Polygenic risk modeling with latent trait-related genetic components
Polygenic risk modeling with latent trait-related genetic components
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DOI：
10.1038/s41431-021-00813-0
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发表时间：
2021-02-08
影响因子：
5.2
通讯作者：
Rivas, Manuel A.
中科院分区：
文献类型：
--
作者：
Aguirre, Matthew;Tanigawa, Yosuke;Rivas, Manuel A.
多基因风险模型在理解复杂疾病及其临床表现方面取得了重大进展。虽然多基因风险评分（PRS）可以有效地预测结果，但它们通常不能解释特征多样性背后的疾病亚型或途径。在这里，我们引入了一个基于遗传关联分解（DeGAs）成分的遗传风险潜在因素模型，我们称之为DeGAs多基因风险评分（dPRS）。我们使用977个性状的遗传关联来计算德加，发现dPRS的表现与标准PRS相当，同时具有更高的可解释性。我们展示了如何在DeGAs成分中分解个体特征的遗传风险，并以英国生物银行中337,151名英国白人的体重指数（BMI）和心肌梗死（心脏病发作）为例，并在25,486名非英国白人中进行了复制。我们发现BMI多基因风险因素与无脂量、脂肪量和身体活动等整体健康指标相关。大多数具有高dPRS BMI的个体都有脂肪质量成分和无脂肪质量成分的强烈贡献，而少数“异常”个体仅有两个成分中的一个的强烈贡献。总的来说，我们的方法能够对复杂性状的遗传风险驱动因素进行精细的解释。
Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.