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A mixed-model approach for genome-wide association studies of correlated traits in structured populations
Open accessAbstract
Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.
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Cites
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
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Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
Variance component model to account for sample structure in genome-wide association studies
Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010) · Nature Genetics · doi:10.1038/ng.548 · Cited by 2,817
Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
The International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2 (2011) · Nature · doi:10.1038/nature10251 · Cited by 2,697
Genome-wide association studies of 14 agronomic traits in rice landraces
Huang, X., Wei, X., Sang, T., Zhao, Q., Feng, Q., Zhao, Y., Li, C., Zhu, C., Lu, T., Zhang, Z., et al. (2010) · Nature Genetics · doi:10.1038/ng.695 · Cited by 2,148
Mixed linear model approach adapted for genome-wide association studies
Zhang, Z., Ersoz, E., Lai, C.-Q., Todhunter, R. J., Tiwari, H. K., Gore, M. A., Bradbury, P. J., Yu, J., Arnett, D. K., Ordovas, J. M., et al. (2010) · Nature Genetics · doi:10.1038/ng.546 · Cited by 2,125
Efficient Control of Population Structure in Model Organism Association Mapping
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Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
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LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
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Efficient Bayesian mixed-model analysis increases association power in large cohorts
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Self-citationComputationally efficient whole-genome regression for quantitative and binary traits
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00870-7 · Cited by 1,502
Yin, L., Zhang, H., Tang, Z., Xu, J., Yin, D., Zhang, Z., Yuan, X., Zhu, M., Zhao, S., Li, X., et al. (2021) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.10.007 · Cited by 1,250
Advantages and pitfalls in the application of mixed-model association methods
Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014) · Nature Genetics · doi:10.1038/ng.2876 · Cited by 981
Metadata
- Authors
- Korte, A., Vilhjálmsson, B. J., Segura, V., Platt, A., Long, Q., & Nordborg, M.
- Venue
- Nature Genetics
- Published
- 2012-08-19
- Volume
- 44
- Issue
- 9
- Pages
- 1066-1071
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W1976587387
- Cited by
- 446
- References
- 40
- FWCI
- 15.24
- Type
- journal article
- Language
- en
- Open access
- green
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