An Adaptive and Memory Efficient Algorithm for Genotype Imputation
Genome wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely on the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to “impute” with high accuracy the genotypes of SNPs that are not collected in the study provided that they are present in a reference data set which contains both SNPs collected in the study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to traditional methods, our method is up to ten times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim .
KeywordsHide Markov Model Inbred Mouse Strain Imputation Accuracy Silent State Genotype Imputation
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- 1.Borevitz, J.O., Hazen, S.P., Michael, T.P., Morris, G.P., Baxter, I.R., Hu, T.T., Chen, H., Werner, J.D., Nordborg, M., Salt, D.E., Kay, S.A., Chory, J., Weigel, D., Jones, J.D., Ecker, J.R.: Genome-wide patterns of single-feature polymorphism in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 104, 12057–12062 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
- 5.Frazer, K.A., Eskin, E., Kang, H.M., Bogue, M.A., Hinds, D.A., Beilharz, E.J., Gupta, R.V., Montgomery, J., Morenzoni, M.M., Nilsen, G.B., Pethiyagoda, C.L., Stuve, L.L., Johnson, F.M., Daly, M.J., Wade, C.M., Cox, D.R.: A sequence-based variation map of 8. 27 million SNPs in inbred mouse strains 448, 1050–1053 (2007)Google Scholar
- 7.International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (October 2007)Google Scholar
- 8.Karlsson, E.K., Baranowska, I., Wade, C.M., Salmon Hillbertz, N.H., Zody, M.C., Anderson, N., Biagi, T.M., Patterson, N., Pielberg, G.R., Kulbokas, E.J., Comstock, K.E., Keller, E.T., Mesirov, J.P., von Euler, H., Kämpe, O., Hedhammar, A., Lander, E.S., Andersson, G., Andersson, L., Lindblad-Toh, K.: Efficient mapping of mendelian traits in dogs through genome-wide association. Nat. Genet. 39, 1321–1328 (2007)CrossRefPubMedGoogle Scholar
- 10.Li, Y., Willer, C.J., Ding, J., Scheet, P., Abecasis, G.R.: Rapid Markov chain haplotyping and genotype inference (in submission) (2006)Google Scholar
- 12.Matsuzaki, H., Dong, S., Loi, H., Di, X., Liu, G., Hubbell, E., Law, J., Berntsen, T., Chadha, M., Hui, H., Yang, G., Kennedy, G.C., Webster, T.A., Cawley, S., Walsh, P.S., Jones, K.W., Fodor, S.P., Mei, R.: Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nat. Methods 1, 109–111 (2004)CrossRefPubMedGoogle Scholar
- 16.The STAR Consortium. SNP and haplotype mapping for genetic analysis in the rat. Nat. Genet. 40, 560–566 (May 2008)Google Scholar
- 17.The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls 447, 661–678 (2007)Google Scholar