Calibrating the Human Mutation Rate via Ancestral Recombination Density in Diploid Genomes

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The rate at which new heritable mutations occur in the human genome is a fundamental parameter in population and evolutionary genetics. However, recent direct family-based estimates of the mutation rate have consistently been much lower than previous results from comparisons with other great ape species. Because split times of species and populations estimated from genetic data are often inversely proportional to the mutation rate, resolving the disagreement would have important implications for understanding human evolution. In our work, we apply a new technique that uses mutations that have accumulated over many generations on either copy of a chromosome in an individual’s genome. Instead of an external reference point, we rely on fine-scale knowledge of the human recombination rate to calibrate the long-term mutation rate. Our procedure accounts for possible errors found in real data, and we also show that it is robust to a range of model violations. Using eight diploid genomes from non-African individuals, we infer a rate of 1.61 ± 0.13 × 10−8 single-nucleotide changes per base per generation, which is intermediate between most phylogenetic and pedigree-based estimates. Thus, our estimate implies reasonable, intermediate-age population split times across a range of time scales.

Vyšlo v časopise: Calibrating the Human Mutation Rate via Ancestral Recombination Density in Diploid Genomes. PLoS Genet 11(11): e32767. doi:10.1371/journal.pgen.1005550
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005550

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Zdroje

1. Scally A, Durbin R (2012) Revising the human mutation rate: implications for understanding human evolution. Nat Rev Genet 13: 745–753. doi: 10.1038/nrg3295 22965354

2. Campbell CD, Eichler EE (2013) Properties and rates of germline mutations in humans. Trends Genet 29: 575–584. doi: 10.1016/j.tig.2013.04.005 23684843

3. Ségurel L, Wyman MJ, Przeworski M (2014) Determinants of mutation rate variation in the human germline. Annu Rev Genomics Hum Genet 15: 47–70. doi: 10.1146/annurev-genom-031714-125740 25000986

4. Li WH, Tanimura M (1987) The molecular clock runs more slowly in man than in apes and monkeys. Nature 326: 93–96. doi: 10.1038/326093a0 3102974

5. Kimura M (1968) Evolutionary rate at the molecular level. Nature 217: 624–626. doi: 10.1038/217624a0 5637732

6. Roach JC, Glusman G, Smit AF, Huff CD, Hubley R, et al. (2010) Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328: 636–639. doi: 10.1126/science.1186802 20220176

7. Conrad DF, Keebler JE, DePristo MA, Lindsay SJ, Zhang Y, et al. (2011) Variation in genome-wide mutation rates within and between human families. Nat Genet 43: 712–714. doi: 10.1038/ng.862 21666693

8. Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, et al. (2012) Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488: 471–475. doi: 10.1038/nature11396 22914163

9. Campbell CD, Chong JX, Malig M, Ko A, Dumont BL, et al. (2012) Estimating the human mutation rate using autozygosity in a founder population. Nat Genet 44: 1277–1281. doi: 10.1038/ng.2418 23001126

10. Besenbacher S, Liu S, Izarzugaza JM, Grove J, Belling K, et al. (2015) Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios. Nat Comm 6: 5969. doi: 10.1038/ncomms6969

11. Fu Q, Li H, Moorjani P, Jay F, Slepchenko SM, et al. (2014) Genome sequence of a 45,000-year-old modern human from western Siberia. Nature 514: 445–449. doi: 10.1038/nature13810 25341783

12. Fenner J (2005) Cross-cultural estimation of the human generation interval for use in genetics-based population divergence studies. Am J Phys Anthropol 128: 415–423. doi: 10.1002/ajpa.20188 15795887

13. Sun JX, Helgason A, Masson G, Ebenesersdóttir SS, Li H, et al. (2012) A direct characterization of human mutation based on microsatellites. Nat Genet 44: 1161–1165. doi: 10.1038/ng.2398 22922873

14. Myers S, Bottolo L, Freeman C, McVean G, Donnelly P (2005) A fine-scale map of recombination rates and hotspots across the human genome. Science 310: 321–324. doi: 10.1126/science.1117196 16224025

15. The International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861. doi: 10.1038/nature06258 17943122

16. Kong A, Thorleifsson G, Gudbjartsson DF, Masson G, Sigurdsson A, et al. (2010) Fine-scale recombination rate differences between sexes, populations and individuals. Nature 467: 1099–1103. doi: 10.1038/nature09525 20981099

17. Hinch AG, Tandon A, Patterson N, Song Y, Rohland N, et al. (2011) The landscape of recombination in African Americans. Nature 476: 170–175. doi: 10.1038/nature10336 21775986

18. Palamara PF, Francioli L, Genovese G, Wilton P, Gusev A, et al. (2015) Leveraging distant relatedness to quantify human mutation and gene conversion rates. Am J Hum Genet. 97. doi: 10.1016/j.ajhg.2015.10.006

19. Li H, Durbin R (2011) Inference of human population history from individual whole-genome sequences. Nature 475: 493–496. doi: 10.1038/nature10231 21753753

20. Sankararaman S, Patterson N, Li H, Pääbo S, Reich D (2012) The date of interbreeding between Neandertals and modern humans. PLoS Genet 8: e1002947. doi: 10.1371/journal.pgen.1002947 23055938

21. Williams A, Geneovese G, Dyer T, Truax K, Jun G, et al. (2015) Non-crossover gene conversions show strong GC bias and unexpected clustering in humans. eLife 4: e04637. doi: 10.7554/eLife.04637

22. Li H (2014) Towards better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30: 2843–2851. doi: 10.1093/bioinformatics/btu356 24974202

23. Kong A, Thorleifsson G, Frigge ML, Masson G, Gudbjartsson DF, et al. (2014) Common and low-frequency variants associated with genome-wide recombination rate. Nat Genet 46: 11–16. doi: 10.1038/ng.2833 24270358

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

25. Cochran G, Harpending H (2013) Paternal age and genetic load. Hum Biol 85: 515–528. doi: 10.3378/027.085.0401 25019186

26. Francioli LC, Polak PP, Koren A, Menelaou A, Chun S, et al. (2015) Genome-wide patterns and properties of de novo mutations in humans. Nat Genet 47: 822–826. doi: 10.1038/ng.3292 25985141

27. Neale BM, Kou Y, Liu L, Maayan A, Samocha KE, et al. (2012) Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485: 242–245. doi: 10.1038/nature11011 22495311

28. Prüfer K, Racimo F, Patterson N, Jay F, Sankararaman S, et al. (2014) The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505: 43–49. doi: 10.1038/nature12886 24352235

29. Genome of the Netherlands Consortium (2014) Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat Genet 46: 818–825. doi: 10.1038/ng.3021 24974849

30. Mailund T, Halager AE, Westergaard M, Dutheil JY, Munch K, et al. (2012) A new isolation with migration model along complete genomes infers very different divergence processes among closely related great ape species. PLoS Genet 8: e1003125. doi: 10.1371/journal.pgen.1003125 23284294

31. Prado-Martinez J, Sudmant PH, Kidd JM, Li H, Kelley JL, et al. (2013) Great ape genetic diversity and population history. Nature 499: 471–475. doi: 10.1038/nature12228 23823723

32. Scally A, Dutheil JY, Hillier LW, Jordan GE, Goodhead I, et al. (2012) Insights into hominid evolution from the gorilla genome sequence. Nature 483: 169–175. doi: 10.1038/nature10842 22398555

33. Schiffels S, Durbin R (2014) Inferring human population size and separation history from multiple genome sequences. Nat Genet 46: 919–925. doi: 10.1038/ng.3015 24952747

34. Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, et al. (2012) De novo gene disruptions in children on the autistic spectrum. Neuron 74: 285–299. doi: 10.1016/j.neuron.2012.04.009 22542183

35. Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, et al. (2014) De novo mutations in schizophrenia implicate synaptic networks. Nature 506: 179–184. doi: 10.1038/nature12929 24463507

36. Iossifov I, ORoak BJ, Sanders SJ, Ronemus M, Krumm N, et al. (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature 515: 216–221. doi: 10.1038/nature13908 25363768

37. Harris K (2015) Evidence for recent, population-specific evolution of the human mutation rate. Proc Natl Acad Sci U S A 112: 3439–3444. doi: 10.1073/pnas.1418652112 25733855

38. McVean GA, Cardin NJ (2005) Approximating the coalescent with recombination. Philos Trans R Soc Lond B Biol Sci 360: 1387–1393. doi: 10.1098/rstb.2005.1673 16048782

39. Marjoram P, Wall JD (2006) Fast “coalescent” simulation. BMC Genet 7: 16. doi: 10.1186/1471-2156-7-16 16539698

40. Coop G, Wen X, Ober C, Pritchard JK, Przeworski M (2008) High-resolution mapping of crossovers reveals extensive variation in fine-scale recombination patterns among humans. Science 319: 1395–1398. doi: 10.1126/science.1151851 18239090

41. Hellenthal G, Stephens M (2007) mshot: modifying hudson’s ms simulator to incorporate crossover and gene conversion hotspots. Bioinformatics 23: 520–521. doi: 10.1093/bioinformatics/btl622 17150995

42. Hudson RR (2002) Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18: 337–338. doi: 10.1093/bioinformatics/18.2.337 11847089

43. Jeffreys AJ, May CA (2004) Intense and highly localized gene conversion activity in human meiotic crossover hot spots. Nat Genet 36: 151–156. doi: 10.1038/ng0404-427a 14704667