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Recombination rates in admixed individuals identified by ancestry-based inference

Nature Genetics volume 43, pages 847–853 (2011)Cite this article

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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.

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Figure 1: Sketch of the haplotype-copying Hidden Markov model used to detect ancestry switch points.
Figure 2: Sensitivity and specificity of inference.
Figure 3: Comparison of the African admixture-based map to existing maps.
Figure 4: Population differences in recombination patterns.
Figure 5: Recombination rates in notable genomic locations.

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Acknowledgements

J.N., D.W. and K.R.V. were funded by a Searle Scholar Program award to J.N. N.B.F. was supported by US National Institutes of Health (NIH) grants R01HL087679 and RL1MH083268. The sample assembled is compiled from the larger efforts and the generous sharing of data from four major consortiums. For the GeneSTAR consortium (L.C.B., D.R.B., L.R.Y. and R.A.M.), support came from NIH grants HL072518 and M01-RR00052. For the CAG-CSGA consortium (D.A.M., D.G.T. and D.L.N.), support came from NIH grants U01 HL49596, R01 HL072414, R01 HL087665 and RC2 HL101651, and special thanks is given to C. Ober. For the GENOA samples (Y.V.S. and S.L.R.K.), support came from NIH grants HL087660 and HL100245, and special thanks is given to E. Boerwinkle. For the GRAAD consortium (K.C.B., N.R., I.R., T.H.B. and R.A.M.), support came from NIH grants HL087699, HL49612, AI50024, AI44840, HL075417, HL072433, AI41040, ES09606, HL072433 and RR03048, US Environmental Protection Agency grant 83213901, and National Institute of General Medical Sciences (NIGMS) grant S06GM08015, and special thanks are given to A.V. Grant, L. Gao, C. Vergara, Y.J. Tsai, P. Gao, M.C. Liu, P. Breysse, M.B. Bracken, J. Hoh, E.W. Pugh, A.F. Scott, G. Abecasis, T. Murray, T. Hand, M. Yang, M. Campbell, C. Foster, J.B. Hetmanski, R. Ashworth, C.M. Ongaco, K.N. Hetrick and K.F. Doheny. K.C.B. was supported in part by the Mary Beryl Patch Turnbull Scholar Program. R.A.M. was supported in part by the Mosaic Initiative Award from Johns Hopkins University. We thank C. Jaquish and the NHLBI STAMPEED program for their support of this collaboration. We also acknowledge G. Coop, A. di Rienzo, K. Lohmueller, and M. Przeworski for helpful discussions and comments on a draft of the manuscript.

Author information

Authors and 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

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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.

Corresponding author

Correspondence to John Novembre.

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Wegmann, D., Kessner, D., Veeramah, K. et al. Recombination rates in admixed individuals identified by ancestry-based inference. Nat Genet 43, 847–853 (2011). https://doi.org/10.1038/ng.894

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