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An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations
Open accessAbstract
Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.
Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387
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
Precision mapping of quantitative trait loci.
Zeng, Z. B. (1994) · Genetics · doi:10.1093/genetics/136.4.1457 · Cited by 3,182
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · cited 1×
“Several methods have been proposed to deal with this issue, including genomic control 3 , structured association 4 , principal components analysis 5 , and mixed linear models 6 .”
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
Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Atwell, S., Huang, Y. S., Vilhjálmsson, B. J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A. M., Hu, T. T., et al. (2010) · Nature · doi:10.1038/nature08800 · Cited by 1,780
Self-citation
Research works printed but not linked: 26
- Cardon, L.R. & Palmer, L.J. Population stratification and spurious allelic association. Lancet 361, 598–604 (2003). · DOI · Paper page
- Pritchard, J.K., Stephens, M., Rosenberg, N.A. & Donnelly, P. Association mapping in structured populations. Am. J. Hum. Genet. 67, 170–181 (2000). · DOI · Paper page
- Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006). · DOI · Paper page
- Zhao, K. et al. An Arabidopsis example of association mapping in structured samples. PLoS Genet. 3, e4 (2007). · DOI · Paper page
- Kang, H.M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008). · DOI · Paper page
- Aulchenko, Y.S., de Koning, D.J. & Haley, C. Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177, 577–585 (2007). · DOI · Paper page
- Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010). · DOI · Paper page
- Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011). · DOI · Paper page
- Jansen, R.C. Interval mapping of multiple quantitative trait loci. Genetics 135, 205–211 (1993). · DOI · Paper page
- Platt, A., Vilhjalmsson, B.J. & Nordborg, M. Conditions under which genome-wide association studies will be positively misleading. Genetics 186, 1045–1052 (2010). · DOI · Paper page
- Allen, A.S., Satten, G.A., Bray, S.L., Dudbridge, F. & Epstein, M.P. Fast and robust association tests for untyped SNPs in case-control studies. Hum. Hered. 70, 167–176 (2010). · doi:10.1159/000308456
- Cordell, H.J. & Clayton, D.G. A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am. J. Hum. Genet. 70, 124–141 (2002). · DOI · Paper page
- Croiseau, P. & Cordell, H.J. Analysis of North American Rheumatoid Arthritis Consortium data using a penalized logistic regression approach. BMC Proc. 3, S61 (2009). · DOI
- Cho, S. et al. Joint identification of multiple genetic variants via elastic-net variable selection in a genome-wide association analysis. Ann. Hum. Genet. 74, 416–428 (2010). · DOI
- Wang, D., Eskridge, K.M. & Crossa, J. Identifying QTLs and epistasis in structured plant populations using adaptive mixed LASSO. J. Agric. Biol. Environ. Stat. 16, 170–184 (2011). · DOI
- Ayers, K.L. & Cordell, H.J. SNP selection in genome-wide and candidate gene studies via penalized logistic regression. Genet. Epidemiol. 34, 879–891 (2010). · DOI · Paper page
- Horton, M.W. et al. Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nat. Genet. 44, 212–216 (2012). · DOI · Paper page
- Chen, J.H. & Chen, Z.H. Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95, 759–771 (2008). · DOI · Paper page
- Astle, W. & Balding, D.J. Population structure and cryptic relatedness in genetic association studies. Stat. Sci. 24, 451–471 (2009). · DOI · Paper page
- Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41, 35–46 (2009). · DOI · Paper page
- Baxter, I. et al. A coastal cline in sodium accumulation in Arabidopsis thaliana is driven by natural variation of the sodium transporter AtHKT1;1. PLoS Genet. 6, e1001193 (2010). · DOI · Paper page
- Valdar, W., Holmes, C.C., Mott, R. & Flint, J. Mapping in structured populations by resample model averaging. Genetics 182, 1263–1277 (2009). · DOI
- Tian, F. et al. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat. Genet. 43, 159–162 (2011). · DOI · Paper page
- Stephens, M. & Balding, D.J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009). · DOI · Paper page
- Servin, B. & Stephens, M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 3, e114 (2007). · DOI · Paper page
- Kass, R.E. & Raftery, A.E. Bayes Factors. J. Am. Stat. Assoc. 90, 773–795 (1995). · DOI · Paper page
Cited by
Efficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507
Self-citationAdvantages 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
Huang, M., Liu, X., Zhou, Y., Summers, R. M., & Zhang, Z. (2019) · GigaScience · doi:10.1093/gigascience/giy154 · Cited by 855
Identifying Causal Variants at Loci with Multiple Signals of Association
Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B., & Eskin, E. (2014) · Genetics · doi:10.1534/genetics.114.167908 · Cited by 465
Wang, S.-B., Feng, J.-Y., Ren, W.-L., Huang, B., Zhou, L., Wen, Y.-J., Zhang, J., Dunwell, J. M., Xu, S., & Zhang, Y.-M. (2016) · Scientific Reports · doi:10.1038/srep19444 · Cited by 513
Metadata
- Authors
- Segura, V., Vilhjálmsson, B. J., Platt, A., Korte, A., Seren, Ü., Long, Q., & Nordborg, M.
- Venue
- Nature Genetics
- Published
- 2012-06-17
- Volume
- 44
- Issue
- 7
- Pages
- 825-830
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W2146374090
- Cited by
- 1,072
- References
- 15
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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