Abstract
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 .
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Collins, F.S., Brooks, L.D., Chakravarti, A.: A DNA polymorphism discovery resource for research on human genetic variation. Genome Res. 8, 1229–1231 (1998)
de Bakker, P.I., Yelensky, R., Pe’er, I., Gabriel, S.B., Daly, M.J., Altshuler, D.: Efficiency and power in genetic association studies. Nat. Genet. 37, 1217–1223 (2005)
Devlin, B., Risch, N.: A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29, 311–322 (1995)
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)
Gunderson, K.L., Steemers, F.J., Lee, G., Mendoza, L.G., Chee, M.S.: A genome-wide scalable SNP genotyping assay using microarray technology. Nat. Genet. 37, 549–554 (2005)
International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (October 2007)
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)
Kingman, J.F.C.: On the genealogy of large populations. Journal of Applied Proability 19, 27–43 (1982)
Li, Y., Willer, C.J., Ding, J., Scheet, P., Abecasis, G.R.: Rapid Markov chain haplotyping and genotype inference (in submission) (2006)
Marchini, J., Howie, B., Myers, S., McVean, G., Donnelly, P.: A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007)
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)
Risch, N., Merikangas, K.: The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996)
Scheet, P., Stephens, M.: A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644 (2006)
Szatkiewicz, J.P., Beane, G.L., Ding, Y., Hutchins, L., de Villena, F.P.-M., Churchill, G.A.: An imputed genotype resource for the laboratory mouse. Mamm. Genome 19, 199–208 (2008)
The STAR Consortium. SNP and haplotype mapping for genetic analysis in the rat. Nat. Genet. 40, 560–566 (May 2008)
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)
Zaitlen, N., Kang, H.M., Eskin, E., Halperin, E.: Leveraging the HapMap correlation structure in association studies. Am. J. Hum. Genet. 80, 683–691 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, H.M., Zaitlen, N.A., Han, B., Eskin, E. (2009). An Adaptive and Memory Efficient Algorithm for Genotype Imputation. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-02008-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02007-0
Online ISBN: 978-3-642-02008-7
eBook Packages: Computer ScienceComputer Science (R0)