AUTHOR=Khalilisamani Nima , Li Zitong , Pettolino Filomena A. , Moncuquet Philippe , Reverter Antonio , MacMillan Colleen P. 

TITLE=Leveraging transcriptomics-based approaches to enhance genomic prediction: integrating SNPs and gene networks for cotton fibre quality improvement

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1420837

DOI=10.3389/fpls.2024.1420837

ISSN=1664-462X

ABSTRACT=Cultivated cotton plants produce the world's largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains ~ 80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improve genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in differentially expressed (DE) genes overall, in target pairwise DE gene-lists informed by gene annotation, and in a novel approach of gene correlation network (GCN) clusters created with Partial Correlation and Information Theory (PCIT) as the prior information in GP models. The GCN-clusters were nucleated with known genes for fibre biomechanics i.e. fasciclin-like arabinogalactan proteins, and cluster-size was evaluated. The most promising improvements in improving GP accuracy were achieved by using GCN-clusters for cotton fibre Elongation by 4.6%, and Strength by 4.7%, where clusters-sizes of 2 and 3 neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN-cluster approach. Non-trait-specific biological timepoints, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN-clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN-clusters make a specific and unique contribution in improving GP of cotton fibre traits. The findings also indicate that there is scope for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits.