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      Genomic formation of Tibeto-Burman speaking populations in Guizhou, Southwest China

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          Abstract

          Sino-Tibetan is the most prominent language family in East Asia. Previous genetic studies mainly focused on the Tibetan and Han Chinese populations. However, due to the sparse sampling, the genetic structure and admixture history of Tibeto-Burman-speaking populations in the low-altitude region of Southwest China still need to be clarified. We collected DNA from 157 individuals from four Tibeto-Burman-speaking groups from the Guizhou province in Southwest China. We genotyped the samples at about 700,000 genome-wide single nucleotide polymorphisms. Our results indicate that the genetic variation of the four Tibeto-Burman-speaking groups in Guizhou is at the intermediate position in the modern Tibetan-Tai-Kadai/Austronesian genetic cline. This suggests that the formation of Tibetan-Burman groups involved a large-scale gene flow from lowland southern Chinese. The southern ancestry could be further modelled as deriving from Vietnam’s Late Neolithic-related inland Southeast Asia agricultural populations and Taiwan’s Iron Age-related coastal rice-farming populations. Compared to the Tibeto-Burman speakers in the Tibetan-Yi Corridor reported previously, the Tibeto-Burman groups in the Guizhou region received additional gene flow from the southeast coastal area of China. We show a difference between the genetic profiles of the Tibeto-Burman speakers of the Tibetan-Yi Corridor and the Guizhou province. Vast mountain ranges and rivers in Southwest China may have decelerated the westward expansion of the southeast coastal East Asians. Our results demonstrate the complex genetic profile in the Guizhou region in Southwest China and support the multiple waves of human migration in the southern area of East Asia.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-023-09767-7.

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          Second-generation PLINK: rising to the challenge of larger and richer datasets

          PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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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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              Ancient admixture in human history.

              Population mixture is an important process in biology. We present a suite of methods for learning about population mixtures, implemented in a software package called ADMIXTOOLS, that support formal tests for whether mixture occurred and make it possible to infer proportions and dates of mixture. We also describe the development of a new single nucleotide polymorphism (SNP) array consisting of 629,433 sites with clearly documented ascertainment that was specifically designed for population genetic analyses and that we genotyped in 934 individuals from 53 diverse populations. To illustrate the methods, we give a number of examples that provide new insights about the history of human admixture. The most striking finding is a clear signal of admixture into northern Europe, with one ancestral population related to present-day Basques and Sardinians and the other related to present-day populations of northeast Asia and the Americas. This likely reflects a history of admixture between Neolithic migrants and the indigenous Mesolithic population of Europe, consistent with recent analyses of ancient bones from Sweden and the sequencing of the genome of the Tyrolean "Iceman."
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                Author and article information

                Contributors
                wang@xmu.edu.cn
                mmm_hj@126.com
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                7 November 2023
                7 November 2023
                2023
                : 24
                : 672
                Affiliations
                [1 ]State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, ( https://ror.org/00mcjh785) Xiamen, 361102 China
                [2 ]Department of Forensic Medicine, Guizhou Medical University, ( https://ror.org/035y7a716) Guiyang, China
                [3 ]Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, ( https://ror.org/00anm2x55) Shanghai, 200063 China
                [4 ]Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, ( https://ror.org/00mcjh785) Xiamen, 361005 China
                [5 ]Department of Anthropology and Human Genetics, Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, ( https://ror.org/013q1eq08) Shanghai, China
                [6 ]State Key Laboratory of Marine Environmental Science, Xiamen University, ( https://ror.org/00mcjh785) Xiamen, 361102 China
                [7 ]Institute of Artificial Intelligence, Xiamen University, ( https://ror.org/00mcjh785) Xiamen, 361005 Fujian China
                Article
                9767
                10.1186/s12864-023-09767-7
                10630991
                37936086
                f9fc5d1a-39ed-4701-b4d5-c54cc4986114
                © The Author(s) 2023

                Open Access This 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 27 May 2023
                : 25 October 2023
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 32270667
                Award Recipient :
                Funded by: National Natural Science Foundation
                Award ID: 82160324, 82260335
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Genetics
                genetic structure,admixture history,gene flow,tibeto-burman,guizhou,southwest china,tibetan-yi corridor,southeast coastal

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