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      Genome-Wide Analysis in Brazilians Reveals Highly Differentiated Native American Genome Regions

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          Abstract

          Despite its population, geographic size, and emerging economic importance, disproportionately little genome-scale research exists into genetic factors that predispose Brazilians to disease, or the population genetics of risk. After identification of suitable proxy populations and careful analysis of tri-continental admixture in 1,538 North-Eastern Brazilians to estimate individual ancestry and ancestral allele frequencies, we computed 400,000 genome-wide locus-specific branch length (LSBL) Fst statistics of Brazilian Amerindian ancestry compared to European and African; and a similar set of differentiation statistics for their Amerindian component compared with the closest Asian 1000 Genomes population (surprisingly, Bengalis in Bangladesh). After ranking SNPs by these statistics, we identified the top 10 highly differentiated SNPs in five genome regions in the LSBL tests of Brazilian Amerindian ancestry compared to European and African; and the top 10 SNPs in eight regions comparing their Amerindian component to the closest Asian 1000 Genomes population. We found SNPs within or proximal to the genes CIITA (rs6498115), SMC6 (rs1834619), and KLHL29 (rs2288697) were most differentiated in the Amerindian-specific branch, while SNPs in the genes ADAMTS9 (rs7631391), DOCK2 (rs77594147), SLC28A1 (rs28649017), ARHGAP5 (rs7151991), and CIITA (rs45601437) were most highly differentiated in the Asian comparison. These genes are known to influence immune function, metabolic and anthropometry traits, and embryonic development. These analyses have identified candidate genes for selection within Amerindian ancestry, and by comparison of the two analyses, those for which the differentiation may have arisen during the migration from Asia to the Americas.

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          An Introduction to the Bootstrap

          Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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            Estimating and interpreting F ST: The impact of rare variants

            In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F ST as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion regarding how to estimate F ST from available data. Considering this confusion, wide variation in published estimates of F ST for pairs of HapMap populations is a cause for concern. These estimates changed—in some cases more than twofold—when comparing estimates from genotyping arrays to those from sequence data. Indeed, changes in F ST from sequencing data might be expected due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of F ST that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating F ST for a single SNP, (2) combining estimates of F ST across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in F ST estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions.
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              Estimation of individual admixture: analytical and study design considerations.

              The genome of an admixed individual represents a mixture of alleles from different ancestries. In the United States, the two largest minority groups, African-Americans and Hispanics, are both admixed. An understanding of the admixture proportion at an individual level (individual admixture, or IA) is valuable for both population geneticists and epidemiologists who conduct case-control association studies in these groups. Here we present an extension of a previously described frequentist (maximum likelihood or ML) approach to estimate individual admixture that allows for uncertainty in ancestral allele frequencies. We compare this approach both to prior partial likelihood based methods as well as more recently described Bayesian MCMC methods. Our full ML method demonstrates increased robustness when compared to an existing partial ML approach. Simulations also suggest that this frequentist estimator achieves similar efficiency, measured by the mean squared error criterion, as Bayesian methods but requires just a fraction of the computational time to produce point estimates, allowing for extensive analysis (e.g., simulations) not possible by Bayesian methods. Our simulation results demonstrate that inclusion of ancestral populations or their surrogates in the analysis is required by any method of IA estimation to obtain reasonable results. (c) 2005 Wiley-Liss, Inc.
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                Author and article information

                Journal
                Mol Biol Evol
                Mol. Biol. Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press
                0737-4038
                1537-1719
                March 2017
                18 January 2017
                18 January 2017
                : 34
                : 3
                : 559-574
                Affiliations
                [1 ]Center for Public Health Genomics, University of Virginia, Charlottesville, VA
                [2 ]Department of Public Health Sciences, University of Virginia, Charlottesville, VA
                [3 ]Departamento de Fisiologia e Farmacologia, Universidade Federal do Ceará, Fortaleza, Brazil
                [4 ]INCT-Instituto de Biomedicina Universidade Federal do Ceará, Fortaleza, Brazil
                [5 ]Center for Global Health, University of Virginia, Charlottesville, VA
                [6 ]Genetics Institute, University of Florida, Gainesville, FL
                [7 ]Department of Pathology Immunology and Laboratory Medicine, University of Florida, Gainesville, FL
                Author notes
                [* ] Corresponding author: E-mail: jcm6t@ 123456virginia.edu.
                Article
                msw249
                10.1093/molbev/msw249
                5430616
                28100790
                58bf6b51-ee84-4cb7-a90d-8fc46877aa4d
                © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 16
                Funding
                Award ID: U01AI026512
                Funded by: National Institute of Allergy and Infectious Diseases http://dx.doi.org/10.13039/100000060
                Categories
                Discoveries

                Molecular biology
                ancestry,selection,admixture,genetic differentiation,native american,brazil.
                Molecular biology
                ancestry, selection, admixture, genetic differentiation, native american, brazil.

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