Patrick Conaghan

Markers associated with heading and aftermath heading in perennial ryegrass full-sib families

BackgroundHeading and aftermath heading are important traits in perennial ryegrass because they impact forage quality. So far, genome-wide association analyses in this major forage species have only identified a small number of genetic variants associated with heading date that overall explained little of the variation. Some possible reasons include rare alleles with large phenotypic affects, allelic heterogeneity, or insufficient marker density. We established a genome-wide association panel with multiple genotypes from multiple full-sib families. This ensured alleles were present at the frequency needed to have sufficient statistical power to identify associations. We genotyped the panel via partial genome sequencing and performed genome-wide association analyses with multi-year phenotype data collected for heading date, and aftermath heading.ResultsGenome wide association using a mixed linear model failed to identify any variants significantly associated with heading date or aftermath heading. Our failure to identify associations for these traits is likely due to the extremely low linkage disequilibrium we observed in this population. However, using single marker analysis within each full-sib family we could identify markers and genomic regions associated with heading and aftermath heading. Using the ryegrass genome we identified putative orthologs of key heading genes, some of which were located in regions of marker-trait associations.ConclusionGiven the very low levels of LD, genome wide association studies in perennial ryegrass populations are going to require very high SNP densities. Single marker analysis within full-sibs enabled us to identify significant marker-trait associations. One of these markers anchored proximal to a putative ortholog of TFL1, homologues of which have been shown to play a key role in continuous heading of some members of the rose family, Rosaceae.

Using variable importance measures to identify a small set of SNPs to predict heading date in perennial ryegrass

Prior knowledge on heading date enables the selection of parents of synthetic cultivars that are well matched with respect to time of heading, which is essential to ensure plants put together will cross pollinate. Heading date of individual plants can be determined via direct phenotyping, which has a time and labour cost. It can also be inferred from family means, although the spread in days to heading within families demands roguing in first generation synthetics. Another option is to predict heading date from molecular markers. In this study we used a large training population consisting of individual plants to develop equations to predict heading date from marker genotypes. Using permutation-based variable selection measures we reduced the marker set from 217,563 to 50 without impacting the predictive ability. Opportunities exist to develop a cheap assay to sequence a small number of regions in linkage disequilibrium with heading date QTL in thousands of samples. Simultaneous use of these markers in non-linkage based marker-assisted selection approaches, such as paternity testing, should enhance the utility of such an approach.

Genomic prediction of crown rust resistance in Lolium perenne

BackgroundGenomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability.ResultsUsing these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set.ConclusionUsing a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.

Extent of Crown Rust Infection in a Perennial Ryegrass (Lolium perenne L.) Association Mapping Population

One of the most damaging foliar diseases on perennial ryegrass is crown rust, caused by Puccinia coronata f. sp. lolli. Crown rust causes severe losses to yield and quality of forage and results in decreased palatability and digestibility for ruminants. Crown rust was scored in 1800 individually spaced plants in two replicates on a scale of 1 (no infection) to 9 (severely infected) at three time points (June, August & September 2014) in a block design experiment. These 1800 individuals represent 30 populations with different population structures: ten synthetic varieties, eight full-sib breeding families, eight half-sib breeding families and four Irish ecotypes. The disease progression was observed in all populations, with highest pressure of crown rust seen in September and lowest disease pressure observed in June. Significant differences (p < 0.001) were recorded among populations, among time points and for the population x time point interaction. The Irish ecotypes were more susceptible to crown rust compared to the other populations. Crown rust phenotypic data will be used to conduct association analysis on these populations to identify significant marker trait associations.