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Testing Genetic Association by Regressing Genotype over Multiple Phenotypes

Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply...

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Detalles Bibliográficos
Autor principal: Wang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4164437/
https://www.ncbi.nlm.nih.gov/pubmed/25221983
http://dx.doi.org/10.1371/journal.pone.0106918
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author Wang, Kai
author_facet Wang, Kai
author_sort Wang, Kai
collection PubMed
description Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply the proportional odds model. In the current research, we introduce an explicit expression for the score test statistic and its non-centrality parameter that determines its power. Same simulation studies as those reported in Galesloot et al. (2014) were conducted to assess its performance. We demonstrate by theoretical arguments and simulation studies that, despite its potential usefulness for multiple phenotypes, the proportional odds model method can be less powerful than regular methods for univariate traits. We also introduce an implementation of the proposed score statistic in an R package named iGasso.
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spelling pubmed-41644372014-09-19 Testing Genetic Association by Regressing Genotype over Multiple Phenotypes Wang, Kai PLoS One Research Article Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply the proportional odds model. In the current research, we introduce an explicit expression for the score test statistic and its non-centrality parameter that determines its power. Same simulation studies as those reported in Galesloot et al. (2014) were conducted to assess its performance. We demonstrate by theoretical arguments and simulation studies that, despite its potential usefulness for multiple phenotypes, the proportional odds model method can be less powerful than regular methods for univariate traits. We also introduce an implementation of the proposed score statistic in an R package named iGasso. Public Library of Science 2014-09-15 /pmc/articles/PMC4164437/ /pubmed/25221983 http://dx.doi.org/10.1371/journal.pone.0106918 Text en © 2014 Kai Wang http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wang, Kai
Testing Genetic Association by Regressing Genotype over Multiple Phenotypes
title Testing Genetic Association by Regressing Genotype over Multiple Phenotypes
title_full Testing Genetic Association by Regressing Genotype over Multiple Phenotypes
title_fullStr Testing Genetic Association by Regressing Genotype over Multiple Phenotypes
title_full_unstemmed Testing Genetic Association by Regressing Genotype over Multiple Phenotypes
title_short Testing Genetic Association by Regressing Genotype over Multiple Phenotypes
title_sort testing genetic association by regressing genotype over multiple phenotypes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4164437/
https://www.ncbi.nlm.nih.gov/pubmed/25221983
http://dx.doi.org/10.1371/journal.pone.0106918
work_keys_str_mv AT wangkai testinggeneticassociationbyregressinggenotypeovermultiplephenotypes