Development of a general logistic model for disease risk prediction using multiple SNPs
Development of a general logistic model for disease risk prediction using multiple SNPs
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使用多个 SNP 开发疾病风险预测的通用逻辑模型
DOI：
10.1002/2211-5463.12722
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发表时间：
2019-09-27
期刊：
影响因子：
2.6
通讯作者：
Fu，Xinmiao
中科院分区：
文献类型：
--
作者：
Long，Cheng;Lv，Guanting;Fu，Xinmiao
人类疾病通常与多位点遗传改变有关，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，并导致有争议的预测结果。在这里，我们提出了一种建立基于logistic模型的DRP算法的通用方法，其中直接使用了来自不同出版物的多个SNP风险因素。其中，将每个SNP的系数β设为报告优势比的自然对数，常数系数β0由每个SNP的系数和频率与人群平均疾病风险综合确定。此外，纯合SNP被认为是一个虚拟变量，如果需要，SNP会被更新（添加、删除和修改）。重要的是，我们将该算法验证为概念验证：从57名中国人中确定了两名肺癌患者为最大风险病例。我们基于logistic模型的DRP算法显然比商业公司探索的算法更直观、更自明，它可能促进DRP在个性化医疗时代的商业化。
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 β0is 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.