Reconstructing past changes in locus-specific recombination rates

BMC Genetics

Jan Schmid²  Matthew C Wilkins²  Barbara R Holland¹  Murray P Cox² 

[1]School of Mathematics and Physics, University of Tasmania, Hobart, Australia

[2]Institute of Fundamental Sciences, Massey University, Private Bag 11 222, Palmerston North, 4442, New Zealand

关键词: Statistical inference;    Reconstruction;    Temporal;    Recombination rate;   

Others  :  1087337
DOI  :  10.1186/1471-2156-14-11

 received in 2012-08-22, accepted in 2013-02-21,  发布年份 2013

【 摘 要 】

Background

Recombination rates vary at the level of the species, population and individual. Now recognized as a transient feature of the genome, recombination rates at a given locus can change markedly over time. Existing inferential methods, predominantly based on linkage disequilibrium patterns, return a long-term average estimate of past recombination rates. Such estimates can be misleading, but no analytical framework to infer recombination rates that have changed over time is currently available.

Results

We apply coalescent modeling in conjunction with a suite of summary statistics to show that the recombination history of a locus can be reconstructed from a time series of genetic samples. More usefully, we describe a new method, based on n-tuple dataset subsampling, to infer past changes in recombination rate from DNA sequences taken at a single time point. This subsampling strategy can correctly assign simulated loci to constant, increasing and decreasing recombination models with an accuracy of 84%.

Conclusions

While providing an important stepping-stone to determining past recombination rates, n-tuple subsampling still exhibits a moderate error rate. Theoretical limitations indicated by coalescent theory suggest that highly accurate inference of past recombination rates will remain challenging. Nevertheless, we show for the first time that reconstructing historic recombination rates is possible in principle.

【 授权许可】

   
2013 Cox et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Paigen K, Petkov P: Mammalian recombination hot spots: properties, control and evolution. Nat Rev Genet 2010, 11:221-233.
• [2]Kong A, Barnard J, Gudbjartsson DF, Thorleifsson G, Jonsdottir G, Sigurdardottir S, Richardsson B, Jonsdottir J, Thorgeirsson T, Frigge ML: Recombination rate and reproductive success in humans. Nat Genet 2004, 36(11):1203-1206.
• [3]Calabrese P: A population genetics model with recombination hotspots that are heterogeneous across the population. Proc Natl Acad Sci USA 2007, 104(11):4748-4752.
• [4]Broman KW, Murray JC, Sheffield VC, White RL, Weber JL: Comprehensive human genetic maps: individual and sex-specific variation in recombination. Am J Hum Genet 1998, 63(3):861-869.
• [5]Neumann R, Jeffreys AJ: Polymorphism in the activity of human crossover hotspots independent of local DNA sequence variation. Hum Mol Genet 2006, 15(9):1401-1411.
• [6]Kong A, Gudbjartsson DF, Sainz J, Jonsdottir GM, Gudjonsson SA, Richardsson B, Sigurdardottir S, Barnard J, Hallbeck B, Masson G: A high-resolution recombination map of the human genome. Nat Genet 2002, 31(3):241-247.
• [7]Khil PP, Camerini-Otero RD: Variation in patterns of human meiotic recombination. Genome Dyn 2009, 5:117-127.
• [8]Smukowski CS, Noor MAF: Recombination rate variation in closely related species. Heredity 2011, 107(6):496-508.
• [9]Webster MT, Hurst LD: Direct and indirect consequences of meiotic recombination: implications for genome evolution. Trends Genet 2012, 28(3):101-109.
• [10]Jeffreys AJ, Neumann R: The rise and fall of a human recombination hot spot. Nat Genet 2009, 41(5):625-629.
• [11]Laayouni H, Montanucci L, Sikora M, Melé M, Dall’Olio GM, Lorente-Galdos B, McGee KM, Graffelman J, Awadalla P, Bosch E: Similarity in recombination rate estimates highly correlates with genetic differentiation in humans. PLoS One 2011, 6(3):e17913.
• [12]Spencer CCA, Deloukas P, Hunt S, Mullikin J, Myers S, Silverman B, Donnelly P, Bentley D, McVean G: The influence of recombination on human genetic diversity. PLoS Genet 2006, 2(9):e148.
• [13]Úbeda F, Wilkins JF: The Red Queen theory of recombination hotspots. J Evol Biol 2011, 24(3):541-553.
• [14]Myers S, Freeman C, Auton A, Donnelly P, McVean G: A common sequence motif associated with recombination hot spots and genome instability in humans. Nat Genet 2008, 40(9):1124-1129.
• [15]Zheng J, Khil P, Camerini-Otero RD, Przytycka T: Detecting sequence polymorphisms associated with meiotic recombination hotspots in the human genome. Genome Biol 2010, 11(10):R103. BioMed Central Full Text
• [16]Coop G, Myers SR: Live hot, die young: transmission distortion in recombination hotspots. PLoS Genet 2007, 3(3):e35.
• [17]Hellenthal G, Pritchard JK, Stephens M: The effects of genotype-dependent recombination, and transmission asymmetry, on linkage disequilibrium. Genetics 2006, 172(3):2001-2005.
• [18]Brick K, Smagulova F, Khil P, Camerini-Otero RD, Petukhova GV: Genetic recombination is directed away from functional genomic elements in mice. Nature 2012, 485:642-645.
• [19]Myers S, Bowden R, Tumian A, Bontrop RE, Freeman C, MacFie TS, McVean G, Donnelly P: Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination. Science 2010, 327(5967):876-879.
• [20]Baudat F, Buard J, Grey C, Fledel-Alon A, Ober C, Przeworski M, Coop G, de Massy B: PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 2010, 327(5967):836-840.
• [21]Kauppi L, Stumpf MPH, Jeffreys AJ: Localized breakdown in linkage disequilibrium does not always predict sperm crossover hot spots in the human MHC class II region. Genomics 2005, 86(1):13-24.
• [22]Jeffreys AJ, Neumann R, Panayi M, Myers S, Donnelly P: Human recombination hot spots hidden in regions of strong marker association. Nat Genet 2005, 37(6):601-606.
• [23]Hudson RR: Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 2002, 18(2):337-338.
• [24]Cox MP, Hammer MF: A question of scale: human migrations writ large and small. BMC Biol 2010, 8:98. BioMed Central Full Text
• [25]Cox MP, Morales DA, Woerner AE, Sozanski J, Wall JD, Hammer MF: Autosomal resequence data reveal late stone Age signals of population expansion in sub-saharan african foraging and farming populations. PLoS One 2009, 4(7):e6366.
• [26]Cox MP, Woerner AE, Wall JD, Hammer MF: Intergenic DNA sequences from the human X chromosome reveal high rates of global gene flow. BMC Genet 2008, 9:e76.
• [27]Wall JD, Cox MP, Mendez FL, Woerner A, Severson T, Hammer MF: A novel DNA sequence database for analyzing human demographic history. Genome Res 2008, 18:1354-1361.
• [28]Schmid J, Cannon RD, Holland BR: A futile act? Thoughts on the reproductive biology of Candida albicans. Mycologist 2004, 18(4):158-163.
• [29]Efron B, Tibshirani R: An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall; 1993.
• [30]Felsenstein J: Confidence limits on phylogenies: an approach using the bootstrap. Evolution 1985, 39(4):783-791.
• [31]Hastie T, Tibshirani R, Friedman J: The elements of statistical learning: data mining, inference and prediction. Berlin: Springer Verlag; 2009.
• [32]Beaumont MA, Nielsen R, Robert C, Hey J, Gaggiotti O, Knowles L, Estoup A, Panchal M, Corander J, Hickerson M: In defence of model-based inference in phylogeography. Mol Ecol 2010, 19(3):436-446.
• [33]Csilléry K, Blum MGB, Gaggiotti OE, François O: Approximate Bayesian Computation (ABC) in practice. Trends Ecol Evol 2010, 25(7):410-418.
• [34]Cox MP: Accuracy of molecular dating with the rho statistic: deviations from coalescent expectations under a range of demographic models. Hum Biol 2008, 80:335-357.
• [35]Cox MP, Mendez FL, Karafet TM, Metni Pilkington M, Kingan SB, Destro-Bisol G, Strassmann BI, Hammer MF: Testing for archaic hominin admixture on the X chromosome: model likelihoods for the modern human RRM2P4 region from summaries of genealogical topology under the structured coalescent. Genetics 2008, 178:427-437.
• [36]Hey J, Nielsen R: Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics 2004, 167(2):747-760.
• [37]Fisher RA: The use of multiple measurements in taxonomic problems. Ann Eug 1936, 7(2):179-188.
• [38]Wu W, Mallet Y, Walczak B, Penninckx W, Massart DL, Heuerding S, Erni F: Comparison of regularized discriminant analysis, linear discriminant analysis and quadratic discriminant analysis, applied to NIR data. Anal Chim Acta 1996, 329:257-265.
• [39]Johnston HR, Cutler DJ: Population demographic history can cause the appearance of recombination hotspots. Am J Hum Genet 2012, 90(5):774-783.
• [40]Kingman JFC: On the genealogy of large populations. J Appl Prob 1982, 19:27-43.
• [41]Kingman JFC: The coalescent. Stochastic Process Appl 1982, 13:235-248.
• [42]Wakeley J: Coalescent Theory: An Introduction. Greenwood Village, Colorado: Roberts & Company Publishers; 2008.
• [43]Kliman RM, Hey J: DNA sequence variation at the period locus within and among species at the Drosophila melanogaster complex. Genetics 1993, 133:375-387.
• [44]Saunders IW, Tavaré S, Watterson GA: On the genealogy of nested subsamples from a haploid population. Adv Appl Prob 1984, 16:471-491.
• [45]Thornton K: libsequence: a C++ class library for evolutionary genetic analysis. Bioinformatics 2003, 19(17):2325-2327.
• [46]Watterson GA: On the number of segregating sites in genetical models without recombination. Theor Popul Biol 1975, 7(2):256-276.
• [47]Hudson RR, Kaplan NL: Statistical properties of the number of recombination events in the history of a sample of DNA sequences. Genetics 1985, 111(1):147-164.
• [48]Myers SR, Griffiths RC: Bounds on the minimum number of recombination events in a sample history. Genetics 2003, 163(1):375-394.
• [49]Nei M: Molecular evolutionary genetics. New York: Columbia University Press; 1987.
• [50]Wall JD: Recombination and the power of statistical tests of neutrality. Genet Res, Camb 1999, 74:65-79.
• [51]Hudson RR: Estimating the recombination parameter of a finite population model without selection. Genet Res 1987, 50:245-250.
• [52]Kelly JK: A test of neutrality based on interlocus associations. Genetics 1997, 146:1197-1206.
• [53]R Development Core Team: A Language and Environment for Statistical Computing. 2012. http://www.r-project.org webcite