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A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much... Full description

Journal Title: The American Journal of Human Genetics 03 May 2018, Vol.102(5), pp.904-919
Main Author: Su, Yu-Ru
Other Authors: Di, Chongzhi , Bien, Stephanie , Huang, Licai , Dong, Xinyuan , Abecasis, Goncalo , Berndt, Sonja , Bezieau, Stephane , Brenner, Hermann , Caan, Bette , Casey, Graham , Chang-Claude, Jenny , Chanock, Stephen , Chen, Sai , Connolly, Charles , Curtis, Keith , Figueiredo, Jane , Gala, Manish , Gallinger, Steven , Harrison, Tabitha
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0002-9297 ; E-ISSN: 1537-6605 ; DOI: 10.1016/j.ajhg.2018.03.019
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recordid: elsevier_sdoi_10_1016_j_ajhg_2018_03_019
title: A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics
format: Article
creator:
  • Su, Yu-Ru
  • Di, Chongzhi
  • Bien, Stephanie
  • Huang, Licai
  • Dong, Xinyuan
  • Abecasis, Goncalo
  • Berndt, Sonja
  • Bezieau, Stephane
  • Brenner, Hermann
  • Caan, Bette
  • Casey, Graham
  • Chang-Claude, Jenny
  • Chanock, Stephen
  • Chen, Sai
  • Connolly, Charles
  • Curtis, Keith
  • Figueiredo, Jane
  • Gala, Manish
  • Gallinger, Steven
  • Harrison, Tabitha
subjects:
  • Mixed-Effects Score Test
  • Functional Annotation
  • Expression Quantitative Trait Locus
  • Data-Adaptive Weight
  • Variance Component Test
  • Set-Based Association
  • Genome-Wide Association Study
  • Biology
ispartof: The American Journal of Human Genetics, 03 May 2018, Vol.102(5), pp.904-919
description: Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate...
language: eng
source:
identifier: ISSN: 0002-9297 ; E-ISSN: 1537-6605 ; DOI: 10.1016/j.ajhg.2018.03.019
fulltext: fulltext
issn:
  • 0002-9297
  • 00029297
  • 1537-6605
  • 15376605
url: Link


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titleA Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics
creatorSu, Yu-Ru ; Di, Chongzhi ; Bien, Stephanie ; Huang, Licai ; Dong, Xinyuan ; Abecasis, Goncalo ; Berndt, Sonja ; Bezieau, Stephane ; Brenner, Hermann ; Caan, Bette ; Casey, Graham ; Chang-Claude, Jenny ; Chanock, Stephen ; Chen, Sai ; Connolly, Charles ; Curtis, Keith ; Figueiredo, Jane ; Gala, Manish ; Gallinger, Steven ; Harrison, Tabitha
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subjectMixed-Effects Score Test ; Functional Annotation ; Expression Quantitative Trait Locus ; Data-Adaptive Weight ; Variance Component Test ; Set-Based Association ; Genome-Wide Association Study ; Biology
descriptionGenome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate...
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Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate...

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Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate...

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date2018-05-03