Qian S Zhang1, Brian L Browning2, Sharon R Browning3. 1. Department of Medicine Department of Biostatistics and. 2. Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA. 3. Department of Biostatistics and.
Abstract
UNLABELLED: In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individual's genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry Specific Allele Frequency Estimation (ASAFE). We demonstrate that ASAFE has low error on simulated data. AVAILABILITY AND IMPLEMENTATION: The R source code for ASAFE is available for download at https://github.com/BiostatQian/ASAFE CONTACT: qszhang@uw.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individual's genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry Specific Allele Frequency Estimation (ASAFE). We demonstrate that ASAFE has low error on simulated data. AVAILABILITY AND IMPLEMENTATION: The R source code for ASAFE is available for download at https://github.com/BiostatQian/ASAFE CONTACT: qszhang@uw.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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