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The advantages and limitations of trait analysis with GWAS: a review
Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Associa...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750305/ https://www.ncbi.nlm.nih.gov/pubmed/23876160 http://dx.doi.org/10.1186/1746-4811-9-29 |
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author | Korte, Arthur Farlow, Ashley |
author_facet | Korte, Arthur Farlow, Ashley |
author_sort | Korte, Arthur |
collection | PubMed |
description | Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation. |
format | Online Article Text |
id | pubmed-3750305 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37503052013-08-24 The advantages and limitations of trait analysis with GWAS: a review Korte, Arthur Farlow, Ashley Plant Methods Review Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation. BioMed Central 2013-07-22 /pmc/articles/PMC3750305/ /pubmed/23876160 http://dx.doi.org/10.1186/1746-4811-9-29 Text en Copyright © 2013 Korte and Farlow; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Korte, Arthur Farlow, Ashley The advantages and limitations of trait analysis with GWAS: a review |
title | The advantages and limitations of trait analysis with GWAS: a review |
title_full | The advantages and limitations of trait analysis with GWAS: a review |
title_fullStr | The advantages and limitations of trait analysis with GWAS: a review |
title_full_unstemmed | The advantages and limitations of trait analysis with GWAS: a review |
title_short | The advantages and limitations of trait analysis with GWAS: a review |
title_sort | advantages and limitations of trait analysis with gwas: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750305/ https://www.ncbi.nlm.nih.gov/pubmed/23876160 http://dx.doi.org/10.1186/1746-4811-9-29 |
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