群体基因组学方法:从经典统计学到有监督学习
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摘要
群体遗传学的一个主要研究目标是理解突变、自然选择、遗传漂变、群体结构和数量变化等进化力量如何共同影响基因组中的遗传变异.通过分析DNA序列多态数据,可以推测曾经作用于基因组的各种力量,进而探讨生物演化的过程.近年来,随着第二代DNA测序技术的快速革新,群体遗传学进入了基因组学时代,相关的方法在不断发展,并可将群体基因组学方法分为经典统计学方法和新兴的机器学习方法.前者包括经典群体遗传学统计量、单一统计量或多统计量联合检测自然选择、群体历史与自然选择的联合估计以及基于溯祖树和祖先重组图的方法.后者主要基于有监督学习,为群体基因组时代的大数据分析带来了全新范式.本文从理论基础出发,全面回顾了群体基因组学方法发展变化的历程,着重介绍了该领域的最新进展,并就未来的发展方向进行了展望.
It is essential to understand how the patterns of genetic variation in organisms have been shaped by different evolutionary forces,such as mutation,natural selection,genetic drift,population structure,and population size change.In recent years,with the rapid innovation of next-generation sequencing technology,we are facing the new era of population genomics.The relevant population genomics methods can be classified as classical statistics and supervised learning.The classical statistics methods include many popular ones for detecting natural selection and inferring the parameters of demography,which are based on single or multiple combined statistics.The supervised learning methods may promise a new paradigm to make sense of large datasets in the genomic era.Here a brief introduction was first given on the important theory in population genomics.Then we overviewed the recent research progress in population genomics and shared our perspectives on its future development.
It is essential to understand how the patterns of genetic variation in organisms have been shaped by different evolutionary forces,such as mutation,natural selection,genetic drift,population structure,and population size change.In recent years,with the rapid innovation of next-generation sequencing technology,we are facing the new era of population genomics.The relevant population genomics methods can be classified as classical statistics and supervised learning.The classical statistics methods include many popular ones for detecting natural selection and inferring the parameters of demography,which are based on single or multiple combined statistics.The supervised learning methods may promise a new paradigm to make sense of large datasets in the genomic era.Here a brief introduction was first given on the important theory in population genomics.Then we overviewed the recent research progress in population genomics and shared our perspectives on its future development.
引文
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2 Huxley J.Evolution:The Modern Synthesis.London:George Allen and Unwin,1942.1-45
3 Crow J F.Population genetics history:a personal view.Annu Rev Genet,1987,21:1-22
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5 Hughes A L.Looking for Darwin in all the wrong places:the misguided quest for positive selection at the nucleotide sequence level.Heredity,2007,99:364-373
6 Voight B F,Kudaravalli S,Wen X,et al.A map of recent positive selection in the human genome.PLo S Biol,2006,4:e72
7 Consortium T I H.A haplotype map of the human genome.Nature,2005,437:1299-1320
8 Gibbs R A,Boerwinkle E,Doddapaneni H,et al.A global reference for human genetic variation.Nature,2015,526:68-74
9 Sudmant P H,Rausch T,Gardner E J,et al.An integrated map of structural variation in 2,504 human genomes.Nature,2015,526:75-81
10 Walter K,Min J L,Huang J,et al.The UK10K project identifies rare variants in health and disease.Nature,2015,526:82-90
11 Geihs M,Yan Y,Walter K,et al.An interactive genome browser of association results from the UK10K cohorts project.Bioinformatics,2015,31:4029-4031
12 Hoban S,Bertorelle G,Gaggiotti O E.Computer simulations:tools for population and evolutionary genetics.Nat Rev Genet,2012,13:110-122
13 Ke Y,Su B,Song X,et al.African origin of modern humans in East Asia:a tale of 12,000 Y chromosomes.Science,2001,292:1151-1153
14 He Y X,Qi X B,Ouzhuluobu X,et al.Blunted nitric oxide regulation in Tibetans under high-altitude hypoxia.Natl Sci Rev,2018,5:516-529
15 Marciniak S,Perry G H.Harnessing ancient genomes to study the history of human adaptation.Nat Rev Genet,2017,18:659-674
16 Wang G D,Shao X J,Bai B,et al.Structural variation during dog domestication:insights from grey wolf and dhole genomes.Natl Sci Rev,2019,doi:10.1093/nsr/nwy076
17 Ling S,Hu Z,Yang Z,et al.Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution.Proc Natl Acad Sci USA,2015,112:E6496-E6505
18 Wu C I,Wang H Y,Ling S,et al.The ecology and evolution of cancer:the ultra-microevolutionary process.Annu Rev Genet,2016,50:347-369
19 Wang H Y,Chen Y X,Tong D,et al.Is the evolution in tumors Darwinian or non-Darwinian?Natl Sci Rev,2018,5:15-17
20 Schneider A,Souvorov A,Sabath N,et al.Estimates of positive Darwinian selection are inflated by errors in sequencing,annotation,and alignment.Genome Biol Evol,2009,1:114-118
21 Wright S.Evolution in Mendelian populations.Genetics,1931,16:97-159
22 Wright S.Breeding structure of populations in relation to speciation.Am Natist,1940,74:232-248
23 Watterson G A.On the number of segregating sites in genetical models without recombination.Theor Populat Biol,1975,7:256-276
24 Tajima F.Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.Genetics,1989,123:585-595
25 Fu Y X,Li W H.Statistical tests of neutrality of mutations.Genetics,1993,133:693-709
26 Fay J C,Wu C I.Hitchhiking under positive Darwinian selection.Genetics,2000,155:1405-1413
27 Zeng K,Fu Y X,Shi S,et al.Statistical tests for detecting positive selection by utilizing high-frequency variants.Genetics,2006,174:1431-1439
28 Fu Y X.A phylogenetic estimator of effective population size or mutation rate.Genetics,1994,136:685-692
29 Fu Y X,Li W H.Maximum likelihood estimation of population parameters.Genetics,1993,134:1261-1270
30 Wright S.The genetical structure of populations.Ann Eugen,1949,15:323-354
31 Weir B S,Cockerham C C.Estimating F-statistics for the analysis of population structure.Evolution,1984,38:1358-1370
32 Hudson R R,Kaplan N L.Statistical properties of the number of recombination events in the history of a sample of DNA sequences.Genetics,1985,111:147-164
33 Nielsen R.Molecular signatures of natural selection.Annu Rev Genet,2005,39:197-218
34 Vitti J J,Grossman S R,Sabeti P C.Detecting natural selection in genomic data.Annu Rev Genet,2013,47:97-120
35 Zhou Q,Wang W.Detecting natural selection at the DNA level(in Chinese).Zool Res,2004,25:73-80[周琦,王文.DNA水平自然选择作用的检测.动物学研究,2004,25:73-80]
36 Lin K,Li H P.Advances in detecting positive selection on genome(in Chinese).Hereditas,2009,31:896-902[林栲,李海鹏.DNA水平上检测正选择方法的研究进展.遗传,2009,31:896-902]
37 Chen H,Hey J,Slatkin M.A hidden Markov model for investigating recent positive selection through haplotype structure.Theor Popul Biol,2015,99:18-30
38 Chen H,Patterson N,Reich D.Population differentiation as a test for selective sweeps.Genome Res,2010,20:393-402
39 Yi X,Liang Y,Huerta-Sanchez E,et al.Sequencing of 50 human exomes reveals adaptation to high altitude.Science,2010,329:75-78
40 Akey J M.Constructing genomic maps of positive selection in humans:where do we go from here?Genome Res,2009,19:711-722
41 Li W H,Wu C I,Luo C C.A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changes.Mol Biol Evol,1985,2:150--174
42 Hurst L D.The Ka/Ksratio:diagnosing the form of sequence evolution.Trends Genets,2002,18:486-487
43 Mc Donald J H,Kreitman M.Adaptive protein evolution at the Adh locus in Drosophila.Nature,1991,351:652-654
44 Pollard K S,Salama S R,King B,et al.Forces shaping the fastest evolving regions in the human genome.PLo S Genet,2006,2:e168
45 Pollard K S,Salama S R,Lambert N,et al.An RNA gene expressed during cortical development evolved rapidly in humans.Nature,2006,443:167-172
46 Kim Y,Stephan W.Detecting a local signature of genetic hitchhiking along a recombining chromosome.Genetics,2002,160:765-777
47 Sabeti P C,Reich D E,Higgins J M,et al.Detecting recent positive selection in the human genome from haplotype structure.Nature,2002,419:832-837
48 Wang E T,Kodama G,Baldi P,et al.Global landscape of recent inferred Darwinian selection for Homo sapiens.Proc Natl Acad Sci USA,2006,103:135-140
49 Lewontin R C,Krakauer J.Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms.Genetics,1973,74:175-195
50 Shriver M D,Kennedy G C,Parra E J,et al.The genomic distribution of population substructure in four populations using 8,525 autosomal SNPs.Hum Genom,2004,1:274-286
51 Jaillon O,Aury J M,Brunet F,et al.Genome duplication in the teleost fish Tetraodon nigroviridis reveals the early vertebrate proto-karyotype.Nature,2004,431:946-957
52 Hufford M B,Xu X,van Heerwaarden J,et al.Comparative population genomics of maize domestication and improvement.Nat Genet,2012,44:808-811
53 Zhou Z,Jiang Y,Wang Z,et al.Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean.Nat Biotechnol,2015,33:408-414
54 Li J,Li H,Jakobsson M,et al.Joint analysis of demography and selection in population genetics:where do we stand and where could we go?Mol Ecol,2012,21:28-44
55 Bank C,Ewing G B,Ferrer-Admettla A,et al.Thinking too positive?Revisiting current methods of population genetic selection inference.Trends Genets,2014,30:540-546
56 Zeng K,Shi S,Wu C I.Compound tests for the detection of hitchhiking under positive selection.Mol Biol Evol,2007,24:1898-1908
57 Watterson G A.The homozygosity test of neutrality.Genetics,1978,88:405-417
58 Grossman S R,Shlyakhter I,Shylakhter I,et al.A composite of multiple signals distinguishes causal variants in regions of positive selection.Science,2010,327:883-886
59 Ewens W J.The sampling theory of selectively neutral alleles.Theor Popul Biol,1972,3:87-112
60 Simonson T S,Yang Y,Huff C D,et al.Genetic evidence for high-altitude adaptation in Tibet.Science,2010,329:72-75
61 Lin K,Li H,Schl?tterer C,et al.Distinguishing positive selection from neutral evolution:boosting the performance of summary statistics.Genetics,2011,187:229-244
62 Nielsen R,Williamson S,Kim Y,et al.Genomic scans for selective sweeps using SNP data.Genome Res,2005,15:1566-1575
63 Pavlidis P,Hutter S,Stephan W.A population genomic approach to map recent positive selection in model species.Mol Ecol,2008,17:3585-3598
64 Li H,Stephan W.Inferring the demographic history and rate of adaptive substitution in Drosophila.PLo S Genet,2006,2:1580-1589
65 Li H,Durbin R.Inference of human population history from individual whole-genome sequences.Nature,2011,475:493-496
66 Schiffels S,Durbin R.Inferring human population size and separation history from multiple genome sequences.Nat Genet,2014,46:919-925
67 Liu X,Fu Y X.Exploring population size changes using SNP frequency spectra.Nat Genet,2015,47:555-559
68 Chen H,Hey J,Chen K.Inferring very recent population growth rate from population-scale sequencing data:using a large-sample coalescent estimator.Mol Biol Evol,2015,32:2996-3011
69 Kaplan N L,Hudson R R,Langley C H.The hitchhiking effect revisited.Genetics,1989,123:887-899
70 Li H.A new test for detecting recent positive selection that is free from the confounding impacts of demography.Mol Biol Evol,2011,28:365-375
71 Li H,Wiehe T.Coalescent tree imbalance and a simple test for selective sweeps based on microsatellite variation.PLo S Comput Biol,2013,9:e1003060
72 Yang Z,Li J,Wiehe T,et al.Detecting recent positive selection with a single locus test bipartitioning the coalescent tree.Genetics,2018,208:791-805
73 Wang M,Huang X,Li R,et al.Detecting recent positive selection with high accuracy and reliability by conditional coalescent tree.Mol Biol Evol,2014,31:3068-3080
74 Disanto F,Schlizio A,Wiehe T.Yule-generated trees constrained by node imbalance.Math Biosci,2013,246:139-147
75 Ronen R,Tesler G,Akbari A,et al.Predicting carriers of ongoing selective sweeps without knowledge of the favored allele.PLo S Genet,2015,11:e1005527
76 Akbari A,Vitti J J,Iranmehr A,et al.Identifying the favored mutation in a positive selective sweep.Nat Meth,2018,15:279-282
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