Diversity analysis and genomic prediction of Sclerotinia resistance in sunflower using a new 25 K SNP genotyping array

Maximizing efficiency in sunflower breeding through historical data optimization

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

Applications of Genomic Tools in Plant Breeding: Crop Biofortification

Crop breeding has mainly been focused on increasing productivity, either directly or by decreasing the losses caused by biotic and abiotic stresses (that is, incorporating resistance to diseases and enhancing tolerance to adverse conditions, respectively). Quite the opposite, little attention has been paid to improve the nutritional value of crops. It has not been until recently that crop biofortification has become an objective within breeding programs, through either conventional methods or genetic engineering. There are many steps along this long path, from the initial evaluation of germplasm for the content of nutrients and health-promoting compounds to the development of biofortified varieties, with the available and future genomic tools assisting scientists and breeders in reaching their objectives as well as speeding up the process. This review offers a compendium of the genomic technologies used to explore and create biodiversity, to associate the traits of interest to the genome, and to transfer the genomic regions responsible for the desirable characteristics into potential new varieties. Finally, a glimpse of future perspectives and challenges in this emerging area is offered by taking the present scenario and the slow progress of the regulatory framework as the starting point.

Development and Validation of Markers for the Fertility Restorer Gene Rf1 in Sunflower

Hybrid breeding in sunflowers based on CMS PET1 requires development of restorer lines carrying, in most cases, the restorer gene Rf1. Markers for marker-assisted selection have been developed, but there is still need for closer, more versatile, and co-dominant markers linked to Rf1. Homology searches against the reference sunflower genome using sequences of cloned markers, as well as Bacterial Artificial Chromosome (BAC)-end sequences of clones hybridizing to them, allowed the identification of two genomic regions of 30 and 3.9 Mb, respectively, as possible physical locations of the restorer gene Rf1 on linkage group 13. Nine potential candidate genes, encoding six pentatricopeptide repeat proteins, one tetratricopeptide-like helical domain, a probable aldehyde dehydrogenase 22A1, and a probable poly(A) polymerase 3 (PAPS3), were identified in these two genomic regions. Amplicon targeted next generation sequencing of these nine candidate genes for Rf1 was performed in an association panel consisting of 27 maintainer and 32 restorer lines and revealed the presence of 210 Single Nucleotide Polymorphisms (SNPs) and 67 Insertions/Deletions (INDELs). Association studies showed significant associations of 10 SNPs with fertility restoration (p-value < 10⁻⁴), narrowing Rf1 down to three candidate genes. Three new markers, one co-dominant marker 67N04_P and two dominant markers, PPR621.5R for restorer, and PPR621.5M for maintainer lines were developed and verified in the association panel of 59 sunflower lines. The versatility of the three newly developed markers, as well as of three existing markers for the restorer gene Rf1 (HRG01 and HRG02, Cleaved Amplified Polymorphic Sequence (CAPS)-marker H13), was analyzed in a large association panel consisting of 557 accessions.

High-throughput genotyping-by-sequencing facilitates molecular tagging of a novel rust resistance gene, R 15 , in sunflower (Helianthus annuus L.)

A novel rust resistance gene, R ₁₅ , derived from the cultivated sunflower HA-R8 was assigned to linkage group 8 of the sunflower genome using a genotyping-by-sequencing approach. SNP markers closely linked to R ₁₅ were identified, facilitating marker-assisted selection of resistance genes. The rust virulence gene is co-evolving with the resistance gene in sunflower, leading to the emergence of new physiologic pathotypes. This presents a continuous threat to the sunflower crop necessitating the development of resistant sunflower hybrids providing a more efficient, durable, and environmentally friendly host plant resistance. The inbred line HA-R8 carries a gene conferring resistance to all known races of the rust pathogen in North America and can be used as a broad-spectrum resistance resource. Based on phenotypic assessments of 140 F₂ individuals derived from a cross of HA 89 with HA-R8, rust resistance in the population was found to be conferred by a single dominant gene (R ₁₅ ) originating from HA-R8. Genotypic analysis with the currently available SSR markers failed to find any association between rust resistance and any markers. Therefore, we used genotyping-by-sequencing (GBS) analysis to achieve better genomic coverage. The GBS data showed that R ₁₅ was located at the top end of linkage group (LG) 8. Saturation with 71 previously mapped SNP markers selected within this region further showed that it was located in a resistance gene cluster on LG8, and mapped to a 1.0-cM region between three co-segregating SNP makers SFW01920, SFW00128, and SFW05824 as well as the NSA_008457 SNP marker. These closely linked markers will facilitate marker-assisted selection and breeding in sunflower.

Sunflower Hybrid Breeding: From Markers to Genomic Selection

In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches combining omic technologies (genomics, transcriptomics, proteomics, metabolomics and phenomics) using bioinformatic tools will facilitate the identification of target genes and markers for complex traits and will give a better insight into the mechanisms behind the traits.

Main and epistatic QTL analyses for Sclerotinia Head Rot resistance in sunflower

Sclerotinia Head Rot (SHR), a disease caused by Sclerotinia sclerotiorum, is one of the most limiting factors in sunflower production. In this study, we identified genomic loci associated with resistance to SHR to support the development of assisted breeding strategies. We genotyped 114 Recombinant Inbred Lines (RILs) along with their parental lines (PAC2 -partially resistant-and RHA266 -susceptible-) by using a 384 single nucleotide polymorphism (SNP) Illumina Oligo Pool Assay to saturate a sunflower genetic map. Subsequently, we tested these lines for SHR resistance using assisted inoculations with S. sclerotiorum ascospores. We also conducted a randomized complete-block assays with three replicates to visually score disease incidence (DI), disease severity (DS), disease intensity (DInt) and incubation period (IP) through four field trials (2010-2014). We finally assessed main effect quantitative trait loci (M-QTLs) and epistatic QTLs (E-QTLs) by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively. As a result of this study, the improved map incorporates 61 new SNPs over candidate genes. We detected a broad range of narrow sense heritability (h2) values (1.86-59.9%) as well as 36 M-QTLs and 13 E-QTLs along 14 linkage groups (LGs). On LG1, LG10, and LG15, we repeatedly detected QTLs across field trials; which emphasizes their putative effectiveness against SHR. In all selected variables, most of the identified QTLs showed high determination coefficients, associated with moderate to high heritability values. Using markers shared with previous Sclerotinia resistance studies, we compared the QTL locations in LG1, LG2, LG8, LG10, LG11, LG15 and LG16. This study constitutes the largest report of QTLs for SHR resistance in sunflower. Further studies focusing on the regions in LG1, LG10, and LG15 harboring the detected QTLs are necessary to identify causal alleles and contribute to unraveling the complex genetic basis governing the resistance.

Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives

There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype-phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.