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Association mapping, using a mixture model for complex traits.

Xiaofeng Zhu·ShuangLin Zhang·Hongyu Zhao·Richard S Cooper
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Abstract

Association mapping for complex diseases using unrelated individuals can be more powerful than family-based analysis in many settings. In addition, this approach has major practical advantages, including greater efficiency in sample recruitment. Association mapping may lead to false-positive findings, however, if population stratification is not properly considered. In this paper, we propose a method that makes it possible to infer the number of subpopulations by a mixture model, using a set of independent genetic markers and then testing the association between a genetic marker and a trait. The proposed method can be effectively applied in the analysis of both qualitative and quantitative traits. Extensive simulations demonstrate that the method is valid in the presence of a population structure.

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AllelesCase-Control StudiesChromosome MappingComputer SimulationGene FrequencyGenetics, PopulationGenotypeHumansModels, GeneticJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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Xiaofeng Zhu, ShuangLin Zhang, Hongyu Zhao, Richard S Cooper. 2002. Association mapping, using a mixture model for complex traits.. https://doi.org/10.1002/gepi.210

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