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Genetic Epidemiology,Volume 42, Issue 2, Page 214-229, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 30, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 2, Page 156-167, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 29, 2017 Category: Epidemiology Source Type: research
On the substructure controls in rare variant analysis: Principal components or variance components?
Genetic Epidemiology,Volume 42, Issue 3, Page 276-287, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 26, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 3, Page 276-287, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 26, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 2, Page 187-200, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 18, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 2, Page 174-186, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 18, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 2, Page 168-173, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 18, 2017 Category: Epidemiology Source Type: research
A deeper look at two concepts of measuring gene –gene interactions: logistic regression and interaction information revisited
ABSTRACT
Detection of gene–gene interactions is one of the most important challenges in genome‐wide case–control studies. Besides traditional logistic regression analysis, recently the entropy‐based methods attracted a significant attention. Among entropy‐based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome‐wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We ...
Source: Genetic Epidemiology - December 18, 2017 Category: Epidemiology Authors: Jan Mielniczuk, Pawe ł Teisseyre Tags: RESEARCH ARTICLE Source Type: research
Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations
ABSTRACT
In genetic association studies, it is important to distinguish direct and indirect genetic effects in order to build truly functional models. For this purpose, we consider a directed acyclic graph setting with genetic variants, primary and intermediate phenotypes, and confounding factors. In order to make valid statistical inference on direct genetic effects on the primary phenotype, it is necessary to consider all potential effects in the graph, and we propose to use the estimating equations method with robust Huber–White sandwich standard errors. We evaluate the proposed causal inference based on estimating eq...
Source: Genetic Epidemiology - December 18, 2017 Category: Epidemiology Authors: Stefan Konigorski, Yuan Wang, Candemir Cigsar, Yildiz E. Yilmaz Tags: RESEARCH ARTICLE Source Type: research
A robust and powerful two ‐step testing procedure for local ancestry adjusted allelic association analysis in admixed populations
Genetic Epidemiology,Volume 42, Issue 3, Page 288-302, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 10, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 2, Page 134-145, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 10, 2017 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 3, Page 288-302, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 10, 2017 Category: Epidemiology Source Type: research
Methods for meta ‐analysis of multiple traits using GWAS summary statistics
ABSTRACT
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. Recently several multivariate methods have been proposed that require individual‐level da...
Source: Genetic Epidemiology - December 10, 2017 Category: Epidemiology Authors: Debashree Ray, Michael Boehnke Tags: RESEARCH ARTICLE Source Type: research
Strategies for phasing and imputation in a population isolate
ABSTRACT
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....
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Anthony Francis Herzig, Teresa Nutile, Marie ‐Claude Babron, Marina Ciullo, Céline Bellenguez, Anne‐Louise Leutenegger Tags: RESEARCH ARTICLE Source Type: research
Inference on phenotype ‐specific effects of genes using multivariate kernel machine regression
ABSTRACT
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. Specifically, we develop an estimation method based on th...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Arnab Maity, Jing Zhao, Patrick F. Sullivan, Jung ‐Ying Tzeng Tags: RESEARCH ARTICLE Source Type: research
