Generalized Admixture Mapping for Complex Traits

TL;DR

This paper introduces GLEAM, a flexible regression method for admixture mapping that improves detection of trait-associated loci in admixed populations, applicable to both quantitative and qualitative traits.

Contribution

GLEAM is a novel generalized linear model-based approach that tests multiple loci simultaneously and adjusts for covariates, enhancing power and reducing false positives in admixture mapping.

Findings

GLEAM outperforms existing methods in simulations with lower type I error and higher power.

Applied to African American data, GLEAM identified a locus on chromosome 2 linked to maternal blood pressure.

Method effectively detects associations in complex traits across diverse populations.

Abstract

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.