Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study.

Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).

Interpreting SNP heritability in admixed populations.

SNP heritability (hsnp2) is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability (h2), being equal to it if all causal variants are known. Despite the simple intuition behind hsnp2, its interpretation and equivalence to h2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in h/np2 estimates. Here we use analytical theory and simulations to demonstrate that hsnp2 estimated with genome-wide restricted maximum likelihood (GREML) can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates linkage disequilibrium (LD), which contributes to the genetic variance, and therefore to heritability. GREML implicitly assumes this component is zero, which may not be true, particularly for traits under divergent or stabilizing selection in the source populations, leading under- or over-estimates of hsnp2 relative to h2. For the same reason, GREML estimates of local ancestry heritability (hγ2) will also be biased. We describe the bias in h/np2 and h^γ2 as a function of admixture history and the genetic architecture of the trait and discuss its implications for genome-wide association and polygenic prediction.

Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data.

We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach-based on the e-values framework-for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption.

Comparing heritability estimators under alternative structures of linkage disequilibrium.

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.

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies.

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.

Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus.

Many individual genetic risk loci have been associated with multiple common human diseases. However, the molecular basis of this pleiotropy often remains unclear. We present an integrative approach to reveal the molecular mechanism underlying the PROCR locus, associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. We identify PROCR-p.Ser219Gly as the likely causal variant at the locus and protein C as a causal factor. Using genetic analyses, human recall-by-genotype and in vitro experimentation, we demonstrate that PROCR-219Gly increases plasma levels of (activated) protein C through endothelial protein C receptor (EPCR) ectodomain shedding in endothelial cells, attenuating leukocyte-endothelial cell adhesion and vascular inflammation. We also associate PROCR-219Gly with an increased pro-thrombotic state via coagulation factor VII, a ligand of EPCR. Our study, which links PROCR-219Gly to CAD through anti-inflammatory mechanisms and to VTE through pro-thrombotic mechanisms, provides a framework to reveal the mechanisms underlying similar cross-phenotype associations.

Estimating SNP heritability in presence of population substructure in biobank-scale datasets.

Single nucleotide polymorphism heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms. Linear mixed models are routinely used to estimate single nucleotide polymorphism heritability for many complex traits, which requires estimation of a genetic relationship matrix among individuals. Heritability is usually estimated by the restricted maximum likelihood or method of moments approaches such as Haseman-Elston regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the genetic relationship matrix as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here, we propose a method of moments approach for estimating single nucleotide polymorphism heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their single nucleotide polymorphism allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.

A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies.

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.

A Bayesian hierarchically structured prior for rare-variant association testing.

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.

Impact of body mass index on pathological complete response following neoadjuvant chemotherapy in operable breast cancer: a meta-analysis.

PURPOSE: 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. METHODS: 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. RESULTS: 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 < 25 vs. BMI ≥ 25). Pooled analyses demonstrated that overweight/obese women were less likely to achieve pCR after NACT as compared to under-/normal weight women (odds ratio (OR) = 0.80; 95% confidence interval (CI): 0.68-0.93). Eleven studies provided data of three BMI groups (BMI < 25, 25 ≤ BMI < 30, BMI ≥ 30). Based on pooled analyses, both overweight and obese groups were less likely to achieve pCR with NACT as compared to under-/normal weight group, (OR = 0.77, 95% CI 0.65-0.93 and OR = 0.68, 95% CI 0.61-0.77, respectively). CONCLUSIONS: Overweight and obese breast cancer patients had a lower pCR rate with NACT compared to patients with under-/normal weight. Further prospective studies may help confirm this finding and investigate possible mechanisms.

Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data.

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.

Long-Term Association of Venous Thromboembolism With Frailty, Physical Functioning, and Quality of Life: The Atherosclerosis Risk in Communities Study.

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 (<10) versus good scores on the Short Physical Performance Battery (OR, 3.59; 95% CI, 2.36-5.47). They also had slower gait speed, less endurance, and lower physical quality of life. VTE survivors were similar to coronary heart disease and stroke survivors on categorical frailty and outcomes on Short Physical Performance Battery assessment. When score on the Short Physical Performance Battery instrument was modeled continuously, VTE survivors performed better than stroke survivors but worse than CHD survivors. 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.

A robust and unified framework for estimating heritability in twin studies using generalized estimating equations.

The 'heritability' of a phenotype measures the proportion of trait variance due to genetic factors in a population. In the past 50 years, studies with monozygotic and dizygotic twins have estimated heritability for 17,804 traits;1 thus twin studies are popular for estimating heritability. Researchers are often interested in estimating heritability for non-normally distributed outcomes such as binary, counts, skewed or heavy-tailed continuous traits. In these settings, the traditional normal ACE model (NACE) and Falconer's method can produce poor coverage of the true heritability. Therefore, we propose a robust generalized estimating equations (GEE2) framework for estimating the heritability of non-normally distributed outcomes. The traditional NACE and Falconer's method are derived within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot adjust for covariates, the corresponding 'GEE2-Falconer' can incorporate mean and variance-level covariate effects (e.g. let heritability vary by sex or age). Given a non-normally distributed outcome, the GEE2 models are shown to attain better coverage of the true heritability compared to traditional methods. Finally, a scenario is demonstrated where NACE produces biased estimates of heritability while Falconer remains unbiased. Therefore, we recommend GEE2-Falconer for estimating the heritability of non-normally distributed outcomes in twin studies.

A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics.

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.

Burden of rare exome sequence variants in PROC gene is associated with venous thromboembolism: a population-based study.

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 < 1%, exome-wide significance P < 1.47 × 10-6 ) separately in each study and ancestry group, and meta-analyzed the results for the EAs in ARIC and CHS. RESULTS: In the meta-analysis of EAs, we identified one gene, PROC, in which the burden of rare, coding variants was significantly associated with increased risk of VTE (HR = 5.42 [3.11, 9.42] for carriers versus non-carriers, P = 2.27 × 10-9 ). In ARIC EAs, carriers of the PROC rare variants had on average 0.75 standard deviation (SD) lower concentrations of plasma protein C and 0.28 SD higher D-dimer (P < .05) than non-carriers. Adjustment for low protein C status did not eliminate the association of PROC burden with VTE. In AAs, rare coding PROC variants were not associated with VTE. CONCLUSIONS: Rare coding variants in PROC contribute to increased VTE risk in EAs in this general population sample.

Reasons for Differences in the Incidence of Venous Thromboembolism in Black Versus White Americans.

INTRODUCTION: Venous thromboembolism incidence rates are 30%-100% higher in US blacks than whites. We examined the degree to which differences in the frequencies of socioeconomic, lifestyle, medical risk factors, and genetic variants explain the excess venous thromboembolism risk in blacks and 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 and white participants of the Atherosclerosis Risk in Communities study in 1987-1989 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 1000 person-years in blacks. The age and sex-adjusted hazard ratio (95% confidence interval) 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 concentration of plasma factor VIII reduced the regression coefficient for race by 75%. There were no significant (P <0.05) 2-way multiplicative interactions of race with any risk factor, except with a 5-single nucleotide polymorphism (5-SNP) genetic risk score (a weaker venous thromboembolism risk factor in blacks) and with hospitalization for heart failure (a stronger venous thromboembolism risk factor in blacks). CONCLUSION: The higher incidence rate of venous thromboembolism in blacks than whites was mostly explained by blacks having higher frequencies of venous thromboembolism risk factors.

Plasma Concentrations of High Molecular Weight Kininogen and Prekallikrein and Venous Thromboembolism Incidence in the General Population.

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.

A large-scale exome array analysis of venous thromboembolism.

Although recent Genome-Wide Association Studies have identified novel associations for common variants, there has been no comprehensive exome-wide search for low-frequency variants that affect the risk of venous thromboembolism (VTE). We conducted a meta-analysis of 11 studies comprising 8,332 cases and 16,087 controls of European ancestry and 382 cases and 1,476 controls of African American ancestry genotyped with the Illumina HumanExome BeadChip. We used the seqMeta package in R to conduct single variant and gene-based rare variant tests. In the single variant analysis, we limited our analysis to the 64,794 variants with at least 40 minor alleles across studies (minor allele frequency [MAF] ~0.08%). We confirmed associations with previously identified VTE loci, including ABO, F5, F11, and FGA. After adjusting for multiple testing, we observed no novel significant findings in single variant or gene-based analysis. Given our sample size, we had greater than 80% power to detect minimum odds ratios greater than 1.5 and 1.8 for a single variant with MAF of 0.01 and 0.005, respectively. Larger studies and sequence data may be needed to identify novel low-frequency and rare variants associated with VTE risk.