Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

ABSTRACT: In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated to large sections of a genome. By analyzing allele sharing between individuals, one may calculate realized genomic relationships from single-nucleotide polymorphisms (SNPs) data rather than relying on known pedigree relationships under polygenic model. The traditional approaches in genome-wide prediction (GWP) of quantitative phenotypes utilize genomic relationships in fixed global covariance modeling, possibly with some nonlinear kernel mapping (for example Gaussian processes). On the other hand, the DL appr

SUBMITTER: Kihlman R 

PROVIDER: S-EPMC11540326 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature