The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions

Oyomoare L. Osazuwa-Peters, Karen Schwander, R. J. Waken, Lisa De Las Fuentes, Tuomas O. Kilpeläinen, Ruth J.F. Loos, Susan B. Racette, Yun Ju Sung, D. C. Rao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci. Objectives: This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification. Method: For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error. Results: In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power. Conclusion: SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.

Original languageEnglish
Pages (from-to)315-332
Number of pages18
JournalHuman heredity
Issue number6
StatePublished - Jun 1 2019


  • Computer simulations
  • Dichotomization
  • Gene-physical activity interactions
  • Misclassification
  • Selecting individuals at extremes of exposure


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