In this article, we propose a flexible and powerful generalized admixture mapping approach, which is based on the generalized linear model and is able to incorporate admixture prior information by using the quadratic normal moment prior and to adjust for covariates.
In this sparse-genotype-based study, admixture mapping was more powerful than association mapping on individual SNP genotypes because it efficiently utilized neighboring markers to infer the ancestry component.
Admixture mapping is a powerful gene mapping approach [ 5, 28].
Admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies.
Admixture mapping is a powerful approach for identifying genetic variants involved in human disease that exploits the unique genomic structure in recently admixed populations.
Another important genetic approach, suitable in admixed population, to identify disease risk variants is admixture mapping, which is powerful when the ancestral populations differ both in allele frequencies and disease prevalence.
In this study, we have shown how LD generated by population admixture can be a powerful tool for learning about history, extending previous work that showed how it can be used for estimating dates of mixture (Moorjani et al. 2011; Patterson et al. 2012).
At the same time, admixture mapping can be more powerful, and can achieve higher mapping resolution than traditional linkage studies, provided that the underlying trait variants occur at sufficiently different frequencies in the ancestral populations.
Admixture mapping is an economical and theoretically powerful approach.
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.
