1
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Buthasane W, Shotelersuk V, Chetruengchai W, Srichomthong C, Assawapitaksakul A, Tangphatsornruang S, Pootakham W, Sonthirod C, Tongsima S, Wangkumhang P, Wilantho A, Thongphakdee A, Sanannu S, Poksawat C, Nipanunt T, Kasorndorkbua C, Koepfli KP, Pukazhenthi BS, Suriyaphol P, Wongsurawat T, Jenjaroenpun P, Suriyaphol G. Comprehensive genome assembly reveals genetic diversity and carcass consumption insights in critically endangered Asian king vultures. Sci Rep 2024; 14:9455. [PMID: 38658744 PMCID: PMC11043450 DOI: 10.1038/s41598-024-59990-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 04/17/2024] [Indexed: 04/26/2024] Open
Abstract
The Asian king vulture (AKV), a vital forest scavenger, is facing globally critical endangerment. This study aimed to construct a reference genome to unveil the mechanisms underlying its scavenger abilities and to assess the genetic relatedness of the captive population in Thailand. A reference genome of a female AKV was assembled from sequencing reads obtained from both PacBio long-read and MGI short-read sequencing platforms. Comparative genomics with New World vultures (NWVs) and other birds in the Family Accipitridae revealed unique gene families in AKV associated with retroviral genome integration and feather keratin, contrasting with NWVs' genes related to olfactory reception. Expanded gene families in AKV were linked to inflammatory response, iron regulation and spermatogenesis. Positively selected genes included those associated with anti-apoptosis, immune response and muscle cell development, shedding light on adaptations for carcass consumption and high-altitude soaring. Using restriction site-associated DNA sequencing (RADseq)-based genome-wide single nucleotide polymorphisms (SNPs), genetic relatedness and inbreeding status of five captive AKVs were determined, revealing high genomic inbreeding in two females. In conclusion, the AKV reference genome was established, providing insights into its unique characteristics. Additionally, the potential of RADseq-based genome-wide SNPs for selecting AKV breeders was demonstrated.
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Affiliation(s)
- Wannapol Buthasane
- Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Henri Dunant Road, Pathumwan, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Wanna Chetruengchai
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Henri Dunant Road, Pathumwan, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Chalurmpon Srichomthong
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Henri Dunant Road, Pathumwan, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Adjima Assawapitaksakul
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Henri Dunant Road, Pathumwan, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Sithichoke Tangphatsornruang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Wirulda Pootakham
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Chutima Sonthirod
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Sissades Tongsima
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Pongsakorn Wangkumhang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Alisa Wilantho
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Ampika Thongphakdee
- Animal Conservation and Research Institute, The Zoological Park Organization of Thailand under the Royal Patronage of H.M. The King, Bangkok, 10300, Thailand
| | - Saowaphang Sanannu
- Animal Conservation and Research Institute, The Zoological Park Organization of Thailand under the Royal Patronage of H.M. The King, Bangkok, 10300, Thailand
| | - Chaianan Poksawat
- Animal Conservation and Research Institute, The Zoological Park Organization of Thailand under the Royal Patronage of H.M. The King, Bangkok, 10300, Thailand
| | - Tarasak Nipanunt
- Huai Kha Khaeng Wildlife Breeding Center, Department of National Parks, Wildlife and Plant Conservation, Uthai Thani, 61160, Thailand
| | - Chaiyan Kasorndorkbua
- Laboratory of Raptor Research and Conservation Medicine, Department of Pathology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok, 10900, Thailand
| | - Klaus-Peter Koepfli
- Smithsonian-Mason School of Conservation, George Mason University, Front Royal, VA, 22630, USA
- Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, 22630, USA
| | - Budhan S Pukazhenthi
- Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, 22630, USA
| | - Prapat Suriyaphol
- Division of Medical Bioinformatics, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Thidathip Wongsurawat
- Division of Medical Bioinformatics, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Piroon Jenjaroenpun
- Division of Medical Bioinformatics, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Gunnaporn Suriyaphol
- Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, 10330, Thailand.
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2
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Pumpitakkul V, Chetruengchai W, Srichomthong C, Phokaew C, Pootakham W, Sonthirod C, Nawae W, Tongsima S, Wangkumhang P, Wilantho A, Utara Y, Thongpakdee A, Sanannu S, Maikaew U, Khuntawee S, Changpetch W, Phromwat P, Raschasin K, Sarnkhaeveerakul P, Supapannachart P, Buthasane W, Pukazhenthi BS, Koepfli KP, Suriyaphol P, Tangphatsornruang S, Suriyaphol G, Shotelersuk V. Comparative genomics and genome-wide SNPs of endangered Eld's deer provide breeder selection for inbreeding avoidance. Sci Rep 2023; 13:19806. [PMID: 37957263 PMCID: PMC10643696 DOI: 10.1038/s41598-023-47014-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/08/2023] [Indexed: 11/15/2023] Open
Abstract
Eld's deer, a conserved wildlife species of Thailand, is facing inbreeding depression, particularly in the captive Siamese Eld's deer (SED) subspecies. In this study, we constructed genomes of a male SED and a male Burmese Eld's deer (BED), and used genome-wide single nucleotide polymorphisms to evaluate the genetic purity and the inbreeding status of 35 SED and 49 BED with limited pedigree information. The results show that these subspecies diverged approximately 1.26 million years ago. All SED were found to be purebred. A low proportion of admixed SED genetic material was observed in some BED individuals. Six potential breeders from male SED with no genetic relation to any female SED and three purebred male BED with no relation to more than 10 purebred female BED were identified. This study provides valuable insights about Eld's deer populations and appropriate breeder selection in efforts to repopulate this endangered species while avoiding inbreeding.
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Affiliation(s)
- Vichayanee Pumpitakkul
- Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Wanna Chetruengchai
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Chalurmpon Srichomthong
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Chureerat Phokaew
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
| | - Wirulda Pootakham
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Chutima Sonthirod
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Wanapinun Nawae
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Sissades Tongsima
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Pongsakorn Wangkumhang
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Alisa Wilantho
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand
| | - Yongchai Utara
- Zoological Park Organization of Thailand, Animal Conservation and Research Institute, Bangkok, 10800, Thailand
| | - Ampika Thongpakdee
- Zoological Park Organization of Thailand, Animal Conservation and Research Institute, Bangkok, 10800, Thailand
| | - Saowaphang Sanannu
- Zoological Park Organization of Thailand, Animal Conservation and Research Institute, Bangkok, 10800, Thailand
| | - Umaporn Maikaew
- Khao Kheow Open Zoo, Zoological Park Organization of Thailand, Chonburi, 20110, Thailand
| | - Suphattharaphonnaphan Khuntawee
- Ubon Ratchathani Zoo, Zoological Park Organization of Thailand, Ubon Ratchathani District, Ubon Ratchathani, 34000, Thailand
| | - Wirongrong Changpetch
- Nakhon Ratchasima Zoo, Zoological Park Organization of Thailand, Nakhon Ratchasima, 30000, Thailand
| | - Phairot Phromwat
- Huai Kha Khaeng Wildlife Breeding Center, Department of National Parks, Wildlife and Plant Conservation, Uthai Thani, 61160, Thailand
| | - Kacharin Raschasin
- Chulabhorn Wildlife Breeding Center, Department of National Parks, Wildlife and Plant Conservation, Sisaket, 33140, Thailand
| | - Phunyaphat Sarnkhaeveerakul
- Banglamung Wildlife Breeding Center, Department of National Parks, Wildlife and Plant Conservation, Chonburi, 20150, Thailand
| | - Pannawat Supapannachart
- Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Wannapol Buthasane
- Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Budhan S Pukazhenthi
- Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, 22630, USA
| | - Klaus-Peter Koepfli
- Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, 22630, USA
- Smithsonian-Mason School of Conservation, George Mason University, Front Royal, VA, 22630, USA
| | - Prapat Suriyaphol
- Office for Research and Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Sithichoke Tangphatsornruang
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, 12120, Thailand.
| | - Gunnaporn Suriyaphol
- Biochemistry Unit, Department of Physiology, Faculty of Veterinary Science, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, 10330, Thailand
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3
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Wangkumhang P, Greenfield M, Hellenthal G. An efficient method to identify, date, and describe admixture events using haplotype information. Genome Res 2022; 32:gr.275994.121. [PMID: 35794007 PMCID: PMC9435750 DOI: 10.1101/gr.275994.121] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 06/28/2022] [Indexed: 11/24/2022]
Abstract
We present fastGLOBETROTTER, an efficient new haplotype-based technique to identify, date, and describe admixture events using genome-wide autosomal data. With simulations, we show how fastGLOBETROTTER reduces computation time by an order of magnitude relative to the related technique GLOBETROTTER without suffering loss of accuracy. We apply fastGLOBETROTTER to a cohort of more than 6000 Europeans from 10 countries, revealing previously unreported admixture signals. In particular, we infer multiple periods of admixture related to East Asian or Siberian-like sources, starting >2000 yr ago, in people living in countries north of the Baltic Sea. In contrast, we infer admixture related to West Asian, North African, and/or Southern European sources in populations south of the Baltic Sea, including admixture dated to ∼300-700 CE, overlapping the fall of the Roman Empire, in people from Belgium, France, and parts of Germany. Our new approach scales to analyzing hundreds to thousands of individuals from a putatively admixed population and, hence, is applicable to emerging large-scale cohorts of genetically homogeneous populations.
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Affiliation(s)
- Pongsakorn Wangkumhang
- University College London Genetics Institute (UGI), Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, United Kingdom
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani 12120, Thailand
| | - Matthew Greenfield
- University College London Genetics Institute (UGI), Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, United Kingdom
| | - Garrett Hellenthal
- University College London Genetics Institute (UGI), Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, United Kingdom
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4
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Shotelersuk V, Wichadakul D, Ngamphiw C, Srichomthong C, Phokaew C, Wilantho A, Pakchuen S, Nakhonsri V, Shaw PJ, Wasitthankasem R, Piriyapongsa J, Wangkumhang P, Assawapitaksakul A, Chetruengchai W, Lapphra K, Khuninthong A, Makarawate P, Suphapeetiporn K, Mahasirimongkol S, Satproedprai N, Porntaveetus T, Pisitkun P, Praphanphoj V, Kantaputra P, Tassaneeyakul W, Tongsima S. The Thai reference exome (T-REx) variant database. Clin Genet 2021; 100:703-712. [PMID: 34496037 DOI: 10.1111/cge.14060] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/31/2021] [Accepted: 09/02/2021] [Indexed: 01/19/2023]
Abstract
To maximize the potential of genomics in medicine, it is essential to establish databases of genomic variants for ethno-geographic groups that can be used for filtering and prioritizing candidate pathogenic variants. Populations with non-European ancestry are poorly represented among current genomic variant databases. Here, we report the first high-density survey of genomic variants for the Thai population, the Thai Reference Exome (T-REx) variant database. T-REx comprises exome sequencing data of 1092 unrelated Thai individuals. The targeted exome regions common among four capture platforms cover 30.04 Mbp on autosomes and chromosome X. 345 681 short variants (18.27% of which are novel) and 34 907 copy number variations were found. Principal component analysis on 38 469 single nucleotide variants present worldwide showed that the Thai population is most genetically similar to East and Southeast Asian populations. Moreover, unsupervised clustering revealed six Thai subpopulations consistent with the evidence of gene flow from neighboring populations. The prevalence of common pathogenic variants in T-REx was investigated in detail, which revealed subpopulation-specific patterns, in particular variants associated with erythrocyte disorders such as the HbE variant in HBB and the Viangchan variant in G6PD. T-REx serves as a pivotal addition to the current databases for genomic medicine.
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Affiliation(s)
- Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Duangdao Wichadakul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Chumpol Ngamphiw
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Chalurmpon Srichomthong
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Chureerat Phokaew
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Alisa Wilantho
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Sujiraporn Pakchuen
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Vorthunju Nakhonsri
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand.,Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Philip James Shaw
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Rujipat Wasitthankasem
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Jittima Piriyapongsa
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Pongsakorn Wangkumhang
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Adjima Assawapitaksakul
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Wanna Chetruengchai
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Keswadee Lapphra
- Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Athiphat Khuninthong
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
| | | | - Kanya Suphapeetiporn
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Surakameth Mahasirimongkol
- Genomic Medicine Center, Division of Genomic Medicine and Innovation Support, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
| | - Nusara Satproedprai
- Genomic Medicine Center, Division of Genomic Medicine and Innovation Support, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
| | - Thantrira Porntaveetus
- Genomics and Precision Dentistry Research Unit, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Prapaporn Pisitkun
- Division of Allergy, Immunology, and Rheumatology, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Verayuth Praphanphoj
- Center for Medical Genetics Research, Rajanukul Institute, Department of Mental Health, Ministry of Public Health Bangkok, Bangkok, Thailand
| | - Piranit Kantaputra
- Division of Pediatric Dentistry, Department of Orthodontics and Pediatric Dentistry, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand
| | | | - Sissades Tongsima
- National Biobank of Thailand, National Science and Technology Development Agency, Pathum Thani, Thailand
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5
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Yahya P, Sulong S, Harun A, Wangkumhang P, Wilantho A, Ngamphiw C, Tongsima S, Zilfalil BA. Ancestry-informative marker (AIM) SNP panel for the Malay population. Int J Legal Med 2019; 134:123-134. [PMID: 31760471 DOI: 10.1007/s00414-019-02184-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 10/15/2019] [Indexed: 10/25/2022]
Abstract
Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
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Affiliation(s)
- Padillah Yahya
- Department of Paediatrics, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Sarina Sulong
- Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Azian Harun
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Pongsakorn Wangkumhang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang District, Pathum Thani, 12120, Thailand
| | - Alisa Wilantho
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang District, Pathum Thani, 12120, Thailand
| | - Chumpol Ngamphiw
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang District, Pathum Thani, 12120, Thailand
| | - Sissades Tongsima
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang District, Pathum Thani, 12120, Thailand
| | - Bin Alwi Zilfalil
- Department of Paediatrics, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia.
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6
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Ongaro L, Scliar MO, Flores R, Raveane A, Marnetto D, Sarno S, Gnecchi-Ruscone GA, Alarcón-Riquelme ME, Patin E, Wangkumhang P, Hellenthal G, Gonzalez-Santos M, King RJ, Kouvatsi A, Balanovsky O, Balanovska E, Atramentova L, Turdikulova S, Mastana S, Marjanovic D, Mulahasanovic L, Leskovac A, Lima-Costa MF, Pereira AC, Barreto ML, Horta BL, Mabunda N, May CA, Moreno-Estrada A, Achilli A, Olivieri A, Semino O, Tambets K, Kivisild T, Luiselli D, Torroni A, Capelli C, Tarazona-Santos E, Metspalu M, Pagani L, Montinaro F. The Genomic Impact of European Colonization of the Americas. Curr Biol 2019; 29:3974-3986.e4. [PMID: 31735679 DOI: 10.1016/j.cub.2019.09.076] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 09/06/2019] [Accepted: 09/30/2019] [Indexed: 12/30/2022]
Abstract
The human genetic diversity of the Americas has been affected by several events of gene flow that have continued since the colonial era and the Atlantic slave trade. Moreover, multiple waves of migration followed by local admixture occurred in the last two centuries, the impact of which has been largely unexplored. Here, we compiled a genome-wide dataset of ∼12,000 individuals from twelve American countries and ∼6,000 individuals from worldwide populations and applied haplotype-based methods to investigate how historical movements from outside the New World affected (1) the genetic structure, (2) the admixture profile, (3) the demographic history, and (4) sex-biased gene-flow dynamics of the Americas. We revealed a high degree of complexity underlying the genetic contribution of European and African populations in North and South America, from both geographic and temporal perspectives, identifying previously unreported sources related to Italy, the Middle East, and to specific regions of Africa.
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Affiliation(s)
- Linda Ongaro
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia; Department of Evolutionary Biology, Institute of Molecular and Cell Biology, Riia 23, Tartu 51010, Estonia.
| | - Marilia O Scliar
- Human Genome and Stem Cell Research Center, Biosciences Institute, University of São Paulo, São Paulo, SP 05508-090, Brazil; Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Rodrigo Flores
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia
| | - Alessandro Raveane
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia 27100, Italy
| | - Davide Marnetto
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia
| | - Stefania Sarno
- Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna 40100, Italy
| | - Guido A Gnecchi-Ruscone
- Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna 40100, Italy; Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena 07745, Germany
| | - Marta E Alarcón-Riquelme
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, Granada 18016, Spain
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Pasteur Institute, UMR2000, CNRS, Paris 75015, France
| | - Pongsakorn Wangkumhang
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Garrett Hellenthal
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | | | - Roy J King
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305-5101, USA
| | - Anastasia Kouvatsi
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Oleg Balanovsky
- Vavilov Institute of General Genetics, Ulitsa Gubkina, 3, Moscow 117971, Russia; Research Centre for Medical Genetics, Moskvorech'ye Ulitsa, 1, Moscow 115478, Russia; Biobank of North Eurasia, Kotlyakovskaya Ulitsa, 3 строение 12, Moscow 115201, Russia
| | - Elena Balanovska
- Vavilov Institute of General Genetics, Ulitsa Gubkina, 3, Moscow 117971, Russia; Research Centre for Medical Genetics, Moskvorech'ye Ulitsa, 1, Moscow 115478, Russia; Biobank of North Eurasia, Kotlyakovskaya Ulitsa, 3 строение 12, Moscow 115201, Russia
| | - Lubov Atramentova
- Department of Genetics and Cytology, V.N. Karazin Kharkiv National University, Kharkiv 61022, Ukraine
| | - Shahlo Turdikulova
- Laboratory of Genomics, Institute of Bioorganic Chemistry, Academy of Sciences Republic of Uzbekistan, Tashkent 100047, Uzbekistan
| | - Sarabjit Mastana
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
| | - Damir Marjanovic
- Department of Genetics and Bioengineering, Faculty of Engineering and Information Technologies, International Burch University, Sarajevo 71000, Bosnia and Herzegovina; Institute for Anthropological Researches, Zagreb, Croatia
| | | | - Andreja Leskovac
- Vinca Institute of Nuclear Sciences, University of Belgrade, M. Petrovica Alasa 12-14, Belgrade 11001, Serbia
| | - Maria F Lima-Costa
- Instituto de Pesquisa Rene Rachou, Fundação Oswaldo Cruz, Belo Horizonte, MG 30190-002, Brazil
| | - Alexandre C Pereira
- Instituto do Coração, Universidade de São Paulo, São Paulo, SP 05403-900, Brazil
| | - Mauricio L Barreto
- Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, BA 0110-040, Brazil; Center of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz (FIOCRUZ), Salvador, BA 41745-715, Brazil
| | - Bernardo L Horta
- Programa de Pós-Graduação em Epidemiologia, Universidade Federal de Pelotas, 464, Pelotas, RS 96001-970, Brazil
| | - Nédio Mabunda
- Instituto Nacional de Saúde, Distrito de Marracuene, Estrada Nacional N 1, Província de Maputo, Maputo 1120, Mozambique
| | - Celia A May
- Department of Genetics & Genome Biology, University of Leicester, Leicester LE1 7RH, UK
| | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Guanajuato 36821, Mexico
| | - Alessandro Achilli
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia 27100, Italy
| | - Anna Olivieri
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia 27100, Italy
| | - Ornella Semino
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia 27100, Italy
| | - Kristiina Tambets
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia
| | - Toomas Kivisild
- Department of Human Genetics, KU Leuven, Herestraat 49 - box 602, Leuven 3000, Belgium
| | - Donata Luiselli
- Department of Cultural Heritage, University of Bologna, Ravenna Campus, Ravenna 48100, Italy
| | - Antonio Torroni
- Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia 27100, Italy
| | | | - Eduardo Tarazona-Santos
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Mait Metspalu
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia
| | - Luca Pagani
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia; Department of Biology, University of Padua, Via Ugo Bassi 58B, Padua 35100, Italy
| | - Francesco Montinaro
- Estonian Biocentre, Institute of Genomics, Riia 23, Tartu 51010, Estonia; Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK.
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7
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Wangkumhang P, Hellenthal G. Statistical methods for detecting admixture. Curr Opin Genet Dev 2018; 53:121-127. [PMID: 30245220 DOI: 10.1016/j.gde.2018.08.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 08/03/2018] [Accepted: 08/09/2018] [Indexed: 10/28/2022]
Abstract
The increasing availability of large-scale autosomal genetic variation data sampled from world-wide geographic areas, coupled with advances in the statistical methodology to analyse these data, is showcasing the power of DNA as a major tool to gain insights into the demographic history of humans and other organisms. Here we review statistical techniques that shed light on a specific aspect of demography: the detection and description of admixture events where two or more genetically distinct groups intermixed at one or more times in the past. In particular we give an overview of some of the widely used methods to identify and describe admixture events using autosomal DNA from unrelated individuals, with a particular focus on analysing biallelic Single-Nucleotide-Polymorphsim (SNP) markers.
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Affiliation(s)
- Pongsakorn Wangkumhang
- University College London Genetics Institute (UGI), Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Garrett Hellenthal
- University College London Genetics Institute (UGI), Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.
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8
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Yahya P, Sulong S, Harun A, Wan Isa H, Ab Rajab NS, Wangkumhang P, Wilantho A, Ngamphiw C, Tongsima S, Zilfalil BA. Analysis of the genetic structure of the Malay population: Ancestry-informative marker SNPs in the Malay of Peninsular Malaysia. Forensic Sci Int Genet 2017; 30:152-159. [DOI: 10.1016/j.fsigen.2017.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 06/23/2017] [Accepted: 07/10/2017] [Indexed: 12/27/2022]
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9
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Liu X, Lu D, Saw WY, Shaw PJ, Wangkumhang P, Ngamphiw C, Fucharoen S, Lert-Itthiporn W, Chin-Inmanu K, Chau TNB, Anders K, Kasturiratne A, de Silva HJ, Katsuya T, Kimura R, Nabika T, Ohkubo T, Tabara Y, Takeuchi F, Yamamoto K, Yokota M, Mamatyusupu D, Yang W, Chung YJ, Jin L, Hoh BP, Wickremasinghe AR, Ong RH, Khor CC, Dunstan SJ, Simmons C, Tongsima S, Suriyaphol P, Kato N, Xu S, Teo YY. Characterising private and shared signatures of positive selection in 37 Asian populations. Eur J Hum Genet 2017; 25:499-508. [PMID: 28098149 DOI: 10.1038/ejhg.2016.181] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 10/22/2016] [Accepted: 11/01/2016] [Indexed: 11/09/2022] Open
Abstract
The Asian Diversity Project (ADP) assembled 37 cosmopolitan and ethnic minority populations in Asia that have been densely genotyped across over half a million markers to study patterns of genetic diversity and positive natural selection. We performed population structure analyses of the ADP populations and divided these populations into four major groups based on their genographic information. By applying a highly sensitive algorithm haploPS to locate genomic signatures of positive selection, 140 distinct genomic regions exhibiting evidence of positive selection in at least one population were identified. We examined the extent of signal sharing for regions that were selected in multiple populations and observed that populations clustered in a similar fashion to that of how the ancestry clades were phylogenetically defined. In particular, populations predominantly located in South Asia underwent considerably different adaptation as compared with populations from the other geographical regions. Signatures of positive selection present in multiple geographical regions were predicted to be older and have emerged prior to the separation of the populations in the different regions. In contrast, selection signals present in a single population group tended to be of lower frequencies and thus can be attributed to recent evolutionary events.
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Affiliation(s)
- Xuanyao Liu
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Dongsheng Lu
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Woei-Yuh Saw
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Philip J Shaw
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Pongsakorn Wangkumhang
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Chumpol Ngamphiw
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Suthat Fucharoen
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, Thailand
| | - Worachart Lert-Itthiporn
- Faculty of Science, Molecular Medicine Graduate Programme, Mahidol University, Bangkok, Thailand.,Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Kwanrutai Chin-Inmanu
- Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Tran Nguyen Bich Chau
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Katie Anders
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam.,Nuffield Department of Clinical Medicine, Centre for Tropical Medicine, University of Oxford, Oxford, UK
| | | | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryosuke Kimura
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara-cho, Japan
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine, Tokyo, Japan
| | - Ken Yamamoto
- Department of Medical Chemistry, Kurume University School of Medicine, Kurume, Japan
| | - Mitsuhiro Yokota
- Department of Genome Science, School of Dentistry, Aichi Gakuin University, Nagoya, Japan
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University, Urumqi, China
| | - Wenjun Yang
- Key Laboratory of Reproduction and Heredity of Ningxia Region, Ningxia Medical University, YinchuanChina
| | - Yeun-Jun Chung
- Department of Microbiology, Integrated Research Center for Genome Polymorphism, The Catholic University Medical College, Seoul, Korea
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE), Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Boon-Peng Hoh
- Faculty of Medicine and Health Sciences, UCSI University, Kuala Lumpur, Malaysia
| | | | - RickTwee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Sarah J Dunstan
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam.,Nuffield Department of Clinical Medicine, Centre for Tropical Medicine, University of Oxford, Oxford, UK.,The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
| | - Cameron Simmons
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam.,Nuffield Department of Clinical Medicine, Centre for Tropical Medicine, University of Oxford, Oxford, UK.,Department of Microbiology and Immunology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sissades Tongsima
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Prapat Suriyaphol
- Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.,Institute of Personalized Genomics and Gene Therapy (IPGG), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine, Tokyo, Japan
| | - Shuhua Xu
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Sciences and Technology, ShanghaiTech University, Shanghai, China.,Collaborative Innovation Center of Genetics and Development, Shanghai, China
| | - Yik-Ying Teo
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine, Tokyo, Japan.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
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10
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Wangkumhang P, Wilantho A, Shaw PJ, Flori L, Moazami-Goudarzi K, Gautier M, Duangjinda M, Assawamakin A, Tongsima S. Genetic analysis of Thai cattle reveals a Southeast Asian indicine ancestry. PeerJ 2015; 3:e1318. [PMID: 26528405 PMCID: PMC4627918 DOI: 10.7717/peerj.1318] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 09/22/2015] [Indexed: 12/20/2022] Open
Abstract
Cattle commonly raised in Thailand have characteristics of Bos indicus (zebu). We do not know when or how cattle domestication in Thailand occurred, and so questions remain regarding their origins and relationships to other breeds. We obtained genome-wide SNP genotypic data of 28 bovine individuals sampled from four regions: North (Kho-Khaolampoon), Northeast (Kho-Isaan), Central (Kho-Lan) and South (Kho-Chon) Thailand. These regional varieties have distinctive traits suggestive of breed-like genetic variations. From these data, we confirmed that all four Thai varieties are Bos indicus and that they are distinct from other indicine breeds. Among these Thai cattle, a distinctive ancestry pattern is apparent, which is the purest within Kho-Chon individuals. This ancestral component is only present outside of Thailand among other indicine breeds in Southeast Asia. From this pattern, we conclude that a unique Bos indicus ancestor originated in Southeast Asia, and native Kho-Chon Thai cattle retain the signal of this ancestry with limited admixture of other bovine ancestors.
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Affiliation(s)
- Pongsakorn Wangkumhang
- Genome Technology Research Unit, National Center for Genetic Engineering and Biotechnology , Pathum Thani , Thailand
| | - Alisa Wilantho
- Genome Technology Research Unit, National Center for Genetic Engineering and Biotechnology , Pathum Thani , Thailand
| | - Philip J Shaw
- Medical Molecular Biology Research Unit, National Center for Genetic Engineering and Biotechnology , Pathum Thani , Thailand
| | - Laurence Flori
- UMR INTERTRYP, CIRAD , Montpellier , France ; UMR 1313 GABI, INRA , Jouy-en-Josas , France
| | | | - Mathieu Gautier
- UMR CBGP (INRA/CIRAD/IRD/Supagro), INRA , Montferrier-sur-Lez , France
| | - Monchai Duangjinda
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University , Khon Kaen , Thailand
| | - Anunchai Assawamakin
- Department of Pharmacology, Faculty of Pharmacy, Mahidol University , Bangkok , Thailand
| | - Sissades Tongsima
- Genome Technology Research Unit, National Center for Genetic Engineering and Biotechnology , Pathum Thani , Thailand
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11
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Wangkumhang P, Shaw PJ, Chaichoompu K, Ngamphiw C, Assawamakin A, Nuinoon M, Sripichai O, Svasti S, Fucharoen S, Praphanphoj V, Tongsima S. Insight into the peopling of Mainland Southeast Asia from Thai population genetic structure. PLoS One 2013; 8:e79522. [PMID: 24223962 PMCID: PMC3817124 DOI: 10.1371/journal.pone.0079522] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 09/23/2013] [Indexed: 12/22/2022] Open
Abstract
There is considerable ethno-linguistic and genetic variation among human populations in Asia, although tracing the origins of this diversity is complicated by migration events. Thailand is at the center of Mainland Southeast Asia (MSEA), a region within Asia that has not been extensively studied. Genetic substructure may exist in the Thai population, since waves of migration from southern China throughout its recent history may have contributed to substantial gene flow. Autosomal SNP data were collated for 438,503 markers from 992 Thai individuals. Using the available self-reported regional origin, four Thai subpopulations genetically distinct from each other and from other Asian populations were resolved by Neighbor-Joining analysis using a 41,569 marker subset. Using an independent Principal Components-based unsupervised clustering approach, four major MSEA subpopulations were resolved in which regional bias was apparent. A major ancestry component was common to these MSEA subpopulations and distinguishes them from other Asian subpopulations. On the other hand, these MSEA subpopulations were admixed with other ancestries, in particular one shared with Chinese. Subpopulation clustering using only Thai individuals and the complete marker set resolved four subpopulations, which are distributed differently across Thailand. A Sino-Thai subpopulation was concentrated in the Central region of Thailand, although this constituted a minority in an otherwise diverse region. Among the most highly differentiated markers which distinguish the Thai subpopulations, several map to regions known to affect phenotypic traits such as skin pigmentation and susceptibility to common diseases. The subpopulation patterns elucidated have important implications for evolutionary and medical genetics. The subpopulation structure within Thailand may reflect the contributions of different migrants throughout the history of MSEA. The information will also be important for genetic association studies to account for population-structure confounding effects.
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Affiliation(s)
- Pongsakorn Wangkumhang
- National Center for Genetic Engineering and Biotechnology (BioTeC), Khlong Luang, Pathum Thani, Thailand
- Inter-Department Program of Biomedical Sciences, Chulalongkorn University, Pathumwan, Bangkok, Thailand
| | - Philip James Shaw
- National Center for Genetic Engineering and Biotechnology (BioTeC), Khlong Luang, Pathum Thani, Thailand
| | - Kridsadakorn Chaichoompu
- National Center for Genetic Engineering and Biotechnology (BioTeC), Khlong Luang, Pathum Thani, Thailand
| | - Chumpol Ngamphiw
- National Center for Genetic Engineering and Biotechnology (BioTeC), Khlong Luang, Pathum Thani, Thailand
- Inter-Department Program of Biomedical Sciences, Chulalongkorn University, Pathumwan, Bangkok, Thailand
| | | | - Manit Nuinoon
- School of Allied Health Sciences and Public Health, Walailak University, Thai Buri, Nakhon Sri Thammarat, Thailand
| | - Orapan Sripichai
- Thalassemia Research Center, Mahidol University, Salaya, Nakhon Pathom, Thailand
| | - Saovaros Svasti
- Thalassemia Research Center, Mahidol University, Salaya, Nakhon Pathom, Thailand
| | - Suthat Fucharoen
- Thalassemia Research Center, Mahidol University, Salaya, Nakhon Pathom, Thailand
| | - Verayuth Praphanphoj
- Center for Medical Genetics Research, Rajanukul Institute, Dindaeng, Bangkok, Thailand
| | - Sissades Tongsima
- National Center for Genetic Engineering and Biotechnology (BioTeC), Khlong Luang, Pathum Thani, Thailand
- * E-mail:
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12
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Limpiti T, Intarapanich A, Assawamakin A, Shaw PJ, Wangkumhang P, Piriyapongsa J, Ngamphiw C, Tongsima S. Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure. BMC Bioinformatics 2011; 12:255. [PMID: 21699684 PMCID: PMC3148578 DOI: 10.1186/1471-2105-12-255] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Accepted: 06/23/2011] [Indexed: 01/20/2023] Open
Abstract
Background The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis. Results A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA. Conclusions The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.
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Affiliation(s)
- Tulaya Limpiti
- Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
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13
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Piriyapongsa J, Ngamphiw C, Assawamakin A, Wangkumhang P, Suwannasri P, Ruangrit U, Agavatpanitch G, Tongsima S. RExPrimer: an integrated primer designing tool increases PCR effectiveness by avoiding 3' SNP-in-primer and mis-priming from structural variation. BMC Genomics 2009; 10 Suppl 3:S4. [PMID: 19958502 PMCID: PMC2788391 DOI: 10.1186/1471-2164-10-s3-s4] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Polymerase chain reaction (PCR) is very useful in many areas of molecular biology research. It is commonly observed that PCR success is critically dependent on design of an effective primer pair. Current tools for primer design do not adequately address the problem of PCR failure due to mis-priming on target-related sequences and structural variations in the genome. METHODS We have developed an integrated graphical web-based application for primer design, called RExPrimer, which was written in Python language. The software uses Primer3 as the primer designing core algorithm. Locally stored sequence information and genomic variant information were hosted on MySQLv5.0 and were incorporated into RExPrimer. RESULTS RExPrimer provides many functionalities for improved PCR primer design. Several databases, namely annotated human SNP databases, insertion/deletion (indel) polymorphisms database, pseudogene database, and structural genomic variation databases were integrated into RExPrimer, enabling an effective without-leaving-the-website validation of the resulting primers. By incorporating these databases, the primers reported by RExPrimer avoid mis-priming to related sequences (e.g. pseudogene, segmental duplication) as well as possible PCR failure because of structural polymorphisms (SNP, indel, and copy number variation (CNV)). To prevent mismatching caused by unexpected SNPs in the designed primers, in particular the 3' end (SNP-in-Primer), several SNP databases covering the broad range of population-specific SNP information are utilized to report SNPs present in the primer sequences. Population-specific SNP information also helps customize primer design for a specific population. Furthermore, RExPrimer offers a graphical user-friendly interface through the use of scalable vector graphic image that intuitively presents resulting primers along with the corresponding gene structure. In this study, we demonstrated the program effectiveness in successfully generating primers for strong homologous sequences. CONCLUSION The improvements for primer design incorporated into RExPrimer were demonstrated to be effective in designing primers for challenging PCR experiments. Integration of SNP and structural variation databases allows for robust primer design for a variety of PCR applications, irrespective of the sequence complexity in the region of interest. This software is freely available at http://www4a.biotec.or.th/rexprimer.
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Affiliation(s)
- Jittima Piriyapongsa
- Genome Institute, National Center for Genetic Engineering and Biotechnology, Pathumthani, Thailand.
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14
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Intarapanich A, Shaw PJ, Assawamakin A, Wangkumhang P, Ngamphiw C, Chaichoompu K, Piriyapongsa J, Tongsima S. Iterative pruning PCA improves resolution of highly structured populations. BMC Bioinformatics 2009; 10:382. [PMID: 19930644 PMCID: PMC2790469 DOI: 10.1186/1471-2105-10-382] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2009] [Accepted: 11/23/2009] [Indexed: 12/12/2022] Open
Abstract
Background Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming. Results A novel population structure analysis algorithm called iterative pruning PCA (ipPCA) was developed which assigns individuals to subpopulations and infers the total number of subpopulations present. Genotypic data from simulated and real population datasets with different degrees of structure were analyzed. For datasets with simple structures, the subpopulation assignments of individuals made by ipPCA were largely consistent with the STRUCTURE, BAPS and AWclust algorithms. On the other hand, highly structured populations containing many closely related subpopulations could be accurately resolved only by ipPCA, and not by other methods. Conclusion The algorithm is computationally efficient and not constrained by the dataset complexity. This systematic subpopulation assignment approach removes the need for prior population labels, which could be advantageous when cryptic stratification is encountered in datasets containing individuals otherwise assumed to belong to a homogenous population.
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Affiliation(s)
- Apichart Intarapanich
- BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand.
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