论文标题
GWAS的深度解释性
Deep interpretability for GWAS
论文作者
论文摘要
全基因组关联研究通常是使用线性模型进行的，以发现与常见疾病相关的遗传变异。在这些研究中，关联测试是按变异的变化进行的，可能会缺少变体之间的非线性相互作用效应。深网可以用来对这些相互作用进行建模，但是它们很难在大型遗传数据集上进行训练和解释。我们提出了一种使用基于梯度的深层可解释性技术DeepLift的方法，以表明可以使用深层模型以及可能的新型关联来鉴定已知的糖尿病遗传危险因素。
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In these studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.
