Genomic selection methods for crop improvement: Current status and prospects

TLDR

Genomic selection links marker and phenotype data but struggles with a high marker‑to‑observation ratio, and recent research focuses on non‑additive effects and multi‑trait/environment analyses, which this review surveys. The paper reviews current genomic selection methods to estimate all loci effects and predict genetic values in untested populations, thereby accelerating crop breeding. The authors discuss models such as GBLUP, Bayes, and machine‑learning algorithms used to improve prediction accuracy.

Abstract

With marker and phenotype information from observed populations, genomic selection (GS) can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in these methods for crop improvement are reviewed in this paper.