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      The genomic footprint of social stratification in admixing American populations

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          Abstract

          Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European, and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification, shaped by socially constructed racial and gender hierarchies, has constrained the admixture processes in the Americas since the European colonization and the subsequent Atlantic slave trade.

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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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              Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color

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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                01 December 2023
                2023
                : 12
                : e84429
                Affiliations
                [1 ] Department of Life Sciences, Silwood Park Campus, Imperial College London ( https://ror.org/041kmwe10) London United Kingdom
                [2 ] Department of Statistical Sciences, University of Bologna ( https://ror.org/01111rn36) Bologna Italy
                [3 ] Department of Computer Science, Haverford College ( https://ror.org/04fnrxr62) Haverford United States
                [4 ] School of Biological and Behavioural Sciences, Queen Mary University of London ( https://ror.org/026zzn846) London United Kingdom
                Brown University ( https://ror.org/05gq02987) United States
                Max Planck Institute for Biology Tübingen ( https://ror.org/0243gzr89) Germany
                Brown University ( https://ror.org/05gq02987) United States
                Brown University ( https://ror.org/05gq02987) United States
                Author information
                https://orcid.org/0000-0002-1712-9404
                https://orcid.org/0000-0002-0484-0838
                Article
                84429
                10.7554/eLife.84429
                10776089
                38038347
                8a7f5e6a-b644-49ca-a766-6b69826eab5f
                © 2023, Mas-Sandoval et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 24 October 2022
                : 22 November 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000275, Leverhulme Trust;
                Award ID: RPG-2018-208
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 865356
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R15HG011528
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Evolutionary Biology
                Genetics and Genomics
                Custom metadata
                Social hierarchies resulting from the European colonization of the Americas stratified the population structure, leading to ancestry-related assortative mating and sex bias patterns that can be inferred from the genomes of the populations across the continent.

                Life sciences
                admixture,genetic structure,population stratification,assortative mating,sex bias,deep learning,human

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