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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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2
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Li Z, Wu J, Yang J, Li K, Chen J, Huang S, Ji Q, Kong X, Xie S, Zhan W, Zhang B, Ye K, Liu Q, Mao Z, Cao Y, Huang H, Yu Y, Wang K, Yu Y, Li D, Chen F, Chen P. Genome-wide association studies combined with k-fold cross-validation identify rs17822931 as an ancestry-informative marker in Han Chinese population. Electrophoresis 2023; 44:1187-1196. [PMID: 37183951 DOI: 10.1002/elps.202200227] [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: 04/29/2022] [Revised: 02/13/2023] [Accepted: 05/04/2023] [Indexed: 05/16/2023]
Abstract
DNA-based ancestry inference has long been a research hot spot in forensic science. The differentiation of Han Chinese population, such as the northern-to-southern substructure, would benefit forensic practice. In the present study, we enrolled participants from northern and southern China, each participant was genotyped at ∼400 K single-nucleotide polymorphisms (SNPs) and data of CHB and CHS from 1000 Genomes Project were used to perform genome-wide association analyses. Meanwhile, a new method combining genome-wide association study (GWAS) analyses with k-fold cross-validation in a small sample size was introduced. As a result, one SNP rs17822931 emerged with a p-value of 7.51E - 6. We also simulated a huge dataset to verify whether k-fold cross-validation could reduce the false-negative rate of GWAS. The identified ABCC11 rs17822931 has been reported to have allele frequencies varied with the geographical gradient distribution in humans. We also found a great difference in the allele frequency distributions of rs17822931 among five different cohorts of the Chinese population. In conclusion, our study demonstrated that even small-scale GWAS can also have potential to identify effective loci with implemented k-fold cross-validation method and shed light on the potential maker of rs17822931 in differentiating the north-to-south substructure of the Han Chinese population.
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Affiliation(s)
- Zheng Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China
| | - Jiayi Wu
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Jiawen Yang
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Kai Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Ji Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Shuainan Huang
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Qiang Ji
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Xiaochao Kong
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Sumei Xie
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Wenxuan Zhan
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Beilei Zhang
- Fujian Zhengtai Judicial Expertise Center, Xiamen, Fujian, P. R. China
| | - Ke Ye
- Institute of Criminal Science and Technology, Xiangtan City Public Security Bureau, Xiangtan, Hunan, P. R. China
| | - Qingfan Liu
- Mayang Miaozu Autonomous County Public Security Bureau, Huaihua, Hunan, P. R. China
| | - Zhengsheng Mao
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Yue Cao
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Huijie Huang
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Youjia Yu
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Kang Wang
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Yanfang Yu
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Ding Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Feng Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
- Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Peng Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
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3
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Pamjav H, Fóthi Á, Dudás D, Tapasztó A, Krizsik V, Fóthi E. The paternal genetic legacy of Hungarian-speaking Rétköz (Hungary) and Váh valley (Slovakia) populations. Front Genet 2022; 13:977517. [PMID: 36324512 PMCID: PMC9619085 DOI: 10.3389/fgene.2022.977517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/03/2022] [Indexed: 11/18/2022] Open
Abstract
One hundred and six Rétköz and 48 Váh valley samples were collected from the contact zones of Hungarian-Slovakian territories and were genotyped for Y-chromosomal haplotypes and haplogroups. The results were compared with contemporary and archaic data from published sources. The genetic composition of the Rétköz population from Hungary and the Váh valley population from Slovakia indicates different histories. In the Rétköz population, the paternal lineages that were also found in the Hungarian Conquerors, such as R1a-Z93, N-M46, Q-M242, and R1b-L23, were better preserved. These haplogroups occurred in 10% of the population. The population of the Váh valley, however, is characterized by the complete absence of these haplogroups. Our study did not detect a genetic link between the Váh valley population and the Hungarian Conquerors; the genetic composition of the Váh valley population is similar to that of the surrounding Indo-European populations. The Hungarian Rétköz males shared common haplotypes with ancient Xiongnu, ancient Avar, Caucasian Avar, Abkhazian, Balkarian, and Circassian males within haplogroups R1a-Z93, N1c-M46, and R1b-L23, indicating a common genetic footprint. Another difference between the two studied Hungarian populations can be concluded from the Fst-based MDS plot. The Váh valley, in the western part of the Hungarian-Slovakian contact zone, is genetically closer to the Western Europeans. In contrast, Rétköz is in the eastern part of that zone and therefore closer to the Eastern Europeans.
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Affiliation(s)
- Horolma Pamjav
- Department of Reference sample analysis, Institute of Forensic Genetics, Hungarian Institutes for Forensic Sciences, Budapest, Hungary
- *Correspondence: Horolma Pamjav, ; Erzsébet Fóthi,
| | - Ábel Fóthi
- Institute of Archaeogenomics, Research Centre for the Humanities, Budapest, Hungary
| | - Dániel Dudás
- Department of Reference sample analysis, Institute of Forensic Genetics, Hungarian Institutes for Forensic Sciences, Budapest, Hungary
- Departmant of Genetics, Eötvös Lorand University, Budapest, Hungary
| | - Attila Tapasztó
- Department of Reference sample analysis, Institute of Forensic Genetics, Hungarian Institutes for Forensic Sciences, Budapest, Hungary
| | - Virág Krizsik
- Institute of Archaeogenomics, Research Centre for the Humanities, Budapest, Hungary
| | - Erzsébet Fóthi
- Institute of Archaeogenomics, Research Centre for the Humanities, Budapest, Hungary
- *Correspondence: Horolma Pamjav, ; Erzsébet Fóthi,
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Xu H, Fang Y, Zhao M, Lan Q, Mei S, Liu L, Bai X, Zhu B. Forensic Features and Genetic Structure Analyses of the Beijing Han Nationality Disclosed by a Self-Developed Panel Containing a Series of Ancestry Informative Deletion/Insertion Polymorphism Loci. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.890153] [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
The utilization of the ancestry informative markers to disclose the ancestral composition of a certain population and explore the genetic affinities between diverse populations is beneficial to inferring the biogeographic ancestry of unknown individuals and assisting in case detection, as well as avoiding the impacts of population stratification during genome-wide association analysis studies. In the present study, we applied an in-house ancestry informative deletion/insertion polymorphic multiplex amplification system to investigate the ancestral compositions of the Beijing Han population and analyze the genetic relationships between the Beijing Han population and 31 global reference populations. The results demonstrated that 32 loci of this self-developed panel containing 39 loci significantly contributed to the inference of genetic information for the Beijing Han population. The results of multiple population genetics statistical analyses indicated that the ancestral component and genetic architecture of the Beijing Han population were analogous to the reference East Asian populations, and that the Beijing Han population was genetically close to the reference East Asian populations.
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Zhao C, Yang J, Xu H, Mei S, Fang Y, Lan Q, Deng Y, Zhu B. Genetic diversity analysis of forty-three insertion/deletion loci for forensic individual identification in Han Chinese from Beijing based on a novel panel. J Zhejiang Univ Sci B 2022; 23:241-248. [PMID: 35261219 DOI: 10.1631/jzus.b2100507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Due to the virtues of no stutter peaks, low rates of mutation, and short amplicon sizes, insertion/deletion (InDel) polymorphism is an indispensable tool for analyzing degraded DNA samples from crime scenes for human identifications (Wang et al., 2021). Herein, a self-developed panel of 43 InDel loci constructed previously by our group was utilized to evaluate the genetic diversities and explore the genetic background of the Han Chinese from Beijing (HCB) including 301 random healthy individuals. The lengths of amplicons at 43 InDel loci in this panel ranged from 87 to 199 bp, which indicated that the panel could be used as an effective tool to utilize highly degraded DNA samples for human identity testing. The loci in this panel were validated and performed well for forensic degraded DNA samples (Jin et al., 2021). The combined discrimination power (PD) and combined probability of exclusion (PE) values in this panel indicated that the 43 InDel loci could be used as the candidate markers in personal identification and parentage testing of HCB. In addition, population genetic relationships between the HCB and 26 reference populations from five continents based on 19 overlapped InDel loci were displayed by constructing a phylogenetic tree, principal component analysis (PCA), and population genetic structure analysis. The results illustrated that the HCB had closer genetic relationships with the Han populations from Chinese different regions.
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Affiliation(s)
- Congying Zhao
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Jinlong Yang
- Beijing Zhongzheng DNA Evidence Institute, Beijing 101318, China
| | - Hui Xu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Shuyan Mei
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Yating Fang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Qiong Lan
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Yajun Deng
- Beijing Zhongzheng DNA Evidence Institute, Beijing 101318, China. ,
| | - Bofeng Zhu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China. .,Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an 710004, China.
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6
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Tseng TE, Lee CC, Yen HK, Groot OQ, Hou CH, Lin SY, Bongers MER, Hu MH, Karhade AV, Ko JC, Lai YH, Yang JJ, Verlaan JJ, Yang RS, Schwab JH, Lin WH. International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment. Clin Orthop Relat Res 2022; 480:367-378. [PMID: 34491920 PMCID: PMC8747677 DOI: 10.1097/corr.0000000000001969] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/17/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data. QUESTIONS/PURPOSES (1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions? METHODS All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study. RESULTS The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1-year survival, respectively. CONCLUSION SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps.io/extremitymetssurvival/. LEVEL OF EVIDENCE Level III, therapeutic study.
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Affiliation(s)
- Ting-En Tseng
- Department of Medical Education, National Taiwan University Hospital, Taipei City, Taiwan
| | - Chia-Che Lee
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | | | - Olivier Q. Groot
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chun-Han Hou
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Shin-Ying Lin
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Michiel E. R. Bongers
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ming-Hsiao Hu
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Aditya V. Karhade
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia-Chi Ko
- Department of Medical Education, National Taiwan University Hospital, Taipei City, Taiwan
| | - Yi-Hsiang Lai
- Department of Medical Education, National Taiwan University Hospital, Taipei City, Taiwan
| | - Jing-Jen Yang
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Jorrit-Jan Verlaan
- Department of Orthopedic Surgery, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | - Joseph H. Schwab
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei-Hsin Lin
- Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
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Cui W, Jin X, Fang Y, Lan Q, Lan J, Chen M, Mei S, Xie T, Zhu B. An interpretation of the genetic polymorphism and population genetic background of Ankang Han population via a novel InDel panel. Forensic Sci Res 2021; 7:694-701. [PMID: 36817236 PMCID: PMC9930792 DOI: 10.1080/20961790.2021.1997368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
In this research, genotyping data of 43 InDel loci in 311 Han individuals in Ankang City, Shaanxi Province, China were detected using a self-developed five-dye multiplex amplification panel. The allelic frequencies and forensic parameters of all InDel loci were calculated. The combined power of discrimination and probability of exclusion values were 0.999 999 999 999 999 998 827 39 and 0.999 887 424, respectively, which demonstrated that this 43-InDel panel was powerful for individual identifications in Ankang Han population. Moreover, genetic distances, pairwise FST values, principal component analyses, phylogenetic trees and STRUCTURE analyses were performed to investigate the genetic affinities between Ankang Han and reference groups. Population genetic investigations indicated that Ankang Han population had a close genetic relationship with Southern Han population compared with other reference groups.
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Affiliation(s)
- Wei Cui
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Xiaoye Jin
- Key laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an, China
| | - Yating Fang
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Qiong Lan
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Jiangwei Lan
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Man Chen
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Shuyan Mei
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Tong Xie
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China,CONTACT Tong Xie ;
| | - Bofeng Zhu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China,Bofeng Zhu
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Lo YH, Cheng HC, Hsiung CN, Yang SL, Wang HY, Peng CW, Chen CY, Lin KP, Kang ML, Chen CH, Chu HW, Lin CF, Lee MH, Liu Q, Satta Y, Lin CJ, Lin M, Chaw SM, Loo JH, Shen CY, Ko WY. Detecting Genetic Ancestry and Adaptation in the Taiwanese Han People. Mol Biol Evol 2021; 38:4149-4165. [PMID: 33170928 PMCID: PMC8476137 DOI: 10.1093/molbev/msaa276] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The Taiwanese people are composed of diverse indigenous populations and the Taiwanese Han. About 95% of the Taiwanese identify themselves as Taiwanese Han, but this may not be a homogeneous population because they migrated to the island from various regions of continental East Asia over a period of 400 years. Little is known about the underlying patterns of genetic ancestry, population admixture, and evolutionary adaptation in the Taiwanese Han people. Here, we analyzed the whole-genome single-nucleotide polymorphism genotyping data from 14,401 individuals of Taiwanese Han collected by the Taiwan Biobank and the whole-genome sequencing data for a subset of 772 people. We detected four major genetic ancestries with distinct geographic distributions (i.e., Northern, Southeastern, Japonic, and Island Southeast Asian ancestries) and signatures of population mixture contributing to the genomes of Taiwanese Han. We further scanned for signatures of positive natural selection that caused unusually long-range haplotypes and elevations of hitchhiked variants. As a result, we identified 16 candidate loci in which selection signals can be unambiguously localized at five single genes: CTNNA2, LRP1B, CSNK1G3, ASTN2, and NEO1. Statistical associations were examined in 16 metabolic-related traits to further elucidate the functional effects of each candidate gene. All five genes appear to have pleiotropic connections to various types of disease susceptibility and significant associations with at least one metabolic-related trait. Together, our results provide critical insights for understanding the evolutionary history and adaption of the Taiwanese Han population.
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Affiliation(s)
- Yun-Hua Lo
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Hsueh-Chien Cheng
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chia-Ni Hsiung
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, Taiwan
| | - Show-Ling Yang
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, Taiwan
| | - Han-Yu Wang
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chia-Wei Peng
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chun-Yu Chen
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Kung-Ping Lin
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Mei-Ling Kang
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, Taiwan
| | - Hou-Wei Chu
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, Taiwan
| | | | - Mei-Hsuan Lee
- Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Quintin Liu
- Department of Evolutionary Studies of Biosystems, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Yoko Satta
- Department of Evolutionary Studies of Biosystems, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan
| | - Cheng-Jui Lin
- Molecular Anthropology and Transfusion Medicine Research Laboratory, Mackay Memorial Hospital, Taipei, Taiwan
| | - Marie Lin
- Molecular Anthropology and Transfusion Medicine Research Laboratory, Mackay Memorial Hospital, Taipei, Taiwan
| | - Shu-Miaw Chaw
- Biodiversity Research Center, Academia Sinica, Taipei City, Taiwan
| | - Jun-Hun Loo
- Molecular Anthropology and Transfusion Medicine Research Laboratory, Mackay Memorial Hospital, Taipei, Taiwan
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, Taiwan
| | - Wen-Ya Ko
- Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, Taiwan
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9
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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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10
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Yu X, Li H. Origin of ethnic groups, linguistic families, and civilizations in China viewed from the Y chromosome. Mol Genet Genomics 2021; 296:783-797. [PMID: 34037863 DOI: 10.1007/s00438-021-01794-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 04/22/2021] [Indexed: 12/20/2022]
Abstract
East Asia, geographically extending to the Pamir Plateau in the west, to the Himalayan Mountains in the southwest, to Lake Baikal in the north and to the South China Sea in the south, harbors a variety of people, cultures, and languages. To reconstruct the natural history of East Asians is a mission of multiple disciplines, including genetics, archaeology, linguistics, and ethnology. Geneticists confirm the recent African origin of modern East Asians. Anatomically modern humans arose in Africa and immigrated into East Asia via a southern route approximately 50,000 years ago. Following the end of the Last Glacial Maximum approximately 12,000 years ago, rice and millet were domesticated in the south and north of East Asia, respectively, which allowed human populations to expand and linguistic families and ethnic groups to develop. These Neolithic populations produced a strong relation between the present genetic structures and linguistic families. The expansion of the Hongshan people from northeastern China relocated most of the ethnic populations on a large scale approximately 5300 years ago. Most of the ethnic groups migrated to remote regions, producing genetic structure differences between the edge and center of East Asia. In central China, pronounced population admixture occurred and accelerated over time, which subsequently formed the Han Chinese population and eventually the Chinese civilization. Population migration between the north and the south throughout history has left a smooth gradient in north-south changes in genetic structure. Observation of the process of shaping the genetic structure of East Asians may help in understanding the global natural history of modern humans.
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Affiliation(s)
- Xueer Yu
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China.,Shanxi Academy of Advanced Research and Innovation, Fudan-Datong Institute of Chinese Origin, Datong, 037006, China
| | - Hui Li
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China. .,Shanxi Academy of Advanced Research and Innovation, Fudan-Datong Institute of Chinese Origin, Datong, 037006, China.
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11
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Yang X, Wang XX, He G, Guo J, Zhao J, Sun J, Li Y, Cheng HZ, Hu R, Wei LH, Chen G, Wang CC. Genomic insight into the population history of Central Han Chinese. Ann Hum Biol 2021; 48:49-55. [PMID: 33191788 DOI: 10.1080/03014460.2020.1851396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND In recent decades, considerable attention has been paid to exploring the population genetic characteristics of Han Chinese, mainly documenting a north-south genetic substructure. However, the central Han Chinese have been largely underrepresented in previous studies. AIM To infer a comprehensive understanding of the homogenisation process and population history of Han Chinese. SUBJECTS AND METHODS We collected samples from 122 Han Chinese from seven counties of Hubei province in central China and genotyped 534,000 genome-wide SNPs. We compared Hubei Han with both ancient and present-day Eurasian populations using Principal Component Analysis, ADMIXTURE, f statistics, qpWave and qpAdm. RESULTS We observed Hubei Han Chinese are at a genetically intermediate position on the north-south Han Chinese cline. We have not detected any significant genetic substructure in the studied groups from seven different counties. Hubei Han show significant evidence of genetic admixture deriving about 63% of ancestry from Tai-Kadai or Austronesian-speaking southern indigenous groups and 37% from Tungusic or Mongolic related northern populations. CONCLUSIONS The formation of Han Chinese has involved extensive admixture with Tai-Kadai or Austronesian-speaking populations in the south and Tungusic or Mongolic speaking populations in the north. The convenient transportation and central location of Hubei make it the key region for the homogenisation of Han Chinese.
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Affiliation(s)
- Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Xiao-Xun Wang
- Department of Medical Laboratory, Taihe Hospital Affiliated to Hubei University of Medicine, Shiyan, China
| | - Guanglin He
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China.,Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine Sichuan University, Chengdu, China
| | - Jianxin Guo
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jing Zhao
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jin Sun
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Yingxiang Li
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Hui-Zhen Cheng
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Rong Hu
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | | | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
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12
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Liu Y, Zhang H, He G, Ren Z, Zhang H, Wang Q, Ji J, Yang M, Guo J, Yang X, Sun J, Ba J, Peng D, Hu R, Wei LH, Wang CC, Huang J. Forensic Features and Population Genetic Structure of Dong, Yi, Han, and Chuanqing Human Populations in Southwest China Inferred From Insertion/Deletion Markers. Front Genet 2020; 11:360. [PMID: 32425974 PMCID: PMC7205039 DOI: 10.3389/fgene.2020.00360] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 03/24/2020] [Indexed: 12/20/2022] Open
Abstract
Guizhou province in southwest China has abundant genetic and cultural diversities, but the forensic features and genetic structure of Guizhou populations remain poorly understood due to the sparse sampling of present-day populations. Here, we present 30 insertion/deletion polymorphisms (InDels) data of 591 human individuals collected from four populations, Dong, Yi, Han, and Chuanqing residing in Guizhou. We calculated the forensic parameters of 30 InDel loci and found that this panel meets the efficiency of forensic personal identification based on the high combined power of discrimination, but it could only be used as a complementary tool in the parentage testing because of the lower combined probability of exclusion values. The studied populations are genetically closer related to geographically adjacent or linguistically related populations in southern China, such as the Tai-Kadai and Hmong-Mien speaking groups. The unrecognized ethnic Chuanqing people show an additional genetic affinity with Han Chinese, highlighting the role of possible military immigrations in their origin.
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Affiliation(s)
- Yubo Liu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Han Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Guanglin He
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China.,Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jianxin Guo
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jin Sun
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jinxing Ba
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Dan Peng
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Rong Hu
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
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13
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Wu WC, Lin HC, Liao WL, Tsai YY, Chen AC, Chen HC, Lin HY, Liao LN, Chao PM. FADS Genetic Variants in Taiwanese Modify Association of DHA Intake and Its Proportions in Human Milk. Nutrients 2020; 12:nu12020543. [PMID: 32093185 PMCID: PMC7071481 DOI: 10.3390/nu12020543] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/14/2020] [Accepted: 02/17/2020] [Indexed: 12/13/2022] Open
Abstract
Our objective was to determine how docosahexaenoic acid (DHA) proportions in human milk are modulated by maternal FADS gene variants and dietary intake in Taiwanese women. Inclusion criteria included being healthy, 20–40 y old, having had a full-term baby that they intended to breast feed for at least 1 month, and willingness to participate in this study. Intake of DHA was assessed by food frequency questionnaire and fatty acids were analyzed in human milk samples collected 3–4 weeks postpartum. Based on multiple linear regression of data from 164 mothers that completed this study, there was 0.28% (FA%) reduction in milk DHA in high versus low genetic risk (stratified by whether minor allele numbers were ≥ 3 in rs1535 and rs174448) and 0.45% reduction in low versus high intake (stratified by whether DHA intake reached 200 mg/d). There was a significant gene–diet interaction; mothers with low genetic risk only had high milk DHA proportions with high DHA intake, whereas for mothers with high genetic risk, dietary effects were quite limited. Therefore, for FADS single nucleotide polymorphism in Taiwanese women, increasing DHA intake did not correct low milk DHA proportions in those with a high-risk genotype. Diet only conferred benefits to those with a low-risk genotype. Trial registration: This trial was retrospectively registered (Feb 12, 2019) in ClinicalTrials.gov (No. NCT03842891, https://clinicaltrials.gov/ct2/show/NCT03842891).
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Affiliation(s)
- Wen-Chieh Wu
- PhD Program for Health Science and Industry, China Medical University, Taichung 404, Taiwan;
| | - Hung-Chih Lin
- Division of Neonatology, Children’s Hospital, China Medical University, Taichung 404, Taiwan; (H.-C.L.); (H.-Y.L.)
- Asia University Hospital, Asia University, Taichung 413, Taiwan
- School of Chinese Medicine, China Medical University, Taichung 404, Taiwan
| | - Wen-Ling Liao
- Graduate Institute of Integrated Medicine, China Medical University, Taichung 404, Taiwan;
- Center for Personalized Medicine, China Medical University Hospital, Taichung 404, Taiwan
| | | | - An-Chyi Chen
- Division of Pediatric Hepatology and Gastroenterology, Children’s Hospital, China Medical University, Taichung 404, Taiwan;
- College of Medicine, China Medical University, Taichung 404, Taiwan
| | | | - Hsiang-Yu Lin
- Division of Neonatology, Children’s Hospital, China Medical University, Taichung 404, Taiwan; (H.-C.L.); (H.-Y.L.)
- College of Medicine, China Medical University, Taichung 404, Taiwan
| | - Li-Na Liao
- Department of Public Health, China Medical University, Taichung 404, Taiwan
- Correspondence: (L.-N.L.); (P.-M.C.); Tel.: (+886)-4-22053366 (ext. 7509) (P.-M.C.); Fax: (+886)-4-22062891 (P.-M.C.)
| | - Pei-Min Chao
- PhD Program for Health Science and Industry, China Medical University, Taichung 404, Taiwan;
- Department of Nutrition, China Medical University, Taichung 404, Taiwan
- Correspondence: (L.-N.L.); (P.-M.C.); Tel.: (+886)-4-22053366 (ext. 7509) (P.-M.C.); Fax: (+886)-4-22062891 (P.-M.C.)
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14
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Chen P, Wu J, Luo L, Gao H, Wang M, Zou X, Li Y, Chen G, Luo H, Yu L, Han Y, Jia F, He G. Population Genetic Analysis of Modern and Ancient DNA Variations Yields New Insights Into the Formation, Genetic Structure, and Phylogenetic Relationship of Northern Han Chinese. Front Genet 2019; 10:1045. [PMID: 31737039 PMCID: PMC6832103 DOI: 10.3389/fgene.2019.01045] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 09/30/2019] [Indexed: 11/30/2022] Open
Abstract
Modern East Asians derived from the admixture of aborigines and incoming farmers expanding from Yellow and Yangtze River Basins. Distinct genetic differentiation and subsequent admixture between Northeast Asians and Southeast Asians subsequently evidenced by the mitochondrial DNA, Y-chromosomal variations, and autosomal SNPs. Recently, population geneticists have paid more attention to the genetic polymorphisms and background of southern-Han Chinese and southern native populations. The genetic legacy of northern-Han remains uncharacterized. Thus, we performed this comprehensive population genetic analyses of modern and ancient genetic variations aiming to yield new insight into the formation of modern Han, and the genetic ancestry and phylogenetic relationship of the northern-Han Chinese population. We first genotyped 25 forensic associated markers in 3,089 northern-Han Chinese individuals using the new-generation of the Huaxia Platinum System. And then we performed the first meta-analysis focused on the genetic affinity between Asian Neolithic∼Iron Age ancients and modern northern-Han Chinese by combining mitochondrial variations in 417 ancient individuals from 13 different archeological sites and 812 modern individuals, as well as Y-chromosomal variations in 114 ancient individuals from 12 Neolithic∼Iron Age sites and 2,810 modern subjects. We finally genotyped 643,897 genome-wide nucleotide polymorphisms (SNPs) in 20 Shanxi Han individuals and combined with 1,927 modern humans and 40 Eurasian ancient genomes to explore the genetic structure and admixture of northern-Han Chinese. We addressed genetic legacy, population structure and phylogenetic relationship of northern-Han Chinese via various analyses. Our population genetic results from five different reference datasets indicated that Shanxi Han shares a closer phylogenetic relationship with northern-neighbors and southern ethnically close groups than with Uyghur and Tibetan. Genome-wide variations revealed that modern northern-Han derived their ancestry from Yakut-related population (25.2%) and She-related population (74.8%). Summarily, the genetic mixing that led to the emergence of a Han Chinese ethnicity occurred at a very early period, probably in Neolithic times, and this mixing involved an ancient Tibeto-Burman population and a local pre-Sinitic population, which may have been linguistically Altaic.
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Affiliation(s)
- Pengyu Chen
- Center of Forensic Expertise, Affiliated Hospital of Zunyi Medical University, Zunyi, China.,Department of Forensic Medicine, Zunyi Medical University, Zunyi, China
| | - Jian Wu
- Center of Forensic Expertise, Affiliated Hospital of Zunyi Medical University, Zunyi, China.,Department of Forensic Medicine, Zunyi Medical University, Zunyi, China
| | - Li Luo
- Center of Forensic Expertise, Affiliated Hospital of Zunyi Medical University, Zunyi, China.,Department of Forensic Medicine, Zunyi Medical University, Zunyi, China
| | - Hongyan Gao
- Center of Forensic Expertise, Affiliated Hospital of Zunyi Medical University, Zunyi, China.,Department of Forensic Medicine, Zunyi Medical University, Zunyi, China
| | - Mengge Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China
| | - Xing Zou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China
| | - Yingxiang Li
- Department of Bioinformatics, WeGene, Shenzhen, China
| | - Gang Chen
- Department of Bioinformatics, WeGene, Shenzhen, China
| | - Haibo Luo
- Department of Forensic Medicine, Zunyi Medical University, Zunyi, China
| | - Limei Yu
- Key Laboratory of Cell Engineering in Guizhou Province, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Yanyan Han
- Department of Nutrition and Food Hygiene, School of Public Health, Zunyi Medical University, Zunyi, China
| | - Fuquan Jia
- Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot, China
| | - Guanglin He
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China
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15
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Ishiya K, Mizuno F, Wang L, Ueda S. MitoIMP: A Computational Framework for Imputation of Missing Data in Low-Coverage Human Mitochondrial Genome. Bioinform Biol Insights 2019; 13:1177932219873884. [PMID: 31523131 PMCID: PMC6732850 DOI: 10.1177/1177932219873884] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 08/13/2019] [Indexed: 11/16/2022] Open
Abstract
The incompleteness of partial human mitochondrial genome sequences makes it difficult to perform relevant comparisons among multiple resources. To deal with this issue, we propose a computational framework for deducing missing nucleotides in the human mitochondrial genome. We applied it to worldwide mitochondrial haplogroup lineages and assessed its performance. Our approach can deduce the missing nucleotides with a precision of 0.99 or higher in most human mitochondrial DNA lineages. Furthermore, although low-coverage mitochondrial genome sequences often lead to a blurred relationship in the multidimensional scaling analysis, our approach can correct this positional arrangement according to the corresponding mitochondrial DNA lineages. Therefore, our framework will provide a practical solution to compensate for the lack of genome coverage in partial and fragmented human mitochondrial genome sequences. In this study, we developed an open-source computer program, MitoIMP, implementing our imputation procedure. MitoIMP is freely available from https://github.com/omics-tools/mitoimp.
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Affiliation(s)
- Koji Ishiya
- Computational Bio Big Data Open Innovation Lab (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST)-Waseda University, Tokyo, Japan
| | - Fuzuki Mizuno
- Department of Legal Medicine, School of Medicine, Toho University, Tokyo, Japan
| | - Li Wang
- School of Medicine, Hangzhou Normal University, Zhejiang, China
| | - Shintaroh Ueda
- Department of Legal Medicine, School of Medicine, Toho University, Tokyo, Japan.,School of Medicine, Hangzhou Normal University, Zhejiang, China.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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16
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Du Z, Ma L, Qu H, Chen W, Zhang B, Lu X, Zhai W, Sheng X, Sun Y, Li W, Lei M, Qi Q, Yuan N, Shi S, Zeng J, Wang J, Yang Y, Liu Q, Hong Y, Dong L, Zhang Z, Zou D, Wang Y, Song S, Liu F, Fang X, Chen H, Liu X, Xiao J, Zeng C. Whole Genome Analyses of Chinese Population and De Novo Assembly of A Northern Han Genome. GENOMICS PROTEOMICS & BIOINFORMATICS 2019; 17:229-247. [PMID: 31494266 PMCID: PMC6818495 DOI: 10.1016/j.gpb.2019.07.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 07/07/2019] [Accepted: 08/07/2019] [Indexed: 12/20/2022]
Abstract
To unravel the genetic mechanisms of disease and physiological traits, it requires comprehensive sequencing analysis of large sample size in Chinese populations. Here, we report the primary results of the Chinese Academy of Sciences Precision Medicine Initiative (CASPMI) project launched by the Chinese Academy of Sciences, including the de novo assembly of a northern Han reference genome (NH1.0) and whole genome analyses of 597 healthy people coming from most areas in China. Given the two existing reference genomes for Han Chinese (YH and HX1) were both from the south, we constructed NH1.0, a new reference genome from a northern individual, by combining the sequencing strategies of PacBio, 10× Genomics, and Bionano mapping. Using this integrated approach, we obtained an N50 scaffold size of 46.63 Mb for the NH1.0 genome and performed a comparative genome analysis of NH1.0 with YH and HX1. In order to generate a genomic variation map of Chinese populations, we performed the whole-genome sequencing of 597 participants and identified 24.85 million (M) single nucleotide variants (SNVs), 3.85 M small indels, and 106,382 structural variations. In the association analysis with collected phenotypes, we found that the T allele of rs1549293 in KAT8 significantly correlated with the waist circumference in northern Han males. Moreover, significant genetic diversity in MTHFR, TCN2, FADS1, and FADS2, which associate with circulating folate, vitamin B12, or lipid metabolism, was observed between northerners and southerners. Especially, for the homocysteine-increasing allele of rs1801133 (MTHFR 677T), we hypothesize that there exists a "comfort" zone for a high frequency of 677T between latitudes of 35-45 degree North. Taken together, our results provide a high-quality northern Han reference genome and novel population-specific data sets of genetic variants for use in the personalized and precision medicine.
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Affiliation(s)
- Zhenglin Du
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Liang Ma
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Hongzhu Qu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wei Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Bing Zhang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xi Lu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Weibo Zhai
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xin Sheng
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yongqiao Sun
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenjie Li
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Meng Lei
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qiuhui Qi
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Na Yuan
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shuo Shi
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jingyao Zeng
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jinyue Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yadong Yang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Liu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yaqiang Hong
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lili Dong
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhewen Zhang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Dong Zou
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanqing Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shuhui Song
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Fan Liu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangdong Fang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hua Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Liu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingfa Xiao
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Changqing Zeng
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Yao L, Xu Z, Wan L. Whole Mitochondrial DNA Sequencing Analysis in 47 Han Populations in Southwest China. Med Sci Monit 2019; 25:6482-6490. [PMID: 31464266 PMCID: PMC6733151 DOI: 10.12659/msm.916275] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background Mitochondrial DNA (mtDNA) sequencing has been used in many areas, including forensic genetics. Due to the rapid development of sequencing technology, whole mtDNA sequencing is now possible and may be used in epidemiological and forensic studies. This study aimed to use whole mtDNA sequencing to investigate 47 Chongqing Han populations in southwest China and the diversity in the mtGenome reference data. Material/Methods The mtDNA of 47 Chongqing Han populations was generated using the Ion Torrent Personal Genome Machine (PGM) system. The extent of the effects of the mtDNA on the subpopulations was investigated and compared with six other populations from published studies. Pairwise fixation index (FST), a measure of population differentiation due to genetic structure, were calculated. Analysis of molecular variance (AMOVA) was performed, and 1257 hypervariable region data sets were added to the principal component analysis (PCA). Results The whole mtDNA sequencing data of 47 southwest Chinese Han populations were successfully recovered. Expanding the sequencing rage increased the discrimination power of mtDNA from three-times to 25-times based on different populations. The subpopulation effects showed 20 times the differences in match probability when compared with south China regions. Conclusions Whole mtDNA sequencing distinguished between individuals from 47 Chongqing Han populations in southwest China and has potential applications that include high-quality forensic identification.
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Affiliation(s)
- Lan Yao
- College of Basic Medicine, Chongqing Medical University, Chongqing, China (mainland)
| | - Zhen Xu
- Key Laboratory of Forensic Genetics, Institute of Forensic Science, Ministry of Public Security, Beijing, China (mainland)
| | - Lihua Wan
- College of Basic Medicine, Chongqing Medical University, Chongqing, China (mainland)
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18
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Zhang J, Tao R, Zhong J, Sun D, Qiao L, Shan S, Yang Z, Zhang J, Zhang S, Li C. Genetic polymorphisms of 27 Y-STR loci in the Dezhou Han population from Shandong province, Eastern China. Forensic Sci Int Genet 2019; 39:e26-e28. [DOI: 10.1016/j.fsigen.2018.11.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 11/26/2018] [Accepted: 11/26/2018] [Indexed: 11/30/2022]
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19
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Chiang CWK, Mangul S, Robles C, Sankararaman S. A Comprehensive Map of Genetic Variation in the World's Largest Ethnic Group-Han Chinese. Mol Biol Evol 2018; 35:2736-2750. [PMID: 30169787 PMCID: PMC6693441 DOI: 10.1093/molbev/msy170] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
As are most non-European populations, the Han Chinese are relatively understudied in population and medical genetics studies. From low-coverage whole-genome sequencing of 11,670 Han Chinese women we present a catalog of 25,057,223 variants, including 548,401 novel variants that are seen at least 10 times in our data set. Individuals from this data set came from 24 out of 33 administrative divisions across China (including 19 provinces, 4 municipalities, and 1 autonomous region), thus allowing us to study population structure, genetic ancestry, and local adaptation in Han Chinese. We identified previously unrecognized population structure along the East-West axis of China, demonstrated a general pattern of isolation-by-distance among Han Chinese, and reported unique regional signals of admixture, such as European influences among the Northwestern provinces of China. Furthermore, we identified a number of highly differentiated, putatively adaptive, loci (e.g., MTHFR, ADH7, and FADS, among others) that may be driven by immune response, climate, and diet in the Han Chinese. Finally, we have made available allele frequency estimates stratified by administrative divisions across China in the Geography of Genetic Variant browser for the broader community. By leveraging the largest currently available genetic data set for Han Chinese, we have gained insights into the history and population structure of the world's largest ethnic group.
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Affiliation(s)
- Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA
| | - Serghei Mangul
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA
- Institute for Quantitative and Computational Bioscience, University of California Los Angeles, Los Angeles, CA
| | - Christopher Robles
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Sriram Sankararaman
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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20
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Zhang C, Ni P, Ahmad HI, Gemingguli M, Baizilaitibei A, Gulibaheti D, Fang Y, Wang H, Asif AR, Xiao C, Chen J, Ma Y, Liu X, Du X, Zhao S. Detecting the Population Structure and Scanning for Signatures of Selection in Horses ( Equus caballus) From Whole-Genome Sequencing Data. Evol Bioinform Online 2018; 14:1176934318775106. [PMID: 29899660 PMCID: PMC5990873 DOI: 10.1177/1176934318775106] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 04/14/2018] [Indexed: 12/18/2022] Open
Abstract
Animal domestication gives rise to gradual changes at the genomic level through selection in populations. Selective sweeps have been traced in the genomes of many animal species, including humans, cattle, and dogs. However, little is known regarding positional candidate genes and genomic regions that exhibit signatures of selection in domestic horses. In addition, an understanding of the genetic processes underlying horse domestication, especially the origin of Chinese native populations, is still lacking. In our study, we generated whole genome sequences from 4 Chinese native horses and combined them with 48 publicly available full genome sequences, from which 15 341 213 high-quality unique single-nucleotide polymorphism variants were identified. Kazakh and Lichuan horses are 2 typical Asian native breeds that were formed in Kazakh or Northwest China and South China, respectively. We detected 1390 loss-of-function (LoF) variants in protein-coding genes, and gene ontology (GO) enrichment analysis revealed that some LoF-affected genes were overrepresented in GO terms related to the immune response. Bayesian clustering, distance analysis, and principal component analysis demonstrated that the population structure of these breeds largely reflected weak geographic patterns. Kazakh and Lichuan horses were assigned to the same lineage with other Asian native breeds, in agreement with previous studies on the genetic origin of Chinese domestic horses. We applied the composite likelihood ratio method to scan for genomic regions showing signals of recent selection in the horse genome. A total of 1052 genomic windows of 10 kB, corresponding to 933 distinct core regions, significantly exceeded neutral simulations. The GO enrichment analysis revealed that the genes under selective sweeps were overrepresented with GO terms, including “negative regulation of canonical Wnt signaling pathway,” “muscle contraction,” and “axon guidance.” Frequent exercise training in domestic horses may have resulted in changes in the expression of genes related to metabolism, muscle structure, and the nervous system.
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Affiliation(s)
- Cheng Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Pan Ni
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Hafiz Ishfaq Ahmad
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - M Gemingguli
- College of Animal Science, Tarim University, Alar, China
| | | | - D Gulibaheti
- College of Animal Science, Tarim University, Alar, China
| | - Yaping Fang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Haiyang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Akhtar Rasool Asif
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Changyi Xiao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jianhai Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Yunlong Ma
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Xiangdong Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Xiaoyong Du
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
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21
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Huang YZ, Pamjav H, Flegontov P, Stenzl V, Wen SQ, Tong XZ, Wang CC, Wang LX, Wei LH, Gao JY, Jin L, Li H. Dispersals of the Siberian Y-chromosome haplogroup Q in Eurasia. Mol Genet Genomics 2018; 293:107-117. [PMID: 28884289 PMCID: PMC5846874 DOI: 10.1007/s00438-017-1363-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 08/27/2017] [Indexed: 12/17/2022]
Abstract
The human Y-chromosome has proven to be a powerful tool for tracing the paternal history of human populations and genealogical ancestors. The human Y-chromosome haplogroup Q is the most frequent haplogroup in the Americas. Previous studies have traced the origin of haplogroup Q to the region around Central Asia and Southern Siberia. Although the diversity of haplogroup Q in the Americas has been studied in detail, investigations on the diffusion of haplogroup Q in Eurasia and Africa are still limited. In this study, we collected 39 samples from China and Russia, investigated 432 samples from previous studies of haplogroup Q, and analyzed the single nucleotide polymorphism (SNP) subclades Q1a1a1-M120, Q1a2a1-L54, Q1a1b-M25, Q1a2-M346, Q1a2a1a2-L804, Q1a2b2-F1161, Q1b1a-M378, and Q1b1a1-L245. Through NETWORK and BATWING analyses, we found that the subclades of haplogroup Q continued to disperse from Central Asia and Southern Siberia during the past 10,000 years. Apart from its migration through the Beringia to the Americas, haplogroup Q also moved from Asia to the south and to the west during the Neolithic period, and subsequently to the whole of Eurasia and part of Africa.
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Affiliation(s)
- Yun-Zhi Huang
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Horolma Pamjav
- National Center of Forensic Experts and Research, Budapest, 1087, Hungary
| | - Pavel Flegontov
- Department of Biology and Ecology, Faculty of Science, University of Ostrava, 71000, Ostrava, Czech Republic
- A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, 127051, Russian Federation
| | - Vlastimil Stenzl
- Institute of Criminalistics, Police of the Czech Republic, 17089, Prague, Czech Republic
| | - Shao-Qing Wen
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Xin-Zhu Tong
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Xiamen University, Xiamen, 361005, China
| | - Ling-Xiang Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Lan-Hai Wei
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China
- Institut National des Langues et Civilisations Orientales, 75013, Paris, France
| | - Jing-Yi Gao
- Faculty of Arts and Humanities, University of Tartu, 50090, Tartu, Estonia
- Faculty of Central European Studies, Beijing International Studies University, Beijing, 100024, China
| | - Li Jin
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Hui Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
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22
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Wang M, Wang Z, Zhang Y, He G, Liu J, Hou Y. Forensic characteristics and phylogenetic analysis of two Han populations from the southern coastal regions of China using 27 Y-STR loci. Forensic Sci Int Genet 2017; 31:e17-e23. [DOI: 10.1016/j.fsigen.2017.10.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Revised: 10/23/2017] [Accepted: 10/24/2017] [Indexed: 11/30/2022]
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23
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Nothnagel M, Fan G, Guo F, He Y, Hou Y, Hu S, Huang J, Jiang X, Kim W, Kim K, Li C, Li H, Li L, Li S, Li Z, Liang W, Liu C, Lu D, Luo H, Nie S, Shi M, Sun H, Tang J, Wang L, Wang CC, Wang D, Wen SQ, Wu H, Wu W, Xing J, Yan J, Yan S, Yao H, Ye Y, Yun L, Zeng Z, Zha L, Zhang S, Zheng X, Willuweit S, Roewer L. Revisiting the male genetic landscape of China: a multi-center study of almost 38,000 Y-STR haplotypes. Hum Genet 2017; 136:485-497. [PMID: 28138773 DOI: 10.1007/s00439-017-1759-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 01/13/2017] [Indexed: 02/05/2023]
Abstract
China has repeatedly been the subject of genetic studies to elucidate its prehistoric and historic demography. While some studies reported a genetic distinction between Northern and Southern Han Chinese, others showed a more clinal picture of small differences within China. Here, we investigated the distribution of Y chromosome variation along administrative as well as ethnic divisions in the mainland territory of the People's Republic of China, including 28 administrative regions and 19 recognized Chinese nationalities, to assess the impact of recent demographic processes. To this end, we analyzed 37,994 Y chromosomal 17-marker haplotype profiles from the YHRD database with respect to forensic diversity measures and genetic distance between groups defined by administrative boundaries and ethnic origin. We observed high diversity throughout all Chinese provinces and ethnicities. Some ethnicities, including most prominently Kazakhs and Tibetans, showed significant genetic differentiation from the Han and other groups. However, differences between provinces were, except for those located on the Tibetan plateau, less pronounced. This discrepancy is explicable by the sizeable presence of Han speakers, who showed high genetic homogeneity all across China, in nearly all studied provinces. Furthermore, we observed a continuous genetic North-South gradient in the Han, confirming previous reports of a clinal distribution of Y chromosome variation and being in notable concordance with the previously observed spatial distribution of autosomal variation. Our findings shed light on the demographic changes in China accrued by a fast-growing and increasingly mobile population.
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Affiliation(s)
- Michael Nothnagel
- Department of Statistical Genetics and Bioinformatics, Cologne Center for Genomics (CCG), University of Cologne, Weyertal 115b, 50931, Cologne, Germany.
| | - Guangyao Fan
- Department of Public Security Technology, The Center for Forensic Science Research, Railway Police College, Zhengzhou, 450053, People's Republic of China
| | - Fei Guo
- Department of Forensic Medicine, National Police University of China, Shenyang, 110854, People's Republic of China
| | - Yongfeng He
- Department of Criminal Investigation, Shaanxi Provincial Public Security Bureau, Xi'an, 710016, People's Republic of China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Shengping Hu
- Molecular Biology and Forensic Genetics Laboratory, Shantou University Medical College, Shantou, People's Republic of China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Beijing Road, 9th, Guiyang, 550004, People's Republic of China
| | - Xianhua Jiang
- Liaoning Criminal and Science Technology Research Institute, Shenyang, 110032, People's Republic of China
| | - Wook Kim
- Department of Biological Sciences, Dankook University, Cheonan, 330-714, Republic of Korea
| | - Kicheol Kim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Chengtao Li
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Sciences, Ministry of Justice, P.R. China, Shanghai, 200063, People's Republic of China
| | - Hui Li
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, People's Republic of China
| | - Liming Li
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, People's Republic of China
| | - Shilin Li
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, People's Republic of China
| | - Zhao Li
- Department of Criminal Investigation, Hebei Provincial Public Security Bureau, Shijiazhuang City, 050000, People's Republic of China
| | - Weibo Liang
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Chao Liu
- Guangzhou Forensic Science Institute, Guangzhou, 510030, People's Republic of China
| | - Di Lu
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, People's Republic of China
| | - Haibo Luo
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Shengjie Nie
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, People's Republic of China
| | - Meisen Shi
- Center of Cooperative Innovation for Judicial Civilization, Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Ministry of Education, Beijing, 100088, People's Republic of China
| | - Hongyu Sun
- Department of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510089, People's Republic of China
| | - Jianpin Tang
- Department of Forensic Medicine, Guangdong Medical University, Dongguan, 523808, People's Republic of China
| | - Lei Wang
- Department of Forensic Sciences, Police Station of Zhengzhou, Zhengzhou, Henan, 450008, People's Republic of China
| | - Chuan-Chao Wang
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany
| | - Dan Wang
- Institute of Forensic Medicine and Laboratory Medicine, Jining Medical University, Jining, Shandong, People's Republic of China
| | - Shao-Qing Wen
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, People's Republic of China
| | - Hongyan Wu
- Xinxiang Medical University School of Basic Medical, Xinxiang, Henan, 453003, People's Republic of China
| | - Weiwei Wu
- Institute of Forensic Science, Zhejiang Provincial Public Security Bureau, Hangzhou, 310009, People's Republic of China
| | - Jiaxin Xing
- School of Forensic Medicine, China Medical University, Shenyang, People's Republic of China
| | - Jiangwei Yan
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, People's Republic of China
| | - Shi Yan
- MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, People's Republic of China
| | - Hongbing Yao
- Key Laboratory of Evidence Science of Gansu Province, Gansu Institute of Political Science and Law, Lanzhou, 730070, People's Republic of China
| | - Yi Ye
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Libing Yun
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Zhaoshu Zeng
- School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450052, People's Republic of China
| | - Lagabaiyila Zha
- Forensic Science Department, School of Basic Medical Sciences, Central South University, Changsha, 410013, People's Republic of China
| | - Suhua Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Sciences, Ministry of Justice, P.R. China, Shanghai, 200063, People's Republic of China
| | - Xiufen Zheng
- Department of Pathology, Department of Surgery, Department of Oncology, University of Western Ontario, Lawson Health Research Institute, London, Canada
| | - Sascha Willuweit
- Department of Forensic Genetics, Institute of Legal Medicine and Forensic Sciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lutz Roewer
- Department of Forensic Genetics, Institute of Legal Medicine and Forensic Sciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
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25
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Xu FL, Yao J, Ding M, Shi ZS, Wu X, Zhang JJ, Wang BJ. Characterization of mitochondrial DNA polymorphisms in the Han population in Liaoning Province, Northeast China. Mitochondrial DNA A DNA Mapp Seq Anal 2017; 29:250-255. [PMID: 28093929 DOI: 10.1080/24701394.2016.1275597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This study characterized the genetic variations of mitochondrial DNA (mtDNA) to elucidate the maternal genetic structure of Liaoning Han Chinese. A total of 317 blood samples of unrelated individuals were collected for analysis in Liaoning Province. The mtDNA samples were analyzed using two distinct methods: sequencing of the hypervariable sequences I and II (HVSI and HVSII), and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis of the coding region. The results indicated a high gene diversity value (0.9997 ± 0.0003), a high polymorphism information content (0.99668) and a random match probability (0.00332). These samples were classified into 305 haplotypes, with 9 shared haplotypes. The most common haplogroup was D4 (12.93%). The principal component analysis map, the phylogenetic tree map, and the genetic distance matrix all indicated that the genetic distance of the Liaoning Han population from the Tibetan group was distant, whereas that from the Miao group was relatively close.
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Affiliation(s)
- Feng-Ling Xu
- a School of Forensic Medicine , China Medical University , Shenyang , China
| | - Jun Yao
- a School of Forensic Medicine , China Medical University , Shenyang , China
| | - Mei Ding
- a School of Forensic Medicine , China Medical University , Shenyang , China
| | - Zhang-Sen Shi
- a School of Forensic Medicine , China Medical University , Shenyang , China
| | - Xue Wu
- a School of Forensic Medicine , China Medical University , Shenyang , China
| | - Jing-Jing Zhang
- a School of Forensic Medicine , China Medical University , Shenyang , China
| | - Bao-Jie Wang
- a School of Forensic Medicine , China Medical University , Shenyang , China
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Zhang Y, Li J, Zhao Y, Wu X, Li H, Yao L, Zhu H, Zhou H. Genetic diversity of two Neolithic populations provides evidence of farming expansions in North China. J Hum Genet 2016; 62:199-204. [PMID: 27581844 DOI: 10.1038/jhg.2016.107] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 07/23/2016] [Accepted: 07/26/2016] [Indexed: 11/09/2022]
Abstract
The West Liao River Valley and the Yellow River Valley are recognized Neolithic farming centers in North China. The population dynamics between these two centers have significantly contributed to the present-day genetic patterns and the agricultural advances of North China. To understand the Neolithic farming expansions between the West Liao River Valley and the Yellow River Valley, we analyzed mitochondrial DNA (mtDNA) and the Y chromosome of 48 individuals from two archeological sites, Jiangjialiang (>3000 BC) and Sanguan (~1500 BC). These two sites are situated between the two farming centers and experienced a subsistence shift from hunting to farming. We did not find a significant difference in the mtDNA, but their genetic variations in the Y chromosome were different. Individuals from the Jiangjialiang belonged to two Y haplogroups, N1 (not N1a or N1c) and N1c. The individuals from the Sanguan are Y haplogroup O3. Two stages of migration are supported. Populations from the West Liao River Valley spread south at about 3000 BC, and a second northward expansion from the Yellow River Valley occurred later (3000-1500 BC).
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Affiliation(s)
- Ye Zhang
- Laboratory of Ancient DNA, School of Life Science, Jilin University, Changchun, China
| | - Jiawei Li
- Laboratory of Ancient DNA, School of Life Science, Jilin University, Changchun, China
| | - Yongbin Zhao
- Laboratory of Ancient DNA, School of Life Science, Jilin University, Changchun, China.,Laboratory of Ancient DNA, College of Life Science, Jilin Normal University, Siping, China
| | - Xiyan Wu
- Laboratory of Ancient DNA, School of Life Science, Jilin University, Changchun, China
| | - Hongjie Li
- Laboratory of Ancient DNA, School of Life Science, Jilin University, Changchun, China.,Laboratory of Anthropology, Research Center for Chinese Frontier Archaeology, Jilin University, Changchun, China
| | - Lu Yao
- Department of Anthropology, Committee on Evolutionary Biology, University of Chicago, Chicago, USA
| | - Hong Zhu
- Laboratory of Anthropology, Research Center for Chinese Frontier Archaeology, Jilin University, Changchun, China
| | - Hui Zhou
- Laboratory of Ancient DNA, School of Life Science, Jilin University, Changchun, China.,Laboratory of Anthropology, Research Center for Chinese Frontier Archaeology, Jilin University, Changchun, China
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Di D, Sanchez-Mazas A, Currat M. Computer simulation of human leukocyte antigen genes supports two main routes of colonization by human populations in East Asia. BMC Evol Biol 2015; 15:240. [PMID: 26530905 PMCID: PMC4632674 DOI: 10.1186/s12862-015-0512-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 10/19/2015] [Indexed: 11/10/2022] Open
Abstract
Background Recent genetic studies have suggested that the colonization of East Asia by modern humans was more complex than a single origin from the South, and that a genetic contribution via a Northern route was probably quite substantial. Results Here we use a spatially-explicit computer simulation approach to investigate the human migration hypotheses of this region based on one-route or two-route models. We test the likelihood of each scenario by using Human Leukocyte Antigen (HLA) − A, −B, and − DRB1 genetic data of East Asian populations, with both selective and demographic parameters considered. The posterior distribution of each parameter is estimated by an Approximate Bayesian Computation (ABC) approach. Conclusions Our results strongly support a model with two main routes of colonization of East Asia on both sides of the Himalayas, with distinct demographic histories in Northern and Southern populations, characterized by more isolation in the South. In East Asia, gene flow between populations originating from the two routes probably existed until a remote prehistoric period, explaining the continuous pattern of genetic variation currently observed along the latitude. A significant although dissimilar level of balancing selection acting on the three HLA loci is detected, but its effect on the local genetic patterns appears to be minor compared to those of past demographic events. Electronic supplementary material The online version of this article (doi:10.1186/s12862-015-0512-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Da Di
- Department of Genetics and Evolution - Anthropology Unit, Laboratory of Anthropology, Genetics and Peopling history (AGP lab), University of Geneva, 12 rue Gustave-Revilliod, Geneva, CH-1211, Geneva 4, Switzerland.
| | - Alicia Sanchez-Mazas
- Department of Genetics and Evolution - Anthropology Unit, Laboratory of Anthropology, Genetics and Peopling history (AGP lab), University of Geneva, 12 rue Gustave-Revilliod, Geneva, CH-1211, Geneva 4, Switzerland. .,Institute of Genetics and Genomics in Geneva (IGE3), University of Geneva Medical Centre (CMU), 1 rue Michel-Servet, Geneva, CH-1211, Geneva 4, Switzerland.
| | - Mathias Currat
- Department of Genetics and Evolution - Anthropology Unit, Laboratory of Anthropology, Genetics and Peopling history (AGP lab), University of Geneva, 12 rue Gustave-Revilliod, Geneva, CH-1211, Geneva 4, Switzerland.
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