Development of a general logistic model for disease risk prediction using multiple SNPs. Issue 11 (27th September 2019)

• Record Type: Journal Article
• Authors: Long, Cheng; Lv, Guanting; Fu, Xinmiao
• Is Part Of: FEBS open bio. Volume 9:Issue 11(2019)
• Page Start: 2006
• Page End: 2012
• Publication Date: 2019-09-27
• Subjects: disease risk prediction -- GWASs -- logistic regression -- personalized medicine -- precise medicine -- SNP; Molecular biology -- Periodicals; Cytology -- Periodicals; Life sciences -- Periodicals; Biological Science Disciplines -- Periodicals; Molecular Biology -- Periodicals; Cell Biology -- Periodicals; Cytology; Life sciences; Molecular biology; Periodicals; 572.805
• DOI: 10.1002/2211-5463.12722
• Languages: English
• ISSNs: 2211-5463

Abstract

Abstract : Single‐nucleotide polymorphisms (SNPs) can be used to predict a person's disease risk. Here, we present a general approach for establishing a logistic model‐based, reliable and robust algorithm, in which multiple SNP risk factors from multiple publications are used. Further, we validated this algorithm in lung cancer risk prediction. Our work may advance the commercialization of SNP‐based disease risk prediction. Abstract : 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 β0 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.