基于祖先重组图重建古今人类群体遗传系谱的研究进展及展望

doi:10.16288/j.yczz.24-150

遗传 ›› 2024, Vol. 46 ›› Issue (10): 849-859.doi: 10.16288/j.yczz.24-150

基于祖先重组图重建古今人类群体遗传系谱的研究进展及展望

杨青鑫¹^,²^,³(), 王萌鸽²^,³(), 刘超⁴(), 袁慧军²(), 何光林²^,³()

1.昆明医科大学法医学院，昆明 650500
2.四川大学华西医院罕见病研究院，成都 610000
3.四川大学考古科学中心，成都 610000
4.广东省毒品实验技术中心(国家毒品实验室广东分中心)，广州 510000

收稿日期:2024-05-27 修回日期:2024-08-21 出版日期:2024-08-30 发布日期:2024-08-30
通讯作者: 何光林，副研究员，研究方向：古基因组学、法医基因学、演化基因组学研究。E-mail: Guanglinhescu@163.com;
刘超，中国工程院院士，研究方向：法医遗传学、法医病理学、法医微生物学及法医毒理相关研究。E-mail: liuchaogzf@163.com;
袁慧军，教授，研究方向：聋病基因诊断与基因治疗研究；罕见病精准诊治与智能决策支持。E-mail: yuanhj301@163.com
作者简介:杨青鑫，硕士研究生，专业方向：法医学。E-mail: fayiyqx@163.com
王萌鸽，助理研究员，研究方向：古基因组学、法医基因学、演化基因组学研究。E-mail: menggewang2021@163.com
第一联系人：
杨青鑫和王萌鸽并列第一作者。
基金资助:
国家自然科学基金项目(82202078);国家社科基金重大项目(23&ZD203);四川大学考古科学中心项目(23SASA01);四川省科技计划项目(2024NSFSC1518)

Advancements and prospects in reconstructing the genetic genealogies of ancient and modern human populations using ancestral recombination graphs

Qingxin Yang¹^,²^,³(), Mengge Wang²^,³(), Chao Liu⁴(), Huijun Yuan²(), Guanglin He²^,³()

1. School of Forensic Medicine, Kunming Medical University, Kunming 650500, China
2. Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
3. Center for Archaeological Science, Sichuan University, Chengdu 610000, China
4. Anti-Drug Technology Center of Guangdong Province, Guangzhou 510000, China

Received:2024-05-27 Revised:2024-08-21 Published:2024-08-30 Online:2024-08-30
Supported by:
National Natural Science Foundation of China(82202078);Major Project of the National Social Science Foundation of China(23&ZD203);Center for Archaeological Science of Sichuan University(23SASA01);Sichuan Science and Technology Program(2024NSFSC1518)

摘要/Abstract

摘要：

随着古今人群大规模基因组资源的公布、计算生物学工具的革新及大数据挖掘相关算力的提升，基因组学领域正在经历一场革命性的变化。这些进步和变化不仅显著加深了人们对人类起源、迁徙及混合等复杂演化历程的理解，而且也揭示了这些过程对人类疾病健康状态的影响，加速了人们对人类健康和疾病遗传学基础的研究，还为挖掘人类基因组中记载的群体演化历史及疾病遗传学基础的演化轨迹提供了新的途径。祖先重组图(ancestral recombination graph, ARG)技术，通过分析全基因组中不同区域的重组事件和共祖特征，重建了基因组片段之间的演化关系。ARG 记录了所研究基因组序列分歧以来的所有共祖和重组事件，并指明了每个基因组位置的完整系谱，是基因组分析的理想数据结构。本文综述了ARG技术的理论基础及其研究进展，并探讨了其在法医基因组学、群体遗传学、演化医学、医学基因组学等多个学科领域的转化应用和发展前景，以期推动该技术在基因组学研究中的应用，进一步深化对人类基因组的理解。

关键词: 祖先重组图, 遗传系谱, 演化基因组学

Abstract:

With the release of large-scale genomic resources from ancient and modern populations, advancements in computational biology tools, and the enhancement of data mining capabilities, the field of genomics is undergoing a revolutionary transformation. These advancements and changes have not only significantly deepened our understanding of the complex evolutionary processes of human origins, migration, and admixture but have also unveiled the impact of these processes on human health and disease. They have accelerated research into the genetic basis of human health and disease and provided new avenues for uncovering the evolutionary trajectories recorded in the human genome related to population history and disease genetics. The ancestral recombination graph (ARG) reconstructs the evolutionary relationships between genomic segments by analyzing recombination events and coalescence patterns across different regions of the genome. An ARG provides a record of all coalescence and recombination events since the divergence of the sequences under study and specifies a complete genealogy at each genomic position, which is the ideal data structure for genomic analysis. Here, we review the theoretical foundations and research advancements of the ARG, and explore its translational applications and future prospects across various disciplines, including forensic genomics, population genetics, evolutionary medicine, and medical genomics. Our goal is to promote the application of this technique in genomic research, thereby deepening our understanding of the human genome.

Key words: ancestral recombination graphs, genetic genealogies, evolutionary genomics

杨青鑫, 王萌鸽, 刘超, 袁慧军, 何光林. 基于祖先重组图重建古今人类群体遗传系谱的研究进展及展望[J]. 遗传, 2024, 46(10): 849-859.

Qingxin Yang, Mengge Wang, Chao Liu, Huijun Yuan, Guanglin He. Advancements and prospects in reconstructing the genetic genealogies of ancient and modern human populations using ancestral recombination graphs[J]. Hereditas(Beijing), 2024, 46(10): 849-859.

图/表 3

图1

图2

表1

参考文献 75

[1]	Griffiths RC, Marjoram P. Ancestral inference from samples of DNA sequences with recombination. J Comput Biol, 1996, 3(4): 479-502. pmid: 9018600
[2]	Kingman JFC. The coalescent. Stochastic Processes Their Appl, 1982, 13(3): 235-248.
[3]	Hudson RR. Properties of a neutral allele model with intragenic recombination. Theor Popul Biol, 1983, 23(2): 183-201. pmid: 6612631
[4]	Tajima F. Evolutionary relationship of DNA sequences in finite populations. Genetics, 1983, 105(2): 437-460. doi: 10.1093/genetics/105.2.437 pmid: 6628982
[5]	Griffiths RC. The two-locus ancestral graph. Lect Notes-Monogr Ser, 1991, 18: 100-117.
[6]	Brandt DYC, Huber CD, Chiang CWK, Ortega-Del Vecchyo D. The promise of inferring the past using the ancestral recombination graph. Genome Biol Evol, 2024, 16(2): evae005.
[7]	Mathieson I, Scally A. What is ancestry? PLoS Genet, 2020, 16(3): e1008624.
[8]	Rosenberg NA, Nordborg M. Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nat Rev Genet, 2002, 3(5): 380-390. doi: 10.1038/nrg795 pmid: 11988763
[9]	Kennett D. Using genetic genealogy databases in missing persons cases and to develop suspect leads in violent crimes. Forensic Sci Int, 2019, 301: 107-117. doi: S0379-0738(19)30201-4 pmid: 31153988
[10]	Wang MG, Chen HY, Luo LT, Huang YG, Duan SH, Yuan HJ, Tang RK, Liu C, He GL. Forensic investigative genetic genealogy: expanding pedigree tracing and genetic inquiry in the genomic era. J Genet Genomics, 2024, doi: 10.1016/j.jgg.2024.06.016.
[11]	Foster EA, Jobling MA, Taylor PG, Donnelly P, de Knijff P, Mieremet R, Zerjal T, Tyler-Smith C. Jefferson fathered slave's last child. Nature, 1998, 396(6706): 27-28.
[12]	Wagner JK, Cooper JD, Sterling R, Royal CD. Tilting at windmills no longer: a data-driven discussion of DTC DNA ancestry tests. Genet Med, 2012, 14(6): 586-593. doi: 10.1038/gim.2011.77 pmid: 22382803
[13]	King TE, Jobling MA. What's in a name? Y chromosomes, surnames and the genetic genealogy revolution. Trends Genet, 2009, 25(8): 351-360. doi: 10.1016/j.tig.2009.06.003 pmid: 19665817
[14]	Khan R, Mittelman D. Consumer genomics will change your life, whether you get tested or not. Genome Biol, 2018, 19(1): 120. doi: 10.1186/s13059-018-1506-1 pmid: 30124172
[15]	Erlich Y, Shor T, Pe'er I, Carmi S. Identity inference of genomic data using long-range familial searches. Science, 2018, 362(6415): 690-694. doi: 10.1126/science.aau4832 pmid: 30309907
[16]	Ram N, Guerrini CJ, McGuire AL. Genealogy databases and the future of criminal investigation. Science, 2018, 360(6393): 1078-1079. doi: 10.1126/science.aau1083 pmid: 29880677
[17]	Lewanski AL, Grundler MC, Bradburd GS. The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics. PLoS Genet, 2024, 20(1): e1011110.
[18]	Baumdicker F, Bisschop G, Goldstein D, Gower G, Ragsdale AP, Tsambos G, Zhu S, Eldon B, Ellerman EC, Galloway JG, Gladstein AL, Gorjanc G, Guo B, Jeffery B, Kretzschumar WW, Lohse K, Matschiner M, Nelson D, Pope NS, Quinto-Cortés CD, Rodrigues MF, Saunack K, Sellinger T, Thornton K, van Kemenade H, Wohns AW, Wong Y, Gravel S, Kern AD, Koskela J, Ralph PL, Kelleher J. Efficient ancestry and mutation simulation with msprime 1.0. Genetics, 2022, 220(3): iyab229.
[19]	Gao F, Li HP. Application of computer simulators in population genetics. Hereditas(Beijing), 2016, 38(8): 707-717. doi: 10.16288/j.yczz.16-100 pmid: 27531609
[20]	Hein J. Reconstructing evolution of sequences subject to recombination using parsimony. Math Biosci, 1990, 98(2):185-200. pmid: 2134501
[21]	Minichiello MJ, Durbin R. Mapping trait loci by use of inferred ancestral recombination graphs. Am J Hum Genet, 2006, 79(5): 910-922. pmid: 17033967
[22]	Rasmussen MD, Hubisz MJ, Gronau I, Siepel A. Genome-wide inference of ancestral recombination graphs. PLoS Genet, 2014, 10(5): e1004342.
[23]	Mirzaei S, Wu YF. RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination. Bioinformatics, 2017, 33(7): 1021-1030. doi: 10.1093/bioinformatics/btw735 pmid: 28065901
[24]	Heine K, Beskos A, Jasra A, Balding D, De Iorio M. Bridging trees for posterior inference on ancestral recombination graphs. Proc Math Phys Eng Sci, 2018, 474(2220): 20180568.
[25]	Speidel L, Forest M, Shi SN, Myers SR. A method for genome-wide genealogy estimation for thousands of samples. Nat Genet, 2019, 51(9): 1321-1329. doi: 10.1038/s41588-019-0484-x pmid: 31477933
[26]	Hubisz MJ, Williams AL, Siepel A. Mapping gene flow between ancient hominins through demography- aware inference of the ancestral recombination graph. PLoS Genet, 2020, 16(8): e1008895.
[27]	Schaefer NK, Shapiro B, Green RE. An ancestral recombination graph of human, Neanderthal, and Denisovan genomes. Sci Adv, 2021, 7(29): eabc0776.
[28]	Ignatieva A, Lyngsø RB, Jenkins PA, Hein J. KwARG: parsimonious reconstruction of ancestral recombination graphs with recurrent mutation. Bioinformatics, 2021, 37(19): 3277-3284. doi: 10.1093/bioinformatics/btab351 pmid: 33970217
[29]	Kelleher J, Wong Y, Wohns AW, Fadil C, Albers PK, McVean G. Inferring whole-genome histories in large population datasets. Nat Genet, 2019, 51(9): 1330-1338. doi: 10.1038/s41588-019-0483-y pmid: 31477934
[30]	Brandt DYC, Wei XZ, Deng Y, Vaughn AH, Nielsen R. Evaluation of methods for estimating coalescence times using ancestral recombination graphs. Genetics, 2022, 221(1): iyac044.
[31]	Mahmoudi A, Koskela J, Kelleher J, Chan YB, Balding D. Bayesian inference of ancestral recombination graphs. PLoS Comput Biol, 2022, 18(3): e1009960.
[32]	McVean GAT, Cardin NJ. Approximating the coalescent with recombination. Philos Trans R Soc Lond B Biol Sci, 2005, 360(1459): 1387-1393.
[33]	Marjoram P, Wall JD. Fast "coalescent" simulation. BMC Genet, 2006, 7: 16. pmid: 16539698
[34]	Li N, Stephens M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics, 2003, 165(4): 2213-2233. doi: 10.1093/genetics/165.4.2213 pmid: 14704198
[35]	Ralph P, Thornton K, Kelleher J. Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes. Genetics, 2020, 215(3): 779-797. doi: 10.1534/genetics.120.303253 pmid: 32357960
[36]	Wohns AW, Wong Y, Jeffery B, Akbari A, Mallick S, Pinhasi R, Patterson N, Reich D, Kelleher J, McVean G. A unified genealogy of modern and ancient genomes. Science, 2022, 375(6583): eabi8264.
[37]	Zhang BC, Biddanda A, Gunnarsson ÁF, Cooper F, Palamara PF. Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits. Nat Genet, 2023, 55(5): 768-776. doi: 10.1038/s41588-023-01379-x pmid: 37127670
[38]	Speidel L, Cassidy L, Davies RW, Hellenthal G, Skoglund P, Myers SR. Inferring population histories for ancient genomes using genome-wide genealogies. Mol Biol Evol, 2021, 38(9): 3497-3511. doi: 10.1093/molbev/msab174 pmid: 34129037
[39]	Palamara PF, Terhorst J, Song YS, Price AL. High- throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nat Genet, 2018, 50(9): 1311-1317.
[40]	Bick AG, Metcalf GA, Mayo KR, Lichtenstein L, Rura S, Carroll RJ, Musick A, Linder JE, Jordan IK, Nagar SD. Genomic data in the All of Us Research Program. Nature, 2024, 627(8003): 340-346.
[41]	Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, Pitsillides AN, LeFaive J, Lee SB, Tian XW, Browning BL, Das S, Emde AK, Clarke WE, Loesch DP, Shetty AC, Blackwell TW, Smith AV, Wong Q, Liu XM, Conomos MP, Bobo DM, Aguet F, Albert C, Alonso A, Ardlie KG, Arking DE, Aslibekyan S, Auer PL, Barnard J, Barr RG, Barwick L, Becker LC, Beer RL, Benjamin EJ, Bielak LF, Blangero J, Boehnke M, Bowden DW, Brody JA, Burchard EG, Cade BE, Casella JF, Chalazan B, Chasman DI, Chen YDI, Cho MH, Choi SH, Chung MK, Clish CB, Correa A, Curran JE, Custer B, Darbar D, Daya M, de Andrade M, DeMeo DL, Dutcher SK, Ellinor PT, Emery LS, Eng C, Fatkin D, Fingerlin T, Forer L, Fornage M, Franceschini N, Fuchsberger C, Fullerton SM, Germer S, Gladwin MT, Gottlieb DJ, Guo XQ, Hall ME, He J, Heard-Costa NL, Heckbert SR, Irvin MR, Johnsen JM, Johnson AD, Kaplan R, Kardia SLR, Kelly T, Kelly S, Kenny EE, Kiel DP, Klemmer R, Konkle BA, Kooperberg C, Köttgen A, Lange LA, Lasky-Su J, Levy D, Lin XH, Lin KH, Liu CY, Loos RJF, Garman L, Gerszten R, Lubitz SA, Lunetta KL, Mak ACY, Manichaikul A, Manning AK, Mathias RA, McManus DD, McGarvey ST, Meigs JB, Meyers DA, Mikulla JL, Minear MA, Mitchell BD, Mohanty S, Montasser ME, Montgomery C, Morrison AC, Murabito JM, Natale A, Natarajan P, Nelson SC, North KE, O'Connell JR, Palmer ND, Pankratz N, Peloso GM, Peyser PA, Pleiness J, Post WS, Psaty BM, Rao DC, Redline S, Reiner AP, Roden D, Rotter JI, Ruczinski I, Sarnowski C, Schoenherr S, Schwartz DA, Seo JS, Seshadri S, Sheehan VA, Sheu WH, Shoemaker MB, Smith NL, Smith JA, Sotoodehnia N, Stilp AM, Tang WH, Taylor KD, Telen M, Thornton TA, Tracy RP, Van Den Berg DJ, Vasan RS, Viaud-Martinez KA, Vrieze S, Weeks DE, Weir BS, Weiss ST, Weng LC, Willer CJ, Zhang YZ, Zhao XT, Arnett DK, Ashley-Koch AE, Barnes KC, Boerwinkle E, Gabriel S, Gibbs R, Rice KM, Rich SS, Silverman EK, Qasba P, Gan WN, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Papanicolaou GJ, Nickerson DA, Browning SR, Zody MC, Zöllner S, Wilson JG, Cupples LA, Laurie CC, Jaquish CE, Hernandez RD, O'Connor TD, Abecasis GR. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature, 2021, 590(7845): 290-299.
[42]	Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G, Hardarson MT, Oddsson A, Jensson BO, Kristmundsdottir S, Sigurpalsdottir BD, Stefansson OA, Beyter D, Holley G, Tragante V, Gylfason A, Olason PI, Zink F, Asgeirsdottir M, Sverrisson ST, Sigurdsson B, Gudjonsson SA, Sigurdsson GT, Halldorsson GH, Sveinbjornsson G, Norland K, Styrkarsdottir U, Magnusdottir DN, Snorradottir S, Kristinsson K, Sobech E, Jonsson H, Geirsson AJ, Olafsson I, Jonsson P, Pedersen OB, Erikstrup C, Brunak S, Ostrowski SR, DBDS Genetic Consortium, Thorleifsson G, Jonsson F, Melsted P, Jonsdottir I, Rafnar T, Holm H, Stefansson H, Saemundsdottir J, Gudbjartsson DF, Magnusson OT, Masson G, Thorsteinsdottir U, Helgason A, Jonsson H, Sulem P, Stefansson K. The sequences of 150,119 genomes in the UK Biobank. Nature, 2022, 607(7920): 732-740.
[43]	Silcocks M, Farlow A, Hermes A, Tsambos G, Patel HR, Huebner S, Baynam G, Jenkins MR, Vukcevic D, Easteal S, Leslie S, National Centre for Indigenous Genomics. Indigenous Australian genomes show deep structure and rich novel variation. Nature, 2023, 624(7992): 593-601.
[44]	Li XP, Wang MG, Su HR, Duan SH, Sun YT, Chen HY, Wang ZY, Sun QX, Yang QX, Chen J, Yang T, Huang YG, Zhong J, Jiang XC, Ma JY, Chen SJ, Liu YH, Luo LT, Lin XY, Nie SJ, He GL. Evolutionary history and biological adaptation of Han Chinese people on the Mongolian Plateau. hLife, 2024, 2(6): 296-313.
[45]	He GL, Wang PX, Chen J, Liu Y, Sun YT, Hu R, Duan SH, Sun QX, Tang RK, Yang JB, Wang ZY, Yun LB, Hu LP, Yan JW, Nie SJ, Wei LH, Liu C, Wang MG. Differentiated genomic footprints suggest isolation and long-distance migration of Hmong-Mien populations. BMC Biol, 2024, 22(1): 18. doi: 10.1186/s12915-024-01828-x pmid: 38273256
[46]	Sun YT, Wang MG, Sun QX, Liu Y, Duan SH, Wang ZY, Zhou YY, Zhong J, Huang YG, Huang XY, Yang QX, Li XP, Su HR, Cai Y, Jiang XC, Chen J, Yan JW, Nie SJ, Hu LP, Yang JB, Tang RK, Wang CC, Liu C, Deng XH, Yun LB, He GL. Distinguished biological adaptation architecture aggravated population differentiation of Tibeto- Burman-speaking people. J Genet Genomics, 2023, 51(5): 517-530.
[47]	Zhang P, Luo HX, Li YY, Wang Y, Wang JJ, Zheng Y, Niu YW, Shi YR, Zhou HH, Song TR, Kang Q, Han100K Initiative, Xu T, He SM. NyuWa Genome resource: A deep whole-genome sequencing-based variation profile and reference panel for the Chinese population. Cell Rep, 2021, 37(7): 110017.
[48]	Cao YN, Li L, Xu M, Feng ZM, Sun XH, Lu JL, Xu Y, Du PN, Wang TG, Hu RY, Ye Z, Shi LX, Tang XL, Yan L, Gao ZN, Chen G, Zhang YF, Chen LL, Ning G, Bi YF, Wang WQ, ChinaMAP Consortium. The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals. Cell Res, 2020, 30(9): 717-731. doi: 10.1038/s41422-020-0322-9 pmid: 32355288
[49]	Huang SJ, Liu SY, Huang MX, He JR, Wang CR, Wang TY, Feng XT, Kuang YS, Lu JH, Gu YQ, Xia XY, Lin SS, Born in Guangzhou Cohort Study (BIGCS) Group, Zhou WH, Fu QM, Xia HM, Qiu X. The Born in Guangzhou Cohort Study enables generational genetic discoveries. Nature, 2024, 626(7999): 565-573.
[50]	Wang MG, Huang YG, Liu KJ, Wang ZY, Zhang MH, Yuan HB, Duan SH, Wei LH, Yao HB, Sun QX, Zhong J, Tang RK, Chen J, Sun YT, Li XP, Su HR, Yang QX, Hu LP, Yun LB, Yang JB, Nie SJ, Cai Y, Yan JW, Zhou K, Wang CC, 10K_CPGDP Consortium, Zhu BF, Liu C, He GL. Multiple human population movements and cultural dispersal events shaped the landscape of Chinese paternal heritage. Mol Biol Evol, 2024, 41(7): msae122.
[51]	Sun YT, Wang MG, Sun QX, Liu Y, Duan SH, Wang ZY, Zhou YY, Zhong J, Huang YG, Huang XY, Yang QX, Li XP, Su HR, Cai Y, Jiang XC, Chen J, Yan JW, Nie SJ, Hu LP, Yang JB, Tang RK, Wang CC, Liu C, Deng XH, Yun LB, He GL. Distinguished biological adaptation architecture aggravated population differentiation of Tibeto-Burman-speaking people. J Genet Genomics, 2024, 51(5): 517-530.
[52]	He GL, Wang MG, Luo LT, Sun QX, Yuan HB, Lv HL, Feng YH, Liu XJ, Cheng J, Bu FX, Zhabagin M, Yuan HJ, Liu C, Xu SH. Population genomics of Central Asian peoples unveil ancient Trans-Eurasian genetic admixture and cultural exchanges. hLife, 2024, doi: 10.1016/j.hlife. 2024.06.006.
[53]	Wang ZY, Wang MG, Liu KJ, Yuan HB, Duan SH, Liu YH, Luo LT, Jiang XC, Chen SJ, Wei LH, Tang RK, Hu LP, Chen J, Li XP, Yang QX, Sun YT, Sun QX, Huang YG, Su HR, Zhong J, Yao HB, Yun LB, Li JB, Yang JB, Cai Y, Deng H, Yan JW, Zhu BF, 10K_CPGDP, Zhou K, Nie SJ, Liu C, He GL. Paternal genomic resources from the YanHuang cohort suggested a Weakly-Differentiated Multi-source Admixture model for the formation of Han’s founding ancestral lineages. bioRxiv, 2023, doi: 10.1101/2023.11.08.566335.
[54]	Fan CQ, Cahoon JL, Dinh BL, Ortega-Del Vecchyo D, Huber C, Edge MD, Mancuso N, Chiang CWK. A likelihood-based framework for demographic inference from genealogical trees. bioRxiv, 2023, doi: 10.1101/ 2023.10.10.561787.
[55]	Olson-Manning CF, Wagner MR, Mitchell-Olds T. Adaptive evolution: evaluating empirical support for theoretical predictions. Nat Rev Genet, 2012, 13(12): 867-877. doi: 10.1038/nrg3322 pmid: 23154809
[56]	Hao Y, Lei FM. Genetic mechanism of adaptive evolution: the example of adaptation to high altitudes. Hereditas(Beijing), 2022, 44(8): 635-654. doi: 10.16288/j.yczz.22-108 pmid: 36384664
	郝艳, 雷富民. 适应性演化的分子遗传机制:以高海拔适应为例. 遗传, 2022, 44(8): 635-654.
[57]	Stern AJ, Wilton PR, Nielsen R. An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data. PLoS Genet, 2019, 15(9): e1008384.
[58]	Hejase HA, Mo ZY, Campagna L, Siepel A. A deep-learning approach for inference of selective sweeps from the ancestral recombination graph. Mol Biol Evol, 2022, 39(1): msab332.
[59]	Marsh JI, Johri P. Biases in ARG-based inference of historical population size in populations experiencing selection. Mol Biol Evol, 2024, 41(7): msae118.
[60]	Stern AJ, Speidel L, Zaitlen NA, Nielsen R. Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies. Am J Hum Genet, 2021, 108(2): 219-239. doi: 10.1016/j.ajhg.2020.12.005 pmid: 33440170
[61]	Gnecchi-Ruscone GA, Rácz Z, Samu L, Szeniczey T, Faragó N, Knipper C, Friedrich R, Zlamalová D, Traverso L, Liccardo S, Wabnitz S, Popli D, Wang K, Radzeviciute R, Gulyás B, Koncz I, Balogh C, Lezsák GM, Mácsai V, Bunbury MME, Spekker O, le Roux P, Szécsényi-Nagy A, Gusztáv Mende B, Colleran H, Hajdu T, Geary P, Pohl W, Vida T, Krause J, Hofmanová Z. Network of large pedigrees reveals social practices of Avar communities. Nature, 2024, 629(8011): 376-383.
[62]	Ping WJ, Liu YC, Fu QM. Exploring the evolution of archaic humans through sedimentary ancient DNA. Hereditas(Beijing), 2022, 44(5): 362-369.
	平婉菁, 刘逸宸, 付巧妹. 沉积物古DNA探秘灭绝古人类演化. 遗传, 2022, 44(5): 362-369.
[63]	Bi CL, Guo GY, Zhang X, Tian YH, Shen YZ. Progresses on Neandertal genomics. Hereditas(Beijing), 2012, 34(6): 659-665.
	秘彩莉, 郭光艳, 张晓, 田彦辉, 沈银柱. 尼安德特人基因组学研究进展. 遗传, 2012, 34(6): 659-665.
[64]	Jacobs GS, Hudjashov G, Saag L, Kusuma P, Darusallam CC, Lawson DJ, Mondal M, Pagani L, Ricaut FX, Stoneking M, Metspalu M, Sudoyo H, Lansing JS, Cox MP. Multiple deeply divergent denisovan ancestries in papuans. Cell, 2019, 177(4): 1010-1021.e32. doi: S0092-8674(19)30218-1 pmid: 30981557
[65]	Sun YD, Tian ZZ, Zhou W, Li M, Huai C, He L, Qin SY. Genome-wide association study on liver function tests in Chinese. Hereditas(Beijing), 2021, 43(3): 249-260. doi: 10.16288/j.yczz.20-435 pmid: 33724209
	孙一丹, 田子钊, 周伟, 李沫, 怀聪, 贺林, 秦胜营. 中国人群肝功能检测指标全基因组关联分析研究. 遗传, 2021, 43(3): 249-260.
[66]	DeHaas D, Pan ZQ, Wei XZ. Genotype representation graphs: enabling efficient analysis of biobank-scale data. bioRxiv, 2024, doi: 10.1101/2024.04.23.590800.
[67]	Mandape SN, Budowle B, Mittelman K, Mittelman D. Dense single nucleotide polymorphism testing revolutionizes scope and degree of certainty for source attribution in forensic investigations. Croat Med J, 2024, 65(3): 249-260.
[68]	Schraiber JG, Akey JM. Methods and models for unravelling human evolutionary history. Nat Rev Genet, 2015, 16(12): 727-740. doi: 10.1038/nrg4005 pmid: 26553329
[69]	Huang X, Rymbekova A, Dolgova O, Lao O, Kuhlwilm M. Harnessing deep learning for population genetic inference. Nat Rev Genet, 2024, 25(1): 61-78.
[70]	Marciniak S, Perry GH. Harnessing ancient genomes to study the history of human adaptation. Nat Rev Genet, 2017, 18(11): 659-674. doi: 10.1038/nrg.2017.65 pmid: 28890534
[71]	Veeramah KR, Hammer MF. The impact of whole-genome sequencing on the reconstruction of human population history. Nat Rev Genet, 2014, 15(3): 149-162. doi: 10.1038/nrg3625 pmid: 24492235
[72]	Racimo F, Sankararaman S, Nielsen R, Huerta-Sánchez E. Evidence for archaic adaptive introgression in humans. Nat Rev Genet, 2015, 16(6): 359-371. doi: 10.1038/nrg3936 pmid: 25963373
[73]	Garrigan D, Hammer MF. Reconstructing human origins in the genomic era. Nat Rev Genet, 2006, 7(9): 669-680. pmid: 16921345
[74]	Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet, 2024, doi: 10.1038/s41576-024-00731-z.
[75]	van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. Genetic correlations of polygenic disease traits: from theory to practice. Nat Rev Genet, 2019, 20(10): 567-581. doi: 10.1038/s41576-019-0137-z pmid: 31171865

编辑推荐

Metrics

www.chinagene.cn
备案号：京ICP备09063187号-4
总访问:,今日访问:,当前在线:

方法	核心框架	优势	缺点	参考文献
ARGweaver	SMC	最准确	样本数<100，慢	[22]
Relate	L&S	支持大样本，古基因组	准确性稍差，拓扑结构固定	[25,38]
tsinfer/tsdate	L&S	支持超大样本(数十万)，古基因组，速度快	对定相错误的鲁棒性差，准确性较差，拓扑结构固定	[29,36]
ARG-Needle	ASMC	支持超大样本(数十万)，可以整合，配套软件	准确性未独立评估	[37]

基于祖先重组图重建古今人类群体遗传系谱的研究进展及展望

Advancements and prospects in reconstructing the genetic genealogies of ancient and modern human populations using ancestral recombination graphs

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 3

参考文献 75

相关文章 0

编辑推荐

Metrics