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Imputation of coding variants in African Americans: better performance using data from the exome sequencing project (2013)

Duan, Q., Liu, E. Y., Auer, P. L., Zhang, G., Lange, E. M., Jun, G., Bizon, C., Jiao, S., Buyske, S., Franceschini, N., Carlson, C. S., Hsu, L., Reiner, A. P., Peters, U., Haessler, J., Curtis, K., Wassel, C. L., Robinson, J. G., Martin, L. W., Haiman, C. A., Le Marchand, L., Matise, T. C., Hindorff, L. A., Crawford, D. C., Assimes, T. L., Kang, H. M., Heiss, G., Jackson, R. D., Kooperberg, C., Wilson, J. G., Abecasis, G. R., North, K. E., Nickerson, D. A., Lange, L. A., Li, Y.

Oxford University Press

In: Bioinformatics

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Publication Date: 2013-10-19

Description: : Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3–11.4% for coding variants with minor allele frequency 〈1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2’s two-panel combination. Contact: yunli@med.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine