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The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions.
Human Heredity ( IF 1.8 ) Pub Date : 2019-06-05 , DOI: 10.1159/000499711
Oyomoare L Osazuwa-Peters 1 , Karen Schwander 2 , R J Waken 2 , Lisa de Las Fuentes 2, 3 , Tuomas O Kilpeläinen 4, 5 , Ruth J F Loos 6, 7 , Susan B Racette 8, 9 , Yun Ju Sung 2 , D C Rao 2
Affiliation  

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.

中文翻译:

在基因-物理活动相互作用的分析中,从极端暴露中选择个体的承诺。

背景技术使用较低的四分位数作为截止点的二分法通常用于协调研究中的异类体力活动(PA)措施。但是,这可能会导致分类错误并阻碍新基因座的发现。目的本研究旨在评估从暴露极限(SIEE)中选择个体的性能,作为减少此类错误分类的替代方法。方法对于Framingham心脏研究中的收缩压和舒张压,我们使用SIEE推导的三个PA变量和其他两种二分法,通过基因-PA相互作用分析进行了全基因组关联研究。我们比较了检测到的基因座数量,并与使用定量PA变量发现的基因座重叠。此外,我们进行了模拟研究,以评估偏差,错误发现率(FDR),暴露组中存在/不存在测量误差的协同/拮抗遗传效应下的能量和能力。结果在实证分析中,SIEE的表现既不是最好也不是最差。在大多数模拟方案中,SIEE在FDR和功率方面始终优于。特别是在以拮抗作用和测量误差为特征的情况下,SIEE的偏差最小,功率最高。结论SIEE的承诺似乎仅限于检测具有拮抗作用的基因座。需要进一步研究以评估SIEE的全部优势。在FDR和电力方面,SIEE的表现始终优于。特别是在以拮抗作用和测量误差为特征的情况下,SIEE的偏差最小,功率最高。结论SIEE的承诺似乎仅限于检测具有拮抗作用的基因座。需要进一步研究以评估SIEE的全部优势。在FDR和电力方面,SIEE的表现始终领先。特别是在以拮抗作用和测量误差为特征的情况下,SIEE的偏差最小,功率最高。结论SIEE的承诺似乎仅限于检测具有拮抗作用的基因座。需要进一步研究以评估SIEE的全部优势。
更新日期:2019-11-01
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