Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

ABSTRACT: The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ∼100k individuals, m ∼500k SNPs, and k = 1000) of the interim release of the UK Biobank. We analyzed a total of five phenotypes: height, bone heel mineral density, body mass index, systolic blood pressure, and waist-hip ratio, with genomic heritabilities ranging from ∼0.20 to 0.70. After hyperparame

SUBMITTER: Bellot P 

PROVIDER: S-EPMC6218236 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature