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Liu J, Li S, Su Y, Wen Y, Qin L, Zhao M, Hui M, Jiang L, Chen X, Hou Y, Wang Z. A proof-of-principle study: The potential application of MiniHap biomarkers in ancestry inference based on the QNome nanopore sequencing. Forensic Sci Int Genet 2024; 68:102947. [PMID: 37862770 DOI: 10.1016/j.fsigen.2023.102947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/25/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023]
Abstract
Haplotyped SNPs convey forensic-related information, and microhaplotypes (MHs), as the most representative of this kind of marker, have proved the potential value for human forensics. In recent years, nanopore sequencing technology has developed rapidly, with its outstanding ability to sequence long continuous DNA fragments and obtain phase information, making the detection of longer haplotype marker possible. In this proof-of-principle study, we proposed a new type of forensic marker, MiniHap, based on five or more SNPs within a molecular distance less than 800 bp, and investigated the haplotype data of 56 selected MiniHaps in five Chinese populations using the QNome nanopore sequencing. The sequencing performance, allele (haplotype) frequencies, forensic parameters, effective number of alleles (Ae), and informativeness (In) were subsequently calculated. In addition, we performed principal component analysis (PCA), phylogenetic tree, and structure analysis to investigate the population genetic relationships and ancestry components among the five investigated populations and 26 worldwide populations. MiniHap-04 exhibited remarkable forensic efficacy, with 148 haplotypes reported and the Ae was 66.9268. In addition, the power of discrimination (PD) was 0.9934, the probability of exclusion (PE) was 0.9898, and the In value was 0.7893. Of the 56 loci, 85.71% had PD values above 0.85, 66.07% had PE values above 0.54, 67.86% had Ae values over 7.0%, and 55.36% were with In values above 0.2 across all samples, indicating that most of the MiniHaps are suitable for individual identification, paternity testing, mixture deconvolution, and ancestry inference. Moreover, the results of PCA, phylogenetic tree and structure analysis demonstrated that this MiniHap panel had the competency in continental population ancestry inference, but the differentiation within intracontinental/linguistically restricted subpopulations was not ideal. Such findings suggested that the QNome device for MiniHap detection was feasible and this novel marker has the potential in ancestry inference. Yet, the establishment of a more comprehensive database with sufficient reference population data remains necessary to screen more suitable MiniHaps.
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Affiliation(s)
- Jing Liu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Suyu Li
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Yonglin Su
- Department of Rehabilitation Medicine, West China Hospital Sichuan University, Chengdu 610041, China
| | - Yufeng Wen
- School of Life Sciences, Jilin University, Changchun 130012, China
| | - Liu Qin
- Qitan Technology Ltd., Chengdu 610044, China
| | - Mengyao Zhao
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Minxiao Hui
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Lirong Jiang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Xiacan Chen
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
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Childebayeva A, Zavala EI. Review: Computational analysis of human skeletal remains in ancient DNA and forensic genetics. iScience 2023; 26:108066. [PMID: 37927550 PMCID: PMC10622734 DOI: 10.1016/j.isci.2023.108066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023] Open
Abstract
Degraded DNA is used to answer questions in the fields of ancient DNA (aDNA) and forensic genetics. While aDNA studies typically center around human evolution and past history, and forensic genetics is often more concerned with identifying a specific individual, scientists in both fields face similar challenges. The overlap in source material has prompted periodic discussions and studies on the advantages of collaboration between fields toward mutually beneficial methodological advancements. However, most have been centered around wet laboratory methods (sampling, DNA extraction, library preparation, etc.). In this review, we focus on the computational side of the analytical workflow. We discuss limitations and considerations to consider when working with degraded DNA. We hope this review provides a framework to researchers new to computational workflows for how to think about analyzing highly degraded DNA and prompts an increase of collaboration between the forensic genetics and aDNA fields.
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Affiliation(s)
- Ainash Childebayeva
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Anthropology, University of Kansas, Lawrence, KS, USA
| | - Elena I. Zavala
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Biology, University of Oregon, Eugene, OR, USA
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Chen J, Zhang H, Yang M, Wang R, Zhang H, Ren Z, Wang Q, Liu Y, Chen J, Ji J, Zhao J, He G, Guo J, Zhu K, Yang X, Ma H, Wang CC, Huang J. Genomic formation of Tibeto-Burman speaking populations in Guizhou, Southwest China. BMC Genomics 2023; 24:672. [PMID: 37936086 PMCID: PMC10630991 DOI: 10.1186/s12864-023-09767-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 10/25/2023] [Indexed: 11/09/2023] Open
Abstract
Sino-Tibetan is the most prominent language family in East Asia. Previous genetic studies mainly focused on the Tibetan and Han Chinese populations. However, due to the sparse sampling, the genetic structure and admixture history of Tibeto-Burman-speaking populations in the low-altitude region of Southwest China still need to be clarified. We collected DNA from 157 individuals from four Tibeto-Burman-speaking groups from the Guizhou province in Southwest China. We genotyped the samples at about 700,000 genome-wide single nucleotide polymorphisms. Our results indicate that the genetic variation of the four Tibeto-Burman-speaking groups in Guizhou is at the intermediate position in the modern Tibetan-Tai-Kadai/Austronesian genetic cline. This suggests that the formation of Tibetan-Burman groups involved a large-scale gene flow from lowland southern Chinese. The southern ancestry could be further modelled as deriving from Vietnam's Late Neolithic-related inland Southeast Asia agricultural populations and Taiwan's Iron Age-related coastal rice-farming populations. Compared to the Tibeto-Burman speakers in the Tibetan-Yi Corridor reported previously, the Tibeto-Burman groups in the Guizhou region received additional gene flow from the southeast coastal area of China. We show a difference between the genetic profiles of the Tibeto-Burman speakers of the Tibetan-Yi Corridor and the Guizhou province. Vast mountain ranges and rivers in Southwest China may have decelerated the westward expansion of the southeast coastal East Asians. Our results demonstrate the complex genetic profile in the Guizhou region in Southwest China and support the multiple waves of human migration in the southern area of East Asia.
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Affiliation(s)
- Jinwen Chen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Han Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Rui Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Yubo Liu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jing Chen
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jing Zhao
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Guanglin He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Jianxin Guo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Xiaomin Yang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Hao Ma
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, 361102, China.
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China.
- Department of Anthropology and Human Genetics, Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, 361102, China.
- Institute of Artificial Intelligence, Xiamen University, Xiamen, 361005, Fujian, China.
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China.
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Yang C, He M, Liu C, Liu X, Lun M, Su Q, Han X, Liu H, Wang M, Chen L, Liu C. Development and validation of a custom panel including 114 InDels using massively parallel sequencing for forensic application. Electrophoresis 2023; 44:1704-1713. [PMID: 37622566 DOI: 10.1002/elps.202300044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/13/2023] [Accepted: 08/04/2023] [Indexed: 08/26/2023]
Abstract
Insertion/deletion polymorphisms (InDels) have particular characteristics, such as a relatively low mutation rate, small amplicon size, and no stutter artifacts when genotyped via the capillary electrophoresis platform. It would be an important complementary tool for individual identification and certain kinship analyses. At present, massively parallel sequencing (MPS) has shown excellent application value in forensic studies. Therefore, in this study, we developed a custom MPS InDel panel that contains 114 InDels [77 autosomal InDels (A-InDels), 32 X-chromosomal InDels (X-InDels), and 5 Y-chromosomal InDels) based on previous studies. To assess this panel's performance, several validation experiments were performed, including sensitivity, inhibitor, degraded DNA testing, species specificity, concordance, repeatability, case-type samples, and population studies. The results showed that the lowest DNA input was 0.25 ng. All genotypes were obtained in the presence of 80 ng/µL humic acid, 2000 µmol/L calcium, 3000 µmol/L EDTA and indigo. In degraded DNA testing, 90% of loci could be detected for 16-day-old formalin-fixed hearts. In addition, this panel has good species specificity. The values of combined power of discrimination and the combined power of exclusion for 77 A-InDels were 1-3.9951 × 10-32 and 1-4.2956 × 10-7 , respectively. The combined mean exclusion chance for 32 X-InDels was 0.99999 in trios and 0.99904 in duos. The validation results indicate that this newly developed MPS multiplex system is a robust tool for forensic applications.
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Affiliation(s)
- Chengliang Yang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, P. R. China
| | - Meiyun He
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, P. R. China
| | - Changhui Liu
- Guangzhou Forensic Science Institute, Guangzhou, P. R. China
| | - Xueyuan Liu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, P. R. China
| | - Miaoqiang Lun
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, P. R. China
| | - Qin Su
- Guangzhou Forensic Science Institute, Guangzhou, P. R. China
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, P. R. China
| | - Xiaolong Han
- Guangzhou Forensic Science Institute, Guangzhou, P. R. China
| | - Hong Liu
- Guangzhou Forensic Science Institute, Guangzhou, P. R. China
| | - Mengge Wang
- Guangzhou Forensic Science Institute, Guangzhou, P. R. China
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, P. R. China
| | - Ling Chen
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, P. R. China
| | - Chao Liu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, P. R. China
- National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou, P. R. China
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Wen Y, Liu J, Su Y, Chen X, Hou Y, Liao L, Wang Z. Forensic biogeographical ancestry inference: recent insights and current trends. Genes Genomics 2023; 45:1229-1238. [PMID: 37081293 DOI: 10.1007/s13258-023-01387-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/01/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND As a powerful complement to the paradigmatic DNA profiling strategy, biogeographical ancestry inference (BGAI) plays a significant part in human forensic investigation especially when a database hit or eyewitness testimony are not available. It indicates one's biogeographical profile based on known population-specific genetic variations, and thus is crucial for guiding authority investigations to find unknown individuals. Forensic biogeographical ancestry testing exploits much of the recent advances in the understanding of human genomic variation and improving of molecular biology. OBJECTIVE In this review, recent development of prospective ancestry informative markers (AIMs) and the statistical approaches of inferring biogeographic ancestry from AIMs are elucidated and discussed. METHODS We highlight the research progress of three potential AIMs (i.e., single nucleotide polymorphisms, microhaplotypes, and Y or mtDNA uniparental markers) and discuss the prospects and challenges of two methods that are commonly used in BGAI. CONCLUSION While BGAI for forensic purposes has been thriving in recent years, important challenges, such as ethics and responsibilities, data completeness, and ununified standards for evaluation, remain for the use of biogeographical ancestry information in human forensic investigations. To address these issues and fully realize the value of BGAI in forensic investigation, efforts should be made not only by labs/institutions around the world independently, but also by inter-lab/institution collaborations.
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Affiliation(s)
- Yufeng Wen
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, Beijing, 100088, China
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
- School of Life Sciences, Jilin University, Changchun, 130012, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Yonglin Su
- Department of Rehabilitation Medicine, West China Hospital Sichuan University, Chengdu, 610041, China
| | - Xiacan Chen
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Linchuan Liao
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China.
| | - Zheng Wang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, Beijing, 100088, China.
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China.
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Zhang Y, Lei F, Xu H, Zhang X, Zhao M, Lan Q, Zhu B. Exploration of the ancestral inference effectiveness of 126 SNPs and the genetic feature of Inner Mongolian Manchu population. Gene 2023; 873:147456. [PMID: 37137381 DOI: 10.1016/j.gene.2023.147456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/13/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023]
Abstract
In addition to the validated ancestry-informative single nucleotide polymorphisms (AI-SNPs) in classic panels, there are many new potential AI-SNPs yet to be explored. Moreover, the search for AI-SNPs with high discriminatory power for ancestry inference in inter- and intra-continental populations has become a realistic need. In this study, 126 novel AI-SNPs were selected to distinguish the African, European, Central/South Asian and East Asian populations, and a random forest model was introduced to assess the performance of the AI-SNP set. This set was further used in the genetic analysis of the Manchu people in Inner Mongolia, China, based on 79 reference populations from seven continental regions. Results showed that the 126 AI-SNPs were able to achieve the ancestry inference for African, East Asian, European, and Central/South Asian populations. Genetic analyses indicated that Inner Mongolian Manchus were genetically typical of East Asians and were more closely related to the northern Han Chinese and Japanese than to other Altaic-speaking populations. Overall, this study provided a selection of new promising loci for ancestry inference of major intercontinental populations and intracontinental subgroups, as well as genetic insights and valuable data for dissecting the genetic structure of the Inner Mongolian Manchu group.
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Affiliation(s)
- Yunying Zhang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Fanzhang Lei
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Hui Xu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Xingru Zhang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, Shaanxi, China
| | - Ming Zhao
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Qiong Lan
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510220, Guangdong, China
| | - Bofeng Zhu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, Guangdong, China; Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, Shaanxi, China.
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Evaluation of a SNP-STR haplotype panel for forensic genotype imputation. Forensic Sci Int Genet 2023; 62:102801. [PMID: 36272212 DOI: 10.1016/j.fsigen.2022.102801] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 10/08/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022]
Abstract
Short tandem repeat polymorphism (STR)-based individual identification is a popular and reliable method in many forensic applications. However, STRs still frequently fail to find any matched records. In such cases, if known STRs could provide more information, it would be very helpful to solve specific problems. Genotype imputation has long been used in the study of single nucleotide polymorphisms (SNPs) and has recently been introduced into forensic fields. The idea is that, through a reference haplotype panel containing SNPs and STRs, we can obtain unknown genetic information through genotype imputation based on known STR or SNP genotypes. Several recent studies have already demonstrated this exciting idea, and a 1000 Genomes SNP-STR haplotype panel has also been released. To further study the performance of genotype imputation in forensic fields, we collected STR, microhaplotype (MH) and SNP array genotypes from Chinese Han population individuals and then performed genotype imputation analysis based on the released reference panel. As a result, the average locus imputation accuracy was ∼83 % (or ∼70 %) when SNPs in the SNP array (or MH SNPs) were imputed from STRs, and was ∼30 % when highly polymorphic markers (STRs and MHs) were imputed from each other. When STRs were imputed from SNP array, the average locus imputation accuracy increased to ∼48 %. After analyzing the match scores between real STRs and the STRs imputed from SNPs, ∼80 % of studied STR records can be connected to corresponding SNP records, which may help for individual identification. Our results indicate that genotype imputation has great potential for forensic applications.
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Challenges in selecting admixture models and marker sets to infer genetic ancestry in a Brazilian admixed population. Sci Rep 2022; 12:21240. [PMID: 36481695 PMCID: PMC9731996 DOI: 10.1038/s41598-022-25521-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
The inference of genetic ancestry plays an increasingly prominent role in clinical, population, and forensic genetics studies. Several genotyping strategies and analytical methodologies have been developed over the last few decades to assign individuals to specific biogeographic regions. However, despite these efforts, ancestry inference in populations with a recent history of admixture, such as those in Brazil, remains a challenge. In admixed populations, proportion and components of genetic ancestry vary on different levels: (i) between populations; (ii) between individuals of the same population, and (iii) throughout the individual's genome. The present study evaluated 1171 admixed Brazilian samples to compare the genetic ancestry inferred by tri-/tetra-hybrid admixture models and evaluated different marker sets from those with small numbers of ancestry informative markers panels (AIMs), to high-density SNPs (HDSNP) and whole-genome-sequence (WGS) data. Analyses revealed greater variation in the correlation coefficient of ancestry components within and between admixed populations, especially for minority ancestral components. We also observed positive correlation between the number of markers in the AIMs panel and HDSNP/WGS. Furthermore, the greater the number of markers, the more accurate the tri-/tetra-hybrid admixture models.
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Huang S, Sheng M, Li Z, Li K, Chen J, Wu J, Wang K, Shi C, Ding H, Zhou H, Ma L, Yang J, Pu Y, Yu Y, Chen F, Chen P. Inferring bio-geographical ancestry with 35 microhaplotypes. Forensic Sci Int 2022; 341:111509. [DOI: 10.1016/j.forsciint.2022.111509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 10/04/2022] [Accepted: 10/30/2022] [Indexed: 11/24/2022]
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Yang M, He G, Ren Z, Wang Q, Liu Y, Zhang H, Zhang H, Chen J, Ji J, Zhao J, Guo J, Zhu K, Yang X, Wang R, Ma H, Wang CC, Huang J. Genomic Insights Into the Unique Demographic History and Genetic Structure of Five Hmong-Mien-Speaking Miao and Yao Populations in Southwest China. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.849195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Southern China was the original center of multiple ancestral populations related to modern Hmong-Mien, Tai-Kadai, Austroasiatic, and Austronesian people. More recent genetic surveys have focused on the fine-scale genetic structure and admixture history of southern Chinese populations, but the genetic formation and diversification of Hmong-Mien speakers are far from clear due to the sparse genetic sampling. Here, we reported nearly 700,000 single-nucleotide polymorphisms (SNPs) data from 130 Guizhou Miao and Yao individuals. We used principal component analysis, ADMIXTURE, f-statistics, qpAdm, phylogenetic tree, fineSTRUCTURE, and ALDER to explore the fine-scale population genetic structure and admixture pattern of Hmong-Mien people. The sharing allele patterns showed that our studied populations had a strong genetic affinity with ancient and modern groups from southern and southeastern East Asia. We identified one unique ancestry component maximized in Yao people, which widely existed in other Hmong-Mien-speaking populations in southern China and Southeast Asia and ancient samples of Guangxi. Guizhou Hmong-Mien speakers harbored the dominant proportions of ancestry related to southern indigenous East Asians and minor proportions of northern ancestry related to Yellow River farmers, suggesting the possibility of genetic admixture between Hmong-Mien people and recent southward Sino-Tibetan-related populations. Furthermore, we found a genetic substructure among geographically different Miao and Yao people in Leishan and Songtao. The Yao and Miao people in Leishan harbored more southern East Asian ancestry, but Miao in Songtao received more northern East Asian genetic influence. We observed high mtDNA but low Y-chromosome diversity in studied Hmong-Mien groups, supporting the role of sex-specific residence in influencing human genetic variation. Our data provide valuable clues for further exploring population dynamics in southern China.
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Alladio E, Poggiali B, Cosenza G, Pilli E. Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field. Sci Rep 2022; 12:8974. [PMID: 35643723 PMCID: PMC9148302 DOI: 10.1038/s41598-022-12903-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 04/13/2022] [Indexed: 11/24/2022] Open
Abstract
The biogeographical ancestry (BGA) of a trace or a person/skeleton refers to the component of ethnicity, constituted of biological and cultural elements, that is biologically determined. Nowadays, many individuals are interested in exploring their genealogy, and the capability to distinguish biogeographic information about population groups and subgroups via DNA analysis plays an essential role in several fields such as in forensics. In fact, for investigative and intelligence purposes, it is beneficial to inference the biogeographical origins of perpetrators of crimes or victims of unsolved cold cases when no reference profile from perpetrators or database hits for comparative purposes are available. Current approaches for biogeographical ancestry estimation using SNPs data are usually based on PCA and Structure software. The present study provides an alternative method that involves multivariate data analysis and machine learning strategies to evaluate BGA discriminating power of unknown samples using different commercial panels. Starting from 1000 Genomes project, Simons Genome Diversity Project and Human Genome Diversity Project datasets involving African, American, Asian, European and Oceania individuals, and moving towards further and more geographically restricted populations, powerful multivariate techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and machine learning techniques such as XGBoost were employed, and their discriminating power was compared. PLS-DA method provided more robust classifications than XGBoost method, showing that the adopted approach might be an interesting tool for forensic experts to infer BGA information from the DNA profile of unknown individuals, but also highlighting that the commercial forensic panels could be inadequate to discriminate populations at intra-continental level.
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Affiliation(s)
- Eugenio Alladio
- Department of Chemistry, University of Turin, Turin, Italy.,Centro Regionale Antidoping e di Tossicologia "A. Bertinaria", Orbassano, Torino, Italy
| | - Brando Poggiali
- Department of Biology, Forensic Molecular Anthropology Laboratory, University of Florence, Florence, Italy
| | - Giulia Cosenza
- Department of Biology, Forensic Molecular Anthropology Laboratory, University of Florence, Florence, Italy
| | - Elena Pilli
- Department of Biology, Forensic Molecular Anthropology Laboratory, University of Florence, Florence, Italy.
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12
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Zou X, He G, Liu J, Jiang L, Wang M, Chen P, Hou Y, Wang Z. Screening and selection of 21 novel microhaplotype markers for ancestry inference in ten Chinese subpopulations. Forensic Sci Int Genet 2022; 58:102687. [DOI: 10.1016/j.fsigen.2022.102687] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/20/2022] [Accepted: 03/11/2022] [Indexed: 11/04/2022]
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13
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Chen J, He G, Ren Z, Wang Q, Liu Y, Zhang H, Yang M, Zhang H, Ji J, Zhao J, Guo J, Chen J, Zhu K, Yang X, Wang R, Ma H, Tao L, Liu Y, Shen Q, Yang W, Wang CC, Huang J. Fine-Scale Population Admixture Landscape of Tai–Kadai-Speaking Maonan in Southwest China Inferred From Genome-Wide SNP Data. Front Genet 2022; 13:815285. [PMID: 35251126 PMCID: PMC8891617 DOI: 10.3389/fgene.2022.815285] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/27/2022] [Indexed: 12/27/2022] Open
Abstract
Guizhou Province harbors extensive ethnolinguistic and cultural diversity with Sino-Tibetan-, Hmong–Mien-, and Tai–Kadai-speaking populations. However, previous genetic analyses mainly focused on the genetic admixture history of the former two linguistic groups. The admixture history of Tai–Kadai-speaking populations in Guizhou needed to be characterized further. Thus, we genotyped genome-wide SNP data from 41 Tai–Kadai-speaking Maonan people and made a comprehensive population genetic analysis to explore their genetic origin and admixture history based on the pattern of the sharing alleles and haplotypes. We found a genetic affinity among geographically different Tai–Kadai-speaking populations, especially for Guizhou Maonan people and reference Maonan from Guangxi. Furthermore, formal tests based on the f3/f4-statistics further identified an adjacent connection between Maonan and geographically adjacent Hmong–Mien and Sino-Tibetan people, which was consistent with their historically documented shared material culture (Zhang et al., iScience, 2020, 23, 101032). Fitted qpAdm-based two-way admixture models with ancestral sources from northern and southern East Asians demonstrated that Maonan people were an admixed population with primary ancestry related to Guangxi historical people and a minor proportion of ancestry from Northeast Asians, consistent with their linguistically supported southern China origin. Here, we presented the landscape of genetic structure and diversity of Maonan people and a simple demographic model for their evolutionary process. Further whole-genome-sequence–based projects can be presented with more detailed information about the population history and adaptative history of the Guizhou Maonan people.
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Affiliation(s)
- Jing Chen
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Guanglin He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
- Institute Of Rare Diseases, West China Hospital of Sichuan University, Chengdu, China
| | - Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Yubo Liu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Han Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jing Zhao
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Jianxin Guo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Jinwen Chen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Rui Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Hao Ma
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Le Tao
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Yilan Liu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Qu Shen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Wenjiao Yang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
- *Correspondence: Chuan-Chao Wang, ; Jiang Huang,
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
- *Correspondence: Chuan-Chao Wang, ; Jiang Huang,
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14
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Development and validation of a novel 133-plex forensic STR panel (52 STRs and 81 Y-STRs) using single-end 400 bp massive parallel sequencing. Int J Legal Med 2021; 136:447-464. [PMID: 34741666 DOI: 10.1007/s00414-021-02738-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/25/2021] [Indexed: 12/15/2022]
Abstract
Short tandem repeats (STRs) are the preferred genetic markers in forensic DNA analysis, routinely measured by capillary electrophoresis (CE) method based on the fragment length features. While, the massive parallel sequencing (MPS) technology could simultaneously target a large number of intriguing forensic STRs, bypassing the intrinsic limitations of amplicon size separation and accessible fluorophores in CE, which is efficient and promising for enabling the identification of forensic biological evidence. Here, we developed a novel MPS-based Forensic Analysis System Multiplecues SetB Kit of 133-plex forensic STR markers (52 STRs and 81 Y-STRs) and one Y-InDel (M175) based on multiplex PCR and single-end 400 bp sequencing strategy. This panel was subjected to developmental validation studies according to the SWGDAM Validation Guidelines. Approximately 2185 MPS-based reactions using 6 human DNA standards and 8 male donors were conducted for substrate studies (filter paper, gauze, cotton swab, four different types of FTA cards, peripheral venous blood, saliva, and exfoliated cells), sensitivity studies (from 2 ng down to 0.0625 ng), mixture studies (two-person DNA mixtures), PCR inhibitor studies (seven commonly encountered PCR inhibitors), species specificity studies (11 non-human species), and repeatability studies. Results of concordance studies (413 Han males and 6 human DNA standards) generated by STRait Razor and in-house Python scripts indicated 99.98% concordance rate in STR calling relative to CE for STRs between 41,900 genotypes at 100 STR markers. Moreover, the limitations of present studies, the nomenclature rules and forensic MPS applications were also described. In conclusion, the validation studies based on ~ 2200 MPS-based and ~ 2500 CE-based DNA profiles demonstrated that the novel MPS-based panel meets forensic DNA quality assurance guidelines with robust, reliable, and reproducible performance on samples of various quantities and qualities, and the STR nomenclature rules should be further regulated to integrate the inconformity between MPS-based and CE-based methods.
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15
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Wang M, Yuan D, Zou X, Wang Z, Yeh HY, Liu J, Wei LH, Wang CC, Zhu B, Liu C, He G. Fine-Scale Genetic Structure and Natural Selection Signatures of Southwestern Hans Inferred From Patterns of Genome-Wide Allele, Haplotype, and Haplogroup Lineages. Front Genet 2021; 12:727821. [PMID: 34504517 PMCID: PMC8421688 DOI: 10.3389/fgene.2021.727821] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 07/29/2021] [Indexed: 12/15/2022] Open
Abstract
The evolutionary and admixture history of Han Chinese have been widely discussed via traditional autosomal and uniparental genetic markers [e.g., short tandem repeats, low-density single nucleotide polymorphisms). However, their fine-scale genetic landscapes (admixture scenarios and natural selection signatures) based on the high-density allele/haplotype sharing patterns have not been deeply characterized. Here, we collected and generated genome-wide data of 50 Han Chinese individuals from four populations in Guizhou Province, one of the most ethnolinguistically diverse regions, and merged it with over 3,000 publicly available modern and ancient Eurasians to describe the genetic origin and population admixture history of Guizhou Hans and their neighbors. PCA and ADMIXTURE results showed that the studied four populations were homogeneous and grouped closely to central East Asians. Genetic homogeneity within Guizhou populations was further confirmed via the observed strong genetic affinity with inland Hmong-Mien people through the observed genetic clade in Fst and outgroup f3/f4-statistics. qpGraph-based phylogenies and f4-based demographic models illuminated that Guizhou Hans were well fitted via the admixture of ancient Yellow River Millet farmers related to Lajia people and southern Yangtze River farmers related to Hanben people. Further ChromoPainter-based chromosome painting profiles and GLOBETROTTER-based admixture signatures confirmed the two best source matches for southwestern Hans, respectively, from northern Shaanxi Hans and southern indigenes with variable mixture proportions in the historical period. Further three-way admixture models revealed larger genetic contributions from coastal southern East Asians into Guizhou Hans compared with the proposed inland ancient source from mainland Southeast Asia. We also identified candidate loci (e.g., MTUS2, NOTCH4, EDAR, ADH1B, and ABCG2) with strong natural selection signatures in Guizhou Hans via iHS, nSL, and ihh, which were associated with the susceptibility of the multiple complex diseases, morphology formation, alcohol and lipid metabolism. Generally, we provided a case and ideal strategy to reconstruct the detailed demographic evolutionary history of Guizhou Hans, which provided new insights into the fine-scale genomic formation of one ethnolinguistically specific targeted population from the comprehensive perspectives of the shared unlinked alleles, linked haplotypes, and paternal and maternal lineages.
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Affiliation(s)
- Mengge Wang
- Guangzhou Forensic Science Institute, Guangzhou, China.,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Didi Yuan
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Xing Zou
- College of Basic Medicine, Chongqing University, Chongqing, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Hui-Yuan Yeh
- School of Humanities, Nanyang Technological University, Singapore, Singapore
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Lan-Hai Wei
- State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Bofeng Zhu
- Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China.,Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.,Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi'an Jiaotong University, Xi'an, China
| | - Chao Liu
- Guangzhou Forensic Science Institute, Guangzhou, China.,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Guanglin He
- School of Humanities, Nanyang Technological University, Singapore, Singapore.,State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
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16
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Chen J, He G, Ren Z, Wang Q, Liu Y, Zhang H, Yang M, Zhang H, Ji J, Zhao J, Guo J, Zhu K, Yang X, Wang R, Ma H, Wang CC, Huang J. Genomic Insights Into the Admixture History of Mongolic- and Tungusic-Speaking Populations From Southwestern East Asia. Front Genet 2021; 12:685285. [PMID: 34239544 PMCID: PMC8258170 DOI: 10.3389/fgene.2021.685285] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/05/2021] [Indexed: 12/17/2022] Open
Abstract
As a major part of the modern Trans-Eurasian or Altaic language family, most of the Mongolic and Tungusic languages were mainly spoken in northern China, Mongolia, and southern Siberia, but some were also found in southern China. Previous genetic surveys only focused on the dissection of genetic structure of northern Altaic-speaking populations; however, the ancestral origin and genomic diversification of Mongolic and Tungusic-speaking populations from southwestern East Asia remain poorly understood because of the paucity of high-density sampling and genome-wide data. Here, we generated genome-wide data at nearly 700,000 single-nucleotide polymorphisms (SNPs) in 26 Mongolians and 55 Manchus collected from Guizhou province in southwestern China. We applied principal component analysis (PCA), ADMIXTURE, f statistics, qpWave/qpAdm analysis, qpGraph, TreeMix, Fst, and ALDER to infer the fine-scale population genetic structure and admixture history. We found significant genetic differentiation between northern and southern Mongolic and Tungusic speakers, as one specific genetic cline of Manchu and Mongolian was identified in Guizhou province. Further results from ADMIXTURE and f statistics showed that the studied Guizhou Mongolians and Manchus had a strong genetic affinity with southern East Asians, especially for inland southern East Asians. The qpAdm-based estimates of ancestry admixture proportion demonstrated that Guizhou Mongolians and Manchus people could be modeled as the admixtures of one northern ancestry related to northern Tungusic/Mongolic speakers or Yellow River farmers and one southern ancestry associated with Austronesian, Tai-Kadai, and Austroasiatic speakers. The qpGraph-based phylogeny and neighbor-joining tree further confirmed that Guizhou Manchus and Mongolians derived approximately half of the ancestry from their northern ancestors and the other half from southern Indigenous East Asians. The estimated admixture time ranged from 600 to 1,000 years ago, which further confirmed the admixture events were mediated via the Mongolians Empire expansion during the formation of the Yuan dynasty.
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Affiliation(s)
- Jing Chen
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Guanglin He
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Yubo Liu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Han Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jing Zhao
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jianxin Guo
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Rui Wang
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Hao Ma
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Marine Environmental Science, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
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