Admixed populations, e.g., Hispanics and African Americans, have historically been understudied in genetic studies of human disease. Though access to samples undoubtedly plays a role, another reason is purely technical: the genetic heterogeneity contained in admixed samples presents significant challenges, in terms of both validity and power, to standard case-control genetic association methods. Often, even studies that recruit African Americans and Hispanics do not use their data as part of the study’s primary analysis.
Admixture mapping offers an alternative to the standard genetic association analysis and leverages the genetic heterogeneity found in admixed samples as an asset and not a liability. Since admixture mapping is based on exploiting frequency differences in disease-causing mutations between the ancestral populations comprising the admixture event, diseases for which disease risk varies substantially between ancestral populations make particularly good targets for admixture mapping studies. This includes a number of diseases of public health consequence in which health disparities affecting minority, admixed, populations are especially apparent, including outcomes as diverse as preterm birth, schizophrenia, stroke and obesity.
We are interested in the development and application of new statistical genetic approaches to gene mapping in admixed samples.
- Local ancestry estimation using GWAS or deep sequence data
- Admixture mapping for gene mapping