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Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases

Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritabilit...

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Autores principales: Mägi, Reedik, Asimit, Jennifer L, Day-Williams, Aaron G, Zeggini, Eleftheria, Morris, Andrew P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3569874/
https://www.ncbi.nlm.nih.gov/pubmed/22951892
http://dx.doi.org/10.1002/gepi.21675
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author Mägi, Reedik
Asimit, Jennifer L
Day-Williams, Aaron G
Zeggini, Eleftheria
Morris, Andrew P
author_facet Mägi, Reedik
Asimit, Jennifer L
Day-Williams, Aaron G
Zeggini, Eleftheria
Morris, Andrew P
author_sort Mägi, Reedik
collection PubMed
description Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.
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spelling pubmed-35698742013-02-25 Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases Mägi, Reedik Asimit, Jennifer L Day-Williams, Aaron G Zeggini, Eleftheria Morris, Andrew P Genet Epidemiol Research Articles Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits. Blackwell Publishing Ltd 2012-12 2012-09-05 /pmc/articles/PMC3569874/ /pubmed/22951892 http://dx.doi.org/10.1002/gepi.21675 Text en © 2012 Wiley Periodicals, Inc. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
spellingShingle Research Articles
Mägi, Reedik
Asimit, Jennifer L
Day-Williams, Aaron G
Zeggini, Eleftheria
Morris, Andrew P
Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
title Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
title_full Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
title_fullStr Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
title_full_unstemmed Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
title_short Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases
title_sort genome-wide association analysis of imputed rare variants: application to seven common complex diseases
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3569874/
https://www.ncbi.nlm.nih.gov/pubmed/22951892
http://dx.doi.org/10.1002/gepi.21675
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