HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies distinguish Eastern European Americans from the general European American population.

Sequence-based typing was used to identify human leukocyte antigen (HLA)-A, -B, -C, and -DRB1 alleles from 558 consecutively recruited US volunteers with Eastern European ancestry for an unrelated hematopoietic stem cell registry. Four of 31 HLA-A alleles, 29 HLA-C alleles, 59 HLA-B alleles, and 42 HLA-DRB1 alleles identified (A*0325, B*440204, Cw*0332, and *0732N) are novel. The HLA-A*02010101g allele was observed at a frequency of 0.28. Two-, three-, and four-locus haplotypes were estimated using the expectation-maximization algorithm. The highest frequency extended haplotypes (A*010101g-Cw*070101g-B*0801g-DRB1*0301 and A*03010101g-Cw*0702-B*0702-DRB1*1501) were observed at frequencies of 0.04 and 0.03, respectively. Linkage disequilibrium values (Dij') of the constituent two-locus haplotypes were highly significant for both extended haplotypes (P values were less than 8 x 10(-10)) but were consistently higher for the more frequent haplotype. Balancing selection was inferred to be acting on all the four loci, with the strongest evidence of balancing selection observed for the HLA-C locus. Comparisons of the A-C-B haplotypes and DRB1 frequencies in this population with those for African, European, and western Asian populations showed high degrees of identity with Czech, Polish, and Slovenian populations and significant differences from the general European American population.

PyPop update--a software pipeline for large-scale multilocus population genomics.

Population genetic statistics from multilocus genotype data inform our understanding of the patterns of genetic variation and their implications for evolutionary studies, generally, and human disease studies in particular. In any given population one can estimate haplotype frequencies, identify deviation from Hardy-Weinberg equilibrium, test for balancing or directional selection, and investigate patterns of linkage disequilibrium. Existing software packages are oriented primarily toward the computation of such statistics on a population-by-population basis, not on comparisons among populations and across different statistics. We developed PyPop (Python for Population Genomics) to facilitate the analyses of population genetic statistics across populations and the relationships among different statistics within and across populations. PyPop is an open-source framework for performing large-scale population genetic analyses on multilocus genotype data. It computes the statistics described above, among others. PyPop deploys a standard Extensible Markup Language (XML) output format and can integrate the results of multiple analyses on various populations that were performed at different times into a common output format that can be read into a spreadsheet. The XML output format allows PyPop to be embedded as part of a larger analysis pipeline. Originally developed to analyze the highly polymorphic genetic data of the human leukocyte antigen region of the human genome, PyPop has applicability to any kind of multilocus genetic data. It is the primary analysis platform for analyzing data collected for the Anthropological component of the 13th and 14th International Histocompatibility Workshops. PyPop has also been successfully used in studies by our group, with collaborators, and in publications by several independent research teams.