Dataset Information

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

ABSTRACT: Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.

SUBMITTER: Pembleton LW

PROVIDER: S-EPMC6096624 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

Pembleton Luke W LW Inch Courtney C Baillie Rebecca C RC Drayton Michelle C MC Thakur Preeti P Ogaji Yvonne O YO Spangenberg German C GC Forster John W JW Daetwyler Hans D HD Cogan Noel O I NOI

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 20180602 9

<h4>Key message</h4>Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size ...[more]

PMID: 29860624

Similar Datasets

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:Identification of loci for agronomic traits and characterization of their genetic architecture are crucial in marker-assisted selection (MAS). Genome-wide association studies (GWAS) have increasingly been used as potent tools in identifying marker-trait associations (MTAs). The introduction of new adaptive alleles in the diverse genetic backgrounds may help to improve grain yield of old or newly developed varieties of wheat to balance supply and demand throughout the world. Landraces collected from different climate zones can be an invaluable resource for such adaptive alleles. RESULTS:GWAS was performed using a collection of 298 Iranian bread wheat varieties and landraces to explore the genetic basis of agronomic traits during 2016-2018 cropping seasons under normal (well-watered) and stressed (rain-fed) conditions. A high-quality genotyping by sequencing (GBS) dataset was obtained using either all original single nucleotide polymorphism (SNP, 10938 SNPs) or with additional imputation (46,862 SNPs) based on W7984 reference genome. The results confirm that the B genome carries the highest number of significant marker pairs in both varieties (49,880, 27.37%) and landraces (55,086, 28.99%). The strongest linkage disequilibrium (LD) between pairs of markers was observed on chromosome 2D (0.296). LD decay was lower in the D genome, compared to the A and B genomes. Association mapping under two tested environments yielded a total of 313 and 394 significant (-log10 P >3) MTAs for the original and imputed SNP data sets, respectively. Gene ontology results showed that 27 and 27.5% of MTAs of SNPs in the original set were located in protein-coding regions for well-watered and rain-fed conditions, respectively. While, for the imputed data set 22.6 and 16.6% of MTAs represented in protein-coding genes for the well-watered and rain-fed conditions, respectively. CONCLUSIONS:Our finding suggests that Iranian bread wheat landraces harbor valuable alleles that are adaptive under drought stress conditions. MTAs located within coding genes can be utilized in genome-based breeding of new wheat varieties. Although imputation of missing data increased the number of MTAs, the fraction of these MTAs located in coding genes were decreased across the different sub-genomes.

Project description:BackgroundIn recent years soybean is becoming one of the most important oilseed crops in Kazakhstan. Only within the last ten years (2006-2016), the area under soybean is expanded from 45 thousand hectares (ha) in 2006 to 120 thousand ha in 2016. The general trend of soybean expansion is from south-eastern to eastern and northern regions of the country, where average temperatures are lower and growing seasons are shorter. These new soybean growing territories were poorly examined in terms of general effects on productivity level among the diverse sample of soybean accessions. In this study, phenotypic data were collected in three separate regions of Kazakhstan and entire soybean sample was genotyped for identification of marker-trait associations (MTA).ResultsIn this study, the collection of 113 accessions representing five different regions of the World was planted in 2015-2016 in northern, eastern, and south-eastern regions of Kazakhstan. It was observed that North American accessions showed the highest yield in four out of six trials especially in Northern Kazakhstan in both years. The entire sample was genotyped with 6 K SNP Illumina array. 4442 SNPs found to be polymorphic and were used for whole genome genotyping purposes. Obtained SNP markers data and field data were used for GWAS (genome-wide association study). 30 SNPs appear to be very significant in 42 MTAs in six studied environments.ConclusionsThe study confirms the efficiency of GWAS for the identification of molecular markers which tag important agronomic traits. Overall thirty SNP markers associated with time to flowering and maturation, plant height, number of fertile nodes, seeds per plant and yield were identified. Physical locations of 32 identified out of 42 total MTAs coincide well with positions of known analogous QTLs. This result indicates importance of revealed MTAs for soybean growing regions in Kazakhstan. Obtained results would serve as required prerequisite for forming and realization of specific breeding programs towards effective adaptation and increased productivity of soybean in three different regions of Kazakhstan.

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:BackgroundGossypium hirsutum L. is grown worldwide and is the largest source of natural fiber crop. We focus on exploring the favorable alleles (FAs) for upland cotton varieties improvement, and further understanding the history of accessions selection and acumination of favorable allele during breeding.ResultsThe genetic basis of phenotypic variation has been studied. But the accumulation of favorable alleles in cotton breeding history in unknown, and potential favorable alleles to enhance key agronomic traits in the future cotton varieties have not yet been identified. Therefore, 419 upland cotton accessions were screened, representing a diversity of phenotypic variations of 7362 G. hirsutum, and 15 major traits were investigated in 6 environments. These accessions were categorized into 3 periods (early, medium, and modern) according to breeding history. All accessions were divided into two major groups using 299 polymorphic microsatellite markers: G1 (high fiber yield and quality, late maturity) and G2 (low fiber yield and quality, early maturity). The proportion of G1 genotype gradually increased from early to modern breeding periods. Furthermore, 21 markers (71 alleles) were significantly associated (-log P > 4) with 15 agronomic traits in multiple environments. Seventeen alleles were identified as FAs; these alleles accumulated more in the modern period than in other periods, consistent with their phenotypic variation trends in breeding history. Our results demonstrate that the favorable alleles accumulated through breeding effects, especially for common favorable alleles. However, the potential elite accessions could be rapidly screened by rare favorable alleles.ConclusionIn our study, genetic variation and genome-wide associations for 419 upland cotton accessions were analyzed. Two favorable allele types were identified during three breeding periods, providing important information for yield/quality improvement of upland cotton germplasm.

Dataset Information

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

Publications

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets