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：

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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

COVID-19 患者的病程从无症状到严重不等。这种症状范围的基础尚不清楚。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异和机器学习分类算法的遗传风险评分在预测 SARS-CoV-2 感染反应严重程度方面的效果。我们将英国生物银行数据集中的 981 名在 2020 年 4 月 27 日之前对 SARS-CoV-2 感染出现严重反应的患者与从英国生物银行一般人群中抽取的类似数量的年龄匹配患者进行了比较。对于每位患者，我们建立了一个包含 88 个数字的档案，描述了其种系 DNA 的染色体尺度长度变异性。每个数字代表 22 个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，可以仅根据一个人的 88 个数字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上区分两个类别 (p = 2 · 10−11)（根据随机对照进行测量）和 (p = 3 · 10−14)（根据随机对照的预期值进行测量）猜测算法（AUC = 0.5）。然而，我们发现分类器的 AUC 只有 0.51，对于临床有用的测试来说太低了。遗传学在 COVID-19 的严重程度中发挥着重要作用，但我们尚无法开发出有用的基因测试来预测严重程度。