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A model-based approach for analysis of spatial structure in genetic data

Nature Genetics volume 44, pages 725–731 (2012)Cite this article

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Abstract

Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two- or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B.

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Figure 1: Examples of the allele frequency slope model.
Figure 2: Model-based mapping convergence with random initialization.
Figure 3: Mapping spatial structure on a globe using HGDP data.
Figure 4: The distribution of SPA scores representing allele frequency gradients.
Figure 5: Selection results of six methods in two chromosomes.

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Acknowledgements

W.-Y.Y. and E.E. are supported by grants from the US National Science Foundation (0513612, 0731455, 0729049, 0916676 and 1065276) and the US National Institutes of Health (K25 HL080079, U01 DA024417, P01 HL30568 and PO1 HL28481). J.N. is supported by National Science Foundation grant (0933731) and by the Searle Scholars Program. E.H. is a faculty fellow of the Edmond J. Safra Program at Tel Aviv University and was supported in part by the Israeli Science Foundation (grant 04514831) and by IBM open collaborative research award program.

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Author notes

  1. Eleazar Eskin and Eran Halperin: These authors contributed equally to this work.

Authors and Affiliations

  1. Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, USA

    Wen-Yun Yang, John Novembre & Eleazar Eskin

  2. Department of Computer Science, University of California, Los Angeles, California, USA

    Wen-Yun Yang & Eleazar Eskin

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

    John Novembre

  4. Department of Human Genetics, University of California, Los Angeles, California, USA

    Eleazar Eskin

  5. International Computer Science Institute, Berkeley, California, USA

    Eran Halperin

  6. School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Eran Halperin

  7. Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel

    Eran Halperin

Authors
  1. Wen-Yun Yang
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  2. John Novembre
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  3. Eleazar Eskin
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Contributions

W.-Y.Y., J.N., E.E. and E.H. designed the methods and experiments. W.-Y.Y. implemented the methods. W.-Y.Y., J.N., E.E. and E.H. jointly performed the analysis. All authors discussed the results and contributed to the writing of the manuscript.

Corresponding author

Correspondence to Eleazar Eskin.

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The authors declare no competing financial interests.

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Supplementary Figures 1–5, Supplementary Tables 1–4 and Supplementary Note (PDF 652 kb)

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Yang, WY., Novembre, J., Eskin, E. et al. A model-based approach for analysis of spatial structure in genetic data. Nat Genet 44, 725–731 (2012). https://doi.org/10.1038/ng.2285

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