Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs

Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing

Conventional pre-genomics breeding methodologies have significantly improved crop yields since the mid-twentieth century. Genomics provides breeders with advanced tools for whole-genome study, enabling a direct genotype-phenotype analysis. This shift has led to precise and efficient crop development through genomics-based approaches, including molecular markers, genomic selection, and genome editing. Molecular markers, such as SNPs, are crucial for identifying genomic regions linked to important traits, enhancing breeding accuracy and efficiency. Genomic resources viz. genetic markers, reference genomes, sequence and protein databases, transcriptomes, and gene expression profiles, are vital in plant breeding and aid in the identification of key traits, understanding genetic diversity, assist in genomic mapping, support marker-assisted selection and speeding up breeding programs. Advanced techniques like CRISPR/Cas9 allow precise gene modification, accelerating breeding processes. Key techniques like Genome-Wide Association study (GWAS), Marker-Assisted Selection (MAS), and Genomic Selection (GS) enable precise trait selection and prediction of breeding outcomes, improving crop yield, disease resistance, and stress tolerance. These tools are handy for complex traits influenced by multiple genes and environmental factors. This paper explores new genomic technologies like molecular markers, genomic selection, and genome editing for plant breeding showcasing their impact on developing new plant varieties.

Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance

KEY MESSAGE

Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.

Genome-Wide Association Mapping and Genomic Prediction of Fusarium Wilt Race 2 Resistance in the USDA Citrullus amarus Collection

Fusarium wilt caused by Fusarium oxysporum f. sp. niveum (Fon) race 2 is a serious disease in watermelon and can reduce yields by 80%. Genome-wide association studies (GWAS) are a valuable tool in dissecting the genetic basis of traits. Citrullus amarus accessions (n = 120) from the USDA germplasm collection were genotyped with whole-genome resequencing, resulting in 2,126,759 single nucleotide polymorphic (SNP) markers that were utilized for GWAS. Three models were used for GWAS with the R package GAPIT. Mixed linear model (MLM) analysis did not identify any significant marker associations. FarmCPU identified four quantitative trait nucleotides (QTN) on three different chromosomes (i.e., chromosomes 1, 5, and 9), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) identified one QTN on chromosome 10 as significantly associated with Fon race 2 resistance. FarmCPU identified four QTN that explained 60% of Fon race 2 resistance, and the single QTN from BLINK explained 27%. Relevant candidate genes were found within the linkage disequilibrium (LD) blocks of these significant SNPs, including genes encoding aquaporins, expansins, 2S albumins, and glutathione S-transferases which have been shown to be involved in imparting resistance to Fusarium spp. Genomic predictions (GP) for Fon race 2 resistance using all 2,126,759 SNPs resulted in a mean prediction accuracy of 0.08 with five-fold cross-validation employing genomic best linear unbiased prediction (gBLUP) or ridge-regression best linear unbiased prediction (rrBLUP). Mean prediction accuracy with gBLUP leave-one-out cross-validation was 0.48. Thus, along with identifying genomic regions associated with Fon race 2 resistance among the accessions, this study observed prediction accuracies that were strongly influenced by population size.

A medium-density genotyping platform for cultivated strawberry using DArTag technology

Genomic prediction in breeding populations containing hundreds to thousands of parents and seedlings is prohibitively expensive with current high-density genetic marker platforms designed for strawberry. We developed mid-density panels of molecular inversion probes (MIPs) to be deployed with the "DArTag" marker platform to provide a low-cost, high-throughput genotyping solution for strawberry genomic prediction. In total, 7742 target single nucleotide polymorphism (SNP) regions were used to generate MIP assays that were tested with a screening panel of 376 octoploid Fragaria accessions. We evaluated the performance of DArTag assays based on genotype segregation, amplicon coverage, and their ability to produce subgenome-specific amplicon alignments to the FaRR1 assembly and subsequent alignment-based variant calls with strong concordance to DArT's alignment-free, count-based genotype reports. We used a combination of marker performance metrics and physical distribution in the FaRR1 assembly to select 3K and 5K production panels for genotyping of large strawberry populations. We show that the 3K and 5K DArTag panels are able to target and amplify homologous alleles within subgenomic sequences with low-amplification bias between reference and alternate alleles, supporting accurate genotype calling while producing marker genotypes that can be treated as functionally diploid for quantitative genetic analysis. The 3K and 5K target SNPs show high levels of polymorphism in diverse F. × ananassa germplasm and UC Davis cultivars, with mean pairwise diversity (π) estimates of 0.40 and 0.32 and mean heterozygous genotype frequencies of 0.35 and 0.33, respectively.

Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses

Use of trifluoromethanesulfonamide (TFMSA), a male gametocide, increases the opportunities to identify promising B-lines because large quantities of F1 seed can be generated prior to the laborious task of B-line sterilization. Combining TFMSA technology with genomic selection could efficiently evaluate sorghum B-lines in hybrid combination to maximize the rates of genetic gain of the crop. This study used two recombinant inbred B-line populations, consisting of 217 lines, which were testcrossed to two R-lines to produce 434 hybrids. Each population of testcross hybrids were evaluated across five environments. Population-based genomic prediction models were assessed across environments using three different cross-validation (CV) schemes, each with 70% training and 30% validation sets. The validation schemes were as follows: CV1-hybrids chosen randomly for validation; CV2-B-lines were randomly chosen, and each chosen B-line had one of the two corresponding testcross hybrids randomly chosen for the validation; and CV3-B-lines were randomly chosen, and each chosen B-line had both corresponding testcross hybrids chosen for the validation. CV1 and CV2 presented the highest prediction accuracies; nonetheless, the prediction accuracies of the CV schemes were not statistically different in many environments. We determined that combining the B-line populations could improve prediction accuracies, and the genomic prediction models were able to effectively rank the poorest 70% of hybrids even when genomic prediction accuracies themselves were low. Results indicate that combining genomic prediction models and TFMSA technology can effectively aid breeders in predicting B-line hybrid performance in early generations prior to the laborious task of generating A/B-line pairs.

Genetic loci regulating the concentrations of anthocyanins and proanthocyanidins in the pericarps of purple and red rice

The pigmented flavonoids, anthocyanins and proanthocyanidins, have health promoting properties. Previous work determined that the genes Pb and Rc turn on and off the biosynthesis of anthocyanins (purple) and proanthocyanidins (red), respectively. Not yet known is how the concentrations of these pigmented flavonoids are regulated in grain pericarps. Quantitative trait locus (QTL) analysis in a population of rice (Oryza sativa L.) F5 recombinant inbred lines from white pericarp "IR36ae" x red+purple pericarp "242" revealed three QTLs associated with grain concentrations of anthocyanins (TAC) or proanthocyanidins (PA). Both TAC and PA independently mapped to a 1.5 Mb QTL region on chromosome 3 between RM3400 (at 15.8 Mb) and RM15123 (17.3 Mb), named qPR3. Across 2 years, qPR3 explained 36.3% of variance in TAC and 35.8% in PA variance not attributable to Pb or Rc. The qPR3 region encompasses Kala3, a MYB transcription factor previously known to regulate purple grain characteristics. Study of PbPbRcrc progeny showed that TAC of RcRc near isogenic lines (NILs) was 2.1-4.5x that of rcrc. Similarly, study of PbPbRcRc NILs, which had 70% higher PA than pbpbRcRc NILs, revealed a mutual enhancement, not a trade-off between these compounds that share precursors. This suggests that Pb and Rc upregulate genes in a shared pathway as they activate TAC and PA synthesis, respectively. This study provides molecular markers for facilitating marker-assisted selection of qPR3, qPR5, and qPR7 to enhance grain concentrations of pigmented flavonoids and documented that stacking Rc and Pb genes further increases both flavonoid compounds.

Characterization of the USDA Cucurbita pepo, C. moschata, and C. maxima germplasm collections

The Cucurbita genus is home to a number of economically and culturally important species. We present the analysis of genotype data generated through genotyping-by-sequencing of the USDA germplasm collections of Cucurbita pepo, C. moschata, and C. maxima. These collections include a mixture of wild, landrace, and cultivated specimens from all over the world. Roughly 1,500 - 32,000 high-quality single nucleotide polymorphisms (SNPs) were called in each of the collections, which ranged in size from 314 to 829 accessions. Genomic analyses were conducted to characterize the diversity in each of the species. Analysis revealed extensive structure corresponding to a combination of geographical origin and morphotype/market class. Genome-wide associate studies (GWAS) were conducted using both historical and contemporary data. Signals were observed for several traits, but the strongest was for the bush (Bu) gene in C. pepo. Analysis of genomic heritability, together with population structure and GWAS results, was used to demonstrate a close alignment of seed size in C. pepo, maturity in C. moschata, and plant habit in C. maxima with genetic subgroups. These data represent a large, valuable collection of sequenced Cucurbita that can be used to direct the maintenance of genetic diversity, for developing breeding resources, and to help prioritize whole-genome re-sequencing.