Family ‐Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis
In this report, we describe one such implementation: the multivariate family‐based rare variant association tool (mFARVAT). mFARVAT is a quasi‐likelihood‐based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software...
Source: Genetic Epidemiology - June 16, 2016 Category: Epidemiology Authors: Longfei Wang, Sungyoung Lee, Jungsoo Gim, Dandi Qiao, Michael Cho, Robert C Elston, Edwin K Silverman, Sungho Won Tags: Research Article Source Type: research
Exposure Enriched Case ‐Control (EECC) Design for the Assessment of Gene–Environment Interaction
ABSTRACT
Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case‐control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case‐control study, the exposure enriched case‐control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, dep...
Source: Genetic Epidemiology - June 16, 2016 Category: Epidemiology Authors: Md Hamidul Huque, Raymond J. Carroll, Nancy Diao, David C. Christiani, Louise M. Ryan Tags: Research Article Source Type: research
Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case‐Control Sequencing Studies
ABSTRACT
Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single‐marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden‐type approaches attempt to identify aggregation of RVs across case‐control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provi...
Source: Genetic Epidemiology - June 16, 2016 Category: Epidemiology Authors: Nicholas B. Larson, Shannon McDonnell, Lisa Cannon Albright, Craig Teerlink, Janet Stanford, Elaine A. Ostrander, William B. Isaacs, Jianfeng Xu, Kathleen A. Cooney, Ethan Lange, Johanna Schleutker, John D. Carpten, Isaac Powell, Joan Bailey‐Wilson, Oli Tags: Research Article Source Type: research
Family‐Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis
In this report, we describe one such implementation: the multivariate family‐based rare variant association tool (mFARVAT). mFARVAT is a quasi‐likelihood‐based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software...
Source: Genetic Epidemiology - June 16, 2016 Category: Epidemiology Authors: Longfei Wang, Sungyoung Lee, Jungsoo Gim, Dandi Qiao, Michael Cho, Robert C Elston, Edwin K Silverman, Sungho Won Tags: Research Article Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - June 15, 2016 Category: Epidemiology Tags: Issue Information Source Type: research
Meta ‐Analysis of Genome‐Wide Association Studies with Correlated Individuals: Application to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)
ABSTRACT
Investigators often meta‐analyze multiple genome‐wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta‐analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta‐analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community. We propose a novel mixed‐effect ...
Source: Genetic Epidemiology - June 2, 2016 Category: Epidemiology Authors: Tamar Sofer, John R. Shaffer, Mariaelisa Graff, Qibin Qi, Adrienne M. Stilp, Stephanie M. Gogarten, Kari E. North, Carmen R. Isasi, Cathy C. Laurie, Adam A. Szpiro Tags: Research Article Source Type: research
G ‐STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies
ABSTRACT
In a large‐scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed‐size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes re...
Source: Genetic Epidemiology - June 2, 2016 Category: Epidemiology Authors: Miaoyan Wang, Johanna Jakobsdottir, Albert V. Smith, Mary Sara McPeek Tags: Research Article Source Type: research
Meta‐Analysis of Genome‐Wide Association Studies with Correlated Individuals: Application to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)
ABSTRACT
Investigators often meta‐analyze multiple genome‐wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta‐analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta‐analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community. We propose a novel mixed‐effect ...
Source: Genetic Epidemiology - June 2, 2016 Category: Epidemiology Authors: Tamar Sofer, John R. Shaffer, Mariaelisa Graff, Qibin Qi, Adrienne M. Stilp, Stephanie M. Gogarten, Kari E. North, Carmen R. Isasi, Cathy C. Laurie, Adam A. Szpiro Tags: Research Article Source Type: research
Identification of Rare Variants in Metabolites of the Carnitine Pathway by Whole Genome Sequencing Analysis
ABSTRACT
We use whole genome sequence data and rare variant analysis methods to investigate a subset of the human serum metabolome, including 16 carnitine‐related metabolites that are important components of mammalian energy metabolism. Medium pass sequence data consisting of 12,820,347 rare variants and serum metabolomics data were available on 1,456 individuals. By applying a penalization method, we identified two genes FGF8 and MDGA2 with significant effects on lysine and cis‐4‐decenoylcarnitine, respectively, using Δ‐AIC and likelihood ratio test statistics. Single variant analyses in these regions did not ide...
Source: Genetic Epidemiology - June 2, 2016 Category: Epidemiology Authors: Akram Yazdani, Azam Yazdani, Xiaoming Liu, Eric Boerwinkle Tags: Research Article Source Type: research
Analyzing Association Mapping in Pedigree ‐Based GWAS Using a Penalized Multitrait Mixed Model
This study has been partly motivated by the analysis of Genetic Analysis Workshop (GAW) 18 data, which have two notable characteristics. First, the subjects are from a small number of pedigrees and hence related. Second, for each subject, multiple correlated traits have been measured. Most of the existing penalization methods assume independence between subjects and traits and can be suboptimal. There are a few methods in the literature based on mixed modeling that can accommodate correlations. However, they cannot fully accommodate the two types of correlations while conducting effective marker selection. In this study, w...
Source: Genetic Epidemiology - May 31, 2016 Category: Epidemiology Authors: Jin Liu, Can Yang, Xingjie Shi, Cong Li, Jian Huang, Hongyu Zhao, Shuangge Ma Tags: Research Article Source Type: research
A Critical Look at Entropy ‐Based Gene‐Gene Interaction Measures
ABSTRACT
Several entropy‐based measures for detecting gene‐gene interaction have been proposed recently. It has been argued that the entropy‐based measures are preferred because entropy can better capture the nonlinear relationships between genotypes and traits, so they can be useful to detect gene‐gene interactions for complex diseases. These suggested measures look reasonable at intuitive level, but so far there has been no detailed characterization of the interactions captured by them. Here we study analytically the properties of some entropy‐based measures for detecting gene‐gene interactions in detail. The...
Source: Genetic Epidemiology - May 26, 2016 Category: Epidemiology Authors: Woojoo Lee, Arvid Sj ölander, Yudi Pawitan Tags: Research Article Source Type: research
Detecting Gene –Environment Interactions for a Quantitative Trait in a Genome‐Wide Association Study
ABSTRACT
A genome‐wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene–environment (G × E) interaction analysis. However, a genome‐wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2‐step methods for GWIS have been p...
Source: Genetic Epidemiology - May 26, 2016 Category: Epidemiology Authors: Pingye Zhang, Juan Pablo Lewinger, David Conti, John L. Morrison, W. James Gauderman Tags: Research Article Source Type: research
An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene‐Lifestyle Interactions Working Group
ABSTRACT
Studying gene‐environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the “joint” framework). The alternative “stratified” framework combines results from genetic main‐effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investiga...
Source: Genetic Epidemiology - May 26, 2016 Category: Epidemiology Authors: Yun Ju Sung, Thomas W. Winkler, Alisa K. Manning, Hugues Aschard, Vilmundur Gudnason, Tamara B. Harris, Albert V. Smith, Eric Boerwinkle, Michael R. Brown, Alanna C. Morrison, Myriam Fornage, Li‐An Lin, Melissa Richard, Traci M. Bartz, Bruce M. Psaty, C Tags: Research Article Source Type: research
Association Between Absolute Neutrophil Count and Variation at TCIRG1: The NHLBI Exome Sequencing Project
ABSTRACT
Neutrophils are a key component of innate immunity. Individuals with low neutrophil count are susceptible to frequent infections. Linkage and association between congenital neutropenia and a single rare missense variant in TCIRG1 have been reported in a single family. Here, we report on nine rare missense variants at evolutionarily conserved sites in TCIRG1 that are associated with lower absolute neutrophil count (ANC; p = 0.005) in 1,058 participants from three cohorts: Atherosclerosis Risk in Communities (ARIC), Coronary Artery Risk Development in Young Adults (CARDIA), and Jackson Heart Study (JHS) of the NHLBI...
Source: Genetic Epidemiology - May 26, 2016 Category: Epidemiology Authors: Elisabeth A. Rosenthal, Vahagn Makaryan, Amber A. Burt, David R. Crosslin, Daniel Seung Kim, Joshua D. Smith, Deborah A. Nickerson, Alex P. Reiner, Stephen S. Rich, Rebecca D. Jackson, Santhi K. Ganesh, Linda M. Polfus, Lihong Qi, David C. Dale, , Gail P. Tags: Research Article Source Type: research
Detecting Gene–Environment Interactions for a Quantitative Trait in a Genome‐Wide Association Study
ABSTRACT
A genome‐wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene–environment (G × E) interaction analysis. However, a genome‐wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2‐step methods for GWIS have been p...
Source: Genetic Epidemiology - May 26, 2016 Category: Epidemiology Authors: Pingye Zhang, Juan Pablo Lewinger, David Conti, John L. Morrison, W. James Gauderman Tags: Research Article Source Type: research
