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Xu H, Schwander K, Brown MR, et al. Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions. Eur J Hum Genet. 2021;29(5):839-850doi: 10.1038/s41431-021-00808-x.
Xu, H., Schwander, K., Brown, M. R., Wang, W., Waken, R. J., Boerwinkle, E., Cupples, L. A., de Las Fuentes, L., van Heemst, D., Osazuwa-Peters, O., de Vries, P. S., van Dijk, K. W., Sung, Y. J., Zhang, X., Morrison, A. C., Rao, D. C., Noordam, R., & Liu, C. T. (2021). Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions. European journal of human genetics : EJHG, 29(5), 839-850. https://doi.org/10.1038/s41431-021-00808-x
Xu, Hanfei, et al. "Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions." European journal of human genetics : EJHG vol. 29,5 (2021): 839-850. doi: https://doi.org/10.1038/s41431-021-00808-x
Xu H, Schwander K, Brown MR, Wang W, Waken RJ, Boerwinkle E, Cupples LA, de Las Fuentes L, van Heemst D, Osazuwa-Peters O, de Vries PS, van Dijk KW, Sung YJ, Zhang X, Morrison AC, Rao DC, Noordam R, Liu CT. Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions. Eur J Hum Genet. 2021 May;29(5):839-850. doi: 10.1038/s41431-021-00808-x. Epub 2021 Jan 26. PMID: 33500576; PMCID: PMC8110957.
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Eur J Hum Genet. 2021 May;29(5):839-850. doi: 10.1038/s41431-021-00808-x. Epub 2021 Jan 26.

Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions.

European journal of human genetics : EJHG

Hanfei Xu, Karen Schwander, Michael R Brown, Wenyi Wang, R J Waken, Eric Boerwinkle, L Adrienne Cupples, Lisa de Las Fuentes, Diana van Heemst, Oyomoare Osazuwa-Peters, Paul S de Vries, Ko Willems van Dijk, Yun Ju Sung, Xiaoyu Zhang, Alanna C Morrison, D C Rao, Raymond Noordam, Ching-Ti Liu

Affiliations

  1. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. [email protected].
  2. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
  3. Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas School of Public health, Houston, TX, USA.
  4. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.
  5. Field and Environmental Data Science, Benson Hill Inc, St. Louis, MO, USA.
  6. The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
  7. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
  8. NHLBI and Boston University Framingham Heart Study, Framingham, MA, USA.
  9. Department of Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA.
  10. Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands.
  11. Department of Population Health Sciences, Duke University, Durham, NC, USA.
  12. Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands.
  13. Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands.
  14. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
  15. Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
  16. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. [email protected].

PMID: 33500576 PMCID: PMC8110957 DOI: 10.1038/s41431-021-00808-x

Abstract

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.

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