Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes.

ABSTRACT: Deep learning is impacting many fields of data science with often spectacular results. However, its application to whole-genome predictions in plant and animal science or in human biology has been rather limited, with mostly underwhelming results. While most works focus on exploring alternative network architectures, in this study we propose an innovative representation of marker genotype data and tested it against the GBLUP (Genomic BLUP) benchmark with linear and nonlinear phenotypes. From publicly available cattle SNP genotype data, different types of genomic kinship matrices are stacked together in a 3D pile from where 2D grayscale slices are extracted and fed to a deep convolutional neural network (DNN). We simulated nine phenotype scenarios with combinations of additivity, dominance

SUBMITTER: Nazzicari N 

PROVIDER: S-EPMC9674857 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature