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Interrogating population structure and its impact on association tests
We found from our analysis of the Genetic Analysis Workshop 17 data that the population structure of the 697 unrelated individuals was an important confounding factor for association studies, even if it was not explicitly considered when simulating the phenotypes. We uncovered structures beyond the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287860/ https://www.ncbi.nlm.nih.gov/pubmed/22373290 http://dx.doi.org/10.1186/1753-6561-5-S9-S25 |
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author | Qin, Huaizhen Elston, Robert C Zhu, Xiaofeng |
author_facet | Qin, Huaizhen Elston, Robert C Zhu, Xiaofeng |
author_sort | Qin, Huaizhen |
collection | PubMed |
description | We found from our analysis of the Genetic Analysis Workshop 17 data that the population structure of the 697 unrelated individuals was an important confounding factor for association studies, even if it was not explicitly considered when simulating the phenotypes. We uncovered structures beyond the reported ethnicities and found ample evidence of phenotype–population structure associations. The first 10 principal components of the genotype data of the 697 individuals demonstrated much stronger associations with Q1, Q2, and the disease than did the individuals’ ethnicities. In addition, we observed that population structure was a confounding factor for the Q1-gene association when identifying the significant genes both with and without adjusting for the causal single-nucleotide polymorphisms, the ethnicities, and the principal components. Many false discoveries remained after adjusting for the causal single-nucleotide polymorphisms. Adjusting for the principal components appeared more effective than did adjusting for ethnicity in terms of preventing false discoveries. This analysis was performed with knowledge of the causal loci. |
format | Online Article Text |
id | pubmed-3287860 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32878602012-02-28 Interrogating population structure and its impact on association tests Qin, Huaizhen Elston, Robert C Zhu, Xiaofeng BMC Proc Proceedings We found from our analysis of the Genetic Analysis Workshop 17 data that the population structure of the 697 unrelated individuals was an important confounding factor for association studies, even if it was not explicitly considered when simulating the phenotypes. We uncovered structures beyond the reported ethnicities and found ample evidence of phenotype–population structure associations. The first 10 principal components of the genotype data of the 697 individuals demonstrated much stronger associations with Q1, Q2, and the disease than did the individuals’ ethnicities. In addition, we observed that population structure was a confounding factor for the Q1-gene association when identifying the significant genes both with and without adjusting for the causal single-nucleotide polymorphisms, the ethnicities, and the principal components. Many false discoveries remained after adjusting for the causal single-nucleotide polymorphisms. Adjusting for the principal components appeared more effective than did adjusting for ethnicity in terms of preventing false discoveries. This analysis was performed with knowledge of the causal loci. BioMed Central 2011-11-29 /pmc/articles/PMC3287860/ /pubmed/22373290 http://dx.doi.org/10.1186/1753-6561-5-S9-S25 Text en Copyright ©2011 Qin et al; 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 | Proceedings Qin, Huaizhen Elston, Robert C Zhu, Xiaofeng Interrogating population structure and its impact on association tests |
title | Interrogating population structure and its impact on association tests |
title_full | Interrogating population structure and its impact on association tests |
title_fullStr | Interrogating population structure and its impact on association tests |
title_full_unstemmed | Interrogating population structure and its impact on association tests |
title_short | Interrogating population structure and its impact on association tests |
title_sort | interrogating population structure and its impact on association tests |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287860/ https://www.ncbi.nlm.nih.gov/pubmed/22373290 http://dx.doi.org/10.1186/1753-6561-5-S9-S25 |
work_keys_str_mv | AT qinhuaizhen interrogatingpopulationstructureanditsimpactonassociationtests AT elstonrobertc interrogatingpopulationstructureanditsimpactonassociationtests AT zhuxiaofeng interrogatingpopulationstructureanditsimpactonassociationtests |