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      How Ancestry Influences the Chances of Finding Unrelated Donors: An Investigation in Admixed Brazilians

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          Abstract

          A match of HLA loci between patients and donors is critical for successful hematopoietic stem cell transplantation. However, the extreme polymorphism of HLA loci – an outcome of millions of years of natural selection – reduces the chances that two individuals will carry identical combinations of multilocus HLA genotypes. Further, HLA variability is not homogeneously distributed throughout the world: African populations on average have greater variability than non-Africans, reducing the chances that two unrelated African individuals are HLA identical. Here, we explore how self-identification (often equated with “ethnicity” or “race”) and genetic ancestry are related to the chances of finding HLA compatible donors in a large sample from Brazil, a highly admixed country. We query REDOME, Brazil’s Bone Marrow Registry, and investigate how different criteria for identifying ancestry influence the chances of finding a match. We find that individuals who self-identify as “Black” and “Mixed” on average have lower chances of finding matches than those who self-identify as “White” (up to 57% reduction). We next show that an individual’s African genetic ancestry, estimated using molecular markers and quantified as the proportion of an individual’s genome that traces its ancestry to Africa, is strongly associated with reduced chances of finding a match (up to 60% reduction). Finally, we document that the strongest reduction in chances of finding a match is associated with having an MHC region of exclusively African ancestry (up to 75% reduction). We apply our findings to a specific condition, for which there is a clinical indication for transplantation: sickle-cell disease. We show that the increased African ancestry in patients with this disease leads to reduced chances of finding a match, when compared to the remainder of the sample, without the condition. Our results underscore the influence of ancestry on chances of finding compatible HLA matches, and indicate that efforts guided to increasing the African component of registries are necessary.

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          Most cited references69

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Fast model-based estimation of ancestry in unrelated individuals.

            Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
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              A linear complexity phasing method for thousands of genomes.

              Human-disease etiology can be better understood with phase information about diploid sequences. We present a method for estimating haplotypes, using genotype data from unrelated samples or small nuclear families, that leads to improved accuracy and speed compared to several widely used methods. The method, segmented haplotype estimation and imputation tool (SHAPEIT), scales linearly with the number of haplotypes used in each iteration and can be run efficiently on whole chromosomes.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                06 November 2020
                2020
                06 November 2020
                : 11
                : 584950
                Affiliations
                [1] 1Laboratory of Evolutionary Genetics, Institute of Biosciences, University of São Paulo , São Paulo, Brazil
                [2] 2Instituto de Matemática e Estatística, Universidade do Estado do Rio de Janeiro , Rio de Janeiro, Brazil
                [3] 3Registro Nacional de Doadores Voluntários de Medula Óssea—REDOME, Instituto Nacional do Câncer, Ministério da Saúde , Rio de Janeiro, Brazil
                [4] 4Fundação Pró Sangue, Hemocentro de São Paulo , São Paulo, Brazil
                [5] 5Instituto Oswaldo Cruz, Fundação Oswaldo Cruz , Rio de Janeiro, Brazil
                [6] 6Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais , Belo Horizonte, Brazil
                [7] 7Serviço de Hematologia, Hemoterapia e Terapia Celular, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo , São Paulo, Brazil
                [8] 8Fundação Hemominas , Belo Horizonte, Brazil
                [9] 9Fundação de Hematologia e Hemoterapia de Pernambuco , HEMOPE, Recife, Brazil
                [10] 10Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Instituto da Criança , São Paulo, Brazil
                [11] 11Fundação Hemorio , Rio de Janeiro, Brazil
                [12] 12Epidemiology, Vitalant Research Institute , San Francisco, CA, United States
                [13] 13University of California San Francisco Benioff Children’s Hospital Oakland , Oakland, CA, United States
                [14] 14Department of Laboratory Medicine, University of California San Francisco , San Francisco, CA, United States
                [15] 15Department of Biostatistics, University of Washington , Seattle, WA, United States
                [16] 16Instituto de Medicina Tropical, Departamento de Moléstias Infecciosas e Parasitárias da Faculdade de Medicina da Universidade de São Paulo , São Paulo, Brazil
                [17] 17Laboratório de Histocompatibilidade e Criopreservação, Universidade do Estado do Rio de Janeiro , Rio de Janeiro, Brazil
                Author notes

                Edited by: Martin Maiers, National Marrow Donor Program, United States

                Reviewed by: Pierre-Antoine Gourraud, Université de Nantes, France; Abeer Madbouly, Center for International Blood and Marrow Transplant Research (CIBMTR), United States

                *Correspondence: Diogo Meyer, diogo@ 123456ib.usp.br ; Kelly Nunes, knunesbio@ 123456gmail.com

                This article was submitted to Alloimmunity and Transplantation, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2020.584950
                7677137
                33240273
                ad147c81-3cff-4583-8b90-5cd46d7fc85c
                Copyright © 2020 Nunes, Aguiar, Silva, Sena, de Oliveira, Dinardo, Kehdy, Tarazona-Santos, Rocha, Carneiro-Proietti, Loureiro, Flor-Park, Maximo, Kelly, Custer, Weir, Sabino, Porto and Meyer

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 July 2020
                : 05 October 2020
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 72, Pages: 14, Words: 7555
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01 GM075091
                Categories
                Immunology
                Original Research

                Immunology
                hla,hematopoietic stem cell transplantation,genetic ancestry,admixture,mhc,brazil
                Immunology
                hla, hematopoietic stem cell transplantation, genetic ancestry, admixture, mhc, brazil

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