Abstract
BackgroundAdmixture 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.ResultsWe developed two panels of AIMs (~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 ~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.ConclusionsDeveloping 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 ~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 ~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.
Highlights
Admixture mapping is a powerful approach for identifying genetic variants involved in human disease that exploits the unique genomic structure in recently admixed populations
The average inter-marker distance was 1.33 cM for the panel based on δ, 1.43 cM for the panel based on FST, 0.124 cM for the panel based on HapMap singlenucleotide polymorphisms (SNPs) included in GWAS data
Developing marker panels for admixture mapping from existing genotype data derived from commercial high density SNP chips offers two major advantages
Summary
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. Admixture mapping is an approach for localizing disease susceptibility loci that attempts to capitalize on the long-range linkage disequilibrium occurring in populations formed by recent mixing of ancestral populations [1,2,3,4,5,6]. The approach uses samples from recently admixed populations to detect susceptibility loci at which the risk alleles have different frequencies in the ancestral parental populations. Admixture mapping requires ~200-500-fold fewer markers, is not susceptible to allelic heterogeneity, and can be used with either case-only or case-control study designs. Admixture mapping has been performed for many complex traits which exhibit strong differences in prevalence across ethnicities, such as end-stage renal disease [7,8], hypertension [9,10,11], multiple sclerosis [12], obesity [13,14,15], peripheral arterial disease [16], prostate cancer [17,18], rheumatoid arthritis [19], serum inflammatory markers [20], systemic lupus erythematosus [21], type 2 diabetes [22], and white blood cell count [23]
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