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VolcanoFinder: Genomic scans for adaptive introgression


Autoři: Derek Setter aff001;  Sylvain Mousset aff001;  Xiaoheng Cheng aff004;  Rasmus Nielsen aff005;  Michael DeGiorgio aff006;  Joachim Hermisson aff001
Působiště autorů: Department of Mathematics, University of Vienna, Vienna, Austria aff001;  Vienna Graduate School of Population Genetics, Vienna, Austria aff002;  School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom aff003;  Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America aff004;  Departments of Integrative Biology and Statistics, University of California, Berkeley, Berkeley, California, USA aff005;  Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA aff006;  Max F. Perutz Laboratories, University of Vienna, Vienna, Austria aff007
Vyšlo v časopise: VolcanoFinder: Genomic scans for adaptive introgression. PLoS Genet 16(6): e32767. doi:10.1371/journal.pgen.1008867
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008867

Souhrn

Recent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method—VolcanoFinder—to detect recent events of adaptive introgression using polymorphism data from the recipient species only. VolcanoFinder detects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity. Using coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show that VolcanoFinder has high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implement VolcanoFinder to detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder.

Klíčová slova:

Alleles – Europe – Genetic footprinting – Genomic signal processing – Haplotypes – Introgression – Neanderthals – Volcanoes


Zdroje

1. Coyne JA, Orr HA, Orr HA. Speciation. Oxford University Press Inc; 2004.

2. Mallet J. Hybridization as an invasion of the genome. Trends in ecology & evolution. 2005;20(5):229–237. doi: 10.1016/j.tree.2005.02.010

3. Baack EJ, Rieseberg LH. A genomic view of introgression and hybrid speciation. Current opinion in genetics & development. 2007;17:513–518. doi: 10.1016/j.gde.2007.09.001

4. Arnold ML, Sapir Y, Martin NH. Review. Genetic exchange and the origin of adaptations: prokaryotes to primates. Philosophical transactions of the Royal Society of London Series B, Biological sciences. 2008;363:2813–2820. doi: 10.1098/rstb.2008.0021 18522920

5. Schwenk K, Brede N, Streit B. Introduction. Extent, processes and evolutionary impact of interspecific hybridization in animals. Philosophical transactions of the Royal Society of London Series B, Biological sciences. 2008;363:2805–2811. doi: 10.1098/rstb.2008.0055 18534946

6. Hedrick PW. Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation. Molecular ecology. 2013;22:4606–4618. doi: 10.1111/mec.12415 23906376

7. Consortium HG. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature. 2012;487:94–98. doi: 10.1038/nature11041

8. Whitney KD, Randell RA, Rieseberg LH. Adaptive introgression of herbivore resistance traits in the weedy sunflower Helianthus annuus. The American naturalist. 2006;167:794–807. doi: 10.1086/504606 16649157

9. Whitney KD, Randell RA, Rieseberg LH. Adaptive introgression of abiotic tolerance traits in the sunflower Helianthus annuus. The New phytologist. 2010;187:230–239. doi: 10.1111/j.1469-8137.2010.03234.x 20345635

10. Song Y, Endepols S, Klemann N, Richter D, Matuschka FR, Shih CH, et al. Adaptive introgression of anticoagulant rodent poison resistance by hybridization between old world mice. Current biology: CB. 2011;21:1296–1301. doi: 10.1016/j.cub.2011.06.043 21782438

11. Norris LC, Main BJ, Lee Y, Collier TC, Fofana A, Cornel AJ, et al. Adaptive introgression in an African malaria mosquito coincident with the increased usage of insecticide-treated bed nets. Proceedings of the National Academy of Sciences of the United States of America. 2015;112:815–820. doi: 10.1073/pnas.1418892112 25561525

12. Paoletti M, Buck KW, Brasier CM. Selective acquisition of novel mating type and vegetative incompatibility genes via interspecies gene transfer in the globally invading eukaryote Ophiostoma novo-ulmi. Molecular ecology. 2006;15:249–262. doi: 10.1111/j.1365-294X.2005.02728.x 16367844

13. Racimo F, Sankararaman S, Nielsen R, Huerta-Sánchez E. Evidence for archaic adaptive introgression in humans. Nature reviews Genetics. 2015;16:359–371. doi: 10.1038/nrg3936 25963373

14. Dannemann M, Racimo F. Something old, something borrowed: admixture and adaptation in human evolution. Current opinion in genetics & development. 2018;53:1–8. doi: 10.1016/j.gde.2018.05.009

15. Dolgova O, Lao O. Evolutionary and Medical Consequences of Archaic Introgression into Modern Human Genomes. Genes. 2018;9. doi: 10.3390/genes9070358 30022013

16. Huerta-Sánchez E, Jin X, Asan, Bianba Z, Peter BM, Vinckenbosch N, et al. Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA. Nature. 2014;512:194–197. doi: 10.1038/nature13408 25043035

17. Dannemann M, Andrés AM, Kelso J. Introgression of Neandertal- and Denisovan-like Haplotypes Contributes to Adaptive Variation in Human Toll-like Receptors. American journal of human genetics. 2016;98:22–33. doi: 10.1016/j.ajhg.2015.11.015 26748514

18. Deschamps M, Laval G, Fagny M, Itan Y, Abel L, Casanova JL, et al. Genomic Signatures of Selective Pressures and Introgression from Archaic Hominins at Human Innate Immunity Genes. American journal of human genetics. 2016;98:5–21. doi: 10.1016/j.ajhg.2015.11.014 26748513

19. Gittelman RM, Schraiber JG, Vernot B, Mikacenic C, Wurfel MM, Akey JM. Archaic Hominin Admixture Facilitated Adaptation to Out-of-Africa Environments. Current biology: CB. 2016;26:3375–3382. doi: 10.1016/j.cub.2016.10.041 27839976

20. Sankararaman S, Mallick S, Dannemann M, Prüfer K, Kelso J, Pääbo S, et al. The genomic landscape of Neanderthal ancestry in present-day humans. Nature. 2014;507:354–357. doi: 10.1038/nature12961 24476815

21. Vernot B, Tucci S, Kelso J, Schraiber JG, Wolf AB, Gittelman RM, et al. Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals. Science (New York, NY). 2016;352:235–239. doi: 10.1126/science.aad9416

22. Sankararaman S, Mallick S, Patterson N, Reich D. The Combined Landscape of Denisovan and Neanderthal Ancestry in Present-Day Humans. Current biology: CB. 2016;26:1241–1247. doi: 10.1016/j.cub.2016.03.037 27032491

23. Plagnol V, Wall JD. Possible ancestral structure in human populations. PLoS genetics. 2006;2:e105. doi: 10.1371/journal.pgen.0020105 16895447

24. Browning SR, Browning BL, Zhou Y, Tucci S, Akey JM. Analysis of Human Sequence Data Reveals Two Pulses of Archaic Denisovan Admixture. Cell. 2018;173:53–61.e9. doi: 10.1016/j.cell.2018.02.031 29551270

25. Durvasula A, Sankararaman S. A statistical model for reference-free inference of archaic local ancestry. PLoS genetics. 2019;15:e1008175. doi: 10.1371/journal.pgen.1008175 31136573

26. Vernot B, Akey JM. Resurrecting surviving Neandertal lineages from modern human genomes. Science (New York, NY). 2014;343:1017–1021. doi: 10.1126/science.1245938

27. Suarez-Gonzalez A, Lexer C, Cronk QCB. Adaptive introgression: a plant perspective. Biology letters. 2018;14. doi: 10.1098/rsbl.2017.0688 29540564

28. Maynard Smith J, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res. 1974;23(1):23–35. doi: 10.1017/S0016672300014634

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

30. Barton NH. The effect of hitchhiking on neutral genealogies. Genet Res. 1998;72:123–133. doi: 10.1017/S0016672398003462

31. Hermisson J, Pennings PS. Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics. 2005;169(4):2335–2352. doi: 10.1534/genetics.104.036947 15716498

32. Przeworski M, Coop G, Wall JD. The signature of positive selection on standing genetic variation. Evolution; international journal of organic evolution. 2005;59:2312–2323. doi: 10.1111/j.0014-3820.2005.tb00941.x

33. Pennings PS, Hermisson J. Soft sweeps II–molecular population genetics of adaptation from recurrent mutation or migration. Mol Biol Evol. 2006;23(5):1076–1084. doi: 10.1093/molbev/msj117 16520336

34. Pennings PS, Hermisson J. Soft sweeps III: the signature of positive selection from recurrent mutation. PLoS Genet. 2006;2(12):e186. doi: 10.1371/journal.pgen.0020186 17173482

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

36. Hermisson J, Pennings PS. Soft sweeps and beyond: understanding the patterns and probabilities of selection footprints under rapid adaptation. Methods in Ecology and Evolution. 2017;8(6):700–716. doi: 10.1111/2041-210X.12808

37. Slatkin M, Wiehe T. Genetic hitchhiking in a subdivised population. Genet Res Camb. 1998;71:155–160. doi: 10.1017/S001667239800319X

38. Santiago E, Caballero A. Variation After a Selective Sweep in a Subdivided Population. Genetics. 2005;169(1):475–483. doi: 10.1534/genetics.104.032813 15489530

39. Wiehe T, Schmid K, Stephan W. Selective sweeps in structured populations—empirical evidence and theoretical studies. In: Nurminsky D, editor. Selective sweeps. Georgetown, US: Landes Biosciences; 2005. p. 104–117.

40. Bierne N. The distinctive footprints of local hitchhiking in a varied environment and global hitchhiking in a subdivided population. Evolution: International Journal of Organic Evolution. 2010;64(11):3254–3272. doi: 10.1111/j.1558-5646.2010.01050.x

41. Oleksyk TK, Smith MW, O’Brien SJ. Genome-wide scans for footprints of natural selection. Philosophical transactions of the Royal Society of London Series B, Biological sciences. 2010;365:185–205. doi: 10.1098/rstb.2009.0219 20008396

42. Hohenlohe PA, Phillips PC, Cresko WA. Using population genomics to detect selection in natural populations: key concepts and methodological considerations. International journal of plant sciences. 2010;171:1059–1071. doi: 10.1086/656306 21218185

43. Chen H, Patterson N, Reich D. Population differentiation as a test for selective sweeps. Genome Res. 2010;20(3):393–402. doi: 10.1101/gr.100545.109 20086244

44. Fariello MI, Boitard S, Naya H, SanCristobal M, Servin B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics. 2013;193:929–941. doi: 10.1534/genetics.112.147231 23307896

45. Vatsiou AI, Bazin E, Gaggiotti OE. Detection of selective sweeps in structured populations: a comparison of recent methods. Molecular ecology. 2016;25:89–103. doi: 10.1111/mec.13360 26314386

46. Kim Y, Stephan W. Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics. 2002;160(2):765–777. 11861577

47. Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C. Genomic scans for selective sweeps using SNP data. Genome Res. 2005;15(11):1566–1575. doi: 10.1101/gr.4252305 16251466

48. DeGiorgio M, Lohmueller KE, Nielsen R. A model-based approach for identifying signatures of ancient balancing selection in genetic data. PLoS Genet. 2014;10(8):e1004561. doi: 10.1371/journal.pgen.1004561 25144706

49. Huber CD, DeGiorgio M, Hellmann I, Nielsen R. Detecting recent selective sweeps while controlling for mutation rate and background selection. Mol Ecol. 2016;25(1):142–156. doi: 10.1111/mec.13351 26290347

50. DeGiorgio M, Huber CD, Hubisz MJ, Hellmann I, Nielsen R. SweepFinder2: increased sensitivity, robustness and flexibility. Bioinformatics (Oxford, England). 2016;32:1895–1897. doi: 10.1093/bioinformatics/btw051

51. Durrett R, Schweinsberg J. Approximating selective sweeps. Theoretical Population Biology. 2004;66(2):129–138. doi: 10.1016/j.tpb.2004.04.002 15302222

52. Zeng K, Fu YX, Shi S, Wu CI. Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics. 2006;174:1431–1439. doi: 10.1534/genetics.106.061432 16951063

53. 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

54. Kelleher J, Etheridge AM, McVean G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS computational biology. 2016;12(5):e1004842. doi: 10.1371/journal.pcbi.1004842 27145223

55. Ewing G, Hermisson J. MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus. Bioinformatics. 2010;26(16):2064–2065. doi: 10.1093/bioinformatics/btq322 20591904

56. Charlesworth D. Balancing Selection and Its Effects on Sequences in Nearby Genome Regions. PLoS Genet. 2006;2(4):1–6. doi: 10.1371/journal.pgen.0020064

57. Charlesworth B, Morgan MT, Charlesworth D. The effect of deleterious mutations on neutral molecular variation. Genetics. 1993;134(4):1289–1303. 8375663

58. Charlesworth D, Charlesworth B, Morgan MT. The pattern of neutral molecular variation under the background selection model. Genetics. 1995;141(4):1619–1632. 8601499

59. Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS genetics. 2009;5(10). doi: 10.1371/journal.pgen.1000695 19851460

60. Hammer MF, Woerner AE, Mendez FL, Watkins JC, Wall JD. Genetic evidence for archaic admixture in Africa. Proceedings of the National Academy of Sciences. 2011;108(37):15123–15128. doi: 10.1073/pnas.1109300108

61. Xu D, Pavlidis P, Taskent RO, Alachiotis N, Flanagan C, DeGiorgio M, et al. Archaic Hominin Introgression in Africa Contributes to Functional Salivary MUC7 Genetic Variation. Molecular Biology and Evolution. 2017;34(10):2704–2715. doi: 10.1093/molbev/msx206 28957509

62. Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, et al. A draft sequence of the Neandertal genome. Science (New York, NY). 2010;328:710–722. doi: 10.1126/science.1188021

63. 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

64. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393

65. Kuderna LF, Tomlinson C, Hillier LW, Tran A, Fiddes I, Armstrong J, et al. A 3-way hybrid approach to generate a new high quality chimpanzee reference genome (Pan_tro_3. 0). GigaScience. 2017. doi: 10.1093/gigascience/gix098

66. Burbano HA, Hodges E, Green RE, Briggs AW, Krause J, Meyer M, et al. Targeted investigation of the Neandertal genome by array-based sequence capture. Science. 2010;328(5979):723–725. doi: 10.1126/science.1188046 20448179

67. Dumont BL, Payseur BA. Evolution of the genomic rate of recombination in mammals. Evolution; international journal of organic evolution. 2008;62:276–294. doi: 10.1111/j.1558-5646.2007.00278.x

68. Charlesworth B. Fundamental concepts in genetics: effective population size and patterns of molecular evolution and variation. Nature reviews Genetics. 2009;10:195–205. doi: 10.1038/nrg2526 19204717

69. Hudson RR, Kreitman M, Aguadé M. A test of neutral molecular evolution based on nucleotide data. Genetics. 1987;116(1):153–159. 3110004

70. Duchateau PN, Pullinger CR, Orellana RE, Kunitake ST, Naya-Vigne J, O’Connor PM, et al. Apolipoprotein L, a new human high density lipoprotein apolipoprotein expressed by the pancreas Identification, cloning, characterization, and plasma distribution of apolipoprotein L. Journal of Biological Chemistry. 1997;272(41):25576–25582. doi: 10.1074/jbc.272.41.25576 9325276

71. Smith EE, Malik HS. The apolipoprotein L family of programmed cell death and immunity genes rapidly evolved in primates at discrete sites of host–pathogen interactions. Genome research. 2009. doi: 10.1101/gr.085647.108

72. Mlitz V, Strasser B, Jaeger K, Hermann M, Ghannadan M, Buchberger M, et al. Trichohyalin-like proteins have evolutionarily conserved roles in the morphogenesis of skin appendages. Journal of Investigative Dermatology. 2014;134(11):2685–2692. doi: 10.1038/jid.2014.204 24780931

73. Lee SC, Wang M, McBride OW, O’Keefe EJ, Kim IG, Steinert PM. Human trichohyalin gene is clustered with the genes for other epidermal structural proteins and calcium-binding proteins at chromosomal locus 1q21. Journal of investigative dermatology. 1993;100(1):65–68. doi: 10.1111/1523-1747.ep12354504 8423399

74. Kypriotou M, Huber M, Hohl D. The human epidermal differentiation complex: cornified envelope precursors, S100 proteins and the’fused genes’ family. Experimental Dermatology. 2012;21(9):643–649. doi: 10.1111/j.1600-0625.2012.01472.x 22507538

75. Uecker H, Setter D, Hermisson J. Adaptive gene introgression after secondary contact. Journal of mathematical biology. 2015;70:1523–1580. doi: 10.1007/s00285-014-0802-y 24992884

76. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100(16):9440–5. doi: 10.1073/pnas.1530509100 12883005

77. Orr HA. The population genetics of speciation: the evolution of hybrid incompatibilities. Genetics. 1995;139:1805–1813. 7789779

78. Rogers AR, Achenbach A, Gwin K, Harris N. Superarchaic admixture confirms a deep separation of Neanderthals and Denisovans. In: AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY. vol. 168. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA; 2019. p. 206–206.

79. Sousa V, Hey J. Understanding the origin of species with genome-scale data: modelling gene flow. Nature reviews Genetics. 2013;14:404–414. doi: 10.1038/nrg3446 23657479

80. Charlesworth B. Measures of divergence between populations and the effect of forces that reduce variability. Mol Biol Evol. 1998;15(5):538–543. doi: 10.1093/oxfordjournals.molbev.a025953 9580982

81. Durand EY, Patterson N, Reich D, Slatkin M. Testing for ancient admixture between closely related populations. Molecular biology and evolution. 2011;28:2239–2252. doi: 10.1093/molbev/msr048 21325092

82. Geneva AJ, Muirhead CA, Kingan SB, Garrigan D. A New Method to Scan Genomes for Introgression in a Secondary Contact Model. PLOS ONE. 2015;10(4):e0118621. doi: 10.1371/journal.pone.0118621 25874895

83. Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 2009;5(10):e1000695. doi: 10.1371/journal.pgen.1000695 19851460

84. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS genetics. 2012;8:e1002967. doi: 10.1371/journal.pgen.1002967 23166502

85. Csilléry K, Blum MG, Gaggiotti OE, François O. Approximate Bayesian computation (ABC) in practice. Trends in ecology & evolution. 2010;25(7):410–418. doi: 10.1016/j.tree.2010.04.001

86. Slatkin M. Linkage disequilibrium–understanding the evolutionary past and mapping the medical future. Nature reviews Genetics. 2008;9:477–485. doi: 10.1038/nrg2361 18427557

87. Hellenthal G, Busby GBJ, Band G, Wilson JF, Capelli C, Falush D, et al. A genetic atlas of human admixture history. Science (New York, NY). 2014;343:747–751. doi: 10.1126/science.1243518

88. Patin E, Lopez M, Grollemund R, Verdu P, Harmant C, Quach H, et al. Dispersals and genetic adaptation of Bantu-speaking populations in Africa and North America. Science (New York, NY). 2017;356:543–546. doi: 10.1126/science.aal1988

89. Patin E, Quintana-Murci L. The demographic and adaptive history of central African hunter-gatherers and farmers. Current opinion in genetics & development. 2018;53:90–97. doi: 10.1016/j.gde.2018.07.008

90. Kelly JK. A test of neutrality based on interlocus associations. Genetics. 1997;146(3):1197–1206. 9215920

91. Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF, et al. Detecting recent positive selection in the Human genome from haplotype structure. Nature. 2002;419(6909):832–837. doi: 10.1038/nature01140 12397357

92. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, et al. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449:913–918. doi: 10.1038/nature06250 17943131

93. Smith J, Coop G, Stephens M, Novembre J. Estimating Time to the Common Ancestor for a Beneficial Allele. Molecular biology and evolution. 2018;35:1003–1017. doi: 10.1093/molbev/msy006 29361025

94. Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O, et al. Positive natural selection in the human lineage. Science (New York, NY). 2006;312:1614–1620. doi: 10.1126/science.1124309

95. 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

96. Knight SJL, Lese CM, Precht KS, Kuc J, Ning Y, Lucas S, et al. An Optimized Set of Human Telomere Clones for Studying Telomere Integrity and Architecture. The American Journal of Human Genetics. 2000;67(2):320–332. doi: 10.1086/302998 10869233

97. Schueler MG, Higgins AW, Rudd MK, Gustashaw K, Willard HF. Genomic and genetic definition of a functional human centromere. Science. 2001;294(5540):109–115. doi: 10.1126/science.1065042 11588252

98. Vernot B, Akey JM. Resurrecting surviving Neandertal lineages from modern human genomes. Science. 2014;343(6174):1017–1021. doi: 10.1126/science.1245938 24476670

99. Mendez FL, Watkins JC, Hammer MF. Neandertal origin of genetic variation at the cluster of OAS immunity genes. Molecular biology and evolution. 2013;30(4):798–801. doi: 10.1093/molbev/mst004 23315957

100. Sams AJ, Dumaine A, Nédélec Y, Yotova V, Alfieri C, Tanner JE, et al. Adaptively introgressed Neandertal haplotype at the OAS locus functionally impacts innate immune responses in humans. Genome biology. 2016;17(1):246. doi: 10.1186/s13059-016-1098-6 27899133

101. Dannemann M, Andrés AM, Kelso J. Introgression of Neandertal-and Denisovan-like haplotypes contributes to adaptive variation in human Toll-like receptors. The American Journal of Human Genetics. 2016;98(1):22–33. doi: 10.1016/j.ajhg.2015.11.015 26748514

102. Quach H, Rotival M, Pothlichet J, Loh YHE, Dannemann M, Zidane N, et al. Genetic adaptation and Neandertal admixture shaped the immune system of human populations. Cell. 2016;167(3):643–656. doi: 10.1016/j.cell.2016.09.024 27768888

103. Deschamps M, Laval G, Fagny M, Itan Y, Abel L, Casanova JL, et al. Genomic signatures of selective pressures and introgression from archaic hominins at human innate immunity genes. The American Journal of Human Genetics. 2016;98(1):5–21. doi: 10.1016/j.ajhg.2015.11.014 26748513

104. Durvasula A, Sankararaman S. Recovering signals of ghost archaic introgression in African populations. Science Advances. 2020;6(7):eaax5097. doi: 10.1126/sciadv.aax5097 32095519

105. Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, et al. A draft sequence of the Neandertal genome. science. 2010;328(5979):710–722. doi: 10.1126/science.1188021 20448178

106. Durand EY, Patterson N, Reich D, Slatkin M. Testing for ancient admixture between closely related populations. Molecular biology and evolution. 2011;28(8):2239–2252. doi: 10.1093/molbev/msr048 21325092

107. Racimo F, Sankararaman S, Nielsen R, Huerta-Sánchez E. Evidence for archaic adaptive introgression in humans. Nature Reviews Genetics. 2015;16(6):359–371. doi: 10.1038/nrg3936 25963373

108. Racimo F, Marnetto D, Huerta-Sánchez E. Signatures of archaic adaptive introgression in present-day human populations. Molecular biology and evolution. 2017;34(2):296–317. doi: 10.1093/molbev/msw216 27756828

109. Plagnol V, Wall JD. Possible ancestral structure in human populations. PLoS genetics. 2006;2(7). doi: 10.1371/journal.pgen.0020105 16895447

110. 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):43–49. doi: 10.1038/nature12886 24352235

111. Liang M, Nielsen R. The lengths of admixture tracts. Genetics. 2014;197(3):953–967. doi: 10.1534/genetics.114.162362 24770332

112. Steinrücken M, Spence JP, Kamm JA, Wieczorek E, Song YS. Model-based detection and analysis of introgressed Neanderthal ancestry in modern humans. Molecular ecology. 2018;27(19):3873–3888. doi: 10.1111/mec.14565 29603507

113. Sankararaman S, Mallick S, Dannemann M, Prüfer K, Kelso J, Pääbo S, et al. The genomic landscape of Neanderthal ancestry in present-day humans. Nature. 2014;507(7492):354. doi: 10.1038/nature12961 24476815

114. Sankararaman S, Mallick S, Patterson N, Reich D. The combined landscape of Denisovan and Neanderthal ancestry in present-day humans. Current Biology. 2016;26(9):1241–1247. doi: 10.1016/j.cub.2016.03.037 27032491

115. Schrider DR, Ayroles J, Matute DR, Kern AD. Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia. PLoS genetics. 2018;14(4):e1007341. doi: 10.1371/journal.pgen.1007341 29684059

116. Durvasula A, Sankararaman S. A statistical model for reference-free inference of archaic local ancestry. PLoS genetics. 2019;15(5):e1008175. doi: 10.1371/journal.pgen.1008175 31136573

117. Kopp P. Human Genome and Diseases: Review The TSH receptor and its role in thyroid disease. Cellular and Molecular Life Sciences CMLS. 2001;58(9):1301–1322. doi: 10.1007/PL00000941 11577986

118. Abe E, Marians RC, Yu W, Wu XB, Ando T, Li Y, et al. TSH is a negative regulator of skeletal remodeling. Cell. 2003;115(2):151–162. doi: 10.1016/S0092-8674(03)00771-2 14567913

119. Novack DV. TSH, the bone suppressing hormone. Cell. 2003;115(2):129–130. doi: 10.1016/S0092-8674(03)00812-2 14567908

120. Slominski A, Pisarchik A, Wortsman J, Kohn L, Ain KB, Venkataraman GM, et al. Expression of Hypothalamic–Pituitary–Thyroid Axis Related Genes in the Human Skin. Journal of Investigative Dermatology. 2002;119(6):1449–1455. doi: 10.1046/j.1523-1747.2002.19617.x 12485453

121. Bodó E, Kromminga A, Bíró T, Borbíró I, Gáspár E, Zmijewski MA, et al. Human female hair follicles are a direct, nonclassical target for thyroid-stimulating hormone. Journal of Investigative Dermatology. 2009;129(5):1126–1139. doi: 10.1038/jid.2008.361 19052559

122. Vidali S, Knuever J, Lerchner J, Giesen M, Tamás B, Klinger M, et al. Hypothalamic-pituitary-thyroid axis hormones stimulate mitochondrial function and biogenesis in human hair follicles. The Journal of investigative dermatology. 2014;134:33–42. doi: 10.1038/jid.2013.286 23949722

123. Sun SC, Hsu PJ, Wu FJ, Li SH, Lu CH, Luo CW. Thyrostimulin, but not thyroid-stimulating hormone (TSH), acts as a paracrine regulator to activate the TSH receptor in mammalian ovary. Journal of Biological Chemistry. 2010;285(6):3758–3765. doi: 10.1074/jbc.M109.066266 19955180

124. Coutelier JP, Kehrl JH, Bellur SS, Kohn LD, Notkins AL, Prabhakar BS. Binding and functional effects of thyroid stimulating hormone on human immune cells. Journal of clinical immunology. 1990;10(4):204–210. doi: 10.1007/BF00918653 2170438

125. Sorisky A, Bell A, Gagnon A. TSH receptor in adipose cells. Hormone and Metabolic Research. 2000;32(11/12):468–474. doi: 10.1055/s-2007-978672 11246811

126. Martinez-deMena R, Anedda A, Cadenas S, Obregon MJ. TSH effects on thermogenesis in rat brown adipocytes. Molecular and cellular endocrinology. 2015;404:151–158. doi: 10.1016/j.mce.2015.01.028 25662278

127. Elgadi A, Zemack H, Marcus C, Norgren S. Tissue-specific knockout of TSHr in white adipose tissue increases adipocyte size and decreases TSH-induced lipolysis. Biochemical and biophysical research communications. 2010;393(3):526–530. doi: 10.1016/j.bbrc.2010.02.042 20152797

128. Draman MS, Stechman M, Scott-Coombes D, Dayan CM, Rees DA, Ludgate M, et al. The role of thyrotropin receptor activation in adipogenesis and modulation of fat phenotype. Frontiers in endocrinology. 2017;8:83. doi: 10.3389/fendo.2017.00083 28469599

129. Endo T, Kobayashi T. Thyroid-stimulating hormone receptor in brown adipose tissue is involved in the regulation of thermogenesis. American Journal of Physiology-Endocrinology and Metabolism. 2008;295(2):E514–E518. doi: 10.1152/ajpendo.90433.2008 18559984

130. Thorgeirsson TE, Gudbjartsson DF, Surakka I, Vink JM, Amin N, Geller F, et al. Sequence variants at CHRNB3–CHRNA6 and CYP2A6 affect smoking behavior. Nature genetics. 2010;42(5):448. doi: 10.1038/ng.573 20418888

131. Hoft NR, Corley RP, McQueen MB, Schlaepfer IR, Huizinga D, Ehringer MA. Genetic association of the CHRNA6 and CHRNB3 genes with tobacco dependence in a nationally representative sample. Neuropsychopharmacology. 2009;34(3):698. doi: 10.1038/npp.2008.122 18704094

132. Cui W, Wang S, Yang J, Yi S, Yoon D, Kim Y, et al. Significant association of CHRNB3 variants with nicotine dependence in multiple ethnic populations. Molecular psychiatry. 2013;18(11):1149. doi: 10.1038/mp.2012.190 23319001

133. Culverhouse RC, Johnson EO, Breslau N, Hatsukami DK, Sadler B, Brooks AI, et al. Multiple distinct CHRNB 3–CHRNA 6 variants are genetic risk factors for nicotine dependence in African Americans and European Americans. Addiction. 2014;109(5):814–822. doi: 10.1111/add.12478 24401102

134. Hoft NR, Corley RP, McQueen MB, Huizinga D, Menard S, Ehringer MA. SNPs in CHRNA6 and CHRNB3 are associated with alcohol consumption in a nationally representative sample. Genes, Brain and Behavior. 2009;8(6):631–637. doi: 10.1111/j.1601-183X.2009.00495.x

135. Haller G, Kapoor M, Budde J, Xuei X, Edenberg H, Nurnberger J, et al. Rare missense variants in CHRNB3 and CHRNA3 are associated with risk of alcohol and cocaine dependence. Hum mol genet. 2013;23(3):810–819. doi: 10.1093/hmg/ddt463 24057674

136. Page NM, Olano-Martin E, Lanaway C, Turner R, Minihane AM. Polymorphisms in the Apolipoprotein L1 gene and their effects on blood lipid and glucose levels in middle age males. Genes & nutrition. 2006;1(2):133–135. doi: 10.1007/BF02829955

137. Pérez-Morga D, Vanhollebeke B, Paturiaux-Hanocq F, Nolan DP, Lins L, Homblé F, et al. Apolipoprotein LI promotes trypanosome lysis by forming pores in lysosomal membranes. Science. 2005;309(5733):469–472. doi: 10.1126/science.1114566 16020735

138. Lambrecht FL. Aspects of evolution and ecology of tsetse flies and trypanosomiasis in prehistoric African environment. The Journal of African History. 1964;5(1):1–24. doi: 10.1017/S0021853700004473

139. Franco JR, Simarro PP, Diarra A, Jannin JG. Epidemiology of human African trypanosomiasis. Clinical epidemiology. 2014;6:257. doi: 10.2147/CLEP.S39728 25125985

140. Lecordier L, Vanhollebeke B, Poelvoorde P, Tebabi P, Paturiaux-Hanocq F, Andris F, et al. C-terminal mutants of apolipoprotein LI efficiently kill both Trypanosoma brucei brucei and Trypanosoma brucei rhodesiense. PLoS pathogens. 2009;5(12):e1000685. doi: 10.1371/journal.ppat.1000685 19997494

141. Farrall M. Cardiovascular twist to the rapidly evolving apolipoprotein L1 story. Circulation research. 2014;114(5):746. doi: 10.1161/CIRCRESAHA.114.303354 24577959

142. Genovese G, Tonna SJ, Knob AU, Appel GB, Katz A, Bernhardy AJ, et al. A risk allele for focal segmental glomerulosclerosis in African Americans is located within a region containing APOL1 and MYH9. Kidney international. 2010;78(7):698–704. doi: 10.1038/ki.2010.251 20668430

143. Rosset S, Tzur S, Behar DM, Wasser WG, Skorecki K. The population genetics of chronic kidney disease: insights from the MYH9–APOL1 locus. Nature Reviews Nephrology. 2011;7(6):313. doi: 10.1038/nrneph.2011.52 21537348

144. Rogers GE, Harding HW, Llewellyn-Smith IJ. The origin of citrulline-containing proteins in the hair follicle and the chemical nature of trichohyalin, an intracellular precursor. Biochimica et Biophysica Acta (BBA)-Protein Structure. 1977;495(1):159–175. doi: 10.1016/0005-2795(77)90250-1

145. Rothnagel JA, Rogers GE. Trichohyalin, an intermediate filament-associated protein of the hair follicle. J Cell Biol. 1986;102(4):1419–1429. doi: 10.1083/jcb.102.4.1419 3958055

146. Steinert PM, Parry DA, Marekov LN. Trichohyalin mechanically strengthens the hair follicle multiple cross-bridging roles in the inner root sheath. Journal of Biological Chemistry. 2003;278(42):41409–41419. doi: 10.1074/jbc.M302037200 12853460

147. Westgate GE, Ginger RS, Green MR. The biology and genetics of curly hair. Experimental Dermatology. 2017;26(6):483–490. doi: 10.1111/exd.13347 28370528

148. Steinert P, Marekov L. Multiple roles for trichohyalin in the inner root sheath. Experimental dermatology. 1999;8(4):331. 10439256

149. Pośpiech E, Karłowska-Pik J, Marcińska M, Abidi S, Andersen JD, van den Berge M, et al. Evaluation of the predictive capacity of DNA variants associated with straight hair in Europeans. Forensic Science International: Genetics. 2015;19:280–288. doi: 10.1016/j.fsigen.2015.09.004

150. Adhikari K, Fontanil T, Cal S, Mendoza-Revilla J, Fuentes-Guajardo M, Chacón-Duque JC, et al. A genome-wide association scan in admixed Latin Americans identifies loci influencing facial and scalp hair features. Nature communications. 2016;7:10815. doi: 10.1038/ncomms10815 26926045

151. Huber M, Siegenthaler G, Mirancea N, Marenholz I, Nizetic D, Breitkreutz D, et al. Isolation and characterization of human repetin, a member of the fused gene family of the epidermal differentiation complex. Journal of investigative dermatology. 2005;124(5):998–1007. doi: 10.1111/j.0022-202X.2005.23675.x 15854042

152. Trzeciak M, Sakowicz-Burkiewicz M, Wesserling M, Gleń J, Dobaczewska D, Bandurski T, et al. Altered expression of genes encoding cornulin and repetin in atopic dermatitis. International archives of allergy and immunology. 2017;172(1):11–19. doi: 10.1159/000453452 28219068

153. Pośpiech E, Lee SD, Kukla-Bartoszek M, Karłowska-Pik J, Woźniak A, Boroń M, et al. Variation in the RPTN gene may facilitate straight hair formation in Europeans and East Asians. Journal of dermatological science. 2018. 29935789

154. Ewing G, Hermisson J. MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus. Bioinformatics. 2010;26(16):2064–2065. doi: 10.1093/bioinformatics/btq322 20591904

155. Peng B, Kimmel M. simuPOP: a forward-time population genetics simulation environment. Bioinformatics (Oxford, England). 2005;21:3686–3687. doi: 10.1093/bioinformatics/bti584

156. Staab PR, Metzler D. coala: an R framework for coalescent simulation. Bioinformatics (Oxford, England). 2016;32:1903–1904. doi: 10.1093/bioinformatics/btw098

157. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467(7319):1061. doi: 10.1038/nature09534

158. Derrien T, Estellé J, Sola SM, Knowles DG, Raineri E, Guigó R, et al. Fast computation and applications of genome mappability. PloS one. 2012;7(1):e30377. doi: 10.1371/journal.pone.0030377 22276185


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