Genome resources for three modern cotton lines guide future breeding efforts

Sreedasyam, Avinash; Lovell, John T.; Mamidi, Sujan; Khanal, Sameer; Jenkins, Jerry W.; Plott, Christopher; Bryan, Kempton B.; Li, Zhigang; Shu, Shengqiang; Carlson, Joseph; Goodstein, David; De Santiago, Luis; Kirkbride, Ryan C.; Calleja, Sebastian; Campbell, Todd; Koebernick, Jenny C.; Dever, Jane K.; Scheffler, Jodi A.; Pauli, Duke; Jenkins, Johnie N.; McCarty, Jack C.; Williams, Melissa; Boston, LoriBeth; Webber, Jenell; Udall, Joshua A.; Chen, Z. Jeffrey; Bourland, Fred; Stiller, Warwick N.; Saski, Christopher A.; Grimwood, Jane; Chee, Peng W.; Jones, Don C.; Schmutz, Jeremy

doi:10.1038/s41477-024-01713-z

Cited by 2 publications

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model

Beegum,

Hassan,

Reddy

et al. 2024

Preprint

View full text Add to dashboard Cite

Background A fiber quality module developed in 2023 and integrated into the process-based mechanistic cotton crop growth and development model, GOSSYM is the first of its kind. In this fiber quality module, the functional relationships between fiber quality and the major factors influencing it (temperature, water, and nutrient status) are established based on experiments spanned four years conducted in the sunlit Soil Plant Atmospheric Research chambers. All these experiments were conducted only on the Texas Marker-1 cotton variety. Therefore, there is a possibility that the functional equations will be more aligned with this specific cultivar. Consequently, it's essential to assess how the model performs for other cotton cultivars and address any variability that arises. In this study, data from experiments conducted on 40 major cultivars currently grown in the USA, including the Texas Marker-1 variety, under the same environmental and management conditions is used to analyze the variability in fiber quality among the varieties. The measured fiber quality is then compared with the GOSSYM model-simulated fiber quality. Based on the relative variation between measured and simulated fiber quality, cultivar-dependent parameters were developed for the fiber quality model. Results Based on the relative variation between measured and simulated fiber quality, cultivar-dependent parameters were developed for the fiber quality model. The GOSSYM model, after incorporating the developed cultivar-dependent parameters, simulated the fiber quality (fiber length, strength, micronaire, and uniformity) with an average Pearson correlation coefficient value of 0.84 and index of agreement of 0.88. Conclusions This study aims to analyze the fiber quality variability among modern cotton cultivars and establish the cultivar-dependent parameters for cotton fiber quality simulation in the GOSSYM model. The parameter estimation methodology adopted and the estimated cultivar-specific parameters improved the simulation capabilities of the model. The model with cultivar-specific parameters for fiber quality will be helpful for model users, requiring less calibration effort and providing more accurate quality simulations.

show abstract

Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model

Beegum,

Hassan,

Reddy

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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

Khalilisamani,

Li,

Pettolino

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Cultivated cotton plants are 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 improving the 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 DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression 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 effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three 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 time points, 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 the GP of cotton fibre traits. The findings also indicate that there is room 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.

show abstract

Genome resources for three modern cotton lines guide future breeding efforts

Cited by 2 publications

References 93 publications

Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model

Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model

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

Contact Info

Product

Resources

About