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      Diversity of HLA Class I and Class II blocks and conserved extended haplotypes in Lacandon Mayans

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          Abstract

          Here we studied HLA blocks and haplotypes in a group of 218 Lacandon Maya Native American using a high-resolution next generation sequencing (NGS) method. We assessed the genetic diversity of HLA class I and class II in this population, and determined the most probable ancestry of Lacandon Maya HLA class I and class II haplotypes. Importantly, this Native American group showed a high degree of both HLA homozygosity and linkage disequilibrium across the HLA region and also lower class II HLA allelic diversity than most previously reported populations (including other Native American groups). Distinctive alleles present in the Lacandon population include HLA-A*24:14 and HLA-B*40:08. Furthermore, in Lacandons we observed a high frequency of haplotypes containing the allele HLA-DRB1*04:11, a relatively frequent allele in comparison with other neighboring indigenous groups. The specific demographic history of the Lacandon population including inbreeding, as well as pathogen selection, may have elevated the frequencies of a small number of HLA class II alleles and DNA blocks. To assess the possible role of different selective pressures in determining Native American HLA diversity, we evaluated the relationship between genetic diversity at HLA-A, HLA-B and HLA-DRB1 and pathogen richness for a global dataset and for Native American populations alone. In keeping with previous studies of such relationships we included distance from Africa as a covariate. After correction for multiple comparisons we did not find any significant relationship between pathogen diversity and HLA genetic diversity (as measured by polymorphism information content) in either our global dataset or the Native American subset of the dataset. We found the expected negative relationship between genetic diversity and distance from Africa in the global dataset, but no relationship between HLA genetic diversity and distance from Africa when Native American populations were considered alone.

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          Gene map of the extended human MHC.

          The major histocompatibility complex (MHC) is the most important region in the vertebrate genome with respect to infection and autoimmunity, and is crucial in adaptive and innate immunity. Decades of biomedical research have revealed many MHC genes that are duplicated, polymorphic and associated with more diseases than any other region of the human genome. The recent completion of several large-scale studies offers the opportunity to assimilate the latest data into an integrated gene map of the extended human MHC. Here, we present this map and review its content in relation to paralogy, polymorphism, immune function and disease.
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            A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC.

            The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and are essential in self versus non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune diseases. Yet identification of causal variants is problematic owing to linkage disequilibrium that extends across multiple HLA and non-HLA genes in the MHC. We therefore set out to characterize the linkage disequilibrium patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common SNPs and deletion-insertion polymorphisms across four population samples. The analysis provides informative tag SNPs that capture much of the common variation in the MHC region and that could be used in disease association studies, and it provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.
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              Estimation of fixation indices and gene diversities.

              Considering the multinomial sampling of genotypes, unbiased estimators of various gene diversity measures in subdivided populations are presented. Using these quantities, formulae for estimating Wright's fixation indices (FIS, FIT, and FST) from a finite sample are developed.
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                Author and article information

                Contributors
                julgrate@yahoo.com
                marcelof@stanford.edu
                edmond_yunis@dfci.harvard.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 February 2020
                24 February 2020
                2020
                : 10
                : 3248
                Affiliations
                [1 ]ISNI 0000 0004 4914 1197, GRID grid.469873.7, Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), ; Jena, Germany
                [2 ]ISNI 0000 0001 2169 9197, GRID grid.462439.e, Laboratory of Molecular Genetics, Escuela Nacional de Antropología e Historia (ENAH), ; Mexico City, Mexico
                [3 ]ISNI 0000 0000 8515 3604, GRID grid.419179.3, Department of Immunology, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas (INER), ; Mexico City, Mexico
                [4 ]ISNI 0000 0001 2203 4701, GRID grid.419886.a, Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, ; Mexico City, Mexico
                [5 ]ISNI 0000 0000 8637 5954, GRID grid.419204.a, Clinical Laboratory of Neurodegenerative Diseases, Instituto Nacional de Neurología y Neurocirugía “Manuel Velasco Suárez”, ; Mexico City, Mexico
                [6 ]ISNI 0000 0000 8809 1613, GRID grid.7372.1, University of Warwick, School of Life Sciences, ; Coventry, United Kingdom
                [7 ]Immunogenetics Unit, Técnicas Genéticas Aplicadas a la Clínica (TGAC), Mexico City, Mexico
                [8 ]ISNI 0000 0001 0698 4037, GRID grid.416850.e, Department of Transplantation, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMSZ), ; Mexico City, Mexico
                [9 ]Public Health State Laboratory for Chiapas, Tuxtla Gutierrez, Chiapas, Mexico
                [10 ]Histocompatibility, Immunogenetics and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA USA
                [11 ]ISNI 0000 0004 0576 3437, GRID grid.8127.c, Biology Department, University of Crete, ; Heraklion, Greece
                [12 ]ISNI 0000 0000 8637 5954, GRID grid.419204.a, Department of Neurogenetics and Molecular Biology, Instituto Nacional de Neurología y Neurocirugía “Manuel Velasco Suárez”, ; Mexico City, Mexico
                [13 ]Department of Pathology, Stanford University, CA USA
                [14 ]ISNI 0000 0001 1091 9430, GRID grid.419157.f, Clinical Analysis Laboratory, Unidad Médica Familiar (UMF) No. 23, Instituto Mexicano del Seguro Social (IMSS), ; Tuxtla Gutiérrez, Chiapas Mexico
                [15 ]ISNI 0000000419368956, GRID grid.168010.e, Stanford Genome Technology Center, ; Palo Alto, CA USA
                [16 ]Department of Cancer Immunology and Virology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA USA
                [17 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, The William Harvey Research Institute, Barts and London School of Medicine, Queen Mary University of London, ; London, United Kingdom
                Author information
                http://orcid.org/0000-0003-0518-4518
                http://orcid.org/0000-0002-7143-0281
                http://orcid.org/0000-0002-9324-229X
                http://orcid.org/0000-0001-5487-5919
                Article
                58897
                10.1038/s41598-020-58897-5
                7039995
                32094421
                f312ea12-5467-4f6a-b357-0612676c82ce
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 April 2019
                : 22 January 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004189, Max-Planck-Gesellschaft (Max Planck Society);
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                © The Author(s) 2020

                Uncategorized
                immunogenetics
                Uncategorized
                immunogenetics

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