The Genomic Ancestry of Individuals from Different Geographical Regions of Brazil Is More Uniform Than Expected

Based on pre-DNA racial/color methodology, clinical and pharmacological trials have traditionally considered the different geographical regions of Brazil as being very heterogeneous. We wished to ascertain how such diversity of regional color categories correlated with ancestry. Using a panel of 40 validated ancestry-informative insertion-deletion DNA polymorphisms we estimated individually the European, African and Amerindian ancestry components of 934 self-categorized White, Brown or Black Brazilians from the four most populous regions of the Country. We unraveled great ancestral diversity between and within the different regions. Especially, color categories in the northern part of Brazil diverged significantly in their ancestry proportions from their counterparts in the southern part of the Country, indicating that diverse regional semantics were being used in the self-classification as White, Brown or Black. To circumvent these regional subjective differences in color perception, we estimated the general ancestry proportions of each of the four regions in a form independent of color considerations. For that, we multiplied the proportions of a given ancestry in a given color category by the official census information about the proportion of that color category in the specific region, to arrive at a “total ancestry” estimate. Once such a calculation was performed, there emerged a much higher level of uniformity than previously expected. In all regions studied, the European ancestry was predominant, with proportions ranging from 60.6% in the Northeast to 77.7% in the South. We propose that the immigration of six million Europeans to Brazil in the 19th and 20th centuries - a phenomenon described and intended as the “whitening of Brazil” - is in large part responsible for dissipating previous ancestry dissimilarities that reflected region-specific population histories. These findings, of both clinical and sociological importance for Brazil, should also be relevant to other countries with ancestrally admixed populations

Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations

Submitted by Nuzia Santos ([email protected]) on 2016-02-19T13:11:37Z No. of bitstreams: 1 Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations..pdf: 501572 bytes, checksum: 45f5ed2fc0a7c2cb73e047a75457edae (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2016-02-19T13:37:09Z (GMT) No. of bitstreams: 1 Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations..pdf: 501572 bytes, checksum: 45f5ed2fc0a7c2cb73e047a75457edae (MD5)Made available in DSpace on 2016-02-19T13:37:09Z (GMT). No. of bitstreams: 1 Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations..pdf: 501572 bytes, checksum: 45f5ed2fc0a7c2cb73e047a75457edae (MD5) Previous issue date: 2015Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade Federal de Pelotas. Programa de Pós-Graduação em Epidemiologia. Pelotas, RS, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade Federal da Bahia. Instituto de Matemática. Departamento de Estatística. Salvador, Bahia, BrasilUniversidade Federal da Bahia. Instituto de Ciências da Saúde. Departamento de Ciências da Biointeração. Salvador, Bahia, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilUniversity of Leicester. Department of Genetics. Leicester, United KingdomWashington University School of Medicine. Department of Molecular Microbiology. St. Louis, MO/University of California. Department of Medicine. San Diego, CAAsociación Benéfica Proyectos en Informática, Salud, Medicina y Agricultura. Biomedical Research Unit. Lima, PeruUniversidade Federal de Santa Catarina. Embriologia e Genética. Departamento de Biologia Celular. Florianópolis, SC, BrasilUniversidade Federal de Minas Gerais. Departamento de Estatística. Belo Horizonte, MG, BrasilUniversità di Ferrara. Dipartimento di Scienze della Vita e Biotecnologie. Ferrara, ItalyJohns Hopkins University. International Health. Bloomberg School of Public Health. Baltimore, MD, USA/Universidade Peruana Cayetano Heredia. Laboratorio de Investigación de Enfermedades Infecciosas. Lima, PeruUniversity of Toronto. Center for Addiction and Mental Health. Department of Psychiatry and Neuroscience Section. Toronto, ON, CanadaUniversidade Federal de Santa Catarina. Embriologia e Genética. Departamento de Biologia Celular. Florianópolis, SC, BrasilUniversidade Federal de Santa Catarina. Embriologia e Genética. Departamento de Biologia Celular. Florianópolis, SC, BrasilInnsbruck Medical University. Molecular and Clinical Pharmacology. Department of Medical Genetics. Division of Genetic Epidemiology. Innsbruck, AustriaFrederick National Laboratory for Cancer Research. Leidos Biomedical Research. Cancer Genomics Research Laboratory. Frederick, MDLondon School of Hygiene and Tropical Medicine. Faculty of Epidemiology. Department of Infectious Disease Epidemiology. London, United KingdomUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilFundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal da Bahia. Instituto de Ciências da Saúde. Departamento de Ciências da Biointeração. Salvador, BA, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Laboratório de Computação Científica. Belo Horizonte, MG, Brasil.Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.Universidade Federal de Pelotas. Programa de Pós-Graduação em Epidemiologia. Pelotas, RS, Brasil.Universidade Federal de Rio Grande do Sul. Centro Nacional de Supercomputação. Porto Alegre, RS, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Grupo de Genômica e Biologia Computacional. Belo Horizonte, MG, Brasil.Universidade Federal de Pelotas. Programa de Pós-Graduação em Epidemiologia. Pelotas, RS, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Laboratório de Computação Científica. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.While South Americans are underrepresented in human genomic diversity studies, Brazil has been a classical model for population genetics studies on admixture.We present the results of the EPIGEN Brazil Initiative, the most comprehensive up-to-date genomic analysis of any Latin-American population. A population-based genomewide analysis of 6,487 individuals was performed in the context of worldwide genomic diversity to elucidate how ancestry, kinship, and inbreeding interact in three populations with different histories from the Northeast (African ancestry: 50%), Southeast, and South (both with European ancestry >70%) of Brazil. We showed that ancestry-positive assortative mating permeated Brazilian history. We traced European ancestry in the Southeast/South to a wider European/Middle Eastern region with respect to the Northeast, where ancestry seems restricted to Iberia. By developing an approximate Bayesian computation framework, we infer more recent European immigration to the Southeast/South than to the Northeast. Also, the observed low Native-American ancestry (6–8%) was mostly introduced in different regions of Brazil soon after the European Conquest. We broadened our understanding of the African diaspora, the major destination of which was Brazil, by revealing that Brazilians display two within-Africa ancestry components: one associated with non-Bantu/western Africans (more evident in the Northeast and African Americans) and one associated with Bantu/eastern Africans (more present in the Southeast/South). Furthermore, the whole-genome analysis of 30 individuals (42-fold deep coverage) shows that continental admixture rather than local post-Columbian history is the main and complex determinant of the individual amount of deleterious genotypes

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.