An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data

English version

Autoři: Aaron J. Stern ^aff001; Peter R. Wilton ^aff002; Rasmus Nielsen ^aff002
Působiště autorů: Graduate Group in Computation Biology, University of California, Berkeley, Berkeley, California, United States of America ^aff001; Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America ^aff002; Department of Statistics, University of California, Berkeley, Berkeley, California, United States of America ^aff003
Vyšlo v časopise: An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data. PLoS Genet 15(9): e32767. doi:10.1371/journal.pgen.1008384
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1008384

Souhrn

Most current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. Our method CLUES treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency trajectory of a selected or neutral allele. We perform extensive simulations to evaluate the method and show that it uniformly improves power to detect selection compared to current popular methods such as nSL and SDS, and can provide reliable inferences of allele frequency trajectories under many conditions. We also explore the potential of our method to detect extremely recent changes in the strength of selection. We use the method to infer the past allele frequency trajectory for a lactase persistence SNP (MCM6) in Europeans. We also infer the trajectory of a SNP (EDAR) in Han Chinese, finding evidence that this allele’s age is much older than previously claimed. We also study a set of 11 pigmentation-associated variants. Several genes show evidence of strong selection particularly within the last 5,000 years, including ASIP, KITLG, and TYR. However, selection on OCA2/HERC2 seems to be much older and, in contrast to previous claims, we find no evidence of selection on TYRP1.

Klíčová slova:

Biology and life sciences – Genetics – Heredity – Genetic mapping – Haplotypes – Physical sciences – Research and analysis methods – Molecular biology – Evolutionary biology – People and places – Computer and information sciences – Evolutionary systematics – Phylogenetics – Phylogenetic analysis – Evolutionary processes – Natural selection – Taxonomy – Molecular genetics – Population biology – Population dynamics – Geographic distribution – Data management – Mathematics – Probability theory – Markov models – Hidden Markov models – Simulation and modeling – Geographical locations – Europe

Zdroje

1. Watterson GA. Testing Selection at a Single Locus. Biometrics. 1982;38(2):323–331. doi: 10.2307/2530446 7115865

2. Mathieson I, McVean G. Estimating selection coefficients in spatially structured populations from time series data of allele frequencies. Genetics. 2013;193(3):973–984. doi: 10.1534/genetics.112.147611 23307902

3. Williamson EG, Slatkin M. Using Maximum Likelihood to Estimate Population Size From Temporal Changes in Allele Frequencies. 1999;.

4. Bollback JP, York TL, Nielsen R. Estimation of 2Nes from temporal allele frequency data. Genetics. 2008;179(1):497–502. doi: 10.1534/genetics.107.085019 18493066

5. Lang GI, Rice DP, Hickman MJ, Sodergren E, Weinstock GM, Botstein D, et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature. 2013;500(7464):571. doi: 10.1038/nature12344 23873039

6. Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM. The dynamics of molecular evolution over 60,000 generations. Nature. 2017;551(7678):45. doi: 10.1038/nature24287 29045390

7. Lazaridis I, Patterson N, Mittnik Alissa et al. Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature. 2014;513(7518):409–13. doi: 10.1038/nature13673 25230663

8. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, et al. Genome-wide patterns of selection in. Nature. 2015;528(7583):499–503. doi: 10.1038/nature16152 26595274

9. Sabeti PC, Reich DE, Higgins JM, Levine HZP, Richter DJ, Schaffner SF, et al. Detecting recent positive selection in the human genome from haplotype structure. 2002;419(October).

10. Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C. Genomic scans for selective sweeps using SNP data. 2005; p. 1566–1575.

11. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome. PLoS biology. 2006;4(3):e72. doi: 10.1371/journal.pbio.0040072 16494531

12. Smith JM, Haigh J. The hitch-hiking effect of a favourable gene. Genetics Research. 1974;23(1):23–35. doi: 10.1017/S0016672300014634

13. Kaplan NL, Hudson R, Langley C. The “hitchhiking effect” revisited. Genetics. 1989;123(4):887–899. 2612899

14. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123(3):585–595. 2513255

15. Stephan W, Wiehe TH, Lenz MW. The effect of strongly selected substitutions on neutral polymorphism: analytical results based on diffusion theory. Theoretical Population Biology. 1992;41(2):237–254. doi: 10.1016/0040-5809(92)90045-U

16. Fu YX, Li WH. Statistical tests of neutrality of mutations. Genetics. 1993;133(3):693–709. 8454210

17. Fay JC, Wu Ci. Hitchhiking Under Positive Darwinian Selection. 2000;.

18. Teshima KM, Coop G, Przeworski M. How reliable are empirical genomic scans for selective sweeps? 2006;(773):702–712.

19. Ferrer-Admetlla A, Liang M, Korneliussen T, Nielsen R. On detecting incomplete soft or hard selective sweeps using haplotype structure. Molecular biology and evolution. 2014;31(5):1275–1291. doi: 10.1093/molbev/msu077 24554778

20. Garud NR, Messer PW, Buzbas EO, Petrov Da. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS genetics. 2015;11(2):e1005004. doi: 10.1371/journal.pgen.1005004 25706129

21. Field Y, Boyle EA, Telis N, Gao Z, Gaulton KJ. Detection of human adaptation during the past 2, 000 years. 2016; p. 1–18.

22. Schrider DR, Kern AD. S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning. PLoS genetics. 2016;12(3):e1005928. doi: 10.1371/journal.pgen.1005928 26977894

23. Schrider DR, Kern AD. Supervised Machine Learning for Population Genetics: A New Paradigm. Trends in Genetics. 2018;.

24. Lin K, Li H, Schlötterer C, Futschik A. Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics. Genetics. 2011;187(1):229–244. doi: 10.1534/genetics.110.122614 21041556

25. Ronen R, Udpa N, Halperin E, Bafna V. Learning natural selection from the site frequency spectrum. Genetics. 2013;195(1):181–193. doi: 10.1534/genetics.113.152587 23770700

26. Sheehan S, Song YS. Deep learning for population genetic inference. PLoS computational biology. 2016;12(3):e1004845. doi: 10.1371/journal.pcbi.1004845 27018908

27. Krone SM, Neuhauser C. Ancestral processes with selection. Theoretical population biology. 1997;51(3):210–237. doi: 10.1006/tpbi.1997.1299 9245777

28. Kaplan NL, Darden T, Hudson RR. The Coalescent Process in Models With Selection. 1988;829(2):819–829.

29. Coop G, Griffiths RC. Ancestral inference on gene trees under selection. Theoretical population biology. 2004;66(3):219–32. doi: 10.1016/j.tpb.2004.06.006 15465123

30. Vy HMT, Kim Y. A composite-likelihood method for detecting incomplete selective sweep from population genomic data. Genetics. 2015;200(2):633–649. doi: 10.1534/genetics.115.175380 25911658

31. Kim Y, Stephan W. Detecting a Local Signature of Genetic Hitchhiking Along a Recombining Chromosome. 2002;777(February):765–777.

32. Peter BM, Huerta-Sanchez E, Nielsen R. Distinguishing between selective sweeps from standing variation and from a de novo mutation. PLoS genetics. 2012;8(10):e1003011. doi: 10.1371/journal.pgen.1003011 23071458

33. Ormond L, Foll M, Ewing GB, Pfeifer SP, Jensen JD. Inferring the age of a fixed beneficial allele. Molecular ecology. 2016;25(1):157–169. doi: 10.1111/mec.13478 26576754

34. Ilardo MA, Moltke I, Korneliussen TS, Cheng J, Stern AJ, Racimo F, et al. Physiological and genetic adaptations to diving in sea nomads. Cell. 2018;173(3):569–580. doi: 10.1016/j.cell.2018.03.054 29677510

35. Corl A, Bi K, Luke C, Challa AS, Stern AJ, Sinervo B, et al. The genetic basis of adaptation following plastic changes in coloration in a novel environment. Current Biology. 2018;28(18):2970–2977. doi: 10.1016/j.cub.2018.06.075 30197088

36. Sugden LA, Atkinson EG, Fischer AP, Rong S, Henn BM, Ramachandran S. Localization of adaptive variants in human genomes using averaged one-dependence estimation. Nature communications. 2018;9(1):703. doi: 10.1038/s41467-018-03100-7 29459739

37. Rasmussen MD, Hubisz MJ, Gronau I, Siepel A. Genome-wide inference of ancestral recombination graphs. PLoS genetics. 2014;10(5):e1004342. doi: 10.1371/journal.pgen.1004342 24831947

38. Edge MD, Coop G. Reconstructing the history of polygenic scores using coalescent trees. Genetics. 2019;211(1):235–262. doi: 10.1534/genetics.118.301687 30389808

39. Berisa T, Pickrell JK. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics. 2016;32(2):283. doi: 10.1093/bioinformatics/btv546 26395773

40. Tavaré S. Line-of-descent and genealogical processes, and their applications in population genetics models. Theoretical population biology. 1984;26(2):119–164. doi: 10.1016/0040-5809(84)90027-3 6505980

41. Griffiths R. Asymptotic line-of-descent distributions. Journal of Mathematical Biology. 1984;21(1):67–75. doi: 10.1007/BF00275223

42. Jewett EM, Rosenberg NA. Theory and applications of a deterministic approximation to the coalescent model. Theoretical population biology. 2014;93:14–29. doi: 10.1016/j.tpb.2013.12.007 24412419

43. Tennessen JA, Bigham AW, O’connor TD, Fu W, Kenny EE, Gravel S, et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. science. 2012;337(6090):64–69. doi: 10.1126/science.1219240 22604720

44. Kern AD, Schrider DR. Discoal: flexible coalescent simulations with selection. Bioinformatics. 2016;32(24):3839. doi: 10.1093/bioinformatics/btw556 27559153

45. Wright S. The distribution of gene frequencies under irreversible mutation. Proceedings of the National Academy of Sciences. 1938;24(7):253–259. doi: 10.1073/pnas.24.7.253

46. Przeworski M. The signature of positive selection at randomly chosen loci. Genetics. 2002;160(3):1179–1189. 11901132

47. Slatkin M. Simulating genealogies of selected alleles in a population of variable size. Genetics Research. 2001;78(1):49–57. doi: 10.1017/S0016672301005183

48. Garud NR, Messer PW, Buzbas EO, Petrov DA. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genet. 2015;11(2):e1005004. doi: 10.1371/journal.pgen.1005004 25706129

49. Haller BC, Messer PW. SLiM 3: forward genetic simulations beyond the Wright–Fisher model. Molecular biology and evolution. 2019;36(3):632–637. doi: 10.1093/molbev/msy228 30517680

50. Barton NH. Linkage and the limits to natural selection. Genetics. 1995;140(2):821–841. 7498757

51. Torres R, Szpiech ZA, Hernandez RD. Human demographic history has amplified the effects of background selection across the genome. PLoS genetics. 2018;14(6):e1007387. doi: 10.1371/journal.pgen.1007387 29912945

52. Meyer M, Kircher M, Gansauge MT, Li H, Racimo F, Mallick S, et al. A high-coverage genome sequence from an archaic Denisovan individual. Science. 2012;338(6104):222–226. doi: 10.1126/science.1224344 22936568

53. Prüfer K, Racimo F, Patterson N, Jay F, Sankararaman S, Sawyer S, et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature. 2014;505(7481):43. doi: 10.1038/nature12886 24352235

54. Prüfer K, de Filippo C, Grote S, Mafessoni F, Korlević P, Hajdinjak M, et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science. 2017;358(6363):655–658. doi: 10.1126/science.aao1887 28982794

55. Mathieson S, Mathieson I. FADS1 and the Timing of Human Adaptation to Agriculture. Molecular Biology and Evolution. 2018;35(12):2957–2970. doi: 10.1093/molbev/msy180 30272210

56. Grossman SR, Shylakhter I, Karlsson EK, Byrne EH, Morales S, Frieden G, et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science. 2010;327(5967):883–886. doi: 10.1126/science.1183863 20056855

57. Kimura R, Yamaguchi T, Takeda M, Kondo O, Toma T, Haneji K, et al. A common variation in EDAR is a genetic determinant of shovel-shaped incisors. The American Journal of Human Genetics. 2009;85(4):528–535. doi: 10.1016/j.ajhg.2009.09.006 19804850

58. Wu S, Tan J, Yang Y, Peng Q, Zhang M, Li J, et al. Genome-wide scans reveal variants at EDAR predominantly affecting hair straightness in Han Chinese and Uyghur populations. Human genetics. 2016;135(11):1279–1286. doi: 10.1007/s00439-016-1718-y 27487801

59. Gravel S, Henn BM, Gutenkunst RN, Indap AR, Marth GT, Clark AG, et al. Demographic history and rare allele sharing among human populations. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(29):11983–8. doi: 10.1073/pnas.1019276108 21730125

60. Marcus JH, Novembre J. Visualizing the geography of genetic variants. Bioinformatics. 2017;33(4):594–595. doi: 10.1093/bioinformatics/btw643 27742697

61. Eriksson N, Macpherson JM, Tung JY, Hon LS, Naughton B, Saxonov S, et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS genetics. 2010;6(6):e1000993. doi: 10.1371/journal.pgen.1000993 20585627

62. Han J, Kraft P, Nan H, Guo Q, Chen C, Qureshi A, et al. A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS genetics. 2008;4(5):e1000074. doi: 10.1371/journal.pgen.1000074 18483556

63. Sulem P, Gudbjartsson DF, Stacey SN, Helgason A, Rafnar T, Magnusson KP, et al. Genetic determinants of hair, eye and skin pigmentation in Europeans. Nature genetics. 2007;39(12):1443. doi: 10.1038/ng.2007.13 17952075

64. Sturm RA, Duffy DL, Zhao ZZ, Leite FP, Stark MS, Hayward NK, et al. A single SNP in an evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color. The American Journal of Human Genetics. 2008;82(2):424–431. doi: 10.1016/j.ajhg.2007.11.005 18252222

65. Huerta-Sánchez E, Jin X, Bianba Z, Peter BM, Vinckenbosch N, Liang Y, et al. Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA. Nature. 2014;512(7513):194. doi: 10.1038/nature13408 25043035

66. Wilde S, Timpson A, Kirsanow K, Kaiser E, Kayser M, Unterländer M, et al. Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans during the last 5,000 y. Proceedings of the National Academy of Sciences. 2014;111(13):4832–4837. doi: 10.1073/pnas.1316513111

67. Gittelman RM, Schraiber JG, Vernot B, Mikacenic C, Wurfel MM, Akey JM. Archaic hominin admixture facilitated adaptation to out-of-Africa environments. Current Biology. 2016;26(24):3375–3382. doi: 10.1016/j.cub.2016.10.041 27839976

68. Frudakis T, Thomas M, Gaskin Z, Venkateswarlu K, Chandra KS, Ginjupalli S, et al. Sequences associated with human iris pigmentation. Genetics. 2003;165(4):2071–2083. 14704187

69. Sulem P, Gudbjartsson DF, Stacey SN, Helgason A, Rafnar T, Jakobsdottir M, et al. Two newly identified genetic determinants of pigmentation in Europeans. Nature genetics. 2008;40(7):835. doi: 10.1038/ng.160 18488028

70. Liu F, Wollstein A, Hysi PG, Ankra-Badu GA, Spector TD, Park D, et al. Digital quantification of human eye color highlights genetic association of three new loci. PLoS genetics. 2010;6(5):e1000934. doi: 10.1371/journal.pgen.1000934 20463881

71. Kenny EE, Timpson NJ, Sikora M, Yee MC, Moreno-Estrada A, Eng C, et al. Melanesian blond hair is caused by an amino acid change in TYRP1. Science. 2012;336(6081):554–554. doi: 10.1126/science.1217849 22556244

72. Mirzaei S, Wu Y. RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination. Bioinformatics. 2016;33(7):1021–1030.

73. Kelleher J, Wong Y, Albers P, Wohns AW, McVean G. Inferring the ancestry of everyone. BioRxiv. 2018; p. 458067.

74. Shchur V, Ziganurova L, Durbin R. Fast and scalable genome-wide inference of local tree topologies from large number of haplotypes based on tree consistent PBWT data structure. bioRxiv. 2019; p. 542035.

75. Speidel L, Forest M, Shi S, Myers S. A method for genome-wide genealogy estimation for thousands of samples. BioRxiv. 2019; p. 550558.

76. Palamara PF, Terhorst J, Song YS, Price AL. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. bioRxiv. 2018; p. 276931.

77. Albers PK, McVean G. Dating genomic variants and shared ancestry in population-scale sequencing data. bioRxiv. 2018;.

78. Galtier N, Depaulis F, Barton NH. Detecting bottlenecks and selective sweeps from DNA sequence polymorphism. Genetics. 2000;155(2):981–987. 10835415