Whole-Exome Sequencing Reveals a Rapid Change in the Frequency of Rare Functional Variants in a Founding Population of Humans

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Whole-exome or gene targeted resequencing in hundreds to thousands of individuals has shown that the majority of genetic variants are at low frequency in human populations. Rare variants are enriched for functional mutations and are expected to explain an important fraction of the genetic etiology of human disease, therefore having a potential medical interest. In this work, we analyze the whole-exome sequences of French-Canadian individuals, a founder population with a unique demographic history that includes an original population bottleneck less than 20 generations ago, followed by a demographic explosion, and the whole exomes of French individuals sampled from France. We show that in less than 20 generations of genetic isolation from the French population, the genetic pool of French-Canadians shows reduced levels of diversity, higher homozygosity, and an excess of rare variants with low variant sharing with Europeans. Furthermore, the French-Canadian population contains a larger proportion of putatively damaging functional variants, which could partially explain the increased incidence of genetic disease in the province. Our results highlight the impact of population demography on genetic fitness and the contribution of rare variants to the human genetic variation landscape, emphasizing the need for deep cataloguing of genetic variants by resequencing worldwide human populations in order to truly assess disease risk.

Vyšlo v časopise: Whole-Exome Sequencing Reveals a Rapid Change in the Frequency of Rare Functional Variants in a Founding Population of Humans. PLoS Genet 9(9): e32767. doi:10.1371/journal.pgen.1003815
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003815

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Zdroje

1. Cavalli-SforzaLL, FeldmanMW (2003) The application of molecular genetic approaches to the study of human evolution. Nat Genet 33 Suppl: 266–275.

2. BarbujaniG, MagagniA, MinchE, Cavalli-SforzaLL (1997) An apportionment of human DNA diversity. Proc Natl Acad Sci U S A 94: 4516–4519.

3. CoventryA, Bull-OttersonLM, LiuX, ClarkAG, MaxwellTJ, et al. (2010) Deep resequencing reveals excess rare recent variants consistent with explosive population growth. Nat Commun 1: 131.

4. KeinanA, ClarkAG (2012) Recent explosive human population growth has resulted in an excess of rare genetic variants. Science 336: 740–743.

5. LiY, VinckenboschN, TianG, Huerta-SanchezE, JiangT, et al. (2010) Resequencing of 200 human exomes identifies an excess of low-frequency non-synonymous coding variants. Nat Genet 42: 969–972.

6. MarthGT, YuF, IndapAR, GarimellaK, GravelS, et al. (2011) The functional spectrum of low-frequency coding variation. Genome Biol 12: R84.

7. NelsonMR, WegmannD, EhmMG, KessnerD, St JeanP, et al. (2012) An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337: 100–104.

8. TennessenJA, BighamAW, O'ConnorTD, FuW, KennyEE, et al. (2012) Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337: 64–69.

9. AbecasisGR, AltshulerD, AutonA, BrooksLD, DurbinRM, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073.

10. AbecasisGR, AutonA, BrooksLD, DePristoMA, DurbinRM, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65.

11. KryukovGV, PennacchioLA, SunyaevSR (2007) Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am J Hum Genet 80: 727–739.

12. BustamanteCD, BurchardEG, De la VegaFM (2011) Genomics for the world. Nature 475: 163–165.

13. GravelS, HennBM, GutenkunstRN, IndapAR, MarthGT, et al. (2011) Demographic history and rare allele sharing among human populations. Proc Natl Acad Sci U S A 108: 11983–11988.

14. BoykoAR, WilliamsonSH, IndapAR, DegenhardtJD, HernandezRD, et al. (2008) Assessing the evolutionary impact of amino acid mutations in the human genome. PLoS Genet 4: e1000083.

15. LohmuellerKE, IndapAR, SchmidtS, BoykoAR, HernandezRD, et al. (2008) Proportionally more deleterious genetic variation in European than in African populations. Nature 451: 994–997.

16. MathiesonI, McVeanG (2012) Differential confounding of rare and common variants in spatially structured populations. Nat Genet 44: 243–246.

17. Charbonneau H, Desjardins B, Guillemette A, Landry Y, Légaré J, et al.. (1993) The First French Canadians: Pioneers in the St. Lawrence Valley. Newark, London and Toronto: University of Delaware Press and Associated University Presses.

18. Charbonneau H, Desjardins B, Légaré J, Denis H (2000) The population of the St-Lawrence Valley, 1608–1760. In: Haines M, Steckel R, editors. A population history of North America. Cambridge: Cambridge University Press. pp. 99–142.

19. ScriverCR (2001) Human genetics: lessons from Quebec populations. Annu Rev Genomics Hum Genet 2: 69–101.

20. Livi-Bacci M (1989) Storia minima della popolazione del mondo. Torino: Loescher Editore.

21. BhererC, LabudaD, Roy-GagnonMH, HoudeL, TremblayM, et al. (2011) Admixed ancestry and stratification of Quebec regional populations. Am J Phys Anthropol 144: 432–441.

22. LabergeAM, MichaudJ, RichterA, LemyreE, LambertM, et al. (2005) Population history and its impact on medical genetics in Quebec. Clin Genet 68: 287–301.

23. MoreauC, BhererC, VezinaH, JompheM, LabudaD, et al. (2011) Deep human genealogies reveal a selective advantage to be on an expanding wave front. Science 334: 1148–1150.

24. GirardSL, GauthierJ, NoreauA, XiongL, ZhouS, et al. (2011) Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet 43: 860–863.

25. CooperGM, StoneEA, AsimenosG, GreenED, BatzoglouS, et al. (2005) Distribution and intensity of constraint in mammalian genomic sequence. Genome Res 15: 901–913.

26. CooperGM, GoodeDL, NgSB, SidowA, BamshadMJ, et al. (2010) Single-nucleotide evolutionary constraint scores highlight disease-causing mutations. Nat Methods 7: 250–251.

27. HodgkinsonA, CasalsF, IdaghdourY, GrenierJC, HernandezRD, et al. (2013) Selective constraint, background selection, and mutation accumulation variability within and between human populations. BMC Genomics 14: 495.

28. GoodeDL, CooperGM, SchmutzJ, DicksonM, GonzalesE, et al. (2010) Evolutionary constraint facilitates interpretation of genetic variation in resequenced human genomes. Genome Res 20: 301–310.

29. AdzhubeiIA, SchmidtS, PeshkinL, RamenskyVE, GerasimovaA, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248–249.

30. Eyre-WalkerA, KeightleyPD (2009) Estimating the rate of adaptive molecular evolution in the presence of slightly deleterious mutations and population size change. Mol Biol Evol 26: 2097–2108.

31. KeightleyPD, Eyre-WalkerA (2007) Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies. Genetics 177: 2251–2261.

32. LabergeAM (2007) Prevalence and distribution of genetic diseases in Quebec: impact of the past on the present. Med Sci (Paris) 23: 997–1001.

33. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

34. NejentsevS, WalkerN, RichesD, EgholmM, ToddJA (2009) Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324: 387–389.

35. PruittKD, HarrowJ, HarteRA, WallinC, DiekhansM, et al. (2009) The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes. Genome Res 19: 1316–1323.

36. McKennaA, HannaM, BanksE, SivachenkoA, CibulskisK, et al. (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20: 1297–1303.

37. LiH, HandsakerB, WysokerA, FennellT, RuanJ, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079.

38. DePristoMA, BanksE, PoplinR, GarimellaKV, MaguireJR, et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43: 491–498.

39. PriceAL, PattersonNJ, PlengeRM, WeinblattME, ShadickNA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.

40. GutenkunstRN, HernandezRD, WilliamsonSH, BustamanteCD (2009) Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet 5: e1000695.

41. MacArthurDG, BalasubramanianS, FrankishA, HuangN, MorrisJ, et al. (2012) A systematic survey of loss-of-function variants in human protein-coding genes. Science 335: 823–828.

42. KleinmanCL, MajewskiJ (2012) Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335: 1302; author reply 1302.

43. LiM, WangIX, LiY, BruzelA, RichardsAL, et al. (2011) Widespread RNA and DNA sequence differences in the human transcriptome. Science 333: 53–58.

44. LinW, PiskolR, TanMH, LiJB (2012) Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335: 1302; author reply 1302.

45. PickrellJK, GiladY, PritchardJK (2012) Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335: 1302; author reply 1302.

46. LoHS, WangZ, HuY, YangHH, GereS, et al. (2003) Allelic variation in gene expression is common in the human genome. Genome Res 13: 1855–1862.

47. HwangDG, GreenP (2004) Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Proc Natl Acad Sci U S A 101: 13994–14001.

48. HernandezRD, WilliamsonSH, ZhuL, BustamanteCD (2007) Context-dependent mutation rates may cause spurious signatures of a fixation bias favoring higher GC-content in humans. Mol Biol Evol 24: 2196–2202.

49. HernandezRD (2008) A flexible forward simulator for populations subject to selection and demography. Bioinformatics 24: 2786–2787.

50. SakharkarMK, ChowVT, KangueaneP (2004) Distributions of exons and introns in the human genome. In Silico Biol 4: 387–393.