Generalized admixture mapping for complex traits.
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Admixture mapping is a popular tool to identify regions of the genome associated with traits in a recently admixed population. Existing methods have been developed primarily for identification of a single locus influencing a dichotomous trait within a case-control study design. We propose a generalized admixture mapping (GLEAM) approach, a flexible and powerful regression method for both quantitative and qualitative traits, which is able to test for association between the trait and local ancestries in multiple loci simultaneously and adjust for covariates. The new method is based on the generalized linear model and uses a quadratic normal moment prior to incorporate admixture prior information. Through simulation, we demonstrate that GLEAM achieves lower type I error rate and higher power than ANCESTRYMAP both for qualitative traits and more significantly for quantitative traits. We applied GLEAM to genome-wide SNP data from the Illumina African American panel derived from a cohort of black women participating in the Healthy Pregnancy, Healthy Baby study and identified a locus on chromosome 2 associated with the averaged maternal mean arterial pressure during 24 to 28 weeks of pregnancy.
Subjectgeneralized linear model
mapping by admixture linkage disequilibrium
quadratic normal moment prior
Genetic Association Studies
Quantitative Trait Loci
Published Version (Please cite this version)10.1534/g3.113.006478
Publication InfoAshley-Koch, A; Dunson, David B; & Zhu, B (2013). Generalized admixture mapping for complex traits. G3 (Bethesda), 3(7). pp. 1165-1175. 10.1534/g3.113.006478. Retrieved from https://hdl.handle.net/10161/15601.
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Professor in Medicine
One of my major research foci is in the genetic basis of psychiatric and neurological disorders. I am currently involved in studies to dissect the genetic etiology of attention deficit hyperactivity disorder (ADHD), autism, chiari type I malformations, essential tremor, and neural tube defects. Additional research foci include genetic modifiers of sickle cell disease, and genetic contributions to birth outcomes, particularly among African American women.
Arts and Sciences Professor of Statistical Science
Development of novel approaches for representing and analyzing complex data. A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty. In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecol
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