Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.

The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may co...

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Autores principales:	Yoshitaka Nagamine, Ricardo Pong-Wong, Pau Navarro, Veronique Vitart, Caroline Hayward, Igor Rudan, Harry Campbell, James Wilson, Sarah Wild, Andrew A Hicks, Peter P Pramstaller, Nicholas Hastie, Alan F Wright, Chris S Haley
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9edfd7b3fa8e490a92d7407a5a566bb9
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spelling	oai:doaj.org-article:9edfd7b3fa8e490a92d7407a5a566bb92021-11-18T08:12:06ZLocalising loci underlying complex trait variation using Regional Genomic Relationship Mapping.1932-620310.1371/journal.pone.0046501https://doaj.org/article/9edfd7b3fa8e490a92d7407a5a566bb92012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23077511/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. This approach takes advantage of very distant (and unrecorded) relationships, and this greatly increases the power of the method, compared with traditional pedigree-based linkage analyses. By integrating variance contributed by founder gametes in the population, our approach provides an estimate of the Regional Heritability attributable to a small genomic region (e.g. 100 SNP window covering ca. 1 Mb of DNA in a 300000 SNP GWAS) and has the power to detect regions containing multiple alleles that individually contribute too little variance to be detectable by GWAS as well as regions with single common GWAS-detectable SNPs. We use genome-wide SNP array data to obtain both a genome-wide relationship matrix and regional relationship ("identity by state" or IBS) matrices for sequential regions across the genome. We then estimate a heritability for each region sequentially in our genome-wide scan. We demonstrate by simulation and with real data that, when compared to traditional ("individual SNP") GWAS, our method uncovers new loci that explain additional trait variation. We analysed data from three Southern European populations and from Orkney for exemplar traits - serum uric acid concentration and height. We show that regional heritability estimates are correlated with results from genome-wide association analysis but can capture more of the genetic variance segregating in the population and identify additional trait loci.Yoshitaka NagamineRicardo Pong-WongPau NavarroVeronique VitartCaroline HaywardIgor RudanHarry CampbellJames WilsonSarah WildAndrew A HicksPeter P PramstallerNicholas HastieAlan F WrightChris S HaleyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 10, p e46501 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yoshitaka Nagamine Ricardo Pong-Wong Pau Navarro Veronique Vitart Caroline Hayward Igor Rudan Harry Campbell James Wilson Sarah Wild Andrew A Hicks Peter P Pramstaller Nicholas Hastie Alan F Wright Chris S Haley Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.
description	The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. This approach takes advantage of very distant (and unrecorded) relationships, and this greatly increases the power of the method, compared with traditional pedigree-based linkage analyses. By integrating variance contributed by founder gametes in the population, our approach provides an estimate of the Regional Heritability attributable to a small genomic region (e.g. 100 SNP window covering ca. 1 Mb of DNA in a 300000 SNP GWAS) and has the power to detect regions containing multiple alleles that individually contribute too little variance to be detectable by GWAS as well as regions with single common GWAS-detectable SNPs. We use genome-wide SNP array data to obtain both a genome-wide relationship matrix and regional relationship ("identity by state" or IBS) matrices for sequential regions across the genome. We then estimate a heritability for each region sequentially in our genome-wide scan. We demonstrate by simulation and with real data that, when compared to traditional ("individual SNP") GWAS, our method uncovers new loci that explain additional trait variation. We analysed data from three Southern European populations and from Orkney for exemplar traits - serum uric acid concentration and height. We show that regional heritability estimates are correlated with results from genome-wide association analysis but can capture more of the genetic variance segregating in the population and identify additional trait loci.
format	article
author	Yoshitaka Nagamine Ricardo Pong-Wong Pau Navarro Veronique Vitart Caroline Hayward Igor Rudan Harry Campbell James Wilson Sarah Wild Andrew A Hicks Peter P Pramstaller Nicholas Hastie Alan F Wright Chris S Haley
author_facet	Yoshitaka Nagamine Ricardo Pong-Wong Pau Navarro Veronique Vitart Caroline Hayward Igor Rudan Harry Campbell James Wilson Sarah Wild Andrew A Hicks Peter P Pramstaller Nicholas Hastie Alan F Wright Chris S Haley
author_sort	Yoshitaka Nagamine
title	Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.
title_short	Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.
title_full	Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.
title_fullStr	Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.
title_full_unstemmed	Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.
title_sort	localising loci underlying complex trait variation using regional genomic relationship mapping.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/9edfd7b3fa8e490a92d7407a5a566bb9
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Localising loci underlying complex trait variation using Regional Genomic Relationship Mapping.

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