Deep Learning captures the effect of epistasis in multifactorial diseases
Vladislav Perelygin,
Alexey Kamelin,
Nikita Syzrantsev
et al.
Abstract:Background
Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as Alzheimer's disease, diabetes, cardiovascular disease, cancer, and others, association between individual SNPs and disease could be non-linear due to epistatic inter… Show more
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