测量噪音对心功能遗传关联研究的影响。

Q2 Computer Science
Milos Vukadinovic, Gauri Renjith, Victoria Yuan, Alan Kwan, Susan C Cheng, Debiao Li, Shoa L Clarke, David Ouyang
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引用次数: 0

摘要

最近的研究有效地利用了成像的定量性状来提高全基因组关联研究(GWAS)的能力,从而进一步了解疾病生物学和各种性状。然而,值得注意的是,表型分析本身存在测量误差和噪声,可能会影响后续的遗传分析。这项研究以左心室射血分数(LVEF)为重点,研究表型测量的不精确性如何影响遗传研究。研究人员评估了几种获取 LVEF 的方法以及模拟测量噪音对后续遗传分析的影响。结果显示,只需引入 7.9% 的测量噪声,就能消除近四万人的 LVEF GWAS 中的所有遗传关联。此外,LVEF 平均绝对误差(MAE)每增加 1%,对 GWAS 功率的影响相当于队列样本量减少 10%。因此,提高表型分析的准确性对于最大限度地提高全基因组关联研究的效果至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Measurement Noise on Genetic Association Studies of Cardiac Function.

Recent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acquiring LVEF, along with simulating measurement noise, were assessed for their effects on ensuing genetic analyses. The results showed that by introducing just 7.9% of measurement noise, all genetic associations in an LVEF GWAS with almost forty thousand individuals could be eliminated. Moreover, a 1% increase in mean absolute error (MAE) in LVEF had an effect equivalent to a 10% reduction in the sample size of the cohort on the power of GWAS. Therefore, enhancing the accuracy of phenotyping is crucial to maximize the effectiveness of genome-wide association studies.

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