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Genetic Epidemiology

Emily Baker, Karl Michael Schmidt, Rebecca Sims, Michael C O'Donovan, Julie Williams, Peter Holmans, Valentina Escott-Price, With The Gerad Consortium
Polygenic risk scores (PRSs) are a method to summarize the additive trait variance captured by a set of SNPs, and can increase the power of set-based analyses by leveraging public genome-wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We propose the application of PRSs as a set-based method with an additional component of adjustment for linkage disequilibrium (LD), with potential extension of the PRS approach to analyze biologically meaningful SNP sets...
March 12, 2018: Genetic Epidemiology
Jihye Kim, Peter Kraft, Kaitlin A Hagan, Laura B Harrington, Sara Lindstroem, Christopher Kabrhel
INTRODUCTION: Venous thromboembolism (VTE) is highly heritable. Physical activity, physical inactivity and body mass index (BMI) are also risk factors, but evidence of interaction between genetic and environmental risk factors is limited. METHODS: Data on 2,134 VTE cases and 3,890 matched controls were obtained from the Nurses' Health Study (NHS), Nurses' Health Study II (NHS II), and Health Professionals Follow-up Study (HPFS). We calculated a weighted genetic risk score (wGRS) using 16 single nucleotide polymorphisms associated with VTE risk in published genome-wide association studies (GWAS)...
March 8, 2018: Genetic Epidemiology
S Taylor Fischer, Yunxuan Jiang, K Alaine Broadaway, Karen N Conneely, Michael P Epstein
There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross-phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family-based designs, including the valuable case-parent trio design...
February 20, 2018: Genetic Epidemiology
Rector Arya, Vidya S Farook, Sharon P Fowler, Sobha Puppala, Geetha Chittoor, Roy G Resendez, Srinivas Mummidi, Jairam Vanamala, Laura Almasy, Joanne E Curran, Anthony G Comuzzie, Donna M Lehman, Christopher P Jenkinson, Jane L Lynch, Ralph A DeFronzo, John Blangero, Daniel E Hale, Ravindranath Duggirala, Vincent P Diego
Knowledge on genetic and environmental (G × E) interaction effects on cardiometabolic risk factors (CMRFs) in children is limited.  The purpose of this study was to examine the impact of G × E interaction effects on CMRFs in Mexican American (MA) children (n = 617, ages 6-17 years). The environments examined were sedentary activity (SA), assessed by recalls from "yesterday" (SAy) and "usually" (SAu) and physical fitness (PF) assessed by Harvard PF scores (HPFS). CMRF data included body mass index (BMI), waist circumference (WC), fat mass (FM), fasting insulin (FI), homeostasis model of assessment-insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, and number of metabolic syndrome components (MSC)...
February 20, 2018: Genetic Epidemiology
Jiebiao Wang, Qianying Liu, Brandon L Pierce, Dezheng Huo, Olufunmilayo I Olopade, Habibul Ahsan, Lin S Chen
There is a growing recognition that gene-environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome-wide analysis remains challenging due to power issues. In genome-wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses...
February 11, 2018: Genetic Epidemiology
Hugues Aschard, Donna Spiegelman, Vincent Laville, Pete Kraft, Molin Wang
The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression...
February 8, 2018: Genetic Epidemiology
Sahir Rai Bhatnagar, Yi Yang, Budhachandra Khundrakpam, Alan C Evans, Mathieu Blanchette, Luigi Bouchard, Celia M T Greenwood
Predicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in the analysis of high-dimensional (HD) data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly used two-step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype...
February 8, 2018: Genetic Epidemiology
Chong Wu, Wei Pan
Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits...
February 7, 2018: Genetic Epidemiology
Canhong Wen, Chintan M Mehta, Haizhu Tan, Heping Zhang
Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate diffusion tensor imaging measurements, and analyzed a genome-wide association study (GWAS) dataset collected by the Pediatric Imaging, Neurocognition, and Genetics (PING) study...
February 7, 2018: Genetic Epidemiology
Anthony Francis Herzig, Teresa Nutile, Marie-Claude Babron, Marina Ciullo, Céline Bellenguez, Anne-Louise Leutenegger
In the search for genetic associations with complex traits, population isolates offer the advantage of reduced genetic and environmental heterogeneity. In addition, cost-efficient next-generation association approaches have been proposed in these populations where only a subsample of representative individuals is sequenced and then genotypes are imputed into the rest of the population. Gene mapping in such populations thus requires high-quality genetic imputation and preliminary phasing. To identify an effective study design, we compare by simulation a range of phasing and imputation software and strategies...
January 10, 2018: Genetic Epidemiology
Arnab Maity, Jing Zhao, Patrick F Sullivan, Jung-Ying Tzeng
We consider the problem of assessing the joint effect of a set of genetic markers on multiple, possibly correlated phenotypes of interest. We develop a kernel machine based multivariate regression framework, where the joint effect of the marker set on each of the phenotypes is modeled using prespecified kernel functions with unknown variance components. Unlike most existing methods that mainly focus on the global association between the marker set and the phenotype set, we develop estimation and testing procedures to study phenotype-specific associations...
January 3, 2018: Genetic Epidemiology
Eden R Martin, Ilker Tunc, Zhi Liu, Susan H Slifer, Ashley H Beecham, Gary W Beecham
Population substructure can lead to confounding in tests for genetic association, and failure to adjust properly can result in spurious findings. Here we address this issue of confounding by considering the impact of global ancestry (average ancestry across the genome) and local ancestry (ancestry at a specific chromosomal location) on regression parameters and relative power in ancestry-adjusted and -unadjusted models. We examine theoretical expectations under different scenarios for population substructure; applying different regression models, verifying and generalizing using simulations, and exploring the findings in real-world admixed populations...
December 30, 2017: Genetic Epidemiology
Ni Zhao, Xiang Zhan, Yen-Tsung Huang, Lynn M Almli, Alicia Smith, Michael P Epstein, Karen Conneely, Michael C Wu
Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association...
December 29, 2017: Genetic Epidemiology
Yiwen Luo, Arnab Maity, Michael C Wu, Chris Smith, Qing Duan, Yun Li, Jung-Ying Tzeng
Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e...
December 26, 2017: Genetic Epidemiology
Debashree Ray, Michael Boehnke
Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses...
March 2018: Genetic Epidemiology
Minsun Song, William Wheeler, Neil E Caporaso, Maria Teresa Landi, Nilanjan Chatterjee
Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population...
March 2018: Genetic Epidemiology
Desmond D Campbell, Yiming Li, Pak C Sham
Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user-friendly interface, extending a reported methodology based on a liability-threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects...
March 2018: Genetic Epidemiology
Haley A Moss, Goli Samimi, Laura J Havrilesky, Mark E Sherman, Evan R Myers
U.S. guidelines recommend BRCA1/2 mutation testing for women diagnosed with high-grade ovarian cancer (HGOC) to increase recognition of carriers, but most remain unidentified and at risk. Accordingly, an approach termed "Traceback" has been proposed in which probands are retrospectively identified by testing archived pathology specimens, and family members are traced to provide genetic counseling and testing. We used population-based data to estimate the number of family members who might be contacted through such a program...
February 2018: Genetic Epidemiology
Wei Xu, Meiling Hao
The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E), and skewed X-chromosome inactivation (XCI-S). Although these expressions are included in various predesigned genetic variation chip platforms, the X-chromosome has generally been excluded from the majority of genome-wide association studies analyses; this is most likely due to the lack of a standardized method in handling X-chromosomal genotype data...
February 2018: Genetic Epidemiology
Frank Dudbridge, Nora Pashayan, Jian Yang
The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk...
February 2018: Genetic Epidemiology
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