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      The genomic analysis of current-day North African populations reveals the existence of trans-Saharan migrations with different origins and dates

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      1 , 2 , 3 , 1 ,
      Human Genetics
      Springer Berlin Heidelberg

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          Abstract

          The Sahara Desert has acted as a barrier to human gene-flow between the northern and central parts of Africa since its aridification. Nonetheless, some contacts between both sides of the desert have occurred throughout history, mainly driven by commercial activity. Part of this was the infamous trans-Saharan slave trade, which forcedly brought peoples from south of the Sahara to North Africa from Roman times until the nineteenth century. Although historical records exist, the genetic aspects of these trans-Saharan migrations have not been deeply studied. In the present study, we assess the genetic influence of trans-Saharan migrations in current-day North Africa and characterize its amount, geographical origin, and dates. We confirm the heterogeneous and generally low-frequency presence of genomic segments of sub-Saharan origin in present-day North Africans acquired in recent historical times, and we show evidence of at least two admixture events: one dated around the thirteenth–fourteenth centuries CE between North Africans and a Western-sub-Saharan-like source similar to current-day Senegambian populations, and another one dated around the seventeenth century CE involving Tunisians and an Eastern-sub-Saharan-like source related to current-day south-Sudan and Kenyan populations. Time and location coincide with the peak of trans-Saharan slave-trade activity between Western African empires and North African powers, and are also concordant with the possibility of continuous recent south-to-north gene-flow. These findings confirm the trans-Saharan human genetic contacts, providing new and precise evidence about its possible dates and geographical origins, which are pivotal to understanding the genomic composition of an underrepresented region such as North Africa.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00439-022-02503-3.

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          Most cited references55

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
            • Record: found
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            Is Open Access

            The variant call format and VCFtools

            Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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              • Article: not found

              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.

                Author and article information

                Contributors
                david.comas@upf.edu
                Journal
                Hum Genet
                Hum Genet
                Human Genetics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-6717
                1432-1203
                28 November 2022
                28 November 2022
                2023
                : 142
                : 2
                : 305-320
                Affiliations
                [1 ]GRID grid.5612.0, ISNI 0000 0001 2172 2676, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Departament de Medicina i Ciències de la Vida, , Universitat Pompeu Fabra, ; Barcelona, Spain
                [2 ]GRID grid.12574.35, ISNI 0000000122959819, Laboratory of Genetics, Immunology, and Human Pathologies, Faculty of Science of Tunis, , University of Tunis El Manar, ; Tunis, Tunisia
                [3 ]GRID grid.412892.4, ISNI 0000 0004 1754 9358, College of Science, Department of Biology, , Taibah University, ; Al Madinah Al Monawarah, Saudi Arabia
                Author information
                http://orcid.org/0000-0001-6741-3959
                http://orcid.org/0000-0002-8980-9716
                http://orcid.org/0000-0002-5075-0956
                Article
                2503
                10.1007/s00439-022-02503-3
                9918576
                36441222
                698d08ad-7f2c-4559-a9f0-98ba01176274
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 August 2022
                : 28 October 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100011033, Agencia Estatal de Investigación;
                Award ID: PID2019-106485GB-I00/AEI/10.13039/501100011033
                Award ID: CEX2018-000792-M
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007136, Secretaría de Estado de Investigación, Desarrollo e Innovación;
                Award ID: PRE2018-084178
                Award Recipient :
                Funded by: Universitat Pompeu Fabra
                Categories
                Original Investigation
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                Genetics
                Genetics

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