated positively with VTE incidence.

In the past decade, many genome-wide association studies (GWASs) have been conducted to explore association of single nucleotide polymorphisms (SNPs) with complex diseases using a case-control design. These GWASs not only collect information on the disease status (primary phenotype, D) and the SNPs (genotypes, X), but also collect extensive data on several risk factors and traits. Recent literature and grant proposals point toward a trend in reusing existing large case-control data for exploring genetic associations of some additional traits (secondary phenotypes, Y) collected during the study. These secondary phenotypes may be correlated, and a proper analysis warrants a multivariate approach. Commonly used multivariate methods are not equipped to properly account for the non-random sampling scheme. Current ad hoc practices include analyses without any adjustment, and analyses with D adjusted as a covariate. Our theoretical and empirical studies suggest that the type I error for testing genetic association of secondary traits can be substantial when X as well as Y are associated with D, even when there is no association between X and Y in the underlying (target) population. Whether using D as a covariate helps maintain type I error depends heavily on the disease mechanism and the underlying causal structure (which is often unknown). To avoid grossly incorrect inference, we have proposed proportional odds model adjusted for propensity score (POM-PS). It uses a proportional odds logistic regression of X on Y and adjusts estimated conditional probability of being diseased as a covariate. We demonstrate the validity and advantage of POM-PS, and compare to some existing methods in extensive simulation experiments mimicking plausible scenarios of dependency among Y, X, and D. Finally, we use POM-PS to jointly analyze four adiposity traits using a type 2 diabetes (T2D) case-control sample from the population-based Metabolic Syndrome in Men (METSIM) study. Only POM-PS analysis of the T2D case-control sample seems to provide valid association signals.

Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed score statistic based genetic association testing approaches to the G-E interaction testing problem. We also propose tests for interaction using gene-based summary measures that pool variants together. Although it has recently been shown that these summary measures can be biased and may lead to inflated type I error, we show that under several realistic scenarios, we can still provide valid tests of interaction. These tests use significantly less degrees of freedom and thus can have much higher power to detect interaction. Additionally, we demonstrate that the iSeq-aSum-min test, which combines a gene-based summary measure test, iSeq-aSum-G, and an interaction-based summary measure test, iSeq-aSum-I, provides a powerful alternative to test G-E interaction. We demonstrate the performance of these approaches using simulation studies and illustrate their performance to study interaction between the SNPs in several candidate genes and family climate environment on alcohol consumption using the Minnesota Center for Twin and Family Research dataset.

INTRODUCTION: Venous thromboembolism incidence rates are 30–100% higher in American blacks than whites. We examined (a) the degree to which differences in the frequencies of socioeconomic, lifestyle, and medical risk factors, and genetic variants explain the excess venous thromboembolism risk in blacks and (b) whether some risk factors are more strongly associated with venous thromboembolism in blacks compared with whites. METHODS: We measured venous thromboembolism risk factors in black or white participants of the Atherosclerosis Risk in Communities study in 1987–89 and followed them prospectively through 2015 for venous thromboembolism incidence. RESULTS: Over a mean of 22 years, we identified 332 venous thromboembolisms in blacks and 578 in whites, yielding 65% higher crude incidence rates per 1,000 person-years in blacks. The age and sex-adjusted hazard ratio (95% CI) of venous thromboembolism for blacks, compared with whites, was 2.04 (1.76, 2.37) for follow-up >10 years, and was attenuated to 1.14 (0.89, 1.46), when adjusted for baseline confounders or mediators of the race association, which tended to be more common in blacks. For example, adjustment for just baseline weight, family income, and plasma factor VIII concentration reduced the regression coefficient for race by 75%. There were no significant (p

Context Vitamin D inadequacy is common in the adult population of the United States. Although the genetic determinants underlying vitamin D inadequacy have been studied in people of European ancestry, less is known about populations with Hispanic or African ancestry. Objective The Trans-Ethnic Evaluation of Vitamin D (TRANSCEN-D) genomewide association study (GWAS) consortium was assembled to replicate genetic associations with 25-hydroxyvitamin D [25(OH)D] concentrations from the Study of Underlying Genetic Determinants of Vitamin D and Highly Related Traits (SUNLIGHT) meta-analyses of European ancestry and to identify genetic variants related to vitamin D concentrations in African and Hispanic ancestries. Design Ancestry-specific (Hispanic and African) and transethnic (Hispanic, African, and European) meta-analyses were performed with Meta-Analysis Helper software (METAL). Patients or Other Participants In total, 8541 African American and 3485 Hispanic American (from North America) participants from 12 cohorts and 16,124 European participants from SUNLIGHT were included in the study. Main Outcome Measures Blood concentrations of 25(OH)D were measured for all participants. Results Ancestry-specific analyses in African and Hispanic Americans replicated single nucleotide polymorphisms (SNPs) in GC (2 and 4 SNPs, respectively). An SNP (rs79666294) near the KIF4B gene was identified in the African American cohort. Transethnic evaluation replicated GC and DHCR7 region SNPs. Additionally, the transethnic analyses revealed SNPs rs719700 and rs1410656 near the ANO6/ARID2 and HTR2A genes, respectively. Conclusions Ancestry-specific and transethnic GWASs of 25(OH)D confirmed findings in GC and DHCR7 for African and Hispanic American samples and revealed findings near KIF4B, ANO6/ARID2, and HTR2A. The biological mechanisms that link these regions with 25(OH)D metabolism warrant further investigation.

Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. Nearly three-quarters of the 143 genetic signals associated with platelet and erythrocyte phenotypes identified by meta-analyses of genome-wide association (GWA) studies are located at non-protein-coding regions. Here, we assessed the role of candidate regulatory variants associated with cell type-restricted, closely related hematological quantitative traits in biologically relevant hematopoietic cell types. We used formaldehyde-assisted isolation of regulatory elements followed by next-generation sequencing (FAIRE-seq) to map regions of open chromatin in three primary human blood cells of the myeloid lineage. In the precursors of platelets and erythrocytes, as well as in monocytes, we found that open chromatin signatures reflect the corresponding hematopoietic lineages of the studied cell types and associate with the cell type-specific gene expression patterns. Dependent on their signal strength, open chromatin regions showed correlation with promoter and enhancer histone marks, distance to the transcription start site, and ontology classes of nearby genes. Cell type-restricted regions of open chromatin were enriched in sequence variants associated with hematological indices. The majority (63.6%) of such candidate functional variants at platelet quantitative trait loci (QTLs) coincided with binding sites of five transcription factors key in regulating megakaryopoiesis. We experimentally tested 13 candidate regulatory variants at 10 platelet QTLs and found that 10 (76.9%) affected protein binding, suggesting that this is a frequent mechanism by which regulatory variants influence quantitative trait levels. Our findings demonstrate that combining large-scale GWA data with open chromatin profiles of relevant cell types can be a powerful means of dissecting the genetic architecture of closely related quantitative traits. Marie-Curie Initial Training Network NETSIM British Heart Foundation RG/09/12/28096 National Institutes for Health RP-PG-0310-1002 Wellcome Trust 098051 info:eu-repo/grantAgreement/EC/FP7/282510

A novel mechanism is described for the agrin-mediated focal delivery of acetylcholine receptors (AChRs) to the postsynaptic membrane of the neuromuscular junction. Microtubule capture mediated by CLASP2 and its interaction partner, LL5β, and an intact subsynaptic actin cytoskeleton are both required for focal AChR transport to the synaptic membrane., A hallmark of the neuromuscular junction (NMJ) is the high density of acetylcholine receptors (AChRs) in the postsynaptic muscle membrane. The postsynaptic apparatus of the NMJ is organized by agrin secreted from motor neurons. The mechanisms that underlie the focal delivery of AChRs to the adult NMJ are not yet understood in detail. We previously showed that microtubule (MT) capture by the plus end–tracking protein CLASP2 regulates AChR density at agrin-induced AChR clusters in cultured myotubes via PI3 kinase acting through GSK3β. Here we show that knockdown of the CLASP2-interaction partner LL5β by RNAi and forced expression of a CLASP2 fragment blocking the CLASP2/LL5β interaction inhibit microtubule capture. The same treatments impair focal vesicle delivery to the clusters. Consistent with these findings, knockdown of LL5β at the NMJ in vivo reduces the density and insertion of AChRs into the postsynaptic membrane. MT capture and focal vesicle delivery to agrin-induced AChR clusters are also inhibited by microtubule- and actin-depolymerizing drugs, invoking both cytoskeletal systems in MT capture and in the fusion of AChR vesicles with the cluster membrane. Combined our data identify a transport system, organized by agrin through PI3 kinase, GSK3β, CLASP2, and LL5β, for precise delivery of AChR vesicles from the subsynaptic nuclei to the overlying synaptic membrane.

Reduced activated partial thromboplastin time (aPTT) is a risk marker for incident and recurrent venous thromboembolism (VTE). Genetic factors influencing aPTT are not well understood, especially in populations of non-European ancestry. The present study aimed to identify aPTT-related gene variants in both European Americans (EAs) and African Americans (AAs). We conducted a genetic association study for aPTT in 9719 EAs and 2799 AAs from the Atherosclerosis Risk in Communities (ARIC) study. Using the Candidate Gene Association Resource (CARe) consortium candidate gene array, the analyses were based on ∼50 000 SNPs in ∼2000 candidate genes. In EAs, the analyses identified a new independent association for aPTT in F5 (rs2239852, P-value = 1.9 × 10(-8)), which clusters with a coding variant rs6030 (P-value = 7.8 × 10(-7)). The remaining significant signals were located on F5, HRG, KNG1, F11, F12 and ABO and have been previously reported in EA populations. In AAs, significant signals were identified in KNG1, HRG, F12, ABO and VWF, with the leading variants in KNG1, HRG and F12 being the same as in the EAs; the significant variant in VWF (rs2229446, P-value = 1.2 × 10(-6)) was specific to the AA sample (minor allele frequency = 19% in AAs and 0.2% in EAs) and has not been previously reported. This is the first study to report aPTT-related genetic variants in AAs. Our findings in AAs demonstrate transferability of previously reported associations with KNG1, HRG and F12 in EAs. We also identified new associations at F5 in EAs and VWF in AAs that have not been previously reported for aPTT.

P3 amplitude is a candidate endophenotype for disinhibitory psychopathology, psychosis, and other disorders. The present study is a comprehensive analysis of the behavioral- and molecular-genetic basis of P3 amplitude and a P3 genetic factor score in a large community sample (N = 4,211) of adolescent twins and their parents, genotyped for 527,829 single nucleotide polymorphisms (SNPs). Biometric models indicated that as much as 65% of the variance in each measure was due to additive genes. All SNPs in aggregate accounted for approximately 40% to 50% of the heritable variance. However, analyses of individual SNPs did not yield any significant associations. Analyses of individual genes did not confirm previous associations between P3 amplitude and candidate genes but did yield a novel association with myelin expression factor 2 (MYEF2). Main effects of individual variants may be too small to be detected by GWAS without larger samples.

Protein C is an endogenous anticoagulant protein with anti-inflammatory properties. Single-nucleotide polymorphisms (SNPs) affect the levels of circulating protein C in European Americans. We performed a genome-wide association (GWA) scan of plasma protein C concentration with approximately 2.5 million SNPs in 2,701 African Americans in the Atherosclerosis Risk in Communities Study. Seventy-nine SNPs from the 20q11 and 2q14 regions reached the genome-wide significance threshold of 5 × 10(-8) . A missense variant rs867186 in the PROCR gene at 20q11 is known to affect protein C levels in individuals of European descent and showed the strongest signal (P = 9.84 × 10(-65) ) in African Americans. The minor allele of this SNP was associated with higher protein C levels (β = 0.49 μg/ml; 10% variance explained). In the 2q14 region, the top SNPs were near or within the PROC gene: rs7580658 (β = 0.15 μg/ml; 2% variance explained, P = 1.7 × 10(-12) ) and rs1799808 (β = 0.15 μg/ml; 2% variance explained, P = 2.03 × 10(-12) ). These two SNPs were in strong linkage disequilibrium (LD) with another SNP rs1158867 that resides in a biochemically functional site and in weak to strong LD with the top PROC variants previously reported in individuals of European descent. In addition, two variants outside the PROC region were significantly and independently associated with protein C levels: rs4321325 in CYP27C1 and rs13419716 in MYO7B. In summary, this first GWA study for plasma protein C levels in African Americans confirms the associations of SNPs in the PROC and PROCR regions with circulating levels of protein C across ethnic populations and identifies new candidates for protein C regulation.

Background Genome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings. Methods We consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study. Results We show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions. Conclusion Our results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies.

Background: Activated partial thromboplastin time (aPTT) is a clinical test used to measure the clotting time between the activation of factor XII and the formation of a fibrin clot. Prolonged aPTT indicates a deficiency in the coagulation pathway while shortened aPTT is associated with prothrombotic risk factors and venus thromboembolism. Despite the high heritability of aPTT (40-80%), previous genome-wide association studies (GWAS) of aPTT have been limited to European descent populations, with only a smaller candidate gene study conducted in African Americans. Methods: We included 13,803 participants of European ancestry (EU) and 2,724 participants of African American ancestry (AA) from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) consortium. aPTT, in seconds (s), was measured using standard protocols in plasma with the use of automated coagulometers. Genotype data were imputed to the 1000 Genomes Phase 1 reference panel, and associations were examined using linear regression assuming an additive genetic model and adjusting for global ancestry, age, sex, and study design. Study-specific results were combined using a fixed-effects, inverse variance weighted meta-analysis. Results: We identified seven loci associated with aPTT in EU populations ( F5, FRMD5, KNG1, F11, F12, HLA, ABO ) and three loci associated with aPTT in AA populations ( KNG1, F12, ABO ) at genome-wide significant levels ( P < 5x10 -8 ). These results are consistent with previously reported genetic studies in EU and AA populations. Effect sizes were larger in AA populations (1.08 to 1.32 s) than in EU populations (0.40 to 1.00 s). Conclusions: We successfully replicated associations with aPTT at seven loci in EU populations and three loci in AA populations. Our results suggest that genetic determinants of aPTT are consistent across race/ethnicity but that studies in AA populations are currently underpowered relative to EU populations. Future work in aPTT genetics should consider more diverse populations.

International audience; Coagulation factor XI (FXI) has become increasingly interesting for its role in pathogenesis of thrombosis. While elevated plasma levels of FXI have been associated with venous thromboembolism and ischemic stroke, its deficiency is associated with mild bleeding. We aimed to determine novel genetic and post-transcriptional plasma FXI regulators.We performed a genome-wide association study (GWAS) for plasma FXI levels, using novel data imputed to the 1000 Genomes reference panel. Individual GWAS analyses, including a total of 16,169 European individuals from the ARIC, GHS, MARTHA and PROCARDIS studies, were meta-analysed and further replicated in 2,045 individuals from the F5L family, GAIT2 and MEGA studies. Additional association with activated partial thromboplastin time (aPTT) was tested for the top SNPs. In addition, a study on the effect of miRNA on FXI regulation was performed using in silico prediction tools and in vitro luciferase assays.Three loci showed robust, replicating association with circulating FXI levels: KNG1 (rs710446, P-value = 2.07 × 10−302), F11 (rs4253417, P-value = 2.86 × 10−193), and a novel association in GCKR (rs780094, P-value = 3.56 ×10−09), here for the first time implicated in FXI regulation. The two first SNPs (rs710446 and rs4253417) also associated with aPTT. Conditional and haplotype analyses demonstrated a complex association signal, with additional novel SNPs modulating plasma FXI levels in both the F11 and KNG1 loci. Finally, eight miRNAs were predicted to bind F11 mRNA. Over-expression of either miR-145 or miR-181 significantly reduced the luciferase activity in cells transfected with a plasmid containing FXI-3’UTR.These results should open the door to new therapeutic targets for thrombosis prevention.

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Although estimating the five parameters of an unknown Generalized Normal Laplace (GNL) density by minimizing the distance between the empirical and true characteristic functions seems appealing, the approach cannot be advocated in practice. This conclusion is based on extensive numerical simulations in which a fast minimization procedure delivers deceiving estimators with values that are quite far away from the truth. These findings can be predicted by the very large values obtained for the true asymptotic variances of the estimators of the five parameters of the true GNL density.

Summary Neurite extension is regulated by multiple signaling cascades that ultimately converge on the actin and microtubule networks [1]. Rho GTPases, molecular switches that oscillate between an inactive, GDP-bound state and an active, GTP-bound state, play a pivotal role in controlling actin cytoskeleton dynamics in the growth cone [2], whereas the dynamic behavior and interactions of microtubules are largely regulated by proteins called plus-end-tracking proteins (+TIPs), which associate with the ends of growing microtubules [3]. Here, we show that the +TIP Navigator 1 (NAV1) is important for neurite outgrowth and interacts and colocalizes with TRIO, a Rho guanine nucleotide exchange factor that enables neurite outgrowth by activating the Rho GTPases Rac1 and RhoG [4, 5]. We find that binding of NAV1 enhances the affinity of TRIO for Rac1 and RhoG, and that NAV1 regulates TRIO-mediated Rac1 activation and neurite outgrowth. TRIO is also a +TIP, as it interacts with the core +TIP EB1 and tracks microtubule plus ends via EB1 and NAV1. Strikingly, the EB1-mediated recruitment of TRIO to microtubule ends is required for proper neurite outgrowth, and stabilization of the microtubule network by paclitaxel affects both the TRIO-NAV1 interaction and the accumulation of these proteins in neurite extensions. We propose that EB1-labeled ends of dynamic microtubules facilitate the formation and localization of functional NAV1-TRIO complexes, which in turn regulate neurite outgrowth by selectively activating Rac1. Our data reveal a novel link between dynamic microtubules, actin cytoskeleton remodeling, and neurite extension.