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.

In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.

BACKGROUND: Cannabis has been used worldwide for centuries for industrial, recreational and medicinal use, however, to date no successful attempts at editing genes involved in cannabinoid biosynthesis have been reported. This study proposes and develops an in silico best practices approach for the design and implementation of genome editing technologies in cannabis to target all genes involved in cannabinoid biosynthesis. RESULTS: A large dataset of reference genomes was accessed and mined to determine copy number variation and associated SNP variants for optimum target edit sites for genotype independent editing. Copy number variance and highly polymorphic gene sequences exist in the genome making genome editing using CRISPR, Zinc Fingers and TALENs technically difficult. Evaluation of allele or additional gene copies was determined through nucleotide and amino acid alignments with comparative sequence analysis performed. From determined gene copy number and presence of SNPs, multiple online CRISPR design tools were used to design sgRNA targeting every gene, accompanying allele and homologs throughout all involved pathways to create knockouts for further investigation. Universal sgRNA were designed for highly homologous sequences using MultiTargeter and visualised using Sequencher, creating unique sgRNA avoiding SNP and shared nucleotide locations targeting optimal edit sites. CONCLUSIONS: Using this framework, the approach has wider applications to all plant species regardless of ploidy number or highly homologous gene sequences. SIGNIFICANCE STATEMENT: Using this framework, a best-practice approach to genome editing is possible in all plant species, including cannabis, delivering a comprehensive in silico evaluation of the cannabinoid pathway diversity from a large set of whole genome sequences. Identification of SNP variants across all genes could improve genome editing potentially leading to novel applications across multiple disciplines, including agriculture and medicine.

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.

Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes.

KEY MESSAGE: Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat. Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.

Genetic map construction and QTL mapping of resistance to blackleg (Leptosphaeria maculans) disease in Australian canola (Brassica napus L.) cultivars.

Genetic map construction and identification of quantitative trait loci (QTLs) for blackleg resistance were performed for four mapping populations derived from five different canola source cultivars. Three of the populations were generated from crosses between single genotypes from the blackleg-resistant cultivars Caiman, Camberra and (AV)Sapphire and the blackleg-susceptible cultivar Westar(10). The fourth population was derived from a cross between genotypes from two blackleg resistant varieties (Rainbow and (AV)Sapphire). Different types of DNA-based markers were designed and characterised from a collection of 20,000 EST sequences generated from multiple Brassica species, including a new set of 445 EST-SSR markers of high value to the international community. Multiple molecular genetic marker systems were used to construct linkage maps with locus numbers varying between 219 and 468, and coverage ranging from 1173 to 1800 cM. The proportion of polymorphic markers assigned to map locations varied from 70 to 89% across the four populations. Publicly available simple sequence repeat markers were used to assign linkage groups to reference nomenclature, and a sub-set of mapped markers were also screened on the Tapidor x Ningyou (T x N) reference population to assist this process. QTL analysis was performed based on percentage survival at low and high disease pressure sites. Multiple QTLs were identified across the four mapping populations, accounting for 13-33% of phenotypic variance (V (p)). QTL-linked marker data are suitable for implementation in breeding for disease resistance in Australian canola cultivars. However, the likelihood of shifts in pathogen race structure across different geographical locations may have implications for the long-term durability of such associations.

Discovery and genetic mapping of single nucleotide polymorphisms in candidate genes for pathogen defence response in perennial ryegrass (Lolium perenne L.).

Susceptibility to foliar pathogens commonly causes significant reductions in productivity of the important temperate forage perennial ryegrass. Breeding for durable disease resistance involves not only the deployment of major genes but also the additive effects of minor genes. An approach based on in vitro single nucleotide polymorphism (SNP) discovery in candidate defence response (DR) genes has been used to develop potential diagnostic genetic markers. SNPs were predicted, validated and mapped for representatives of the pathogenesis-related (PR) protein-encoding and reactive oxygen species (ROS)-generating gene classes. The F(1)(NA(6) x AU(6)) two-way pseudo-test cross population was used for SNP genetic mapping and detection of quantitative trait loci (QTLs) in response to a crown rust field infection. Novel resistance QTLs were coincident with mapped DR gene SNPs. QTLs on LG3 and LG7 also coincided with both herbage quality QTLs and candidate genes for lignin biosynthesis. Multiple DR gene SNP loci additionally co-located with QTLs for grey leaf spot, bacterial wilt and crown rust resistance from other published studies. Further functional validation of DR gene SNP loci using methods such as fine-mapping and association genetics will improve the efficiency of parental selection based on superior allele content.

Validation of in silico-predicted genic SNPs in white clover (Trifolium repens L.), an outbreeding allopolyploid species.

White clover (Trifolium repens L.) is an obligate outbreeding allotetraploid forage legume. Gene-associated SNPs provide the optimum genetic system for improvement of such crop species. An EST resource obtained from multiple cDNA libraries constructed from numerous genotypes of a single cultivar has been used for in silico SNP discovery and validation. A total of 58 from 236 selected sequence clusters (24.5%) were fully validated as containing polymorphic SNPs by genotypic analysis across the parents and progeny of several two-way pseudo-testcross mapping families. The clusters include genes belonging to a broad range of predicted functional categories. Polymorphic SNP-containing ESTs have also been used for comparative genomic analysis by comparison with whole genome data from model legume species, as well as Arabidopsis thaliana. A total of 29 (50%) of the 58 clusters detected putative ortholoci with known chromosomal locations in Medicago truncatula, which is closely related to white clover within the Trifolieae tribe of the Fabaceae. This analysis provides access to translational data from model species. The efficiency of in silico SNP discovery in white clover is limited by paralogous and homoeologous gene duplication effects, which are resolved unambiguously by the transmission test. This approach will also be applicable to other agronomically important cross-pollinating allopolyploid plant species.

QTL analysis and comparative genomics of herbage quality traits in perennial ryegrass (Lolium perenne L.).

Genetic control of herbage quality variation was assessed through the use of the molecular marker-based reference genetic map of perennial ryegrass (Lolium perenne L.). The restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP) and genomic DNA-derived simple sequence repeat-based (SSR) framework marker set was enhanced, with RFLP loci corresponding to genes for key enzymes involved in lignin biosynthesis and fructan metabolism. Quality traits such as crude protein (CP) content, estimated in vivo dry matter digestibility (IVVDMD), neutral detergent fibre content (NDF), estimated metabolisable energy (EstME) and water soluble carbohydrate (WSC) content were measured by near infrared reflectance spectroscopy (NIRS) analysis of herbage harvests. Quantitative trait locus (QTL) analysis was performed using single-marker regression, simple interval mapping and composite interval mapping approaches, detecting a total of 42 QTLs from six different sampling experiments varying by developmental stage (anthesis or vegetative growth), location or year. Coincident QTLs were detected on linkage groups (LGs) 3, 5 and 7. The region on LG3 was associated with variation for all measured traits across various experimental datasets. The region on LG7 was associated with variation for all traits except CP, and is located in the vicinity of the lignin biosynthesis gene loci xlpomt1 (caffeic acid-O-methyltransferase), xlpccr1 (cinnamoyl CoA-reductase) and xlpssrcad 2.1 (cinnamyl alcohol dehydrogenase). Comparative genomics analysis of these gene classes with wheat (Triticum aestivum L.) provides evidence for conservation of gene order over evolutionary time and the basis for cross-specific genetic information transfer. The identification of co-location between QTLs and functionally associated genetic markers is critical for the implementation of marker-assisted selection programs and for linkage disequilibrium studies, which will enable future improvement strategies for perennial ryegrass.

Functionally associated molecular genetic marker map construction in perennial ryegrass (Lolium perenne L.).

A molecular marker-based map of perennial ryegrass (Lolium perenne L.) has been constructed through the use of polymorphisms associated with expressed sequence tags (ESTs). A pair-cross between genotypes from a North African ecotype and the cultivar Aurora was used to generate a two-way pseudo-testcross population. A selection of 157 cDNAs assigned to eight different functional categories associated with agronomically important biological processes was used to detect polymorphic EST-RFLP loci in the F(1)(NA(6) x AU(6)) population. A comprehensive set of EST-SSR markers was developed from the analysis of 14,767 unigenes, with 310 primer pairs showing efficient amplification and detecting 113 polymorphic loci. Two parental genetic maps were produced: the NA(6) genetic map contains 88 EST-RFLP and 71 EST-SSR loci with a total map length of 963 cM, while the AU(6) genetic map contains 67 EST-RFLP and 58 EST-SSR loci with a total map length of 757 cM. Bridging loci permitted the alignment of homologous chromosomes between the parental maps, and a sub-set of genomic DNA-derived SSRs was used to relate linkage groups to the perennial ryegrass reference map. Regions of segregation distortion were identified, in some instances in common with other perennial ryegrass maps. The EST-derived marker-based map provides the basis for in silico comparative genetic mapping, as well as the evaluation of co-location between QTLs and functionally associated genetic loci.