title: Genome-wide detection of natural selection in African Americans pre- and post-admixture.
abstract: It is particularly meaningful to investigate natural selection in African Americans (AfA) due to the high mortality their African ancestry has experienced in history. In this study, we examined 491,526 autosomal single nucleotide polymorphisms (SNPs) genotyped in 5210 individuals and conducted a genome-wide search for selection signals in 1890 AfA. Several genomic regions showing an excess of African or European ancestry, which were considered the footprints of selection since population admixture, were detected based on a commonly used approach. However, we also developed a new strategy to detect natural selection both pre- and post-admixture by reconstructing an ancestral African population (AAF) from inferred African components of ancestry in AfA and comparing it with indigenous African populations (IAF). Interestingly, many selection-candidate genes identified by the new approach were associated with AfA-specific high-risk diseases such as prostate cancer and hypertension, suggesting an important role these disease-related genes might have played in adapting to a new environment. CD36 and HBB, whose mutations confer a degree of protection against malaria, were also located in the highly differentiated regions between AAF and IAF. Further analysis showed that the frequencies of alleles protecting against malaria in AAF were lower than those in IAF, which is consistent with the relaxed selection pressure of malaria in the New World. There is no overlap between the top candidate genes detected by the two approaches, indicating the different environmental pressures AfA experienced pre- and post-population admixture. We suggest that the new approach is reasonably powerful and can also be applied to other admixed populations such as Latinos and Uyghurs.
authors: Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
authors: Wenfei
authors: W
authors: Jin
authors: Shuhua
authors: S
authors: Xu
authors: Haifeng
authors: H
authors: Wang
authors: Yongguo
authors: Y
authors: Yu
authors: Yiping
authors: Y
authors: Shen
authors: Bailin
authors: B
authors: Wu
authors: Li
authors: L
authors: Jin
mesh_terms: D001741:Black or African American / Q000235:genetics*; D000483:Alleles; D044383:Black People / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D005787:Gene Frequency; D055106:Genome-Wide Association Study; D006801:Humans; D012641:Selection, Genetic; D044465:White People / Q000235:genetics