A meta ‐analytic framework for detection of genetic interactions
ABSTRACT
With varying, but substantial, proportions of heritability remaining unexplained by summaries of single‐SNP genetic variation, there is a demand for methods that extract maximal information from genetic association studies. One source of variation that is difficult to assess is genetic interactions. A major challenge for naive detection methods is the large number of possible combinations, with a requisite need to correct for multiple testing. Assumptions of large marginal effects, to reduce the search space, may be restrictive and miss higher order interactions with modest marginal effects. In this paper, we pr...
Source: Genetic Epidemiology - August 16, 2016 Category: Epidemiology Authors: Yulun Liu, Yong Chen, Paul Scheet Tags: Research Article Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - August 9, 2016 Category: Epidemiology Tags: Issue Information Source Type: research
A global test for gene ‐gene interactions based on random matrix theory
ABSTRACT
Statistical interactions between markers of genetic variation, or gene‐gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene‐gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene‐gene interactions. In these cases, a global test for gene‐gene interactions may be the best option. Global tests have much greater ...
Source: Genetic Epidemiology - July 6, 2016 Category: Epidemiology Authors: H. Robert Frost, Christopher I. Amos, Jason H. Moore Tags: Research Article Source Type: research
A global test for gene‐gene interactions based on random matrix theory
ABSTRACT
Statistical interactions between markers of genetic variation, or gene‐gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene‐gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene‐gene interactions. In these cases, a global test for gene‐gene interactions may be the best option. Global tests have much greater ...
Source: Genetic Epidemiology - July 6, 2016 Category: Epidemiology Authors: H. Robert Frost, Christopher I. Amos, Jason H. Moore Tags: Research Article Source Type: research
Obesity and associated lifestyles modify the effect of glucose metabolism ‐related genetic variants on impaired glucose homeostasis among postmenopausal women
ConclusionsOur data support the important role of obesity in modifying glucose homeostasis in response to glucose metabolism–relevant variants. These findings may inform research on the role of glucose homeostasis in the etiology of chronic disease and the development of intervention strategies to reduce risk in postmenopausal women. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - July 4, 2016 Category: Epidemiology Authors: Su Yon Jung, Eric M. Sobel, Jeanette C. Papp, Carolyn J. Crandall, Alan N. Fu, Zuo ‐Feng Zhang Tags: Research Article Source Type: research
Prioritizing individual genetic variants after kernel machine testing using variable selection
ABSTRACT
Kernel machine learning methods, such as the SNP‐set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single‐SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi‐SNP testing approaches, kernel machine testing can draw conclusion only at the SNP‐set level, and does not directly inform on which one(s) o...
Source: Genetic Epidemiology - June 30, 2016 Category: Epidemiology Authors: Qianchuan He, Tianxi Cai, Yang Liu, Ni Zhao, Quaker E. Harmon, Lynn M. Almli, Elisabeth B. Binder, Stephanie M. Engel, Kerry J. Ressler, Karen N. Conneely, Xihong Lin, Michael C. Wu Tags: Research Article Source Type: research
Toward the integration of Omics data in epidemiological studies: still a “long and winding road”
ABSTRACT
Primary and secondary prevention can highly benefit a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavor requires a precise characterization of the individual exposome, genome, and phenome. Massive molecular omics data representing the different layers of the biological processes of the host and the nonhost will enable to build more accurate risk prediction models. Epidemiologists aim to integrate omics data along with important...
Source: Genetic Epidemiology - June 30, 2016 Category: Epidemiology Authors: Evangelina Maturana, Sílvia Pineda, Angela Brand, Kristel Steen, Núria Malats Tags: Review Article Source Type: research
A perspective on interaction effects in genetic association studies
ABSTRACT
The identification of gene–gene and gene–environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression‐based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypo...
Source: Genetic Epidemiology - June 30, 2016 Category: Epidemiology Authors: Hugues Aschard Tags: Research Article Source Type: research
Obesity and associated lifestyles modify the effect of glucose metabolism‐related genetic variants on impaired glucose homeostasis among postmenopausal women
ConclusionsOur data support the important role of obesity in modifying glucose homeostasis in response to glucose metabolism–relevant variants. These findings may inform research on the role of glucose homeostasis in the etiology of chronic disease and the development of intervention strategies to reduce risk in postmenopausal women. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - June 30, 2016 Category: Epidemiology Authors: Su Yon Jung, Eric M. Sobel, Jeanette C. Papp, Carolyn J. Crandall, Alan N. Fu, Zuo‐Feng Zhang Tags: Research Article Source Type: research
A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits
ABSTRACT
In association studies of complex traits, fixed‐effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance‐component tests based on mixed models were developed for region‐based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT‐O), and a combined sum test of rare and common variant effect (SKAT‐C). Although there are some comparison studies to evaluate the performance of mixed and fixed model...
Source: Genetic Epidemiology - June 30, 2016 Category: Epidemiology Authors: Ruzong Fan, Chi‐yang Chiu, Jeesun Jung, Daniel E. Weeks, Alexander F. Wilson, Joan E. Bailey‐Wilson, Christopher I. Amos, Zhen Chen, James L. Mills, Momiao Xiong Tags: Research Article Source Type: research
FARVATX: Family‐Based Rare Variant Association Test for X‐Linked Genes
In this report, we propose new statistical methods for rare X‐linked variant genetic association analysis of dichotomous phenotypes with family‐based samples. The proposed methods are computationally efficient and can complete X‐linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare‐variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X‐linked genes were identified, illustrating the practical importance of the proposed methods. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - June 21, 2016 Category: Epidemiology Authors: Sungkyoung Choi, Sungyoung Lee, Dandi Qiao, Megan Hardin, Michael H. Cho, Edwin K Silverman, Taesung Park, Sungho Won Tags: Research Article Source Type: research
FARVATX: Family ‐Based Rare Variant Association Test for X‐Linked Genes
In this report, we propose new statistical methods for rare X‐linked variant genetic association analysis of dichotomous phenotypes with family‐based samples. The proposed methods are computationally efficient and can complete X‐linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare‐variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X‐linked genes were identified, illustrating the practical importance of the proposed methods. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - June 20, 2016 Category: Epidemiology Authors: Sungkyoung Choi, Sungyoung Lee, Dandi Qiao, Megan Hardin, Michael H. Cho, Edwin K Silverman, Taesung Park, Sungho Won Tags: Research Article Source Type: research
A Clustered Multiclass Likelihood‐Ratio Ensemble Method for Family‐Based Association Analysis Accounting for Phenotypic Heterogeneity
ABSTRACT
Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family‐based association analysis allowing for phenotypic heterogeneity, we propose a clustered mu...
Source: Genetic Epidemiology - June 20, 2016 Category: Epidemiology Authors: Yalu Wen, Qing Lu Tags: Research Article Source Type: research
A Clustered Multiclass Likelihood ‐Ratio Ensemble Method for Family‐Based Association Analysis Accounting for Phenotypic Heterogeneity
ABSTRACT
Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family‐based association analysis allowing for phenotypic heterogeneity, we propose a clustered mu...
Source: Genetic Epidemiology - June 18, 2016 Category: Epidemiology Authors: Yalu Wen, Qing Lu 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 17, 2016 Category: Epidemiology Authors: Md Hamidul Huque, Raymond J. Carroll, Nancy Diao, David C. Christiani, Louise M. Ryan Tags: Research Article Source Type: research
