Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies

Haplotype analysis has been increasingly used to study the genetic basis of human diseases, but models for characterizing genetic interactions between haplotypes from different chromosomal regions have not been well developed in the current literature. In this article, we describe a statistical mode...

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Main Authors: Li eZhang, Ruitao eLiu, zhong ewang, Daniel eCulver, Rongling eWu
Format: Article
Language:English
Published: Frontiers Media S.A. 2012-01-01
Series:Frontiers in Genetics
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00002/full
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author Li eZhang
Ruitao eLiu
zhong ewang
Daniel eCulver
Rongling eWu
author_facet Li eZhang
Ruitao eLiu
zhong ewang
Daniel eCulver
Rongling eWu
author_sort Li eZhang
collection DOAJ
description Haplotype analysis has been increasingly used to study the genetic basis of human diseases, but models for characterizing genetic interactions between haplotypes from different chromosomal regions have not been well developed in the current literature. In this article, we describe a statistical model for testing haplotype-haplotype interactions for human diseases with a case-control genetic association design. The model is formulated on a contingency table in which cases and controls are typed for the same set of molecular markers. By integrating well-established quantitative genetic principles, the model is equipped with a capacity to characterize physiologically meaningful epistasis arising from interactions between haplotypes from different chromosomal regions. The model allows the partition of epistasis into different components due to additive × additive, additive × dominance, dominance × additive, and dominance × dominance interactions. We derive the EM algorithm to estimate and test the effects of each of these components on differences in the pattern of genetic variation between cases and controls and, therefore, examine their role in the pathogenesis of human diseases. The method was further extended to investigate gene-environment interactions expressed at the haplotype level. The statistical properties of the models were investigated through simulation studies and its usefulness and utilization validated by analyzing the genetic association of sarcoidosis from a human genetics project.
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spelling doaj.art-bfa2bba2b0ff4a659be9b172ccbfa9562022-12-21T20:01:19ZengFrontiers Media S.A.Frontiers in Genetics1664-80212012-01-01310.3389/fgene.2012.0000216720Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association StudiesLi eZhang0Ruitao eLiu1zhong ewang2Daniel eCulver3Rongling eWu4Cleveland ClinicUniversity of IowaPennsylvania State UniversityCleveland ClinicPennsylvania State UniversityHaplotype analysis has been increasingly used to study the genetic basis of human diseases, but models for characterizing genetic interactions between haplotypes from different chromosomal regions have not been well developed in the current literature. In this article, we describe a statistical model for testing haplotype-haplotype interactions for human diseases with a case-control genetic association design. The model is formulated on a contingency table in which cases and controls are typed for the same set of molecular markers. By integrating well-established quantitative genetic principles, the model is equipped with a capacity to characterize physiologically meaningful epistasis arising from interactions between haplotypes from different chromosomal regions. The model allows the partition of epistasis into different components due to additive × additive, additive × dominance, dominance × additive, and dominance × dominance interactions. We derive the EM algorithm to estimate and test the effects of each of these components on differences in the pattern of genetic variation between cases and controls and, therefore, examine their role in the pathogenesis of human diseases. The method was further extended to investigate gene-environment interactions expressed at the haplotype level. The statistical properties of the models were investigated through simulation studies and its usefulness and utilization validated by analyzing the genetic association of sarcoidosis from a human genetics project.http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00002/fullLinkage DisequilibriumEpistasisEM algorithmhaplotyperisk haplotype
spellingShingle Li eZhang
Ruitao eLiu
zhong ewang
Daniel eCulver
Rongling eWu
Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
Frontiers in Genetics
Linkage Disequilibrium
Epistasis
EM algorithm
haplotype
risk haplotype
title Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
title_full Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
title_fullStr Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
title_full_unstemmed Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
title_short Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
title_sort modeling haplotype haplotype interactions in case control genetic association studies
topic Linkage Disequilibrium
Epistasis
EM algorithm
haplotype
risk haplotype
url http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00002/full
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AT ruitaoeliu modelinghaplotypehaplotypeinteractionsincasecontrolgeneticassociationstudies
AT zhongewang modelinghaplotypehaplotypeinteractionsincasecontrolgeneticassociationstudies
AT danieleculver modelinghaplotypehaplotypeinteractionsincasecontrolgeneticassociationstudies
AT ronglingewu modelinghaplotypehaplotypeinteractionsincasecontrolgeneticassociationstudies