Background: Admixture mapping is a powerful approach for identifying genetic variants involved in human disease that exploits the unique genomic structure in recently admixed populations. To use existing published panels of ancestry-informative markers (AIMs) for admixture mapping, markers have to be genotyped de novo for each admixed study sample and samples representing the ancestral parental populations. The increased availability of dense marker data on commercial chips has made it feasible to develop panels wherein the markers need not be predetermined.
Results: We developed two panels of AIMs (approximately 2,000 markers each) based on the Affymetrix Genome-Wide Human SNP Array 6.0 for admixture mapping with African American samples. These two AIM panels had good map power that was higher than that of a denser panel of approximately 20,000 random markers as well as other published panels of AIMs. As a test case, we applied the panels in an admixture mapping study of hypertension in African Americans in the Washington, D.C. metropolitan area.
Conclusions: Developing marker panels for admixture mapping from existing genome-wide genotype data offers two major advantages: (1) no de novo genotyping needs to be done, thereby saving costs, and (2) markers can be filtered for various quality measures and replacement markers (to minimize gaps) can be selected at no additional cost. Panels of carefully selected AIMs have two major advantages over panels of random markers: (1) the map power from sparser panels of AIMs is higher than that of approximately 10-fold denser panels of random markers, and (2) clusters can be labeled based on information from the parental populations. With current technology, chip-based genome-wide genotyping is less expensive than genotyping approximately 20,000 random markers. The major advantage of using random markers is the absence of ascertainment effects resulting from the process of selecting markers. The ability to develop marker panels informative for ancestry from SNP chip genotype data provides a fresh opportunity to conduct admixture mapping for disease genes in admixed populations when genome-wide association data exist or are planned.