Testing for genetic associations in arbitrarily structured populations
We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping da...
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ftpubmed:oai:pubmedcentral.nih.gov:4464830 2023-05-15T17:42:27+02:00 Testing for genetic associations in arbitrarily structured populations Song, Minsun Hao, Wei Storey, John D. 2015-03-30 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464830/ http://www.ncbi.nlm.nih.gov/pubmed/25822090 https://doi.org/10.1038/ng.3244 en eng http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464830/ http://www.ncbi.nlm.nih.gov/pubmed/25822090 http://dx.doi.org/10.1038/ng.3244 Article Text 2015 ftpubmed https://doi.org/10.1038/ng.3244 2015-11-08T01:29:57Z We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as that measured in genome-wide association studies (GWAS). We also derive a new set of methodologies, called a genotype-conditional association test (GCAT), shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and environmental contributions to the trait. We demonstrate the proposed method on a large simulation study and on the Northern Finland Birth Cohort study. In the Finland study, we identify several new significant loci that other methods do not detect. Our proposed framework provides a substantially different approach to the problem from existing methods, such as the linear mixed model and principal component approaches. Text Northern Finland PubMed Central (PMC) Nature Genetics 47 5 550 554 |
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Article Song, Minsun Hao, Wei Storey, John D. Testing for genetic associations in arbitrarily structured populations |
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We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as that measured in genome-wide association studies (GWAS). We also derive a new set of methodologies, called a genotype-conditional association test (GCAT), shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and environmental contributions to the trait. We demonstrate the proposed method on a large simulation study and on the Northern Finland Birth Cohort study. In the Finland study, we identify several new significant loci that other methods do not detect. Our proposed framework provides a substantially different approach to the problem from existing methods, such as the linear mixed model and principal component approaches. |
format |
Text |
author |
Song, Minsun Hao, Wei Storey, John D. |
author_facet |
Song, Minsun Hao, Wei Storey, John D. |
author_sort |
Song, Minsun |
title |
Testing for genetic associations in arbitrarily structured populations |
title_short |
Testing for genetic associations in arbitrarily structured populations |
title_full |
Testing for genetic associations in arbitrarily structured populations |
title_fullStr |
Testing for genetic associations in arbitrarily structured populations |
title_full_unstemmed |
Testing for genetic associations in arbitrarily structured populations |
title_sort |
testing for genetic associations in arbitrarily structured populations |
publishDate |
2015 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464830/ http://www.ncbi.nlm.nih.gov/pubmed/25822090 https://doi.org/10.1038/ng.3244 |
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Northern Finland |
genre_facet |
Northern Finland |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464830/ http://www.ncbi.nlm.nih.gov/pubmed/25822090 http://dx.doi.org/10.1038/ng.3244 |
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https://doi.org/10.1038/ng.3244 |
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Nature Genetics |
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47 |
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5 |
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550 |
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554 |
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1766144315324628992 |