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Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.
medRxiv - Genetic and Genomic Medicine Pub Date : 2020-07-07 , DOI: 10.1101/2020.07.06.20147637
Chris Toh , James P. Brody

The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification. We found that the XGBoost classifier could differentiate between the two classes at a significant level (p=2∙10^(-11)) as measured against a randomized control and (p=3∙10^(-14)) as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test. Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.

中文翻译:

使用人类染色体尺度长度变异评估COVID-19严重程度的遗传风险评分。

患者的COVID-19病程从无症状到严重。此症状范围的依据尚不清楚。一种可能性是遗传变异是造成高度可变反应的部分原因。我们评估了基于染色体尺度长度变化和机器学习分类算法的遗传风险评分可以预测对SARS-CoV-2感染反应的严重程度。我们将英国生物库数据集中的981名在2020年4月27日之前对SARS-CoV-2感染产生严重反应的患者与英国生物库人群的年龄匹配患者进行了比较。对于每位患者,我们建立了一个88个数字的档案,以表征其种系DNA的染色体规模长度变异性。每个数字代表22种常染色体的四分之一。我们使用机器学习算法XGBoost构建了一个分类器,该分类器可以仅基于88号分类来预测一个人是否会对COVID-19产生严重反应。我们发现XGBoost分类器可以在显着水平(p = 2∙10 ^(-11))和随机对照的情况下(p = 3∙10 ^(-14))进行区分。随机猜测算法的期望值(AUC = 0.5)。但是,我们发现分类器的AUC仅为0.51,对于临床有用的测试而言太低了。遗传因素在COVID-19的严重程度中起着作用,但我们尚无法开展有用的遗传测试来预测严重程度。我们发现XGBoost分类器可以在显着水平(p = 2∙10 ^(-11))和随机对照的情况下(p = 3∙10 ^(-14))进行区分。随机猜测算法的期望值(AUC = 0.5)。但是,我们发现分类器的AUC仅为0.51,对于临床有用的测试而言太低了。遗传因素在COVID-19的严重程度中起着作用,但我们尚无法开展有用的遗传测试来预测严重程度。我们发现XGBoost分类器可以在显着水平(p = 2∙10 ^(-11))和随机对照的情况下(p = 3∙10 ^(-14))进行区分。随机猜测算法的期望值(AUC = 0.5)。但是,我们发现分类器的AUC仅为0.51,对于临床有用的测试而言太低了。遗传因素在COVID-19的严重程度中起着作用,但我们尚无法开展有用的遗传测试来预测严重程度。
更新日期:2020-07-07
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