Generalization of Rare Variant Association Tests for Longitudinal Family Studies
ABSTRACT
Given the functional relevance of many rare variants, their identification is frequently critical for dissecting disease etiology. Functional variants are likely to be aggregated in family studies enriched with affected members, and this aggregation increases the statistical power to detect rare variants associated with a trait of interest. Longitudinal family studies provide additional information for identifying genetic and environmental factors associated with disease over time. However, methods to analyze rare variants in longitudinal family data remain fairly limited. These methods should be capable of accoun...
Source: Genetic Epidemiology - January 18, 2016 Category: Epidemiology Authors: Li‐Chu Chien, Fang‐Chi Hsu, Donald W. Bowden, Yen‐Feng Chiu Tags: Research Article Source Type: research
Sequence Kernel Association Test of Multiple Continuous Phenotypes
ABSTRACT
Genetic studies often collect multiple correlated traits, which could be analyzed jointly to increase power by aggregating multiple weak effects and provide additional insights into the etiology of complex human diseases. Existing methods for multiple trait association tests have primarily focused on common variants. There is a surprising dearth of published methods for testing the association of rare variants with multiple correlated traits. In this paper, we extend the commonly used sequence kernel association test (SKAT) for single‐trait analysis to test for the joint association of rare variant sets with mul...
Source: Genetic Epidemiology - January 18, 2016 Category: Epidemiology Authors: Baolin Wu, James S. Pankow Tags: Research Article Source Type: research
A Pipeline for Classifying Relationships Using Dense SNP/SNV Data and Putative Pedigree Information
ABSTRACT
When genome‐wide association studies (GWAS) or sequencing studies are performed on family‐based datasets, the genotype data can be used to check the structure of putative pedigrees. Even in datasets of putatively unrelated people, close relationships can often be detected using dense single‐nucleotide polymorphism/variant (SNP/SNV) data. A number of methods for finding relationships using dense genetic data exist, but they all have certain limitations, including that they typically use average genetic sharing, which is only a subset of the available information. Here, we present a set of approaches for class...
Source: Genetic Epidemiology - December 29, 2015 Category: Epidemiology Authors: Zhen Zeng, Daniel E Weeks, Wei Chen, Nandita Mukhopadhyay, Eleanor Feingold Tags: Research Article Source Type: research
Adaptive Set‐Based Methods for Association Testing
ABSTRACT
With a typical sample size of a few thousand subjects, a single genome‐wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)‐at‐a‐time methods can only detect genetic variants conferring a sizable effect on disease risk. Set‐based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self‐contained set‐based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set‐based approache...
Source: Genetic Epidemiology - December 28, 2015 Category: Epidemiology Authors: Yu‐Chen Su, William James Gauderman, Kiros Berhane, Juan Pablo Lewinger Tags: Research Article Source Type: research
Regionally Smoothed Meta‐Analysis Methods for GWAS Datasets
In this study, we propose regionally smoothed meta‐analysis methods and compare their performance on real and simulated data. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 28, 2015 Category: Epidemiology Authors: Ferdouse Begum, Monir H. Sharker, Stephanie L. Sherman, George C. Tseng, Eleanor Feingold Tags: Research Article Source Type: research
A Framework for Interpreting Type I Error Rates from a Product‐Term Model of Interaction Applied to Quantitative Traits
ABSTRACT
Adequate control of type I error rates will be necessary in the increasing genome‐wide search for interactive effects on complex traits. After observing unexpected variability in type I error rates from SNP‐by‐genome interaction scans, we sought to characterize this variability and test the ability of heteroskedasticity‐consistent standard errors to correct it. We performed 81 SNP‐by‐genome interaction scans using a product‐term model on quantitative traits in a sample of 1,053 unrelated European Americans from the NHLBI Family Heart Study, and additional scans on five simulated datasets. We found th...
Source: Genetic Epidemiology - December 15, 2015 Category: Epidemiology Authors: Tara J. Rao, Michael A. Province Tags: Research Article Source Type: research
Contents
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 14, 2015 Category: Epidemiology Tags: Contents Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 14, 2015 Category: Epidemiology Tags: Issue Information Source Type: research
USAT: A Unified Score‐Based Association Test for Multiple Phenotype‐Genotype Analysis
ABSTRACT
Genome‐wide association studies (GWASs) for complex diseases often collect data on multiple correlated endo‐phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where MANOVA may not ha...
Source: Genetic Epidemiology - December 7, 2015 Category: Epidemiology Authors: Debashree Ray, James S. Pankow, Saonli Basu Tags: Research Article Source Type: research
Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias
ABSTRACT
Machine learning methods, including Random Forests (RF), are increasingly used for genetic data analysis. However, the standard RF algorithm does not correctly model the effects of X chromosome single nucleotide polymorphisms (SNPs), leading to biased estimates of variable importance. We propose extensions of RF to correctly model X SNPs, including a stratified approach and an approach based on the process of X chromosome inactivation. We applied the new and standard RF approaches to case‐control alcohol dependence data from the Study of Addiction: Genes and Environment (SAGE), and compared the performance of th...
Source: Genetic Epidemiology - December 7, 2015 Category: Epidemiology Authors: Stacey J. Winham, Gregory D. Jenkins, Joanna M. Biernacka Tags: Research Article Source Type: research
Meta‐Analysis of Rare Variant Association Tests in Multiethnic Populations
In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single‐ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single‐ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component‐based tests had greater power when some causal ...
Source: Genetic Epidemiology - December 7, 2015 Category: Epidemiology Authors: Akweley Mensah‐Ablorh, Sara Lindstrom, Christopher A. Haiman, Brian E. Henderson, Loic Le Marchand, Seunngeun Lee, Daniel O. Stram, A. Heather Eliassen, Alkes Price, Peter Kraft Tags: Research Article Source Type: research
Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies
Abstract
Kernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem ...
Source: Genetic Epidemiology - December 7, 2015 Category: Epidemiology Authors: Jun Chen, Wenan Chen, Ni Zhao, Michael C. Wu, Daniel J. Schaid Tags: Research Article Source Type: research
Identifying a Deletion Affecting Total Lung Capacity Among Subjects in the COPDGene Study Cohort
ABSTRACT
Chronic obstructive pulmonary disease (COPD) is a progressive disease with both environmental and genetic risk factors. Genome‐wide association studies (GWAS) have identified multiple genomic regions influencing risk of COPD. To thoroughly investigate the genetic etiology of COPD, however, it is also important to explore the role of copy number variants (CNVs) because the presence of structural variants can alter gene expression and can be causal for some diseases. Here, we investigated effects of polymorphic CNVs on quantitative measures of pulmonary function and chest computed tomography (CT) phenotypes among ...
Source: Genetic Epidemiology - December 1, 2015 Category: Epidemiology Authors: Ferdouse Begum, Ingo Ruczinski, Shengchao Li, Edwin K. Silverman, Michael H. Cho, David A. Lynch, Douglas Curran‐Everett, James Crapo, Robert B. Scharpf, Margaret M. Parker, Jacqueline B. Hetmanski, Terri H. Beaty Tags: Research Article Source Type: research
Three Approaches to Modeling Gene‐Environment Interactions in Longitudinal Family Data: Gene‐Smoking Interactions in Blood Pressure
ABSTRACT
Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene‐smoking interactions have detected novel BP loci in cross‐sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Hear...
Source: Genetic Epidemiology - December 1, 2015 Category: Epidemiology Authors: Jacob Basson, Yun Ju Sung, Lisa de las Fuentes, Karen L. Schwander, Ana Vazquez, Dabeeru C. Rao Tags: Research Article Source Type: research
Causal Genetic Inference Using Haplotypes as Instrumental Variables
In this study, we propose to replace SNPs with haplotypes as IVs to increase the variant‐expression association and thus improve the casual effect inference of the expression. In the classical two‐stage procedure, we developed a haplotype regression model combined with a model selection procedure to identify optimal instruments. The performance of the new method was evaluated through simulations and compared with the IV approaches using observed multiple SNPs. Our results showed the gain of power to detect a causal effect of gene or protein on the outcome using haplotypes compared with using only observed SNPs, under e...
Source: Genetic Epidemiology - December 1, 2015 Category: Epidemiology Authors: Fan Wang, Nuala J. Meyer, Keith R. Walley, James A. Russell, Rui Feng Tags: Research Article Source Type: research
