Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

On powerful GWAS in admixed populations

The Original Article was published on 18 January 2021

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1

Data availability

This research was conducted using the UK Biobank Resource under application 33297. We thank the participants of UK Biobank for making this work possible. The UK Biobank genotype and phenotype data are available by application from https://www.ukbiobank.ac.uk/. Extended results can be accessed at our Zenodo repository https://doi.org/10.5281/zenodo.5308562.

Code availability

Software and extended results, including an implementation of the Tractor association models, can be found at our Zenodo repository. (The Tractor software currently does not include logistic models for association; https://github.com/eatkinson/Tractor accessed 22 February 2021.)

References

  1. 1.

    Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).

    CAS  Article  Google Scholar 

  3. 3.

    Zhang, J. & Stram, D. O. The role of local ancestry adjustment in association studies using admixed populations. Genet. Epidemiol. 38, 502–515 (2014).

    CAS  Article  Google Scholar 

  4. 4.

    Seldin, M. F., Pasaniuc, B. & Price, A. L. New approaches to disease mapping in admixed populations. Nat. Rev. Genet. 12, 523–528 (2011).

    CAS  Article  Google Scholar 

  5. 5.

    Pasaniuc, B. et al. Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium. PLoS Genet. 7, e1001371 (2011).

    CAS  Article  Google Scholar 

  6. 6.

    Tang, H., Siegmund, D. O., Johnson, N. A., Romieu, I. & London, S. J. Joint testing of genotype and ancestry association in admixed families. Genet. Epidemiol. 34, 783–791 (2010).

    Article  Google Scholar 

  7. 7.

    Shriner, D., Adeyemo, A. & Rotimi, C. N. Joint ancestry and association testing in admixed individuals. PLoS Comput. Biol. 7, e1002325 (2011).

    CAS  Article  Google Scholar 

  8. 8.

    Yorgov, D., Edwards, K. L. & Santorico, S. A. Use of admixture and association for detection of quantitative trait loci in the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) study. BMC Proc. 8, S6 (2014).

    Article  Google Scholar 

  9. 9.

    Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).

    CAS  Article  Google Scholar 

  10. 10.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  11. 11.

    de Candia, T. R. et al. Additive genetic variation in schizophrenia risk is shared by populations of African and European descent. Am. J. Hum. Genet. 93, 463–470 (2013).

    CAS  Article  Google Scholar 

  12. 12.

    Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).

    CAS  Article  Google Scholar 

  13. 13.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  Article  Google Scholar 

  14. 14.

    Shi, H. et al. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12, 1098 (2021).

    CAS  Article  Google Scholar 

  15. 15.

    Van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).

    CAS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Contributions

K.H. and B.P. conceived and designed the experiments. K.H. performed the experiments and statistical analyses. K.H., K.S.B., R.M. and A.B. collected and managed the data. K.H., K.S.B., R.M., A.B. and B.P. wrote the manuscript.

Corresponding author

Correspondence to Bogdan Pasaniuc.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Loïc Yengo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hou, K., Bhattacharya, A., Mester, R. et al. On powerful GWAS in admixed populations. Nat Genet (2021). https://doi.org/10.1038/s41588-021-00953-5

Download citation

Search

Quick links