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Estimating the Relative Contribution of Environmental and Genetic Risk Factors to Different Aging Traits by Combining Correlated Variables into Weighted Risk Scores

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  • Claudia Wigmann

    (IUF—Leibniz Research Institute for Environmental Medicine, 40225 Duesseldorf, Germany)

  • Anke Hüls

    (Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
    Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA)

  • Jean Krutmann

    (IUF—Leibniz Research Institute for Environmental Medicine, 40225 Duesseldorf, Germany
    The Human Phenome Institute, Shanghai 200433, China)

  • Tamara Schikowski

    (IUF—Leibniz Research Institute for Environmental Medicine, 40225 Duesseldorf, Germany)

Abstract

Genetic and exposomal factors contribute to the development of human aging. For example, genetic polymorphisms and exposure to environmental factors (air pollution, tobacco smoke, etc.) influence lung and skin aging traits. For prevention purposes it is highly desirable to know the extent to which each category of the exposome and genetic factors contribute to their development. Estimating such extents, however, is methodologically challenging, mainly because the predictors are often highly correlated. Tackling this challenge, this article proposes to use weighted risk scores to assess combined effects of categories of such predictors, and a measure of relative importance to quantify their relative contribution. The risk score weights are determined via regularized regression and the relative contributions are estimated by the proportion of explained variance in linear regression. This approach is applied to data from a cohort of elderly Caucasian women investigated in 2007–2010 by estimating the relative contribution of genetic and exposomal factors to skin and lung aging. Overall, the models explain 17% (95% CI: [9%, 28%]) of the outcome’s variance for skin aging and 23% ([11%, 34%]) for lung function parameters. For both aging traits, genetic factors make up the largest contribution. The proposed approach enables us to quantify and rank contributions of categories of exposomal and genetic factors to human aging traits and facilitates risk assessment related to common human diseases in general. Obtained rankings can aid political decision making, for example, by prioritizing protective measures such as limit values for certain exposures.

Suggested Citation

  • Claudia Wigmann & Anke Hüls & Jean Krutmann & Tamara Schikowski, 2022. "Estimating the Relative Contribution of Environmental and Genetic Risk Factors to Different Aging Traits by Combining Correlated Variables into Weighted Risk Scores," IJERPH, MDPI, vol. 19(24), pages 1-13, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16746-:d:1002376
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    References listed on IDEAS

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    1. Alicia Guillien & Solène Cadiou & Rémy Slama & Valérie Siroux, 2021. "The Exposome Approach to Decipher the Role of Multiple Environmental and Lifestyle Determinants in Asthma," IJERPH, MDPI, vol. 18(3), pages 1-14, January.
    2. Frank Dudbridge, 2013. "Power and Predictive Accuracy of Polygenic Risk Scores," PLOS Genetics, Public Library of Science, vol. 9(3), pages 1-17, March.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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