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Aghakhanian F, Hoh BP, Yew CW, Kumar Subbiah V, Xue Y, Tyler-Smith C, Ayub Q, Phipps ME. Sequence analyses of Malaysian Indigenous communities reveal historical admixture between Hoabinhian hunter-gatherers and Neolithic farmers. Sci Rep 2022; 12:13743. [PMID: 35962005 PMCID: PMC9374673 DOI: 10.1038/s41598-022-17884-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 04/08/2022] [Indexed: 11/09/2022] Open
Abstract
Southeast Asia comprises 11 countries that span mainland Asia across to numerous islands that stretch from the Andaman Sea to the South China Sea and Indian Ocean. This region harbors an impressive diversity of history, culture, religion and biology. Indigenous people of Malaysia display substantial phenotypic, linguistic, and anthropological diversity. Despite this remarkable diversity which has been documented for centuries, the genetic history and structure of indigenous Malaysians remain under-studied. To have a better understanding about the genetic history of these people, especially Malaysian Negritos, we sequenced whole genomes of 15 individuals belonging to five indigenous groups from Peninsular Malaysia and one from North Borneo to high coverage (30X). Our results demonstrate that indigenous populations of Malaysia are genetically close to East Asian populations. We show that present-day Malaysian Negritos can be modeled as an admixture of ancient Hoabinhian hunter-gatherers and Neolithic farmers. We observe gene flow from South Asian populations into the Malaysian indigenous groups, but not into Dusun of North Borneo. Our study proposes that Malaysian indigenous people originated from at least three distinct ancestral populations related to the Hoabinhian hunter-gatherers, Neolithic farmers and Austronesian speakers.
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Affiliation(s)
- Farhang Aghakhanian
- MUM Genomics Facility, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.,TropMed and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia.,Department of Medicine, Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, 56000, Cheras, Kuala Lumpur, Malaysia.,Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor, Malaysia
| | - Boon-Peng Hoh
- Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Chee-Wei Yew
- Biotechnology Research Institute, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Vijay Kumar Subbiah
- Biotechnology Research Institute, University Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Yali Xue
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Chris Tyler-Smith
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Qasim Ayub
- MUM Genomics Facility, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.,TropMed and Biology Multidisciplinary Platform, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia
| | - Maude E Phipps
- MUM Genomics Facility, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia. .,Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor, Malaysia.
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2
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Hoh BP, Deng L, Xu S. The Peopling and Migration History of the Natives in Peninsular Malaysia and Borneo: A Glimpse on the Studies Over the Past 100 years. Front Genet 2022; 13:767018. [PMID: 35154269 PMCID: PMC8829068 DOI: 10.3389/fgene.2022.767018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 01/07/2022] [Indexed: 12/05/2022] Open
Abstract
Southeast Asia (SEA) has one of the longest records of modern human habitation out-of-Africa. Located at the crossroad of the mainland and islands of SEA, Peninsular Malaysia is an important piece of puzzle to the map of peopling and migration history in Asia, a question that is of interest to many anthropologists, archeologists, and population geneticists. This review aims to revisit our understanding to the population genetics of the natives from Peninsular Malaysia and Borneo over the past century based on the chronology of the technology advancement: 1) Anthropological and Physical Characterization; 2) Blood Group Markers; 3) Protein Markers; 4) Mitochondrial and Autosomal DNA Markers; and 5) Whole Genome Analysis. Subsequently some missing gaps of the study are identified. In the later part of this review, challenges of studying the population genetics of natives will be elaborated. Finally, we conclude our review by reiterating the importance of unveiling migration history and genetic diversity of the indigenous populations as a steppingstone towards comprehending disease evolution and etiology.
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Affiliation(s)
- Boon-Peng Hoh
- Faculty of Medicine and Health Sciences, UCSI University, UCSI Hospital, Port Dickson, Malaysia
- *Correspondence: Boon-Peng Hoh,
| | - Lian Deng
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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3
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Sellinger TPP, Abu-Awad D, Tellier A. Limits and convergence properties of the sequentially Markovian coalescent. Mol Ecol Resour 2021; 21:2231-2248. [PMID: 33978324 DOI: 10.1111/1755-0998.13416] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/19/2021] [Accepted: 04/29/2021] [Indexed: 02/07/2023]
Abstract
Several methods based on the sequentially Markovian coalescent (SMC) make use of full genome sequence data from samples to infer population demographic history including past changes in population size, admixture, migration events and population structure. More recently, the original theoretical framework has been extended to allow the simultaneous estimation of population size changes along with other life history traits such as selfing or seed banking. The latter developments enhance the applicability of SMC methods to nonmodel species. Although convergence proofs have been given using simulated data in a few specific cases, an in-depth investigation of the limitations of SMC methods is lacking. In order to explore such limits, we first develop a tool inferring the best case convergence of SMC methods assuming the true underlying coalescent genealogies are known. This tool can be used to quantify the amount and type of information that can be confidently retrieved from given data sets prior to the analysis of the real data. Second, we assess the inference accuracy when the assumptions of SMC approaches are violated due to departures from the model, namely the presence of transposable elements, variable recombination and mutation rates along the sequence, and SNP calling errors. Third, we deliver a new interpretation of SMC methods by highlighting the importance of the transition matrix, which we argue can be used as a set of summary statistics in other statistical inference methods, uncoupling the SMC from hidden Markov models (HMMs). We finally offer recommendations to better apply SMC methods and build adequate data sets under budget constraints.
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Affiliation(s)
| | - Diala Abu-Awad
- Department of Life Science Systems, Technical University of Munich, Munchen, Germany
| | - Aurélien Tellier
- Department of Life Science Systems, Technical University of Munich, Munchen, Germany
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4
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The forensic landscape and the population genetic analyses of Hainan Li based on massively parallel sequencing DNA profiling. Int J Legal Med 2021; 135:1295-1317. [PMID: 33847803 DOI: 10.1007/s00414-021-02590-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/26/2021] [Indexed: 12/30/2022]
Abstract
Due to the formation of the Qiongzhou Strait by climate change and marine transition, Hainan island was isolated from the mainland southern China during the Last Glacial Maximum. Hainan island, located at the southernmost part of China and separated from the Leizhou Peninsula by the Qiongzhou Strait, laid on one of the modern human northward migration routes from Southeast Asia to East Asia. The Hlai language-speaking Li minority, the second largest population after Han Chinese in Hainan island, is the direct descendants of the initial migrants in Hainan island and has unique ethnic properties and derived characteristics; however, the forensic-associated studies on Hainan Li population are still insufficient. Hence, 136 Hainan Li individuals were genotyped in this study using the MPS-based ForenSeq™ DNA Signature Prep Kit (DNA Primer Set A, DPMA) to characterize the forensic genetic polymorphism landscape, and DNA profiles were obtained from 152 different molecular genetic markers (27 autosomal STRs, 24 Y-STRs, 7 X-STRs, and 94 iiSNPs). A total of 419 distinct length variants and 586 repeat sequence sub-variants, with 31 novel alleles (at 17 loci), were identified across the 58 STR loci from the DNA profiles of Hainan Li population. We evaluated the forensic characteristics and efficiencies of DPMA, demonstrating that the STRs and iiSNPs in DPMA were highly polymorphic in Hainan Li population and could be employed in forensic applications. In addition, we set up three datasets, which included the genetic data of (i) iiSNPs (27 populations, 2640 individuals), (ii) Y-STRs (42 populations, 8281 individuals), and (iii) Y haplogroups (123 populations, 4837 individuals) along with the population ancestries and language families, to perform population genetic analyses separately from different perspectives. In conclusion, the phylogenetic analyses indicated that Hainan Li, with a southern East Asia origin and Tai-Kadai language-speaking language, is an isolated population relatively. But the genetic pool of Hainan Li influenced by the limited gene flows from other Tai-Kadai populations and Hainan populations. Furthermore, the establishment of isolated population models will be beneficial to clarify the exquisite population structures and develop specific genetic markers for subpopulations in forensic genetic fields.
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Yanagida T, Swastika K, Dharmawan NS, Sako Y, Wandra T, Ito A, Okamoto M. Origin of the pork tapeworm Taenia solium in Bali and Papua, Indonesia. Parasitol Int 2021; 83:102285. [PMID: 33486126 DOI: 10.1016/j.parint.2021.102285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/23/2020] [Accepted: 01/11/2021] [Indexed: 11/17/2022]
Abstract
Global distributions of zoonotic pathogens have been strongly affected by the history of human dispersal and domestication of livestock. The pork tapeworm Taenia solium is distributed worldwide as the cause of neurocysticercosis, one of the most serious neglected tropical diseases. T. solium has been reported in Indonesia but only endemic to restricted areas such as Bali and Papua. Previous studies indicated the distinctiveness of a mitochondrial haplotype confirmed in Papua, but only one isolate has been examined to date. In this study, genetic characterization of T. solium and pigs in Bali and Papua was conducted to clarify the distributional history of the parasite. Mitochondrial haplotype network analysis clearly showed that Indonesian T. solium comprises a unique haplogroup which was the first to diverge among Asian genotypes, indicating its single origin and the fact that it was not introduced in the recent past from other area in Asia in which it is endemic. Although phylogenetic analysis based on the mitochondrial D-loop revealed multiple origins of pigs in Bali and Papua, the majority of pigs belonged to the Pacific Clade, which is widely dispersed throughout the Island Southeast Asia (ISEA) and Oceania due to Neolithic human dispersal. Given the results of our network analysis, it is likely that the Pacific Clade pigs played a key role in the dispersal of T. solium. The data suggest that T. solium was introduced from mainland Asia into Western Indonesia, including Bali, by modern humans in the late Pleistocene, or in the early to middle Holocene along with the Pacific Clade pigs. Introduction into New Guinea most likely occurred in the late Holocene through the spread of Pacific Clade pigs. Over time, T. solium has been eradicated from most of Indonesia through the middle to modern ages owing to religious and cultural practices.
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Affiliation(s)
- Tetsuya Yanagida
- Laboratory of Parasitology, Joint Faculty of Veterinary Medicine, Yamaguchi University, Yamaguchi 1677-1, Japan
| | - Kadek Swastika
- Department of Parasitology, Faculty of Medicine Udayana University, Denpasar, Bali, Indonesia; Center for Human Evolution Modeling Research, Primate Research Institute, Kyoto University, Inuyama 484-8506, Japan
| | - Nyoman Sadra Dharmawan
- Department of Parasitology, Faculty of Veterinary Medicine Udayana University, Denpasar, Bali, Indonesia
| | - Yasuhito Sako
- Department of Parasitology, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Toni Wandra
- Directorate of Postgraduate, Sari Mutiara Indonesia University, Medan, North Sumatra, Indonesia
| | - Akira Ito
- Department of Parasitology, Asahikawa Medical University, Asahikawa 078-8510, Japan
| | - Munehiro Okamoto
- Center for Human Evolution Modeling Research, Primate Research Institute, Kyoto University, Inuyama 484-8506, Japan.
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6
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Hoh BP, Rahman TA. The indigenous populations as the model by nature to understand human genomic-phenomics interactions. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-021-0251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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7
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Hoh BP, Zhang X, Deng L, Yuan K, Yew CW, Saw WY, Hoque MZ, Aghakhanian F, Phipps ME, Teo YY, Subbiah VK, Xu S. Shared Signature of Recent Positive Selection on the TSBP1-BTNL2-HLA-DRA Genes in Five Native Populations from North Borneo. Genome Biol Evol 2020; 12:2245-2257. [PMID: 33022050 PMCID: PMC7738747 DOI: 10.1093/gbe/evaa207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2020] [Indexed: 11/17/2022] Open
Abstract
North Borneo (NB) is home to more than 40 native populations. These natives are believed to have undergone local adaptation in response to environmental challenges such as the mosquito-abundant tropical rainforest. We attempted to trace the footprints of natural selection from the genomic data of NB native populations using a panel of ∼2.2 million genome-wide single nucleotide polymorphisms. As a result, an ∼13-kb haplotype in the Major Histocompatibility Complex Class II region encompassing candidate genes TSBP1–BTNL2–HLA-DRA was identified to be undergoing natural selection. This putative signature of positive selection is shared among the five NB populations and is estimated to have arisen ∼5.5 thousand years (∼220 generations) ago, which coincides with the period of Austronesian expansion. Owing to the long history of endemic malaria in NB, the putative signature of positive selection is postulated to be driven by Plasmodium parasite infection. The findings of this study imply that despite high levels of genetic differentiation, the NB populations might have experienced similar local genetic adaptation resulting from stresses of the shared environment.
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Affiliation(s)
- Boon-Peng Hoh
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, Malaysia Cheras, Kuala Lumpur
| | - Xiaoxi Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Lian Deng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Chee-Wei Yew
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah, Malaysia
| | - Woei-Yuh Saw
- Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore
| | - Mohammad Zahirul Hoque
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah, Malaysia
| | - Farhang Aghakhanian
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
| | - Maude E Phipps
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
| | - Yik-Ying Teo
- Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore.,NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Vijay Kumar Subbiah
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah, Malaysia
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,Collaborative Innovation Centre of Genetics and Development, Shanghai, China
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8
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Hakim HM, Khan HO, Lalung J, Nelson BR, Chambers GK, Edinur HA. Autosomal STR Profiling and Databanking in Malaysia: Current Status and Future Prospects. Genes (Basel) 2020; 11:genes11101112. [PMID: 32977385 PMCID: PMC7597947 DOI: 10.3390/genes11101112] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 08/23/2020] [Accepted: 08/25/2020] [Indexed: 12/28/2022] Open
Abstract
Science and technology are extensively used in criminal investigation. From the mid- to late-1980s, one of the scientific discoveries that has had a particularly remarkable impact on this field has been the use of highly variable DNA sequence regions (minisatellites) in the human genome for individual identification. The technique was initially referred to as DNA fingerprinting, but is now more widely referred to as DNA profiling. Since then, many new developments have occurred within this area of science. These include the introduction of new genetic markers (microsatellites also known as short tandem repeats/STRs), the use of the polymerase chain reaction for target amplification, the development of DNA databases (databanking), and the advancement and/or improvement of genotyping protocols and technologies. In 2019, we described the progress of DNA profiling and DNA databanking in Malaysia for the first time. This report included information on DNA analysis regulations and legislation, STR genotyping protocols, database management, and accreditation status. Here, we provide an update on the performance of our DNA databank (numbers of DNA profiles and hits) plus the technical issues associated with correctly assigning the weight of evidence for DNA profiles in an ethnically diverse population, and the potential application of rapid DNA testing in the country. A total of 116,534 DNA profiles were obtained and stored in the Forensic DNA Databank of Malaysia (FDDM) by 2019, having increased from 70,570 in 2017. The number of hits increased by more than three-fold in just two years, where 17 and 69 hits between the DNA profiles stored in the FDDM and those from crime scenes, suspects, detainees, drug users, convicts, missing persons, or volunteers were recorded in 2017 and 2019, respectively. Forensic DNA analysis and databanking are thus progressing well in Malaysia and have already contributed to many criminal investigations. However, several other issues are discussed here, including the need for STR population data for uncharacterized population groups, and pilot trials for adopting rapid DNA profiling technology. These aspects should be considered by policy makers and law enforcement agencies in order to increase the reliability and efficiency of DNA profiling in criminal cases and in kinship analysis in Malaysia.
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Affiliation(s)
- Hashom Mohd Hakim
- DNA Databank Division (D13), Criminal Investigation Department, Royal Malaysian Police, Cheras 43200, Selangor, Malaysia;
- School of Industrial Technology, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia;
- Correspondence: (H.M.H.); (H.A.E.)
| | - Hussein Omar Khan
- DNA Databank Division (D13), Criminal Investigation Department, Royal Malaysian Police, Cheras 43200, Selangor, Malaysia;
| | - Japareng Lalung
- School of Industrial Technology, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia;
| | - Bryan Raveen Nelson
- Institute of Tropical Biodiversity and Sustainable Development, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia;
| | - Geoffrey Keith Chambers
- School of Biological Sciences, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand;
| | - Hisham Atan Edinur
- Institute of Tropical Biodiversity and Sustainable Development, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia;
- Forensic Science Programme, School of Health Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
- Environmental Futures Research Institute, Griffith University, Nathan, QLD 4111, Australia
- Correspondence: (H.M.H.); (H.A.E.)
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Sellinger TPP, Abu Awad D, Moest M, Tellier A. Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data. PLoS Genet 2020; 16:e1008698. [PMID: 32251472 PMCID: PMC7173940 DOI: 10.1371/journal.pgen.1008698] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 04/21/2020] [Accepted: 02/24/2020] [Indexed: 02/04/2023] Open
Abstract
Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations' past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.
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Affiliation(s)
| | - Diala Abu Awad
- Department of Population Genetics, Technische Universitaet Muenchen, Freising, Germany
| | - Markus Moest
- Department of Ecology, University of Innsbruck, Innsbruck, Austria
| | - Aurélien Tellier
- Department of Population Genetics, Technische Universitaet Muenchen, Freising, Germany
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10
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Hunting practices of the Jahai indigenous community in northern peninsular Malaysia. Glob Ecol Conserv 2020. [DOI: 10.1016/j.gecco.2019.e00815] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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11
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Deng L, Lou H, Zhang X, Thiruvahindrapuram B, Lu D, Marshall CR, Liu C, Xie B, Xu W, Wong LP, Yew CW, Farhang A, Ong RTH, Hoque MZ, Thuhairah AR, Jong B, Phipps ME, Scherer SW, Teo YY, Kumar SV, Hoh BP, Xu S. Analysis of five deep-sequenced trio-genomes of the Peninsular Malaysia Orang Asli and North Borneo populations. BMC Genomics 2019; 20:842. [PMID: 31718558 PMCID: PMC6852992 DOI: 10.1186/s12864-019-6226-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
Background Recent advances in genomic technologies have facilitated genome-wide investigation of human genetic variations. However, most efforts have focused on the major populations, yet trio genomes of indigenous populations from Southeast Asia have been under-investigated. Results We analyzed the whole-genome deep sequencing data (~ 30×) of five native trios from Peninsular Malaysia and North Borneo, and characterized the genomic variants, including single nucleotide variants (SNVs), small insertions and deletions (indels) and copy number variants (CNVs). We discovered approximately 6.9 million SNVs, 1.2 million indels, and 9000 CNVs in the 15 samples, of which 2.7% SNVs, 2.3% indels and 22% CNVs were novel, implying the insufficient coverage of population diversity in existing databases. We identified a higher proportion of novel variants in the Orang Asli (OA) samples, i.e., the indigenous people from Peninsular Malaysia, than that of the North Bornean (NB) samples, likely due to more complex demographic history and long-time isolation of the OA groups. We used the pedigree information to identify de novo variants and estimated the autosomal mutation rates to be 0.81 × 10− 8 – 1.33 × 10− 8, 1.0 × 10− 9 – 2.9 × 10− 9, and ~ 0.001 per site per generation for SNVs, indels, and CNVs, respectively. The trio-genomes also allowed for haplotype phasing with high accuracy, which serves as references to the future genomic studies of OA and NB populations. In addition, high-frequency inherited CNVs specific to OA or NB were identified. One example is a 50-kb duplication in DEFA1B detected only in the Negrito trios, implying plausible effects on host defense against the exposure of diverse microbial in tropical rainforest environment of these hunter-gatherers. The CNVs shared between OA and NB groups were much fewer than those specific to each group. Nevertheless, we identified a 142-kb duplication in AMY1A in all the 15 samples, and this gene is associated with the high-starch diet. Moreover, novel insertions shared with archaic hominids were identified in our samples. Conclusion Our study presents a full catalogue of the genome variants of the native Malaysian populations, which is a complement of the genome diversity in Southeast Asians. It implies specific population history of the native inhabitants, and demonstrated the necessity of more genome sequencing efforts on the multi-ethnic native groups of Malaysia and Southeast Asia.
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Affiliation(s)
- Lian Deng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Haiyi Lou
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoxi Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | | | - Dongsheng Lu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Christian R Marshall
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada.,Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.,Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Chang Liu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Bo Xie
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wanxing Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Lai-Ping Wong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore
| | - Chee-Wei Yew
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Aghakhanian Farhang
- Jefrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Sunway, 46150, Subang Jaya, Selangor, Malaysia.,Tropical Medicine and Biology Platform, Monash University Malaysia, Jalan Lagoon Selatan, 46150 Sunway, Subang Jaya, Selangor, Malaysia
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore
| | - Mohammad Zahirul Hoque
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
| | - Abdul Rahman Thuhairah
- Clinical Pathology Diagnostic Centre Research Laboratory, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, 47000 Sg Buloh, Subang Jaya, Selangor, Malaysia
| | - Bhak Jong
- Personal Genomics Institute, Genome Research Foundation, Suwon, Republic of Korea.,Geromics, Ulsan, 44919, Republic of Korea.,Biomedical Engineering Department, The Genomics Institute, UNIST, Ulsan, Republic of Korea
| | - Maude E Phipps
- Tropical Medicine and Biology Platform, Monash University Malaysia, Jalan Lagoon Selatan, 46150 Sunway, Subang Jaya, Selangor, Malaysia
| | - Stephen W Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada.,Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore.,NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 117456, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, 138672, Singapore
| | - Subbiah Vijay Kumar
- Biotechnology Research Institute, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
| | - Boon-Peng Hoh
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,Faculty of Medicine and Health Sciences, UCSI University, Jalan Menara Gading, Taman Connaught, Cheras, 56000, Kuala Lumpur, Malaysia.
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China. .,Collaborative Innovation Center of Genetics and Development, Shanghai, 200438, China. .,Human Phenome Institute, Fudan University, Shanghai, 201203, China.
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Forensic characteristics and phylogenetic analysis of both Y-STR and Y-SNP in the Li and Han ethnic groups from Hainan Island of China. Forensic Sci Int Genet 2019; 39:e14-e20. [DOI: 10.1016/j.fsigen.2018.11.016] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 10/24/2018] [Accepted: 11/26/2018] [Indexed: 11/21/2022]
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