West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
FEBS Open Bio. 2019 Nov;9(11):2006-2012. doi: 10.1002/2211-5463.12722. Epub 2019 Sep 27.
Human diseases are usually linked to multiloci genetic alterations, including single-nucleotide polymorphisms (SNPs). Methods to use these SNPs for disease risk prediction (DRP) are of clinical interest. DRP algorithms explored by commercial companies to date have tended to be complex and led to controversial prediction results. Here, we present a general approach for establishing a logistic model-based DRP algorithm, in which multiple SNP risk factors from different publications are directly used. In particular, the coefficient β of each SNP is set as the natural logarithm of the reported odds ratio, and the constant coefficient β is comprehensively determined by the coefficient and frequency of each SNP and the average disease risk in populations. Furthermore, homozygous SNP is considered a dummy variable, and the SNPs are updated (addition, deletion and modification) if necessary. Importantly, we validated this algorithm as a proof of concept: two patients with lung cancer were identified as the maximum risk cases from 57 Chinese individuals. Our logistic model-based DRP algorithm is apparently more intuitive and self-evident than the algorithms explored by commercial companies, and it may facilitate DRP commercialization in the era of personalized medicine.
人类疾病通常与多基因遗传改变有关，包括单核苷酸多态性（SNP）。利用这些 SNP 进行疾病风险预测（DRP）的方法具有临床意义。迄今为止，商业公司探索的 DRP 算法往往很复杂，导致预测结果存在争议。在这里，我们提出了一种基于逻辑回归模型的 DRP 算法的通用方法，其中直接使用来自不同出版物的多个 SNP 风险因素。具体来说，每个 SNP 的系数β被设置为报告的比值比的自然对数，常数系数β由每个 SNP 的系数和频率以及人群中的平均疾病风险综合确定。此外，如果需要，还考虑同型 SNP 为哑变量，并对 SNP 进行更新（添加、删除和修改）。重要的是，我们验证了该算法作为一个概念验证：从 57 位中国人中确定了两名肺癌患者为最大风险病例。我们基于逻辑回归模型的 DRP 算法明显比商业公司探索的算法更直观和显而易见，它可能有助于个性化医疗时代的 DRP 商业化。
