  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 utilizes 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 existing
methods 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 woman 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.
