Year: 2014
Type: article
Abstract: Admixture mapping is a disease-mapping strategy to identify disease susceptibility variants in an admixed population that is a result of mating between 2 historically separated populations differing in allele frequencies and disease prevalence. With the increasing availability of high-density genotyping data generated in genome-wide association studies, it is of interest to investigate how to apply admixture mapping in the context of the genome-wide association studies and how to adjust for admixture in association tests. In this study, we first evaluated 3 different local ancestry inference methods, LAMP, LAMP-LD, and MULTIMIX. Then we applied admixture mapping analysis based on estimated local ancestry. Finally, we performed association tests with adjustment for local ancestry.
Source: BMC Proceedings
Institution: Yale University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,290
Funders: U.S. Department of Veterans Affairs, Texas Biomedical Research Institute, China Scholarship Council, National Institutes of Health, National Institute on Drug Abuse, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences
Awards: G1731745734, Discovery of Functional Variants in Type 2 Diabetes Genes in Mexican Americans, Deep sequencing studies for cannabis and stimulant dependence, G725404114, Yale University Clinical and Translational Science Award Program, Genetics of Cocaine Dependence, G9000019908, G9000029359, G9000033359, G9000043073, G9000073520, G9000114294, G9000127894, G9000182967, G9000244037, G9000337347
OpenAlex ID: W2135649969