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Genetic Epidemiology

Silviu-Alin Bacanu, Kenneth S Kendler
To argue for increased sample collection for disorders without significant findings, researchers resorted to plotting, for multiple traits, the number of significant findings as a function of the sample size. However, for polygenic traits, the prevalence of the disorder confounds the relationship between the number of significant findings and the sample size. To adjust the number of significant findings for prevalence, we develop a method that uses the expected noncentrality of the contrast between liabilities of cases and controls...
May 14, 2018: Genetic Epidemiology
Ni Zhao, Xiang Zhan, Katherine A Guthrie, Caroline M Mitchell, Joseph Larson
The human microbiome is a dynamic system that changes due to diseases, medication, change in diet, etc. The paired design is a common approach to evaluate the microbial changes while controlling for the inherent differences between people. For example, microbiome data may be collected from the same individuals before and after a treatment. Two challenges exist in analyzing this type of data. First, microbiome data are compositional such that the reads for all taxa in each sample are constrained to sum to a constant...
May 7, 2018: Genetic Epidemiology
Jingjing Yang, Sai Chen, Gonçalo Abecasis
Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings...
April 25, 2018: Genetic Epidemiology
Jacob M Keaton, Chuan Gao, Meijian Guan, Jacklyn N Hellwege, Nicholette D Palmer, James S Pankow, Myriam Fornage, James G Wilson, Adolfo Correa, Laura J Rasmussen-Torvik, Jerome I Rotter, Yii-Der I Chen, Kent D Taylor, Stephen S Rich, Lynne E Wagenknecht, Barry I Freedman, Maggie C Y Ng, Donald W Bowden
Although type 2 diabetes (T2D) results from metabolic defects in insulin secretion and insulin sensitivity, most of the genetic risk loci identified to date relates to insulin secretion. We reported that T2D loci influencing insulin sensitivity may be identified through interactions with insulin secretion loci, thereby leading to T2D. Here, we hypothesize that joint testing of variant main effects and interaction effects with an insulin secretion locus increases power to identify genetic interactions leading to T2D...
April 24, 2018: Genetic Epidemiology
Eszter Szekely, Tae-Hwi Linus Schwantes-An, Cristina M Justice, Jeremy A Sabourin, Philip R Jansen, Ryan L Muetzel, Wendy Sharp, Henning Tiemeier, Heejong Sung, Tonya J White, Alexander F Wilson, Philip Shaw
Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants)...
April 22, 2018: Genetic Epidemiology
Xiaoyu Liang, Qiuying Sha, Yeonwoo Rho, Shuanglin Zhang
Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases...
April 22, 2018: Genetic Epidemiology
Pratyaydipta Rudra, K Alaine Broadaway, Erin B Ware, Min A Jhun, Lawrence F Bielak, Wei Zhao, Jennifer A Smith, Patricia A Peyser, Sharon L R Kardia, Michael P Epstein, Debashis Ghosh
Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant...
March 30, 2018: Genetic Epidemiology
Stephannie Shih, Yen-Tsung Huang, Hwai-I Yang
Previous work suggested a genetic component affecting the risk of hepatocellular carcinoma (HCC) and mediation analyses have elucidated potential indirect pathways of these genetic effects. Specifically, the effects of alcohol dehydrogenase (ADH1B) and aldehyde dehydrogenase (ALDH2) genes on HCC risk vary based on alcohol consumption habits. However, alcohol consumption may not be the only mediator in the identified pathway: factors related to alcohol consumption may contribute to the same indirect pathway...
March 30, 2018: 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
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...
April 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...
April 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...
April 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...
April 2018: 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...
April 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...
March 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...
March 2018: 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...
March 2018: Genetic Epidemiology
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