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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
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

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 ...
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 estimatin...
Source: Genetic Epidemiology - December 18, 2017 Category: Epidemiology Authors: Stefan Konigorski, Yuan Wang, Candemir Cigsar, Yildiz E. Yilmaz Tags: RESEARCH ARTICLE 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

Properties of global ‐ and local‐ancestry adjustments in genetic association tests in admixed populations
Abstract 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 ...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Eden R. Martin, Ilker Tunc, Zhi Liu, Susan H. Slifer, Ashley H. Beecham, Gary W. Beecham Tags: RESEARCH ARTICLE Source Type: research

Kernel machine methods for integrative analysis of genome ‐wide methylation and genotyping studies
ABSTRACT 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 asso...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Ni Zhao, Xiang Zhan, Yen ‐Tsung Huang, Lynn M Almli, Alicia Smith, Michael P. Epstein, Karen Conneely, Michael C. Wu Tags: RESEARCH ARTICLE Source Type: research

On the substructure controls in rare variant analysis: Principal components or variance components?
Abstract 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.g., common, less frequent, rare, or all variants) should be used t...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Yiwen Luo, Arnab Maity, Michael C. Wu, Chris Smith, Qing Duan, Yun Li, Jung ‐Ying Tzeng Tags: RESEARCH ARTICLE Source Type: research

Integrating genome ‐wide association study summaries and element‐gene interaction datasets identified multiple associations between elements and complex diseases
In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element‐gene interaction datasets and genome‐wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element‐gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD). GWAS summary datasets of 24 complex diseases or traits were downloaded from the dbGaP or GEFOS websites. We observed significant associations between 7 elements and 13 complex diseases or traits ...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Awen He, Wenyu Wang, N. Tejo Prakash, Alexey A. Tinkov, Anatoly V. Skaln, Yan Wen, Jingcan Hao, Xiong Guo, Feng Zhang Tags: RESEARCH ARTICLE Source Type: research

Two ‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing
ABSTRACT We evaluate two‐phase designs to follow‐up findings from genome‐wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation‐maximization‐based inference under a semiparametric maximum likelihood formulation tailored for post‐GWAS inference. A GWAS‐SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT‐SNP‐dependent sampling and...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Osvaldo Espin ‐Garcia, Radu V. Craiu, Shelley B. Bull Tags: RESEARCH ARTICLE Source Type: research

A robust and powerful two ‐step testing procedure for local ancestry adjusted allelic association analysis in admixed populations
ABSTRACT Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we pr...
Source: Genetic Epidemiology - December 1, 2017 Category: Epidemiology Authors: Qing Duan, Zheng Xu, Laura M. Raffield, Suhua Chang, Di Wu, Ethan M. Lange, Alex P. Reiner, Yun Li Tags: RESEARCH ARTICLE Source Type: research

Estimating the number of potential family members eligible for BRCA1 and BRCA2 mutation testing in a “Traceback” approach
ABSTRACT 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. We used incidence data from the Surveillance, Epidemiology, and End Result...
Source: Genetic Epidemiology - November 30, 2017 Category: Epidemiology Authors: Haley A. Moss, Goli Samimi, Laura J. Havrilesky, Mark E. Sherman, Evan R. Myers Tags: RESEARCH ARTICLE Source Type: research

Gene ‐based genetic association test with adaptive optimal weights
ABSTRACT It is well known that using proper weights for genetic variants is crucial in enhancing the power of gene‐ or pathway‐based association tests. To increase the power, we propose a general approach that adaptively selects weights among a class of weight families and apply it to the popular sequencing kernel association test. Through comprehensive simulation studies, we demonstrate that the proposed method can substantially increase power under some conditions. Applications to real data are also presented. This general approach can be extended to all current set‐based rare variant association tests whose perfor...
Source: Genetic Epidemiology - November 27, 2017 Category: Epidemiology Authors: Zhongxue Chen, Yan Lu, Tong Lin, Qingzhong Liu, Kai Wang Tags: RESEARCH ARTICLE Source Type: research

A unified partial likelihood approach for X ‐chromosome association on time‐to‐event outcomes
Abstract 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. To analyze the X‐linked genetic association for time‐to‐event outcomes with the actu...
Source: Genetic Epidemiology - November 26, 2017 Category: Epidemiology Authors: Wei Xu, Meiling Hao Tags: RESEARCH ARTICLE Source Type: research

Multifactorial disease risk calculator: Risk prediction for multifactorial disease pedigrees
ABSTRACT 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...
Source: Genetic Epidemiology - November 26, 2017 Category: Epidemiology Authors: Desmond D Campbell, Yiming Li, Pak C Sham Tags: BRIEF REPORT Source Type: research

Using imputed genotype data in the joint score tests for genetic association and gene –environment interactions in case‐control studies
ABSTRACT 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 ...
Source: Genetic Epidemiology - November 26, 2017 Category: Epidemiology Authors: Minsun Song, William Wheeler, Neil E. Caporaso, Maria Teresa Landi, Nilanjan Chatterjee Tags: RESEARCH ARTICLE Source Type: research

Predictive accuracy of combined genetic and environmental risk scores
ABSTRACT 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...
Source: Genetic Epidemiology - November 26, 2017 Category: Epidemiology Authors: Frank Dudbridge, Nora Pashayan, Jian Yang Tags: RESEARCH ARTICLE Source Type: research

Family ‐based tests for associating haplotypes with general phenotype data
ABSTRACT For family‐based association studies, Horvath et al. proposed an algorithm for the association analysis between haplotypes and arbitrary phenotypes when the phase of the haplotypes is unknown, that is, genotype data is given. Their approach to haplotype analysis maintains the original features of the TDT/FBAT‐approach, that is, complete robustness against genetic confounding and misspecification of the phenotype. The algorithm has been implemented in the FBAT and PBAT software package and has been used in numerous substantive manuscripts. Here, we propose a simplification of the original algorithm that ma...
Source: Genetic Epidemiology - November 21, 2017 Category: Epidemiology Authors: Julian Hecker, Xin Xu, F. William Townes, Heide Loehlein Fier, Chris Corcoran, Nan Laird, Christoph Lange Tags: BRIEF COMMUNICATION Source Type: research

Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - November 19, 2017 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research

Integrative sparse principal component analysis of gene expression data
In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - November 8, 2017 Category: Epidemiology Authors: Mengque Liu, Xinyan Fan, Kuangnan Fang, Qingzhao Zhang, Shuangge Ma Tags: RESEARCH ARTICLE Source Type: research

On meta ‐ and mega‐analyses for gene–environment interactions
ABSTRACT Gene‐by‐environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega‐ versus meta‐analysis continues. In principle, data from different sites can be integrated at the individual level into a “mega” data set, which can be fit by a joint “mega‐analysis.” Al...
Source: Genetic Epidemiology - November 7, 2017 Category: Epidemiology Authors: Jing Huang, Yulun Liu, Steve Vitale, Trevor M. Penning, Alexander S. Whitehead, Ian A. Blair, Anil Vachani, Margie L. Clapper, Joshua E. Muscat, Philip Lazarus, Paul Scheet, Jason H. Moore, Yong Chen Tags: RESEARCH ARTICLE Source Type: research

Identification of 16q21 as a modifier of nonsyndromic orofacial cleft phenotypes
ABSTRACT Orofacial clefts (OFCs) are common, complex birth defects with extremely heterogeneous phenotypic presentations. Two common subtypes—cleft lip alone (CL) and CL plus cleft palate (CLP)—are typically grouped into a single phenotype for genetic analysis (i.e., CL with or without cleft palate, CL/P). However, mounting evidence suggests there may be unique underlying pathophysiology and/or genetic modifiers influencing expression of these two phenotypes. To this end, we performed a genome‐wide scan for genetic modifiers by directly comparing 450 CL cases with 1,692 CLP cases from 18 recruitment sites acr...
Source: Genetic Epidemiology - November 1, 2017 Category: Epidemiology Authors: Jenna C. Carlson, Jennifer Standley, Aline Petrin, John R. Shaffer, Azeez Butali, Carmen J. Bux ó, Eduardo Castilla, Kaare Christensen, Frederic W.‐D. Deleyiannis, Jacqueline T. Hecht, L. Leigh Field, Ariuntuul Garidkhuu, Lina M. Moreno Uribe, Natsume Tags: RESEARCH ARTICLE Source Type: research

An integrative approach to assess X ‐chromosome inactivation using allele‐specific expression with applications to epithelial ovarian cancer
ABSTRACT X‐chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two‐stage statistical framework to assess skewed XCI and evaluate gene‐level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele‐specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison. We model ASE using a two‐component mi...
Source: Genetic Epidemiology - November 1, 2017 Category: Epidemiology Authors: Nicholas B. Larson, Zachary C. Fogarty, Melissa C. Larson, Kimberly R. Kalli, Kate Lawrenson, Simon Gayther, Brooke L. Fridley, Ellen L. Goode, Stacey J. Winham Tags: RESEARCH ARTICLE Source Type: research

An ancestry ‐based approach for detecting interactions
ConclusionWe show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - November 1, 2017 Category: Epidemiology Authors: Danny S. Park, Itamar Eskin, Eun Yong Kang, Eric R. Gamazon, Celeste Eng, Christopher R. Gignoux, Joshua M. Galanter, Esteban Burchard, Chun J. Ye, Hugues Aschard, Eleazar Eskin, Eran Halperin, Noah Zaitlen Tags: RESEARCH ARTICLE Source Type: research

Multiethnic polygenic risk scores improve risk prediction in diverse populations
ABSTRACT Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a ...
Source: Genetic Epidemiology - November 1, 2017 Category: Epidemiology Authors: Carla M árquez‐Luna, Po‐Ru Loh, , , Alkes L. Price Tags: RESEARCH ARTICLE Source Type: research

Testing for the indirect effect under the null for genome ‐wide mediation analyses
We examined the performance of commonly used mediation testing methods for the indirect effect in genome‐wide mediation studies. When there is no association between the exposure and the mediator and no association between the mediator and the outcome, we show that these common tests are overly conservative. This is a case that will arise frequently in genome‐wide mediation studies. Caution is hence needed when applying the commonly used mediation tests in genome‐wide mediation studies. We evaluated the performance of these methods using simulation studies, and performed an epigenome‐wide mediation association stud...
Source: Genetic Epidemiology - October 30, 2017 Category: Epidemiology Authors: Richard Barfield, Jincheng Shen, Allan C. Just, Pantel S. Vokonas, Joel Schwartz, Andrea A. Baccarelli, Tyler J. VanderWeele, Xihong Lin Tags: RESEARCH ARTICLE Source Type: research

Rare ‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)
Abstract Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene‐based association tests have been proposed to aggregate the genetic variants within a gene, pathway, or specific genomic region as opposed to a one‐at‐a‐time single variant analysis. In addition, in longitudinal studies, statistical power to detect disease susceptibility rare variants can be improved through jointly testing repeatedly measured outcomes, which better describes the temporal development of the trait of interes...
Source: Genetic Epidemiology - October 27, 2017 Category: Epidemiology Authors: Zihuai He, Seunggeun Lee, Min Zhang, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Iuliana Ionita ‐Laza, Bhramar Mukherjee Tags: RESEARCH ARTICLE Source Type: research

On the testing of Hardy ‐Weinberg proportions and equality of allele frequencies in males and females at biallelic genetic markers
ABSTRACT Standard statistical tests for equality of allele frequencies in males and females and tests for Hardy‐Weinberg equilibrium are tightly linked by their assumptions. Tests for equality of allele frequencies assume Hardy‐Weinberg equilibrium, whereas the usual chi‐square or exact test for Hardy‐Weinberg equilibrium assume equality of allele frequencies in the sexes. In this paper, we propose ways to break this interdependence in assumptions of the two tests by proposing an omnibus exact test that can test both hypotheses jointly, as well as a likelihood ratio approach that permits these phenomena to be teste...
Source: Genetic Epidemiology - October 26, 2017 Category: Epidemiology Authors: Jan Graffelman, Bruce S. Weir Tags: RESEARCH ARTICLE Source Type: research

Impact of sample collection participation on the validity of estimated measures of association in the National Birth Defects Prevention Study when assessing gene ‐environment interactions
Abstract To better understand the impact that nonresponse for specimen collection has on the validity of estimates of association, we examined associations between self‐reported maternal periconceptional smoking, folic acid use, or pregestational diabetes mellitus and six birth defects among families who did and did not submit buccal cell samples for DNA following a telephone interview as part of the National Birth Defects Prevention Study (NBDPS). Analyses included control families with live born infants who had no birth defects (N = 9,465), families of infants with anorectal atresia or stenosis (N = 873), limb reductio...
Source: Genetic Epidemiology - October 25, 2017 Category: Epidemiology Authors: Mary M. Jenkins, Jennita Reefhuis, Amy H. Herring, Margaret A. Honein Tags: RESEARCH ARTICLE Source Type: research

Estimation of a significance threshold for epigenome ‐wide association studies
ABSTRACT Epigenome‐wide association studies (EWAS) are designed to characterise population‐level epigenetic differences across the genome and link them to disease. Most commonly, they assess DNA‐methylation status at cytosine‐guanine dinucleotide (CpG) sites, using platforms such as the Illumina 450k array that profile a subset of CpGs genome wide. An important challenge in the context of EWAS is determining a significance threshold for declaring a CpG site as differentially methylated, taking multiple testing into account. We used a permutation method to estimate a significance threshold specifically for the 450k ...
Source: Genetic Epidemiology - October 16, 2017 Category: Epidemiology Authors: Ayden Saffari, Matt J. Silver, Patrizia Zavattari, Loredana Moi, Amedeo Columbano, Emma L. Meaburn, Frank Dudbridge Tags: RESEARCH ARTICLE Source Type: research

Corrigendum
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 16, 2017 Category: Epidemiology Tags: CORRIGENDUM Source Type: research

Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 16, 2017 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research

Mendelian randomization with fine ‐mapped genetic data: Choosing from large numbers of correlated instrumental variables
ABSTRACT Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine‐mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be used to assess this causal relationship. However, using too many genetic variants in the analysis can lead to spurious estimates and inflated Type 1 error rates. But if only a few genetic variants are used, then the majority of the data is ignored and estimates are highly sensitive to the particular choice of variants. We propose an approach based on summarized data only (gene...
Source: Genetic Epidemiology - September 25, 2017 Category: Epidemiology Authors: Stephen Burgess, Verena Zuber, Elsa Valdes ‐Marquez, Benjamin B Sun, Jemma C Hopewell Tags: RESEARCH ARTICLE Source Type: research

Analysis of cancer gene expression data with an assisted robust marker identification approach
In this study, we develop an ARMI (assisted robust marker identification) approach for analyzing cancer studies with measurements on GEs as well as regulators. The proposed approach borrows information from regulators and can be more effective than analyzing GE data alone. A robust objective function is adopted to accommodate long‐tailed distributions. Marker identification is effectively realized using penalization. The proposed approach has an intuitive formulation and is computationally much affordable. Simulation shows its satisfactory performance under a variety of settings. TCGA (The Cancer Genome Atlas) data on me...
Source: Genetic Epidemiology - September 14, 2017 Category: Epidemiology Authors: Hao Chai, Xingjie Shi, Qingzhao Zhang, Qing Zhao, Yuan Huang, Shuangge Ma Tags: RESEARCH ARTICLE Source Type: research

Phenotype validation in electronic health records based genetic association studies
Abstract The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR‐derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome‐wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case‐control sampling strategy to select subjects for phenotype validation. We develo...
Source: Genetic Epidemiology - September 1, 2017 Category: Epidemiology Authors: Lu Wang, Scott M. Damrauer, Hong Zhang, Alan X. Zhang, Rui Xiao, Jason H. Moore, Jinbo Chen Tags: RESEARCH ARTICLE Source Type: research

Evolutionarily derived networks to inform disease pathways
ABSTRACT Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET). ET utilizes the hypothesis that alleles affecting d...
Source: Genetic Epidemiology - September 1, 2017 Category: Epidemiology Authors: Britney E. Graham, Christian Darabos, Minjun Huang, Louis J. Muglia, Jason H. Moore, Scott M. Williams Tags: RESEARCH ARTICLE Source Type: research

The more you test, the more you find: The smallest P ‐values become increasingly enriched with real findings as more tests are conducted
ABSTRACT The increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow specific scientific hypotheses with statistical analysis, researchers can now test many possible relationships and let statistics generate hypotheses for them. The field of genetic epidemiology is an illustrative case, where testing of candidate genetic variants for association with an outcome has been replaced by agnostic screening of the entire genome. Poor replication rates of candidate gene studies have improved dramatically with the increase in genomic coverage, due to fa...
Source: Genetic Epidemiology - September 1, 2017 Category: Epidemiology Authors: Olga A. Vsevolozhskaya, Chia ‐Ling Kuo, Gabriel Ruiz, Luda Diatchenko, Dmitri V. Zaykin Tags: RESEARCH ARTICLE Source Type: research

Iterative hard thresholding for model selection in genome ‐wide association studies
ABSTRACT A genome‐wide association study (GWAS) correlates marker and trait variation in a study sample. Each subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here, we assume that subjects are randomly collected unrelateds and that trait values are normally distributed or can be transformed to normality. Over the past decade, geneticists have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges. Penalized regression with the ℓ1 penalty (LASSO) or minimax c...
Source: Genetic Epidemiology - September 1, 2017 Category: Epidemiology Authors: Kevin L. Keys, Gary K. Chen, Kenneth Lange Tags: RESEARCH ARTICLE Source Type: research

A multivariate distance ‐based analytic framework for microbial interdependence association test in longitudinal study
ABSTRACT Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high‐throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longit...
Source: Genetic Epidemiology - September 1, 2017 Category: Epidemiology Authors: Yilong Zhang, Sung Won Han, Laura M. Cox, Huilin Li Tags: RESEARCH ARTICLE Source Type: research

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels
ABSTRACT The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels—1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population‐based Nord Tr&oslas...
Source: Genetic Epidemiology - September 1, 2017 Category: Epidemiology Authors: Wei Zhou, Lars G. Fritsche, Sayantan Das, He Zhang, Jonas B. Nielsen, Oddgeir L. Holmen, Jin Chen, Maoxuan Lin, Maiken B. Elvestad, Kristian Hveem, Goncalo R. Abecasis, Hyun Min Kang, Cristen J. Willer Tags: RESEARCH ARTICLE Source Type: research

A functional U ‐statistic method for association analysis of sequencing data
Abstract Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease‐associated genes, the different ty...
Source: Genetic Epidemiology - August 29, 2017 Category: Epidemiology Authors: Sneha Jadhav, Xiaoran Tong, Qing Lu Tags: RESEARCH ARTICLE Source Type: research

The 2017 Annual Meeting of the International Genetic Epidemiology Society
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - August 22, 2017 Category: Epidemiology Tags: ABSTRACTS Source Type: research

Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - August 16, 2017 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research

An efficient study design to test parent ‐of‐origin effects in family trios
ABSTRACT Increasing evidence has shown that genes may cause prenatal, neonatal, and pediatric diseases depending on their parental origins. Statistical models that incorporate parent‐of‐origin effects (POEs) can improve the power of detecting disease‐associated genes and help explain the missing heritability of diseases. In many studies, children have been sequenced for genome‐wide association testing. But it may become unaffordable to sequence their parents and evaluate POEs. Motivated by the reality, we proposed a budget‐friendly study design of sequencing children and only genotyping their parents through sing...
Source: Genetic Epidemiology - July 1, 2017 Category: Epidemiology Authors: Xiaobo Yu, Gao Chen, Rui Feng Tags: RESEARCH ARTICLE Source Type: research