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Imputation of coding variants in African Americans: better performance using data from the exome sequencing project BIOINFORMATICS 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. 2013; 29 (21): 2744-2749

Abstract

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.yunli@med.unc.edu.Supplementary data are available at Bioinformatics online.

View details for DOI 10.1093/bioinformatics/btt477

View details for Web of Science ID 000325997500011

View details for PubMedID 23956302

View details for PubMedCentralID PMC3799474