Title: PYPOP: A SOFTWARE FRAMEWORK FOR POPULATION GENOMICS: ANALYZING LARGE-SCALE MULTI-LOCUS GENOTYPE DATA
Abstract: Biocomputing 2003, pp. 514-525 (2002) No AccessPYPOP: A SOFTWARE FRAMEWORK FOR POPULATION GENOMICS: ANALYZING LARGE-SCALE MULTI-LOCUS GENOTYPE DATAALEX LANCASTER, MARK P. NELSON, DIOGO MEYER, GLENYS THOMSON, and RICHARD M. SINGLEALEX LANCASTERDepartment of Integrative Biology, University of California, Berkeley, 3060 Valley Life Sciences, Berkeley, CA, 94720, USA, MARK P. NELSONDepartment of Integrative Biology, University of California, Berkeley, 3060 Valley Life Sciences, Berkeley, CA, 94720, USA, DIOGO MEYERDepartment of Integrative Biology, University of California, Berkeley, 3060 Valley Life Sciences, Berkeley, CA, 94720, USA, GLENYS THOMSONDepartment of Integrative Biology, University of California, Berkeley, 3060 Valley Life Sciences, Berkeley, CA, 94720, USA, and RICHARD M. SINGLEDepartment of Biometry, University of Vermont, Hills Science Building, Burlington, VT, 05405, USAhttps://doi.org/10.1142/9789812776303_0048Cited by:4 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Software to analyze multi-locus genotype data for entire populations is useful for estimating haplotype frequencies, deviation from Hardy-Weinberg equilibrium and patterns of linkage disequilibrium. These statistical results are important to both those interested in human genome variation and disease predisposition as well as evolutionary genetics. As part of the 13th International Histocompatibility and Immunogenetics Working Group (IHWG), we have developed a software framework (PyPop). The primary novelty of this package is that it allows integration of statistics across large numbers of data-sets by heavily utilizing the XML file format and the R statistical package to view graphical output, while retaining the ability to inter-operate with existing software. Largely developed to address human population data, it can, however, be used for population based data for any organism. We tested our software on the data from the 13th IHWG which involved data sets from at least 50 laboratories each of up to 1000 individuals with 9 MHC loci (both class I and class II) and found that it scales to large numbers of data sets well. FiguresReferencesRelatedDetailsCited By 4Mapping the Human Leukocyte Antigen Diversity among Croatian Regions: Implication in TransplantationZorana Grubic, Marija Maskalan, Katarina Stingl Jankovic, Marija Burek Kamenaric and Renata Zunec et al.7 Apr 2021 | Journal of Immunology Research, Vol. 2021HLA Class II Genes HLA-DRB1, HLA-DPB1, and HLA-DQB1 Are Associated With the Antibody Response to Inactivated Japanese Encephalitis VaccineYufeng Yao, Huijuan Yang, Lei Shi, Shuyuan Liu and Chuanying Li et al.8 March 2019 | Frontiers in Immunology, Vol. 10KIR-HLA profiling shows presence of higher frequencies of strong inhibitory KIR-ligands among prognostically poor risk AML patientsMeixin Shen, Yeh-Ching Linn and Ee-Chee Ren9 December 2015 | Immunogenetics, Vol. 68, No. 2The power and promise of population genomics: from genotyping to genome typingGordon Luikart, Phillip R. England, David Tallmon, Steve Jordan and Pierre Taberlet1 Dec 2003 | Nature Reviews Genetics, Vol. 4, No. 12 Biocomputing 2003Metrics History PDF download