Abstract
Recently, the use of a mixed model methodology in genome-wide association studies (GWAS) has been considered effective for controlling population stratification and explaining the polygenic effects of complex traits. However, estimating polygenic variance components and heritability was biased when the mixed model was used. This bias results from a diluted genetic relationship covariance structure, particularly with a limited number of underlying causal variants. We simulated disease and quantitative phenotypes with a variety of heritabilities (0.1, 0.2, 0.3, 0.4, and 0.5), prevalence rates (0.1, 0.2, 0.3, and 0.5), and causal variant numbers (10, 30, 50, and 100). Heritabilities from the simulated data using restricted maximum likelihood were underestimated in many populations (P<0.05). The underestimation increased with a large heritability, a small prevalence, and a small number of causal variants. The underestimation was larger in analyzing disease traits compared with quantitative traits. This study suggests an underestimated heritability in GWAS upon using the mixed model methodology with an excessively larger number of variants versus causal variants.
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Acknowledgements
We thank the editor and anonymous reviewers for their valuable comments that helped us to improve the original manuscript. We are grateful to the National Institute of Health in Korea for providing the genotypic and epidemiological data to the KARE Analysis Consortium. This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (Grant No. 2012002096).
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Ryoo, H., Lee, C. Underestimation of heritability using a mixed model with a polygenic covariance structure in a genome-wide association study for complex traits. Eur J Hum Genet 22, 851–854 (2014). https://doi.org/10.1038/ejhg.2013.236
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DOI: https://doi.org/10.1038/ejhg.2013.236
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