Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding

Genetic dissection of green pod yield in dolichos bean, an orphan vegetable legume, using new molecular markers

In the era of genomic-assisted breeding for crop improvement, developing new molecular markers and validating them for use in breeding programs are the prelude. Dolichos bean is one of the most important vegetable legume crops owing to its nutrient-rich green pods used as vegetables. Limitations in genomic resources, including molecular markers, restrict the accelerated improvement of the crop. In the present investigation, a set of 430 new simple sequence repeat markers was developed from sequence information of a reference variety. These markers included di- and tri-nucleotide repeats. The markers were assayed on an association panel, which was evaluated for green pod yield over 5 years. A multi-locus model, FarmCPU, was used to assess the marker-trait association analysis. A total of 106 marker-trait associations were identified using an efficient mixed-model approach. Tri-nucleotide repeats were more informative and predominantly associated with trait. Among these markers, 17 were associated with a high level of significance. Markers LP-D-68 and LP-D-14 were identified with a high level of significance in 5-year pooled data and explained 12.70% and 12% of the phenotypic variance, respectively. These markers associated with a high level of confidence have significant scope for use in marker-assisted selection programmes. Other associated markers may be utilized for improving parents through marker-assisted recurrent selection or genomic selection programs.

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Genome-wide association studies for a comprehensive understanding of the genetic architecture of culm strength and yield traits in rice

Lodging resistance in rice is a complex trait determined by culm morphological and culm physical strength traits, and these traits are a major determinant of yield. We made a detailed analysis of various component traits with the aim of deriving optimized parameters for measuring culm strength. Genotyping by sequencing (GBS)-based genome-wide association study (GWAS) was employed among 181 genotypes for dissecting the genetic control of culm strength traits. The VanRaden kinship algorithm using 6,822 filtered single-nucleotide polymorphisms (SNPs) revealed the presence of two sub-groups within the association panel with kinship values concentrated at<0.5 level, indicating greater diversity among the genotypes. A wide range of phenotypic variation and high heritability for culm strength and yield traits were observed over two seasons, as reflected in best linear unbiased prediction (BLUP) estimates. The multi-locus model for GWAS resulted in the identification of 15 highly significant associations (p< 0.0001) for culm strength traits. Two novel major effect marker-trait associations (MTAs) for section modulus and bending stress were identified on chromosomes 2 and 12 with a phenotypic variance of 21.87% and 10.14%, respectively. Other MTAs were also noted in the vicinity of previously reported putative candidate genes for lodging resistance, providing an opportunity for further research on the biochemical basis of culm strength. The quantitative trait locus (QTL) hotspot identified on chromosome 12 with the synergistic association for culm strength trait (section modulus, bending stress, and internode breaking weight) and grain number can be considered a novel genomic region that can serve a dual purpose of enhancing culm strength and grain yield. Elite donors in the indica background with beneficial alleles of the identified major QTLs could be a valuable resource with greater significance in practical plant breeding programs focusing on improving lodging resistance in rice.