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      Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals


      1 , * , 1 , 1 , 2 , 3 , 4 , 4 , 5 , 5 , 2 , 6 , 2 , 6 , 6 , 6 , 7 , 7 , 7 , 1 , 1 , 7 , 8 , 9 , 9 , 10 , 11 , 12 , 13 , 13 , 14 , 7 , 7 , 7 , 6 , 15 , 5 , 4 , 1 , 3

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          The current genetic makeup of Latin America has been shaped by a history of extensive admixture between Africans, Europeans and Native Americans, a process taking place within the context of extensive geographic and social stratification. We estimated individual ancestry proportions in a sample of 7,342 subjects ascertained in five countries (Brazil, Chile, Colombia, México and Perú). These individuals were also characterized for a range of physical appearance traits and for self-perception of ancestry. The geographic distribution of admixture proportions in this sample reveals extensive population structure, illustrating the continuing impact of demographic history on the genetic diversity of Latin America. Significant ancestry effects were detected for most phenotypes studied. However, ancestry generally explains only a modest proportion of total phenotypic variation. Genetically estimated and self-perceived ancestry correlate significantly, but certain physical attributes have a strong impact on self-perception and bias self-perception of ancestry relative to genetically estimated ancestry.

          Author Summary

          Latin America has a history of extensive mixing between Native Americans and people arriving from Europe and Africa. As a result, individuals in the region have a highly heterogeneous genetic background and show great variation in physical appearance. Latin America offers an excellent opportunity to examine the genetic basis of the differentiation in physical appearance between Africans, Europeans and Native Americans. The region is also an advantageous setting in which to examine the interplay of genetic, physical and social factors in relation to ethnic/racial self-perception. Here we present the most extensive analysis of genetic ancestry, physical diversity and self-perception of ancestry yet conducted in Latin America. We find significant geographic variation in ancestry across the region, this variation being consistent with demographic history and census information. We show that genetic ancestry impacts many aspects of physical appearance. We observe that self-perception is highly influenced by physical appearance, and that variation in physical appearance biases self-perception of ancestry relative to genetically estimated ancestry.

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          Most cited references 48

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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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            Common SNPs explain a large proportion of the heritability for human height.

            SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.
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              Is Open Access

              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.

                Author and article information

                Role: Editor
                PLoS Genet
                PLoS Genet
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                September 2014
                25 September 2014
                : 10
                : 9
                [1 ]Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom
                [2 ]National Institute of Anthropology and History, México City, México
                [3 ]Centro Nacional Patagónico, CONICET, Puerto Madryn, Argentina
                [4 ]Universidad de Antioquia, Medellín, Colombia
                [5 ]Instituto de Alta Investigación Universidad de Tarapacá, Programa de Genética Humana ICBM Facultad de Medicina Universidad de Chile and Centro de Investigaciones del Hombre en el Desierto, Arica, Chile
                [6 ]Facultad de Medicina and Facultad de Química, UNAM, México City, México
                [7 ]Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brasil
                [8 ]Remote Sensing and Digital Imaging Laboratory, Graduate Program on Geology, Vale do Rio dos Sinos University, São Leopoldo, Brazil
                [9 ]The Institute for Fiscal Studies, London, United Kingdom
                [10 ]Department of Economics, University College London, United Kingdom
                [11 ]Department of Anthropology, University College London, London, United Kingdom
                [12 ]Institute for Environmental Sciences, University of Geneva, Carouge, Switzerland
                [13 ]Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Perúana Cayetano Heredia, Lima, Perú
                [14 ]Departamento de Antropología. Facultad de Ciencias Sociales y Humanas. Universidad de Antioquia, Medellín, Colombia
                [15 ]Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”, México City, México
                University of Chicago, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: ARL RGJ DB GB JR CG GP FR JGV VAA SG. Performed the experiments: CJ WA VAA MQS MF MP PE GP FdA JGV PLM TH VR CCSdC MWB EK. Analyzed the data: ARL KA RGJ MQS MZdO MRV MRC OA NR. Contributed reagents/materials/analysis tools: CJ WA GB MF MP PE FdA JGV PLM VAA TH VR CCSdC MWB LSF FMS MCB SCQ. Wrote the paper: ARL KA.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 13
                This work was funded by grants from the Leverhulme Trust (F/07 134/DF to ARL) and BBSRC (BB/I021213/1 to ARL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Biology and Life Sciences
                Human Genetics



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