Association mapping, using a mixture model for complex traits
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Updated: Sep 29, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Xiaofeng Zhu1, ShuangLin Zhang, Hongyu Zhao
1Department of Preventive Medicine and Epidemiology, Loyola University Medical Center, Maywood, Illinois 60153, USA. xzhul@lumc.edu
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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