Periodic Reporting for period 1 - GenSPaD (Genomic Selection for Pasture Digestibility)
Periodo di rendicontazione: 2020-05-18 al 2022-05-17
Breeding for improved perennial ryegrass cultivars to support pastoral based production systems for milk and meat is a critically important goal. However, studies have shown that genetic gains for key traits such as forage quality have been modest over recent decades. New breeding technologies such as Genomic Selection (GS) can assist traditional breeding programmes. GS uses genome wide DNA markers to estimate breeding values on selection candidates. The main ways that GS can assist perennial ryegrass breeding is by shortening the selection cycle, increasing the accuracy of selection, and increasing the number of selection candidates that can be evaluated; thereby increasing the rate of genetic gain for key traits such as forage quality. Improving digestibility of the forage leads to an increase in animal performance, and is therefore an important target trait for forage breeders. Furthermore, it has already been shown that increases in organic matter digestibility can reduce methane emissions; a key target of the EUs climate and energy policy. The specific research objectives of the action were: (1) develop Near-Infrared Spectroscopy (NIRS) calibrations for a suite of forage quality parameters impacting animal performance and use NIRS to phenotype a training population, (2) develop and optimize genomic prediction equations on the training population, and (3) validate these prediction equations in subsequent generations. This action has led to the development of genomic breeding tools to improve forage quality in perennial ryegrass and assist in development of new cultivars for pastoral farming systems.
NIRS calibrations for key forage quality traits impacting animal performance (including organic matter digestibility, neutral detergent fiber, and crude protein) were developed and used to phenotype a large training population that had been sampled for forage quality analysis across multiple years and seasons (over 14,000 samples). In building calibrations, a number of spectral and mathematical pretreatments were evaluated and optimal treatments selected; resulting in development of calibrations with strong predictive performance. A Shiny application created to assist calibration development has been published from this work. These data were first used to determine genotypic and environmental variance components for forage quality traits in this material, and to identify spectral bands strongly associated with quality parameters. The study looked at two complementary quality traits: one determining the digestible portion of the sample, and one related to the estimation of the un-digestible portion. It was also found that time of cut, days-to-heading and precipitation measured over 28 days were key parameters for ash and crude protein; whereas sum of mean temperature measured over 14 and 28 days were the most important parameters impacting digestibility. NIRS calibrations can now be exploited to phenotype forage material during routine breeding, and the phenotyping of the training population can be exploited to identify families, cultivars, and accessions with excellent forage quality for use as germplasm in new cycles of breeding.
The phenotype data above was combined with existing genotype data on the training population to develop genomic prediction equations for forage quality traits. Predictive modeling was carried out using a number of different approaches; these included approaches based on using the DNA data directly and approaches using the genomic relationship matrix derived from the DNA data. Model performance was evaluated using two methods of cross-validation, (i) setting aside random sets of individuals for testing or (ii) leaving out complete families/cultivars/accessions from the training data for testing. Results of the latter highlighted the importance of close relationship between training data and selection candidates for effective implementation of genomic selection in forage breeding. Encouragingly, good predictive accuracies were achieved for forage quality traits, highlighting their potential to use GS for indirect selection. To further validate these predictive models in subsequent generations of the training population, samples were collected from a field trial of F2 families derived from crossing plants of the training population. These samples were collected from two trials (over 1,200 samples) and scanned using NIRS to determine forage quality parameters using developed calibrations. F2 families were also genotyped and a common marker set used to validate the predictive models developed on the training population. The encouraging results demonstrated the potential of using genomic selection to improve forage quality in perennial ryegrass breeding, particularly given that GS will be implemented on seedlings from F2 families. These models are now being exploited to quantify the added value of among-and-within-family genomic selection through an empirical evaluation. A range of synthetic cultivars will be developed using positive genomic selection, negative genomic selection, and random selection, and compared using field evaluations to quantify genetic gains associated with among-and-within-family genomic selection.
A genome wide association study was also carried out using the available data to identify DNA polymorphisms associated with forage quality traits, and a number of significant marker-trait associations were found. In addition to the GWAS, a study was carried out to evaluate the use of genetic algorithms to identify important variables contributing to predictive accuracy of genomic selection and the work disseminated at an international conference.
The phenotype data above was combined with existing genotype data on the training population to develop genomic prediction equations for forage quality traits. Predictive modeling was carried out using a number of different approaches; these included approaches based on using the DNA data directly and approaches using the genomic relationship matrix derived from the DNA data. Model performance was evaluated using two methods of cross-validation, (i) setting aside random sets of individuals for testing or (ii) leaving out complete families/cultivars/accessions from the training data for testing. Results of the latter highlighted the importance of close relationship between training data and selection candidates for effective implementation of genomic selection in forage breeding. Encouragingly, good predictive accuracies were achieved for forage quality traits, highlighting their potential to use GS for indirect selection. To further validate these predictive models in subsequent generations of the training population, samples were collected from a field trial of F2 families derived from crossing plants of the training population. These samples were collected from two trials (over 1,200 samples) and scanned using NIRS to determine forage quality parameters using developed calibrations. F2 families were also genotyped and a common marker set used to validate the predictive models developed on the training population. The encouraging results demonstrated the potential of using genomic selection to improve forage quality in perennial ryegrass breeding, particularly given that GS will be implemented on seedlings from F2 families. These models are now being exploited to quantify the added value of among-and-within-family genomic selection through an empirical evaluation. A range of synthetic cultivars will be developed using positive genomic selection, negative genomic selection, and random selection, and compared using field evaluations to quantify genetic gains associated with among-and-within-family genomic selection.
A genome wide association study was also carried out using the available data to identify DNA polymorphisms associated with forage quality traits, and a number of significant marker-trait associations were found. In addition to the GWAS, a study was carried out to evaluate the use of genetic algorithms to identify important variables contributing to predictive accuracy of genomic selection and the work disseminated at an international conference.
In contrast to forage yield, studies have demonstrated that there has been limited improvements in perennial ryegrass digestibility over decades of breeding. This has likely reflected a lack of emphasis on this trait, owing to difficulties in routine evaluation of forage quality during routine breeding. In this action NIRS calibrations for rapid and high throughput measurement of forage quality parameters have been established and applications for updating calibrations developed; now enabling the host institute to evaluate forage quality in its breeding programme. This is directly impacting cultivar development at the host institute and ensuring material with higher forage quality is being brought forward during selection. The current state of the art in forage breeding is recurrent selection based on field evaluations of selection candidates, followed by selection of plants to use either as parents to produce new cultivars or as parents to start a new cycle of selection. Simulation studies have shown that genomic selection can accelerate genetic gain in forage breeding by increasing selection intensity, improving selection accuracy and reducing the length of the selection cycle. In this action, very promising genomic predictive equations have been developed for forage quality traits and are now being exploited to carry out an empirical evaluation of among-and-within-family genomic selection, while at the same time developing new cultivars with improved forage quality. It is anticipated that the work initiated in this action will lead to the release of new cultivars with excellent forage quality; thereby improving the digestibility of pastures, which has the potential to lower methane emissions from pastoral farming and improve animal performance.