Genomic Prediction and Genotype-by-Environment Interaction Analysis of Crown and Stem Rust in Ryegrasses in European Multi-Site Trials

Fois, Mattia; Malinowska, Marta; Schubiger, F. X.; Asp, Torben

doi:10.3390/agronomy11061119

Cited by 7 publications

(8 citation statements)

References 64 publications

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“…Including GxE in genomic prediction has been investigated in many crops including maize (Windhausen et al, 2012;Malosetti et al, 2013a), barley (Malosetti et al, 2016), ryegrass (Fois et al, 2021) and coffee (Ferrão et al, 2019), to name just a few. Due to its ploidy and heterozygosity, GxE interactions in tetraploid potato has been theorized to be substantial (Jansky and Spooner, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In GP, it is typical to combine various sources of information, in an attempt to increase prediction accuracy. In plant breeding, multi-environment trials are commonplace and therefore the combining of information from multiple environments has been applied across a variety of crops and shown to improve prediction accuracy (Windhausen et al, 2012;Malosetti et al, 2013aMalosetti et al, , 2016Ferrão et al, 2019;Fois et al, 2021). In animals, combining information across different breeds or subspecies is of special interest to breeders, and has shown to be beneficial (de Roos et al, 2009;Raymond et al, 2018a).…”

Section: Combining Information To Increase Prediction Accuracymentioning

confidence: 99%

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Statistical considerations for applying genomic prediction to potato

Wilson

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Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4 to 14% in cross-validation scenarios, and 2 to 8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5 to 10.5% and 0.4 to 4.5%. No interaction was found between sampling method and the statistical model for the traits analysed.

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Section: Discussionmentioning

confidence: 99%

Section: Combining Information To Increase Prediction Accuracymentioning

confidence: 99%

Statistical considerations for applying genomic prediction to potato

Wilson

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“…Furthermore, QTL analysis and quantitative genetics studies aimed at identifying genomic regions associated with quantitative traits of interest in breeding and/or adaptive traits [ 63 ], can now be performed with greater resolution as most genetic markers can be anchored to the chromosomes. In addition, the comprehensive gene set combined with scaffold contiguity supports identification of genes flanking genetic markers in search of the molecular mechanisms underlying agronomic traits [ 64 ], adaptive traits, or survival strategies [ 65 – 67 ]. Furthermore, the new, advanced perennial ryegrass reference genome and annotation presented here, might significantly expand the potential of pan-genomic studies in the Festuca-Lolium complex.…”

Section: Discussionmentioning

confidence: 99%

Chromosome-scale assembly and annotation of the perennial ryegrass genome

et al. 2022

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Background The availability of chromosome-scale genome assemblies is fundamentally important to advance genetics and breeding in crops, as well as for evolutionary and comparative genomics. The improvement of long-read sequencing technologies and the advent of optical mapping and chromosome conformation capture technologies in the last few years, significantly promoted the development of chromosome-scale genome assemblies of model plants and crop species. In grasses, chromosome-scale genome assemblies recently became available for cultivated and wild species of the Triticeae subfamily. Development of state-of-the-art genomic resources in species of the Poeae subfamily, which includes important crops like fescues and ryegrasses, is lagging behind the progress in the cereal species. Results Here, we report a new chromosome-scale genome sequence assembly for perennial ryegrass, obtained by combining PacBio long-read sequencing, Illumina short-read polishing, BioNano optical mapping and Hi-C scaffolding. More than 90% of the total genome size of perennial ryegrass (approximately 2.55 Gb) is covered by seven pseudo-chromosomes that show high levels of collinearity to the orthologous chromosomes of Triticeae species. The transposon fraction of perennial ryegrass was found to be relatively low, approximately 35% of the total genome content, which is less than half of the genome repeat content of cultivated cereal species. We predicted 54,629 high-confidence gene models, 10,287 long non-coding RNAs and a total of 8,393 short non-coding RNAs in the perennial ryegrass genome. Conclusions The new reference genome sequence and annotation presented here are valuable resources for comparative genomic studies in grasses, as well as for breeding applications and will expedite the development of productive varieties in perennial ryegrass and related species.

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“…The SI consists of 14 original research contributions focusing on diverse crops including cereals (winter wheat, maize, rice and ryegrass) [1][2][3][4][5], legumes (grass pea, soybean, chickpea) [6][7][8], rapeseed oil [9], eggplant [10], blackgram [11], bambara groundnut [12], strawberry [13] and sugarcane [14]. Investigations combined different types of germplasm, study strategies and application of statistical models.…”

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confidence: 99%

“…Environmental effects on biotic stress resistance due to crown and stem rust were instead determined in 54 ryegrass accessions grown across 34 European locations in three cycles performed in 12 years [5]. Such comprehensive work highlighted the effect of the environment on natural infections and indicated that the tetraploid varieties are better performing than diploid ones; furthermore, it showed how the large number of observations allowed better accuracy in trait performance prediction.…”

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confidence: 99%