A directed learning strategy integrating multiple omic data improves genomic prediction.

ABSTRACT: Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic data alone has reached a bottleneck, and previous studies on transcriptomic and metabolomic predictions ignored genomic information. Here, we designed a novel strategy of GP called multilayered least absolute shrinkage and selection operator (MLLASSO) by integrating multiple omic data into a single model that iteratively learns three layers of genetic features (GFs) supervised by observed transcriptome and metabolome. Significantly, MLLASSO learns higher order information of gene interactions, which enables us to achieve a significant improvemen

SUBMITTER: Hu X 

PROVIDER: S-EPMC6737184 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature