Dataset Information

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

ABSTRACT: In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

SUBMITTER: Arojju SK

PROVIDER: S-EPMC7426495 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

Arojju Sai Krishna SK Cao Mingshu M Trolove Michael M Barrett Brent A BA Inch Courtney C Eady Colin C Stewart Alan A Faville Marty J MJ

Frontiers in plant science 20200807

In perennial ryegrass (<i>Lolium perenne</i> L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutri ...[more]

PMID: 32849742

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Project description:Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2 g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

Project description:BACKGROUND: Perennial ryegrass (Lolium perenne L.) is one of the most important forage and turf grass species of temperate regions worldwide. Its mitochondrial genome is inherited maternally and contains genes that can influence traits of agricultural importance. Moreover, the DNA sequence of mitochondrial genomes has been established and compared for a large number of species in order to characterize evolutionary relationships. Therefore, it is crucial to understand the organization of the mitochondrial genome and how it varies between and within species. Here, we report the first de novo assembly and annotation of the complete mitochondrial genome from perennial ryegrass. RESULTS: Intact mitochondria from perennial ryegrass leaves were isolated and used for mtDNA extraction. The mitochondrial genome was sequenced to a 167-fold coverage using the Roche 454 GS-FLX Titanium platform, and assembled into a circular master molecule of 678,580 bp. A total of 34 proteins, 14 tRNAs and 3 rRNAs are encoded by the mitochondrial genome, giving a total gene space of 48,723 bp (7.2%). Moreover, we identified 149 open reading frames larger than 300 bp and covering 67,410 bp (9.93%), 250 SSRs, 29 tandem repeats, 5 pairs of large repeats, and 96 pairs of short inverted repeats. The genes encoding subunits of the respiratory complexes - nad1 to nad9, cob, cox1 to cox3 and atp1 to atp9 - all showed high expression levels both in absolute numbers and after normalization. CONCLUSIONS: The circular master molecule of the mitochondrial genome from perennial ryegrass presented here constitutes an important tool for future attempts to compare mitochondrial genomes within and between grass species. Our results also demonstrate that mitochondria of perennial ryegrass contain genes crucial for energy production that are well conserved in the mitochondrial genome of monocotyledonous species. The expression analysis gave us first insights into the transcriptome of these mitochondrial genes in perennial ryegrass.

Project description:BackgroundSingle nucleotide polymorphisms (SNPs) are increasingly becoming the DNA marker system of choice due to their prevalence in the genome and their ability to be used in highly multiplexed genotyping assays. Although needed in high numbers for genome-wide marker profiles and genomics-assisted breeding, a surprisingly low number of validated SNPs are currently available for perennial ryegrass.ResultsA perennial ryegrass unigene set representing 9,399 genes was used as a reference for the assembly of 802,156 high quality reads generated by 454 transcriptome sequencing and for in silico SNP discovery. Out of more than 15,433 SNPs in 1,778 unigenes fulfilling highly stringent assembly and detection parameters, a total of 768 SNP markers were selected for GoldenGate genotyping in 184 individuals of the perennial ryegrass mapping population VrnA, a population being previously evaluated for important agronomic traits. A total of 592 (77%) of the SNPs tested were successfully called with a cluster separation above 0.9. Of these, 509 (86%) genic SNP markers segregated in the VrnA mapping population, out of which 495 were assigned to map positions. The genetic linkage map presented here comprises a total of 838 DNA markers (767 gene-derived markers) and spans 750 centi Mogan (cM) with an average marker interval distance of less than 0.9?cM. Moreover, it locates 732 expressed genes involved in a broad range of molecular functions of different biological processes in the perennial ryegrass genome.ConclusionsHere, we present an efficient approach of using next generation sequencing (NGS) data for SNP discovery, and the successful design of a 768-plex Illumina GoldenGate genotyping assay in a complex genome. The ryegrass SNPs along with the corresponding transcribed sequences represent a milestone in the establishment of genetic and genomics resources available for this species and constitute a further step towards molecular breeding strategies. Moreover, the high density genetic linkage map predominantly based on gene-associated DNA markers provides an important tool for the assignment of candidate genes to quantitative trait loci (QTL), functional genomics and the integration of genetic and physical maps in perennial ryegrass, one of the most important temperate grassland species.

Dataset Information

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

Publications

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

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