Recombination rates in admixed individuals identified by ancestry-based inference

Journal name:
Nature Genetics
Volume:
43,
Pages:
847–853
Year published:
DOI:
doi:10.1038/ng.894
Received
Accepted
Published online

Abstract

Studies of recombination and how it varies depend crucially on accurate recombination maps. We propose a new approach for constructing high-resolution maps of relative recombination rates based on the observation of ancestry switch points among admixed individuals. We show the utility of this approach using simulations and by applying it to SNP genotype data from a sample of 2,565 African Americans and 299 African Caribbeans and detecting several hundred thousand recombination events. Comparison of the inferred map with high-resolution maps from non-admixed populations provides evidence of fine-scale differentiation in recombination rates between populations. Overall, the admixed map is well predicted by the average proportion of admixture and the recombination rate estimates from the source populations. The exceptions to this are in areas surrounding known large chromosomal structural variants, specifically inversions. These results suggest that outside of structurally variable regions, admixture does not substantially disrupt the factors controlling recombination rates in humans.

At a glance

Figures

  1. Sketch of the haplotype-copying Hidden Markov model used to detect ancestry switch points.
    Figure 1: Sketch of the haplotype-copying Hidden Markov model used to detect ancestry switch points.

    (a) Yellow and blue represent the chromosomal segments of different ancestry and the shades of each color represent different haplotypes from each ancestry. Recombination creates a mosaic of haplotypes regardless of origin but recombination events between haplotypes of different ancestries leave signatures that can be detected in descendant, admixed individuals. (b) The genotypes observed for such an individual form observed states of a Hidden Markov model in which underlying states are based on which haplotypes from a reference population each allele of the genotype is copied.

  2. Sensitivity and specificity of inference.
    Figure 2: Sensitivity and specificity of inference.

    (a) Estimated number of switches (cjk(i)) between neighboring SNPs obtained for a simulated individual with two ancestry switches (vertical dashed lines). Below, the comparison at the 50-kb scale of the estimated rates (rjk) and the underlying recombination map used to perform the simulations for this segment. Both maps are normalized to the same total rate. (b) The inferred number of switch points (cjk(i)) as function of the size of the interval between locations j and k. The black line represents the median for symmetric intervals around a single, isolated switch point. The red line represents the median for intervals with zero simulated switch points and which are located at least 1 Mb away from the closest switch point. Dashed lines mark the 2.5% and 97.5% quantiles. (c) Comparison of the inferred rates (rjk) with the true rates across all segments at 10-kb (blue), 50-kb (orange) and 1-Mb (red) scales. The 2.5% and 97.5% quantiles are shown with dashed lines. All maps have been normalized to the same total rate for comparison.

  3. Comparison of the African admixture-based map to existing maps.
    Figure 3: Comparison of the African admixture-based map to existing maps.

    (a) Example of 1-Mb–scale map from 50 Mb of chromosome 1. (b) Example of 50-kb–scale map from the 2.5-Mb section of chromosome 1 indicated by the gray box in a. (c) Proportion of the total recombination in various proportions of sequence intervals at the 50-kb scale.

  4. Population differences in recombination patterns.
    Figure 4: Population differences in recombination patterns.

    (ad) Independent of scale, the AfAdm map correlates better (a) and shares more hotspots (c) with the HapMapYRI than the HapMapCEU map. In contrast, the deCODE map correlates better (b) and shares more hotspots (d) with the HapMapCEU than the HapMapYRI map. Hotspots are defined as the 50-kb intervals with the top 1% largest rates.

  5. Recombination rates in notable genomic locations.
    Figure 5: Recombination rates in notable genomic locations.

    (a) The region with the largest deficit of the AfAdm map just outside the known inversion on chromosome 8p23.1–8p22 (gray). (b) The region with a large deficit of the AfAdm map on chromosome 9 near the boundary of multiple known polymorphic inversions. (c) The inversion on chromosome 17q21.31 (gray). (d) A region on chromosome 14 with an elevated average European-ancestry proportion (gray) framed by local peaks of recombination.

References

  1. Crawford, D.C. et al. Evidence for substantial fine-scale variation in recombination rates across the human genome. Nat. Genet. 36, 700706 (2004).
  2. Evans, D.M. & Cardon, L. A comparison of linkage disequilibrium patterns and estimated population recombination rates across multiple populations. Am. J. Hum. Genet. 76, 681687 (2005).
  3. Graffelman, J., Balding, D., Gonzalez-Neira, A. & Bertranpetit, J. Variation in estimated recombination rates across human populations. Hum. Genet. 122, 301310 (2007).
  4. Serre, D., Nadon, R. & Hudson, T.J. Large-scale recombination rate patterns are conserved among human populations. Genome Res. 15, 15471552 (2005).
  5. Laayouni, H. et al. Similarity in recombination rate estimates highly correlates with genetic differentiation in humans. PLoS ONE 6, e17913 (2011).
  6. Clark, A.G., Wang, X. & Matise, T. Contrasting methods of quantifying fine structure of human recombination. Annu. Rev. Genomics Hum. Genet. 11, 4564 (2010).
  7. Kong, A. et al. A high-resolution recombination map of the human genome. Nat. Genet. 31, 241247 (2002).
  8. Kong, A. et al. Fine-scale recombination rate differences between sexes, populations and individuals. Nature 467, 10991103 (2010).
  9. Broman, K.W., Murray, J.C., Sheffield, V.C., White, R.L. & Weber, J.L. Comprehensive human genetic maps: individual and sex-specific variation in recombination. Am. J. Hum. Genet. 63, 861869 (1998).
  10. Coop, G., Wen, X., Ober, C., Pritchard, J.K. & Przeworski, M. High-resolution mapping of crossovers reveals extensive variation in fine-scale recombination patterns among humans. Science 319, 13951398 (2008).
  11. Jorgenson, E. et al. Ethnicity and human genetic linkage maps. Am. J. Hum. Genet. 76, 276290 (2005).
  12. Ju, Y.S. et al. A genome-wide Asian genetic map and ethnic comparison: the GENDISCAN study. BMC Genomics 9, 554 (2008).
  13. International HapMap Consortium. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851861 (2007).
  14. Myers, S., Bottolo, L., Freeman, C., McVean, G. & Donnelly, P.A. Fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321324 (2005).
  15. O'Reilly, P.F., Birney, E. & Balding, D.J. Confounding between recombination and selection, and the Ped/Pop method for detecting selection. Genome Res. 18, 13041313 (2008).
  16. McVean, G.A.T. et al. The fine-scale structure of recombination rate variation in the human genome. Science 304, 581584 (2004).
  17. Price, A.L. et al. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet. 5, e1000519 (2009).
  18. Johnson, A.D. et al. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat. Genet. 42, 608613 (2010).
  19. Daniels, P.R. et al. Familial aggregation of hypertension treatment and control in the Genetic Epidemiology Network of Arteriopathy (GENOA) study. Am. J. Med. 116, 676681 (2004).
  20. FBPP Investigators. Multi-center genetic study of hypertension: The Family Blood Pressure Program (FBPP). Hypertension 39, 39 (2002).
  21. Barnes, K.C. et al. Linkage of asthma and total serum IgE concentration to markers on chromosome 12q: evidence from Afro-Caribbean and Caucasian populations. Genomics 37, 4150 (1996).
  22. Mathias, R.A. et al. A genome-wide association study on African-ancestry populations for asthma. J. Allergy Clin. Immunol. 125, 336346.e4 (2010).
  23. Zambelli-Weiner, A. et al. Evaluation of the CD14/-260 polymorphism and house dust endotoxin exposure in the Barbados Asthma Genetics Study. J. Allergy Clin. Immunol. 115, 12031209 (2005).
  24. Moore, W.C. et al. Characterization of the severe asthma phenotype by the National Heart, Lung, and Blood Institute's Severe Asthma Research Program. J. Allergy Clin. Immunol. 119, 405413 (2007).
  25. Bryc, K. et al. Genome-wide patterns of population structure and admixture in West Africans and African Americans. Proc. Natl. Acad. Sci. USA 107, 786791 (2010).
  26. Murray, T. et al. African and non-African admixture components in African Americans and an African Caribbean population. Genet. Epidemiol. 34, 561568 (2010).
  27. Parra, E.J. et al. Estimating African American admixture proportions by use of population-specific alleles. Am. J. Hum. Genet. 63, 18391851 (1998).
  28. Tang, H. et al. Recent genetic selection in the ancestral admixture of Puerto Ricans. Am. J. Hum. Genet. 81, 626633 (2007).
  29. Antonacci, F. et al. Characterization of six human disease-associated inversion polymorphisms. Hum. Mol. Genet. 18, 25552566 (2009).
  30. Deng, L. et al. An unusual haplotype structure on human chromosome 8p23 derived from the inversion polymorphism. Hum. Mutat. 29, 12091216 (2008).
  31. Giglio, S. et al. Olfactory receptor-gene clusters, genomic-inversion polymorphisms, and common chromosome rearrangements. Am. J. Hum. Genet. 68, 874883 (2001).
  32. Kidd, J.M. et al. Mapping and sequencing of structural variation from eight human genomes. Nature 453, 5664 (2008).
  33. Redon, R. et al. Global variation in copy number in the human genome. Nature 444, 444454 (2006).
  34. Stefansson, H. et al. A common inversion under selection in Europeans. Nat. Genet. 37, 129137 (2005).
  35. Bergström, T.F., Josefsson, A., Erlich, H.A. & Gyllensten, U. Recent origin of HLA-DRB1 alleles and implications for human evolution. Nat. Genet. 18, 237242 (1998).
  36. The 1,000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 10611073 (2010).
  37. Berg, I.L. et al. PRDM9 variation strongly influences recombination hot-spot activity and meiotic instability in humans. Nat. Genet. 42, 859863 (2010).
  38. Baudat, F. et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327, 836840 (2010).
  39. Bansal, V., Bashir, A. & Bafna, V. Evidence for large inversion polymorphisms in the human genome from HapMap data. Genome Res. 17, 219230 (2007).
  40. Price, A.L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132135, author reply 135–139 (2008).
  41. Feuk, L. et al. Discovery of human inversion polymorphisms by comparative analysis of human and chimpanzee DNA sequence assemblies. PLoS Genet. 1, e56 (2005).
  42. Conrad, D.F. et al. Origins and functional impact of copy number variation in the human genome. Nature 464, 704712 (2010).
  43. Itsara, A. et al. Population analysis of large copy number variants and hotspots of human genetic disease. Am. J. Hum. Genet. 84, 148161 (2009).
  44. Tuzun, E. et al. Fine-scale structural variation of the human genome. Nat. Genet. 37, 727732 (2005).
  45. McKernan, K.J. et al. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res. 19, 15271541 (2009).
  46. Zogopoulos, G. et al. Germ-line DNA copy number variation frequencies in a large North American population. Hum. Genet. 122, 345353 (2007).
  47. de Smith, A.J. et al. Array CGH analysis of copy number variation identifies 1,284 new genes variant in healthy white males: implications for association studies of complex diseases. Hum. Mol. Genet. 16, 27832794 (2007).
  48. Long, J.C. The genetic structure of admixed populations. Genetics 127, 417428 (1991).
  49. Pfaff, C.L. et al. Population structure in admixed populations: effect of admixture dynamics on the pattern of linkage disequilibrium. Am. J. Hum. Genet. 68, 198207 (2001).
  50. Pool, J.E. & Nielsen, R. Inference of historical changes in migration rate from the lengths of migrant tracts. Genetics 181, 711719 (2009).
  51. Chen, G.K., Marjoram, P. & Wall, J.D. Fast and flexible simulation of DNA sequence data. Genome Res. 19, 136142 (2009).
  52. Schaffner, S.F. et al. Calibrating a coalescent simulation of human genome sequence variation. Genome Res. 15, 15761583 (2005).
  53. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

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Author information

Affiliations

  1. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA.

    • Daniel Wegmann,
    • Krishna R Veeramah &
    • John Novembre
  2. Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, USA.

    • Darren E Kessner &
    • John Novembre
  3. Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

    • Rasika A Mathias,
    • Lisa R Yanek,
    • Lewis C Becker,
    • Kathleen C Barnes &
    • Diane M Becker
  4. For the Genetic Study of Atherosclerosis Risk (GeneSTAR) consortium.

    • Rasika A Mathias,
    • Lisa R Yanek,
    • Lewis C Becker &
    • Diane M Becker
  5. For the Genetic Research on Asthma in the African Diaspora (GRAAD) consortium.

    • Rasika A Mathias,
    • Nicholas Rafaels,
    • Ingo Ruczinski,
    • Terri H Beaty &
    • Kathleen C Barnes
  6. Department of Medicine, University of Chicago, Chicago, Illinois, USA.

    • Dan L Nicolae
  7. Department of Statistics, University of Chicago, Chicago, Illinois, USA.

    • Dan L Nicolae
  8. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.

    • Dan L Nicolae &
    • Dara G Torgerson
  9. For the Chicago Asthma Genetics (CAG) and Collaborative Study on the Genetics of Asthma (CSGA) consortium.

    • Dan L Nicolae &
    • Dara G Torgerson
  10. Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA.

    • Yan V Sun &
    • Sharon L R Kardia
  11. For the Genetic Epidemiology Network of Arteriopathy (GENOA) consortium.

    • Yan V Sun,
    • Thomas Mosley &
    • Sharon L R Kardia
  12. Department of Epidemiology, Emory University, Atlanta, Georgia, USA.

    • Yan V Sun
  13. For the Severe Asthma Research Program (SARP) consortium.

    • Dara G Torgerson &
    • Deborah A Meyers
  14. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

    • Nicholas Rafaels &
    • Ingo Ruczinski
  15. Department of Medicine, University of Mississippi, Jackson, Mississippi, USA.

    • Thomas Mosley
  16. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

    • Terri H Beaty
  17. Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.

    • Deborah A Meyers
  18. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California, USA.

    • Nelson B Freimer

Contributions

J.N. and N.B.F. conceived of the project, and D.W., J.N., N.B.F. and D.L.N. designed the analyses. D.G.T. and D.L.N. worked as part of the Chicago Asthma Genetics (CAG) and Collaborative Study on the Genetics of Asthma (CGSA) consortium to gather and prepare primary data for subsequent analysis. R.A.M., L.R.Y., L.C.B. and D.M.B. worked as part of the Genetic Study of Atherosclerosis Risk (GeneSTAR) Consortium to gather and prepare primary data for subsequent analysis. I.R., N.R., R.A.M., T.H.B. and K.C.B. worked as part of the Genetic Research on Asthma in the African Diaspora (GRAAD) consortium to gather and prepare primary data for subsequent analysis. Y.V.S., T.M. and S.L.R.K. worked as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) consortium to gather and prepare primary data for subsequent analysis. D.G.T. and D.A.M. worked as part of the Severe Asthma Research Program (SARP) to gather and prepare primary data for subsequent analysis. D.W., D.E.K., K.R.V. and J.N. developed tools for the analysis and performed the analysis. D.W., N.B.F. and J.N. drafted the manuscript and revised it with D.E.K., K.R.V., R.A.M., D.L.N., L.R.Y., Y.V.S., L.C.B., N.R., I.R., T.H.B., S.L.R.K., D.A.M., K.C.B. and D.M.B.

Competing financial interests

The authors declare no competing financial interests.

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    Supplementary Note, Supplementary Tables 1–4 and Supplementary Figures 1–14.

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