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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| Vydáno v: | PloS one Ročník 7; číslo 10; s. e46501 |
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| Jazyk: | angličtina |
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Public Library of Science
15.10.2012
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Wilson, James Haley, Chris S. Rudan, Igor Campbell, Harry Pong-Wong, Ricardo Nagamine, Yoshitaka Hastie, Nicholas Wild, Sarah Hicks, Andrew A. Pramstaller, Peter P. Vitart, Veronique Wright, Alan F. Navarro, Pau Hayward, Caroline |
| AuthorAffiliation | 6 Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy. Affiliated Institute of the University of Lübeck, Germany 3 MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom 5 Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split, Croatia University of Queensland, Australia 4 Centre for Population Health Sciences, University of Edinburgh, Medical School, Edinburgh, United Kingdom 7 Department of Neurology, General Central Hospital, Bolzano, Italy 8 Department of Neurology, University of Lübeck, Lübeck, Germany 1 National Institute of Livestock and Grassland Science, Tsukuba, Japan 2 The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom |
| AuthorAffiliation_xml | – name: 4 Centre for Population Health Sciences, University of Edinburgh, Medical School, Edinburgh, United Kingdom – name: University of Queensland, Australia – name: 3 MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom – name: 6 Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy. Affiliated Institute of the University of Lübeck, Germany – name: 5 Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split, Croatia – name: 8 Department of Neurology, University of Lübeck, Lübeck, Germany – name: 1 National Institute of Livestock and Grassland Science, Tsukuba, Japan – name: 7 Department of Neurology, General Central Hospital, Bolzano, Italy – name: 2 The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom |
| Author_xml | – sequence: 1 givenname: Yoshitaka surname: Nagamine fullname: Nagamine, Yoshitaka – sequence: 2 givenname: Ricardo surname: Pong-Wong fullname: Pong-Wong, Ricardo – sequence: 3 givenname: Pau surname: Navarro fullname: Navarro, Pau – sequence: 4 givenname: Veronique surname: Vitart fullname: Vitart, Veronique – sequence: 5 givenname: Caroline surname: Hayward fullname: Hayward, Caroline – sequence: 6 givenname: Igor surname: Rudan fullname: Rudan, Igor – sequence: 7 givenname: Harry surname: Campbell fullname: Campbell, Harry – sequence: 8 givenname: James surname: Wilson fullname: Wilson, James – sequence: 9 givenname: Sarah surname: Wild fullname: Wild, Sarah – sequence: 10 givenname: Andrew A. surname: Hicks fullname: Hicks, Andrew A. – sequence: 11 givenname: Peter P. surname: Pramstaller fullname: Pramstaller, Peter P. – sequence: 12 givenname: Nicholas surname: Hastie fullname: Hastie, Nicholas – sequence: 13 givenname: Alan F. surname: Wright fullname: Wright, Alan F. – sequence: 14 givenname: Chris S. surname: Haley fullname: Haley, Chris S. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23077511$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2012 Public Library of Science Nagamine et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2012 Nagamine et al 2012 Nagamine et al |
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| DOI | 10.1371/journal.pone.0046501 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: YN RP PN CSH. Performed the experiments: YN RP PN CSH. Analyzed the data: YN RP PN CSH. Contributed reagents/materials/analysis tools: VV CH IR HC JW SW AH PP NH AW. Wrote the paper: YN RP PN CSH VV. Competing Interests: The authors have declared that no competing interests exist. |
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| SubjectTerms | Alleles Analysis Association analysis Bioinformatics Biology Blood serum Chromosome Mapping Data processing Deoxyribonucleic acid DNA Gametes Gene mapping Genetic diversity Genetic Linkage Genetic variance Genome, Human Genome-wide association studies Genome-Wide Association Study Genomes Genomics Health sciences Heritability Hospitals Humans Loci Mapping Medical schools Medicine Pedigree Polymorphism, Single Nucleotide Population Power Quantitative genetics Quantitative Trait Loci Regional analysis Regional development Simulation Single nucleotide polymorphisms Single-nucleotide polymorphism Studies Uric acid Variance analysis Variation |
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| Title | Localising Loci underlying Complex Trait Variation Using Regional Genomic Relationship Mapping |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/23077511 https://www.proquest.com/docview/1326560810 https://www.proquest.com/docview/1113980822 https://pubmed.ncbi.nlm.nih.gov/PMC3471913 https://doaj.org/article/9edfd7b3fa8e490a92d7407a5a566bb9 http://dx.doi.org/10.1371/journal.pone.0046501 |
| Volume | 7 |
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