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On powerful GWAS in admixed populations

Nature Genetics volume 53, pages 1631–1633 (2021)Cite this article

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Matters Arising to this article was published on 25 November 2021

The Original Article was published on 18 January 2021

arising from E. G. Atkinson et al. Nature Genetics https://doi.org/10.1038/s41588-020-00766-y (2021).

Improving statistical power for genome-wide association studies (GWAS) in admixed populations is imperative since more and larger genomic studies in admixed populations are desperately needed to accelerate genomic medicine and reduce health inequities1. Recently, Atkinson et al.2 introduced a statistical framework (Tractor) for GWAS in admixed populations (for example, African Americans) that corrects for population structure through the use of local ancestry and concluded that GWAS in admixed populations increases discovery power over traditional GWAS only in the presence of allelic effect size heterogeneity by ancestry; a decrease in power is expected when allelic effects at tested variants are similar across ancestries. However, the conclusion reached by Atkinson et al.2 is specific to their particular choice of statistical association test, which prioritizes allelic effect size heterogeneity by ancestry and does not hold for other existing tests for GWAS in admixed populations. Existing association tests attain increased power over traditional GWAS in admixed populations, even when the causal variant has similar allelic effects across ancestries3,4,5. Therefore GWAS in admixed populations increase the power for discovery over homogeneous populations in either scenario—similar or different ancestry-specific allelic effects.

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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Authors and Affiliations

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

    Kangcheng Hou, Kathryn S. Burch & Bogdan Pasaniuc

  2. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Arjun Bhattacharya & Bogdan Pasaniuc

  3. Graduate Program in Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Rachel Mester

  4. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

  5. Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

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  1. Kangcheng Hou
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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.

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Correspondence to Bogdan Pasaniuc.

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The authors declare no competing interests.

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

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Hou, K., Bhattacharya, A., Mester, R. et al. On powerful GWAS in admixed populations. Nat Genet 53, 1631–1633 (2021). https://doi.org/10.1038/s41588-021-00953-5

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