To better understand the population substructure of African Americans living in coastal South Carolina, we used restriction site polymorphisms and an insertion/deletion in mitochondrial DNA (mtDNA) to construct seven-position haplotypes across 1,395 individuals from Sierra Leone, Africa, from U.S. European Americans, and from the New World African-derived populations of Jamaica, Gullah-speaking African Americans of the South Carolina Sea Islands (Gullahs), African Americans living in Charleston, South Carolina, and West Coast African Americans. Analyses showed a high degree of similarity within the New World African-derived populations, where haplotype frequencies and diversities were similar. Phi-statistics indicated that very little genetic differentiation has occurred within New World African-derived populations, but that there has been significant differentiation of these populations from Sierra Leoneans. Genetic distance estimates indicated a close relationship of Gullahs and Jamaicans with Sierra Leoneans, while African Americans living in Charleston and the West Coast were progressively more distantly related to the Sierra Leoneans. We observed low maternal European American admixture in the Jamaican and Gullah samples (m = 0.020 and 0.064, respectively) that increased sharply in a clinal pattern from Charleston African Americans to West Coast African Americans (m = 0.099 and 0.205, respectively). The appreciably reduced maternal European American admixture noted in the Gullah indicates that the Gullah may be uniquely situated to allow genetic epidemiology studies of complex diseases in African Americans with low European American admixture.

Background: Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE © titles and abstracts by applying a probabilistic topic model.

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