SimPEL: Simulation ‐based power estimation for sequencing studies of low‐prevalence conditions
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - May 23, 2018 Category: Epidemiology Authors: LaurenMak , MinghaoLi , ChenCao , PaulGordon , MajaTarailo ‐Graovac , ChadBousman , PeiWang , QuanLong Source Type: research

A univariate perspective of multivariate genome ‐wide association analysis
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - May 21, 2018 Category: Epidemiology Authors: XiaoboGuo , JunxianZhu , QiaoFan , MingguangHe , XueqinWang , HepingZhang Source Type: research

Issue Information
Genetic Epidemiology,Volume 42, Issue 4, Page 317-319, June 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - May 17, 2018 Category: Epidemiology Source Type: research

Method to estimate the approximate samples size that yield a certain number of significant GWAS signals in polygenic traits
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - May 15, 2018 Category: Epidemiology Authors: Silviu ‐AlinBacanu , Kenneth S.Kendler Source Type: research

Generalized Hotelling's test for paired compositional data with application to human microbiome studies
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - May 8, 2018 Category: Epidemiology Authors: NiZhao , XiangZhan , Katherine AGuthrie , Caroline MMitchell , JosephLarson Source Type: research

Improved score statistics for meta ‐analysis in single‐variant and gene‐level association studies
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - April 26, 2018 Category: Epidemiology Authors: JingjingYang , SaiChen , Gon çaloAbecasis , Source Type: research

Genome ‐wide interaction with the insulin secretion locus MTNR1B reveals CMIP as a novel type 2 diabetes susceptibility gene in African Americans
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - April 25, 2018 Category: Epidemiology Authors: Jacob M.Keaton , ChuanGao , MeijianGuan , Jacklyn N.Hellwege , Nicholette D.Palmer , James S.Pankow , MyriamFornage , James G.Wilson , AdolfoCorrea , Laura J.Rasmussen ‐Torvik , Jerome I.Rotter , Yii‐Der I.Chen , Kent D.Taylo Source Type: research

A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - April 23, 2018 Category: Epidemiology Authors: XiaoyuLiang , QiuyingSha , YeonwooRho , ShuanglinZhang Source Type: research

Genetic associations with childhood brain growth, defined in two longitudinal cohorts
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - April 23, 2018 Category: Epidemiology Authors: EszterSzekely , Tae ‐ Hwi LinusSchwantes‐An , Cristina M.Justice , Jeremy A.Sabourin , Philip R.Jansen , Ryan L.Muetzel , WendySharp , HenningTiemeier , HeejongSung , Tonya J.White , Alexander F.Wilson , PhilipShaw Source Type: research

Testing cross ‐phenotype effects of rare variants in longitudinal studies of complex traits
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 30, 2018 Category: Epidemiology Source Type: research

A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 30, 2018 Category: Epidemiology Source Type: research

Issue Information
Genetic Epidemiology,Volume 42, Issue 3, Page 231-232, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 14, 2018 Category: Epidemiology Source Type: research

Cover Image
Genetic Epidemiology,Volume 42, Issue 3, Page i-i, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 14, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology,Volume 42, Issue 3, Page 231-232, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 14, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology,Volume 42, Issue 3, Page i-i, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 14, 2018 Category: Epidemiology Source Type: research

Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 14, 2018 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research

Cover Image
The cover image, by Canhong Wen et al., is based on the Research Article Whole genome association study of brain‐wide imaging phenotypes: A study of the ping cohort, DOI: 10.1002/gepi.22111. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 14, 2018 Category: Epidemiology Authors: Canhong Wen, Chintan M. Mehta, Haizhu Tan, Heping Zhang Tags: COVER IMAGE Source Type: research

POLARIS: Polygenic LD ‐adjusted risk score approach for set‐based analysis of GWAS data
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 12, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology, Ahead of Print. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 12, 2018 Category: Epidemiology Source Type: research

Interaction of a genetic risk score with physical activity, physical inactivity, and body mass index in relation to venous thromboembolism risk
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 8, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology, Ahead of Print. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 8, 2018 Category: Epidemiology Source Type: research

POLARIS: Polygenic LD ‐adjusted risk score approach for set‐based analysis of GWAS data
ABSTRACT 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. We cal...
Source: Genetic Epidemiology - March 1, 2018 Category: Epidemiology Authors: Emily Baker, Karl Michael Schmidt, Rebecca Sims, Michael C. O'Donovan, Julie Williams, Peter Holmans, Valentina Escott ‐Price, with the GERAD Consortium Tags: RESEARCH ARTICLE Source Type: research

Interaction of a genetic risk score with physical activity, physical inactivity, and body mass index in relation to venous thromboembolism risk
ConclusionWe found a synergetic effect between a genetic risk score and high BMI on the risk of VTE. Intervention efforts lowering BMI to decrease VTE risk may have particularly large beneficial effects among individuals with high genetic risk. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 1, 2018 Category: Epidemiology Authors: Jihye Kim, Peter Kraft, Kaitlin A. Hagan, Laura B. Harrington, Sara Lindstroem, Christopher Kabrhel Tags: RESEARCH ARTICLE Source Type: research

Powerful and robust cross ‐phenotype association test for case‐parent trios
Abstract 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. In this paper, we describe a rob...
Source: Genetic Epidemiology - February 20, 2018 Category: Epidemiology Authors: S. Taylor Fischer, Yunxuan Jiang, K. Alaine Broadaway, Karen N. Conneely, Michael P. Epstein Tags: RESEARCH ARTICLE Source Type: research

Powerful and robust cross ‐phenotype association test for case‐parent trios
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 20, 2018 Category: Epidemiology Source Type: research

Genetic and environmental (physical fitness and sedentary activity) interaction effects on cardiometabolic risk factors in Mexican American children and adolescents
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 20, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology, Ahead of Print. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 20, 2018 Category: Epidemiology Source Type: research

Genetic and environmental (physical fitness and sedentary activity) interaction effects on cardiometabolic risk factors in Mexican American children and adolescents
We examined potential G × E interaction in the phenotypic expression of CMRFs using variance component models and likelihood‐based statistical inference. Significant G × SA interactions were identified for six CMRFs: BMI, WC, FI, HOMA‐IR, MSC, and HDL, and significant G × HPFS interactions were observed for four CMRFs: BMI, WC, FM, and HOMA‐IR. However, after correcting for multiple hypothesis testing, only WC × SAy, FM × SAy, and FI × SAu interactions became marginally significant. After correcting for multiple testing, most of CMRFs exhibited significant G × E interactions ...
Source: Genetic Epidemiology - February 20, 2018 Category: Epidemiology Authors: 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. DeF Tags: RESEARCH ARTICLE Source Type: research

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Genetic Epidemiology,Volume 42, Issue 2, Page 127-129, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 13, 2018 Category: Epidemiology Source Type: research

Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 13, 2018 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research

A meta ‐analysis approach with filtering for identifying gene‐level gene–environment interactions
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 11, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology, Ahead of Print. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 11, 2018 Category: Epidemiology Source Type: research

A test for gene –environment interaction in the presence of measurement error in the environmental variable
Genetic Epidemiology,Volume 42, Issue 3, Page 250-264, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research

An analytic approach for interpretable predictive models in high ‐dimensional data in the presence of interactions with exposures
Genetic Epidemiology,Volume 42, Issue 3, Page 233-249, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology,Volume 42, Issue 3, Page 250-264, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology,Volume 42, Issue 3, Page 233-249, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research

A test for gene –environment interaction in the presence of measurement error in the environmental variable
Abstract 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, ...
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Authors: Hugues Aschard, Donna Spiegelman, Vincent Laville, Pete Kraft, Molin Wang Tags: RESEARCH ARTICLE Source Type: research

Whole genome association study of brain ‐wide imaging phenotypes: A study of the ping cohort
Genetic Epidemiology,Volume 42, Issue 3, Page 265-275, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research

Integrating eQTL data with GWAS summary statistics in pathway ‐based analysis with application to schizophrenia
Genetic Epidemiology,Volume 42, Issue 3, Page 303-316, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology,Volume 42, Issue 3, Page 265-275, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research

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Genetic Epidemiology,Volume 42, Issue 3, Page 303-316, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research

Whole genome association study of brain ‐wide imaging phenotypes: A study of the ping cohort
Abstract 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 ...
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Authors: Canhong Wen, Chintan M. Mehta, Haizhu Tan, Heping Zhang Tags: RESEARCH ARTICLE Source Type: research

A meta ‐analysis approach with filtering for identifying gene‐level gene–environment interactions
Abstract 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 analyse...
Source: Genetic Epidemiology - February 1, 2018 Category: Epidemiology Authors: Jiebiao Wang, Qianying Liu, Brandon L. Pierce, Dezheng Huo, Olufunmilayo I. Olopade, Habibul Ahsan, Lin S. Chen Tags: RESEARCH ARTICLE Source Type: research

An analytic approach for interpretable predictive models in high ‐dimensional data in the presence of interactions with exposures
ABSTRACT 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. ...
Source: Genetic Epidemiology - February 1, 2018 Category: Epidemiology Authors: Sahir Rai Bhatnagar, Yi Yang, Budhachandra Khundrakpam, Alan C. Evans, Mathieu Blanchette, Luigi Bouchard, Celia M.T. Greenwood Tags: RESEARCH ARTICLE Source Type: research

Integrating eQTL data with GWAS summary statistics in pathway ‐based analysis with application to schizophrenia
ABSTRACT 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 statist...
Source: Genetic Epidemiology - February 1, 2018 Category: Epidemiology Authors: Chong Wu, Wei Pan Tags: RESEARCH ARTICLE Source Type: research

Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - January 15, 2018 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research

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Genetic Epidemiology,Volume 42, Issue 2, Page 201-213, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - January 10, 2018 Category: Epidemiology Source Type: research

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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