Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

Version 1 : Received: 20 October 2020 / Approved: 22 October 2020 / Online: 22 October 2020 (11:59:41 CEST)
Version 2 : Received: 15 November 2020 / Approved: 18 November 2020 / Online: 18 November 2020 (11:21:50 CET)

How to cite: McGowan, M.; Zhang, Z.; Wang, J.; Dong, H.; Liu, X.; Jia, Y.; Wang, X.; Iwata, H.; Li, Y.; Lipka, A. Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review. Preprints 2020, 2020100460 (doi: 10.20944/preprints202010.0460.v1). McGowan, M.; Zhang, Z.; Wang, J.; Dong, H.; Liu, X.; Jia, Y.; Wang, X.; Iwata, H.; Li, Y.; Lipka, A. Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review. Preprints 2020, 2020100460 (doi: 10.20944/preprints202010.0460.v1).

Abstract

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

Subject Areas

plant breeding; genomic selection; Bayes; BLUP; machine learning

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