Longitudinal evaluation of iron status during pregnancy: a prospective cohort study in a high-resource setting.

Background: Iron deficiency affects a large proportion of pregnant women worldwide, with potentially serious consequences for perinatal and infant outcomes, but well-powered, comprehensive analyses of longitudinal iron status during pregnancy are scarce.

Objectives: This study aimed to evaluate the longitudinal changes in iron biomarkers across pregnancy and prevalence of iron deficiency in primiparous women in a high-resource setting and propose early pregnancy iron status cutoffs that predict iron deficiency in the third trimester.

Methods: In a prospective cohort of primiparous women with low-risk, singleton pregnancies in Ireland, iron [ferritin, soluble transferrin receptors (sTfR), total body iron (TBI)] and inflammatory markers (C-reactive protein, α-glycoprotein) were measured at 3 study visits: 15, 20, and 33 wk of gestation.

Putting computational models of immunity to the test - an invited challenge to predict vaccination outcomes.

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting booster responses and generate experimental data for the explicit purpose of model evaluation.

Variability in addictive and carcinogenic potential of smokeless tobacco products marketed in Mumbai, India: a surveillance study.

Background: India has the highest incidence worldwide of smokeless tobacco (SLT)-associated oral cancer, accounting for nearly 70% of all SLT users globally. Nicotine and tobacco-specific -nitrosamines (TSNA) play critical roles in the addictive and carcinogenic potential, respectively, of SLT products. Our group has previously reported substantial variability in nicotine and TSNA levels across a small SLT product sample in India, calling for systematic surveillance.

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.

Interpreting SNP heritability in admixed populations.

SNP heritability is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability ( ), being equal to it if all causal variants are known. Despite the simple intuition behind , its interpretation and equivalence to is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in estimates because of confounding due to linkage disequilibrium (LD) or shared environment.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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; 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.

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.

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.

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.

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 ( = 612) through 2015 (mean follow-up = 18 years).

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.

Prospective Study of Endogenous Hormones and Incidence of Venous Thromboembolism: The Atherosclerosis Risk in Communities Study.

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.

A linear mixed model framework for gene-based gene-environment interaction tests in twin studies.

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.