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      Dissecting the genetic structure and admixture of four geographical Malay populations

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          Abstract

          The Malay people are an important ethnic composition in Southeast Asia, but their genetic make-up and population structure remain poorly studied. Here we conducted a genome-wide study of four geographical Malay populations: Peninsular Malaysian Malay (PMM), Singaporean Malay (SGM), Indonesian Malay (IDM) and Sri Lankan Malay (SLM). All the four Malay populations showed substantial admixture with multiple ancestries. We identified four major ancestral components in Malay populations: Austronesian (17%–62%), Proto-Malay (15%–31%), East Asian (4%–16%) and South Asian (3%–34%). Approximately 34% of the genetic makeup of SLM is of South Asian ancestry, resulting in its distinct genetic pattern compared with the other three Malay populations. Besides, substantial differentiation was observed between the Malay populations from the north and the south, and between those from the west and the east. In summary, this study revealed that the genetic identity of the Malays comprises a mixed entity of multiple ancestries represented by Austronesian, Proto-Malay, East Asian and South Asian, with most of the admixture events estimated to have occurred 175 to 1,500 years ago, which in turn suggests that geographical isolation and independent admixture have significantly shaped the genetic architectures and the diversity of the Malay populations.

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          Most cited references10

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          Mapping human genetic diversity in Asia.

          Asia harbors substantial cultural and linguistic diversity, but the geographic structure of genetic variation across the continent remains enigmatic. Here we report a large-scale survey of autosomal variation from a broad geographic sample of Asian human populations. Our results show that genetic ancestry is strongly correlated with linguistic affiliations as well as geography. Most populations show relatedness within ethnic/linguistic groups, despite prevalent gene flow among populations. More than 90% of East Asian (EA) haplotypes could be found in either Southeast Asian (SEA) or Central-South Asian (CSA) populations and show clinal structure with haplotype diversity decreasing from south to north. Furthermore, 50% of EA haplotypes were found in SEA only and 5% were found in CSA only, indicating that SEA was a major geographic source of EA populations.
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            Estimating F-statistics.

            A moment estimator of, the coancestry coefficient for alleles within a population, was described by Weir & Cockerham in 1984 (100) and is still widely cited. The estimate is used by population geneticists to characterize population structure, by ecologists to estimate migration rates, by animal breeders to describe genetic variation, and by forensic scientists to quantify the strength of matching DNA profiles. This review extends the work of Weir & Cockerham by allowing different levels of coancestry for different populations, and by allowing non-zero coancestries between pairs of populations. All estimates are relative to the average value of theta between pairs of populations. Moment estimates for within- and between-population theta values are likely to have large sampling variances, although these may be reduced by combining information over loci. Variances also decrease with the numbers of alleles at a locus, and with the numbers of populations sampled. This review also extends the work of Weir & Cockerham by employing maximum likelihood methods under the assumption that allele frequencies follow the normal distribution over populations. For the case of equal theta values within populations and zero theta values between populations, the maximum likelihood estimate is the same as that given by Robertson & Hill in 1984 (70). The review concludes by relating functions of theta values to times of population divergence under a pure drift model.
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              Deep whole-genome sequencing of 100 southeast Asian Malays.

              Whole-genome sequencing across multiple samples in a population provides an unprecedented opportunity for comprehensively characterizing the polymorphic variants in the population. Although the 1000 Genomes Project (1KGP) has offered brief insights into the value of population-level sequencing, the low coverage has compromised the ability to confidently detect rare and low-frequency variants. In addition, the composition of populations in the 1KGP is not complete, despite the fact that the study design has been extended to more than 2,500 samples from more than 20 population groups. The Malays are one of the Austronesian groups predominantly present in Southeast Asia and Oceania, and the Singapore Sequencing Malay Project (SSMP) aims to perform deep whole-genome sequencing of 100 healthy Malays. By sequencing at a minimum of 30× coverage, we have illustrated the higher sensitivity at detecting low-frequency and rare variants and the ability to investigate the presence of hotspots of functional mutations. Compared to the low-pass sequencing in the 1KGP, the deeper coverage allows more functional variants to be identified for each person. A comparison of the fidelity of genotype imputation of Malays indicated that a population-specific reference panel, such as the SSMP, outperforms a cosmopolitan panel with larger number of individuals for common SNPs. For lower-frequency (<5%) markers, a larger number of individuals might have to be whole-genome sequenced so that the accuracy currently afforded by the 1KGP can be achieved. The SSMP data are expected to be the benchmark for evaluating the value of deep population-level sequencing versus low-pass sequencing, especially in populations that are poorly represented in population-genetics studies. Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                23 September 2015
                2015
                : 5
                : 14375
                Affiliations
                [1 ]Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
                [2 ]Faculty of Medicine and Health Sciences, UCSI University , Jalan Merana Gading, Taman Connought, 56000, Kuala Lumpur, Malaysia
                [3 ]Saw Swee Hock School of Public Health, National University of Singapore , Singapore
                [4 ]Life Sciences Institute, National University of Singapore , Singapore
                [5 ]Department of Public Health, Faculty of Medicine, University of Kelaniya , Ragama 11010, Sri Lanka
                [6 ]Department of Medicine, Faculty of Medicine, University of Kelaniya , Ragama 11010, Sri Lanka
                [7 ]Department of Pediatrics, School of Medical Sciences, Universiti Sains Malaysia , Kelantan 16150, Malaysia
                [8 ]Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine , Tokyo 1628655, Japan
                [9 ]NUS Graduate School for Integrative Science and Engineering, National University of Singapore , Singapore
                [10 ]Genome Institute of Singapore, Agency for Science, Technology and Research , Singapore
                [11 ]Department of Statistics and Applied Probability, National University of Singapore , Singapore
                [12 ]School of Life Science and Technology, ShanghaiTec University , Shanghai 200031, China
                [13 ]Collaborative Innovation Center of Genetics and Development , Shanghai 200438, China
                Author notes
                Article
                srep14375
                10.1038/srep14375
                4585825
                26395220
                a6744fb1-1276-4e3f-8fb8-6feb96fd43e9
                Copyright © 2015, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 08 April 2015
                : 24 August 2015
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