Developing an integrated genomic selection approach beyond biomass for varietal protection and nutritive traits in perennial ryegrass (Lolium perenne L.).

KEY MESSAGE: Breeding target traits can be broadened to include nutritive value and plant breeder's rights traits in perennial ryegrass by using in-field regression-based spectroscopy phenotyping and genomic selection. Perennial ryegrass breeding has focused on biomass yield, but expansion into a broader set of traits is needed to benefit livestock industries whilst also providing support for intellectual property protection of cultivars. Numerous breeding objectives can be targeted simultaneously with the development of sensor-based phenomics and genomic selection (GS). Of particular interest are nutritive value (NV), which has been difficult and expensive to measure using traditional phenotyping methods, resulting in limited genetic improvement to date, and traits required to obtain varietal protection, known as plant breeder's rights (PBR) traits. In order to assess phenotyping requirements for NV improvement and potential for genetic improvement, in-field reflectance-based spectroscopy was assessed and GS evaluated in a single population for three key NV traits, captured across four timepoints. Using three prediction approaches, the possibility of targeting PBR traits using GS was evaluated for five traits recorded across three years of a breeding program. Prediction accuracy was generally low to moderate for NV traits and moderate to high for PBR traits, with heritability highly correlated with GS accuracy. NV did not show significant or consistent correlation between timepoints highlighting the need to incorporate seasonal NV into selection indexes and the value of being able to regularly monitor NV across seasons. This study has demonstrated the ability to implement GS for both NV and PBR traits in perennial ryegrass, facilitating the expansion of ryegrass breeding targets to agronomically relevant traits while ensuring necessary varietal protection is achieved.

Meta-analysis of GWAS in canola blackleg (Leptosphaeria maculans) disease traits demonstrates increased power from imputed whole-genome sequence.

Blackleg disease causes yield losses in canola (Brassica napus L.). To identify resistance genes and genomic regions, genome-wide association studies (GWAS) of 585 diverse winter and spring canola accessions were performed using imputed whole-genome sequence (WGS) and transcriptome genotype-by-sequencing (GBSt). Blackleg disease phenotypes were collected across three years in six trials. GWAS were performed in several ways and their respective power was judged by the number of significant single nucleotide polymorphisms (SNP), the false discovery rate (FDR), and the percentage of SNP that validated in additional field trials in two subsequent years. WGS GWAS with 1,234,708 million SNP detected a larger number of significant SNP, achieved a lower FDR and a higher validation rate than GBSt with 64,072 SNP. A meta-analysis combining survival and average internal infection resulted in lower FDR but also lower validation rates. The meta-analysis GWAS identified 79 genomic regions (674 SNP) conferring potential resistance to L. maculans. While several GWAS signals localised in regions of known Rlm genes, fifty-three new potential resistance regions were detected. Seventeen regions had underlying genes with putative functions related to disease defence or stress response in Arabidopsis thaliana. This study provides insight into the genetic architecture and potential molecular mechanisms underlying canola L. maculans resistance.

Assessment of low-coverage nanopore long read sequencing for SNP genotyping in doubled haploid canola (Brassica napus L.).

Despite the high accuracy of short read sequencing (SRS), there are still issues with attaining accurate single nucleotide polymorphism (SNP) genotypes at low sequencing coverage and in highly duplicated genomes due to misalignment. Long read sequencing (LRS) systems, including the Oxford Nanopore Technologies (ONT) minION, have become popular options for de novo genome assembly and structural variant characterisation. The current high error rate often requires substantial post-sequencing correction and would appear to prevent the adoption of this system for SNP genotyping, but nanopore sequencing errors are largely random. Using low coverage ONT minION sequencing for genotyping of pre-validated SNP loci was examined in 9 canola doubled haploids. The minION genotypes were compared to the Illumina sequences to determine the extent and nature of genotype discrepancies between the two systems. The significant increase in read length improved alignment to the genome and the absence of classical SRS biases results in a more even representation of the genome. Sequencing errors are present, primarily in the form of heterozygous genotypes, which can be removed in completely homozygous backgrounds but requires more advanced bioinformatics in heterozygous genomes. Developments in this technology are promising for routine genotyping in the future.