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Covariate selection for association screening in multiphenotype genetic studies

Abstract

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

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Figure 1: Variance components of adjusted variables.
Figure 2: Examples of shared variance in real data and equivalent increases in sample size.
Figure 3: Conditional and unconditional distribution.
Figure 4: Power and robustness quantile–quantile plots under the null and alternate distributions of P values from a series of simulations.
Figure 5: Analysis of the gEUVADIS data.

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Acknowledgements

H.A. and N.Z. were supported by NIH grant R03DE025665. H.A. was also supported by NIH grant R21HG007687, and N.Z. was also supported by NIH career development award K25HL121295 and NIH grant U01HG009080. C.J.P. was supported by NIH grant R00 ES023504.

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Authors

Contributions

H.A. conceived the approach and performed all real-data analyses. H.A., N.Z., B.V., C.J.P., D.S., and P.K. contributed substantially to improving the approach and the study design. C.J.Y. contributed to the quality control and analysis of the gEUVADIS data. B.W. collected the metabolite data and contributed to quality control and analysis of the metabolite data. H.A. and N.Z. conceptualized and performed the simulation study. V.G. contributed to the simulation study. H.A. and N.Z. wrote the manuscript.

Corresponding author

Correspondence to Hugues Aschard.

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The authors declare no competing financial interests.

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Supplementary Text and Figures

Supplementary Figures 1–45, Supplementary Tables 1–8 and Supplementary Note (PDF 11510 kb)

Life Sciences Reporting Summary (PDF 128 kb)

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Aschard, H., Guillemot, V., Vilhjalmsson, B. et al. Covariate selection for association screening in multiphenotype genetic studies. Nat Genet 49, 1789–1795 (2017). https://doi.org/10.1038/ng.3975

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