Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives

English version

Autoři: Madison Caballero ^aff001; Daniel N. Seidman ^aff002; Ying Qiao ^aff002; Jens Sannerud ^aff002; Thomas D. Dyer ^aff003; Donna M. Lehman ^aff004; Joanne E. Curran ^aff003; Ravindranath Duggirala ^aff003; John Blangero ^aff003; Shai Carmi ^aff005; Amy L. Williams ^aff002
Působiště autorů: Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America ^aff001; Department of Computational Biology, Cornell University, Ithaca, New York, United States of America ^aff002; South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, United States of America ^aff003; Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America ^aff004; Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel ^aff005
Vyšlo v časopise: Crossover interference and sex-specific genetic maps shape identical by descent sharing in close relatives. PLoS Genet 15(12): e32767. doi:10.1371/journal.pgen.1007979
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1007979

Souhrn

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of pairwise IBD proportions by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD sharing ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ped-sim.

Klíčová slova:

Haplotypes – Gene mapping – Simulation and modeling – Genetic interference – Meiosis – Probability density – Physical mapping – Crossover interference

Zdroje

1. Weir BS, Anderson AD, Hepler AB. Genetic relatedness analysis: modern data and new challenges. Nature Reviews Genetics. 2006;7(10):771–780. doi: 10.1038/nrg1960 16983373

2. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–209. doi: 10.1038/s41586-018-0579-z 30305743

3. Staples J, Maxwell EK, Gosalia N, Gonzaga-Jauregui C, Snyder C, Hawes A, et al. Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes. The American Journal of Human Genetics. 2018;102(5):874–889. doi: 10.1016/j.ajhg.2018.03.012 29727688

4. Staples J, Witherspoon DJ, Jorde LB, Nickerson DA, Below JE, Huff CD, et al. PADRE: Pedigree-Aware Distant-Relationship Estimation. The American Journal of Human Genetics. 2016;99(1):154–162. doi: 10.1016/j.ajhg.2016.05.020 27374771

5. Ko A, Nielsen R. Composite likelihood method for inferring local pedigrees. PLOS Genetics. 2017;13(8):e1006963. doi: 10.1371/journal.pgen.1006963 28827797

6. Ramstetter MD, Shenoy SA, Dyer TD, Lehman DM, Curran JE, Duggirala R, et al. Inferring Identical-by-Descent Sharing of Sample Ancestors Promotes High-Resolution Relative Detection. The American Journal of Human Genetics. 2018;103(1):30–44. doi: 10.1016/j.ajhg.2018.05.008 29937093

7. Staples J, Qiao D, Cho MH, Silverman EK, Nickerson DA, Below JE. PRIMUS: Rapid Reconstruction of Pedigrees from Genome-wide Estimates of Identity by Descent. The American Journal of Human Genetics. 2014;95(5):553–564. doi: 10.1016/j.ajhg.2014.10.005 25439724

8. Epstein MP, Duren WL, Boehnke M. Improved Inference of Relationship for Pairs of Individuals. The American Journal of Human Genetics. 2000;67(5):1219–1231. doi: 10.1016/S0002-9297(07)62952-8 11032786

9. Hill W, Weir B. Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genetics Research. 2011;93(01):47–64. doi: 10.1017/S0016672310000480 21226974

10. Hill WG, White IMS. Identification of Pedigree Relationship from Genome Sharing. G3: Genes, Genomes, Genetics. 2013;3(9):1553–1571. doi: 10.1534/g3.113.007500

11. Henn BM, Hon L, Macpherson JM, Eriksson N, Saxonov S, Pe’er I, et al. Cryptic Distant Relatives Are Common in Both Isolated and Cosmopolitan Genetic Samples. PLOS ONE. 2012;7(4):e34267. doi: 10.1371/journal.pone.0034267 22509285

12. Ball CA, Barber MJ, Byrnes J, Carbonetto P, Chahine KG, Curtis RE, et al. AncestryDNA Matching White Paper. AncestryDNA; 2016.

13. Broman KW, Weber JL. Characterization of human crossover interference. The American Journal of Human Genetics. 2000;66(6):1911–1926. doi: 10.1086/302923 10801387

14. Housworth E, Stahl F. Crossover interference in humans. The American Journal of Human Genetics. 2003;73(1):188–197. doi: 10.1086/376610 12772089

15. Campbell CL, Furlotte NA, Eriksson N, Hinds D, Auton A. Escape from crossover interference increases with maternal age. Nature Communications. 2015;6:6260. doi: 10.1038/ncomms7260 25695863

16. Bhérer C, Campbell CL, Auton A. Refined genetic maps reveal sexual dimorphism in human meiotic recombination at multiple scales. Nature Communications. 2017;8. doi: 10.1038/ncomms14994 28440270

17. Kong A, Thorleifsson G, Gudbjartsson DF, Masson G, Sigurdsson A, Jonasdottir A, et al. Fine-scale recombination rate differences between sexes, populations and individuals. Nature. 2010;467(7319):1099–1103. doi: 10.1038/nature09525 20981099

18. Donnelly KP. The probability that related individuals share some section of genome identical by descent. Theoretical Population Biology. 1983;23(1):34–63. doi: 10.1016/0040-5809(83)90004-7 6857549

19. Renwick JH. The mapping of human chromosomes. Annual Review of Genetics. 1971;5(1):81–120. doi: 10.1146/annurev.ge.05.120171.000501 16097652

20. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449(7164):851–861. doi: 10.1038/nature06258 17943122

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

22. Ottolini CS, Newnham LJ, Capalbo A, Natesan SA, Joshi HA, Cimadomo D, et al. Genome-wide maps of recombination and chromosome segregation in human oocytes and embryos show selection for maternal recombination rates. Nature Genetics. 2015;47:727–735. doi: 10.1038/ng.3306 25985139

23. Hou Y, Fan W, Yan L, Li R, Lian Y, Huang J, et al. Genome Analyses of Single Human Oocytes. Cell. 2013;155(7):1492–1506. doi: 10.1016/j.cell.2013.11.040 24360273

24. Wang J, Fan HC, Behr B, Quake SR. Genome-wide Single-Cell Analysis of Recombination Activity and De Novo Mutation Rates in Human Sperm. Cell. 2012;150(2):402–412. doi: 10.1016/j.cell.2012.06.030 22817899

25. Lu S, Zong C, Fan W, Yang M, Li J, Chapman AR, et al. Probing Meiotic Recombination and Aneuploidy of Single Sperm Cells by Whole-Genome Sequencing. Science. 2012;338(6114):1627–1630. doi: 10.1126/science.1229112 23258895

26. Kirkness EF, Grindberg RV, Yee-Greenbaum J, Marshall CR, Scherer SW, Lasken RS, et al. Sequencing of isolated sperm cells for direct haplotyping of a human genome. Genome Research. 2013;23(5):826–832. doi: 10.1101/gr.144600.112 23282328

27. Bell AD, Mello CJ, Nemesh J, Brumbaugh SA, Wysoker A, McCarroll SA. Insights about variation in meiosis from 31,228 human sperm genomes. bioRxiv. 2019.

28. Sturtevant AH. The linear arrangement of six sex-linked factors in Drosophila, as shown by their mode of association. Journal of Experimental Zoology. 1913;14(1):43–59. doi: 10.1002/jez.1400140104

29. Foss E, Lande R, Stahl FW, Steinberg CM. Chiasma interference as a function of genetic distance. Genetics. 1993;133(3):681–691. 8454209

30. Zhao H, Speed TP, McPeek MS. Statistical analysis of crossover interference using the chi-square model. Genetics. 1995;139(2):1045–1056. 7713407

31. Vigeland MD. IBDsim: Simulation of Chromosomal Regions Shared by Family Members; 2019. Available from: https://CRAN.R-project.org/package=IBDsim.

32. Mitchell BD, Kammerer CM, Blangero J, Mahaney MC, Rainwater DL, Dyke B, et al. Genetic and environmental contributions to cardiovascular risk factors in Mexican Americans. Circulation. 1996;94(9):2159–2170. doi: 10.1161/01.cir.94.9.2159 8901667

33. Duggirala R, Blangero J, Almasy L, Dyer TD, Williams KL, Leach RJ, et al. Linkage of type 2 diabetes mellitus and of age at onset to a genetic location on chromosome 10q in Mexican Americans. American Journal of Human Genetics. 1999;64(4):1127–1140. doi: 10.1086/302316 10090898

34. Hunt KJ, Lehman DM, Arya R, Fowler S, Leach RJ, Göring HH, et al. Genome-Wide Linkage Analyses of Type 2 Diabetes in Mexican Americans. Diabetes. 2005;54(9):2655–2662. doi: 10.2337/diabetes.54.9.2655 16123354

35. Hemani G, Yang J, Vinkhuyzen A, Powell JE, Willemsen G, Hottenga JJ, et al. Inference of the Genetic Architecture Underlying BMI and Height with the Use of 20,240 Sibling Pairs. The American Journal of Human Genetics. 2013;93(5):865–875. doi: 10.1016/j.ajhg.2013.10.005 24183453

36. Ralph P, Coop G. The Geography of Recent Genetic Ancestry across Europe. PLOS Biology. 2013;11(5):e1001555. doi: 10.1371/journal.pbio.1001555 23667324

37. Browning BL, Browning SR. Improving the Accuracy and Efficiency of Identity-by-Descent Detection in Population Data. Genetics. 2013;194(2):459–471. doi: 10.1534/genetics.113.150029 23535385

38. Williams AL, Housman D, Rinard M, Gifford D. Rapid haplotype inference for nuclear families. Genome Biology. 2010;11(10):R108. doi: 10.1186/gb-2010-11-10-r108 21034477

39. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30(1):97–101. doi: 10.1038/ng786 11731797

40. Browning SR, Browning BL. Haplotype phasing: existing methods and new developments. Nature Reviews Genetics. 2011;12(10):703–714. doi: 10.1038/nrg3054 21921926

41. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26(22):2867–2873. doi: 10.1093/bioinformatics/btq559 20926424

42. Karlin S, Taylor HM. A First Course in Stochastic Processes. 2nd ed. Academic Press; 1975.

43. Yakovlev G, Rundle JB, Shcherbakov R, Turcotte DL. Inter-arrival time distribution for the non-homogeneous Poisson process. arXiv. 2005;cond-mat:0507657.

44. Qiao Y, Sannerud J, Basu-Roy S, Hayward C, Williams AL. Distinguishing pedigree relationships using multi-way identical by descent sharing and sex-specific genetic maps. bioRxiv. 2019.

45. Gudbjartsson DF, Thorvaldsson T, Kong A, Gunnarsson G, Ingolfsdottir A. Allegro version 2. Nature Genetics. 2005;37(10):1015–1016. doi: 10.1038/ng1005-1015 16195711

46. Dietter J, Mattheisen M, Fürst R, Rüschendorf F, Wienker TF, Strauch K. Linkage analysis using sex-specific recombination fractions with GENEHUNTER-MODSCORE. Bioinformatics. 2006;23(1):64–70. doi: 10.1093/bioinformatics/btl539 17060360

47. Fingerlin TE, Abecasis GR, Boehnke M. Using sex-averaged genetic maps in multipoint linkage analysis when identity-by-descent status is incompletely known. Genetic Epidemiology. 2006;30(5):384–396. doi: 10.1002/gepi.20151 16685713

48. Mukhopadhyay N, Weeks DE. Linkage analysis of adult height with parent-of-origin effects in the Framingham Heart Study. BMC Genetics. 2003;4(1):S76. doi: 10.1186/1471-2156-4-S1-S76 14975144

49. Browning S. Pedigree Data Analysis With Crossover Interference. Genetics. 2003;164(4):1561–1566. 12930760

50. Thompson EA. MCMC Estimation of Multi-locus Genome Sharing and Multipoint Gene Location Scores. International Statistical Review. 2000;68(1):53–73. doi: 10.1111/j.1751-5823.2000.tb00387.x

51. Lin S, Speed TP. Relative efficiencies of the Chi-square recombination models for gene mapping with human pedigree data. Annals of Human Genetics. 1999;63(1):81–95. doi: 10.1046/j.1469-1809.1999.6310081.x 10738522

52. Kong A, Thorleifsson G, Frigge ML, Masson G, Gudbjartsson DF, Villemoes R, et al. Common and low-frequency variants associated with genome-wide recombination rate. Nature Genetics. 2013;46:11–16. doi: 10.1038/ng.2833 24270358

53. Consortium IMSG, 2 WTCCC, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011;476(7359):214–219. doi: 10.1038/nature10251

54. Williams AL, Genovese G, Dyer T, Altemose N, Truax K, Jun G, et al. Non-crossover gene conversions show strong GC bias and unexpected clustering in humans. eLife. 2015. doi: 10.7554/eLife.04637

55. Ramstetter MD, Dyer TD, Lehman DM, Curran JE, Duggirala R, Blangero J, et al. Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives. Genetics. 2017;207(1):75–82. doi: 10.1534/genetics.117.1122 28739658

56. Lander ES, Green P. Construction of multilocus genetic linkage maps in humans. Proceedings of the National Academy of Sciences. 1987;84(8):2363–2367. doi: 10.1073/pnas.84.8.2363

57. Seidman DN, Shenoy SA, Kim M, Babu R, Dyer TD, Lehman DM, et al. Rapid, Phase-Free Detection of Long Identical by Descent Segments Enables Fast Relationship Classification. (Under review). 2019.

58. Gravel S. Population Genetics Models of Local Ancestry. Genetics. 2012;191:607–619. doi: 10.1534/genetics.112.139808 22491189

59. Cox DR, Smith WL. On the Superposition of Renewal Processes. Biometrika. 1954;41:91–99. doi: 10.2307/2333008