A collection of 100 wheat varieties representing more than 100 years of wheat-breeding history in Scandinavia was established in order to identify marker-trait associations for plant height (PH), grain yield (GY), and biomass potential for bioethanol production. The field-grown material showed variations in PH from 54 to 122 cm and in GY from 2 to 6.61 t ha-1. The release of monomeric sugars was determined by high-throughput enzymatic treatment of ligno-cellulosic material and varied between 0.169 and 0.312 g/g dm for glucose (GLU) and 0.146 and 0.283 g/g dm for xylose (XYL). As expected, PH and GY showed to be highly influenced by genetic factors with repeatability (R) equal to 0.75 and 0.53, respectively, while this was reduced for GLU and XYL (R = 0.09 for both). The study of trait correlations showed how old, low-yielding, tall varieties released higher amounts of monomeric sugars after straw enzymatic hydrolysis, showing reduced recalcitrance to bioconversion compared to modern varieties. Ninety-three lines from the collection were genotyped with the DArTseq® genotypic platform and 5525 markers were used for genome-wide association mapping. Six quantitative trait loci (QTLs) for GY, PH, and GLU released from straw were mapped. One QTL for PH was previously reported, while the remaining QTLs constituted new genomic regions linked to trait variation. This paper is one of the first studies in wheat to identify QTLs that are important for bioethanol production based on a genome-wide association approach.

A collection of 112 winter barley varieties (Hordeum vulgare L.) was grown in the field for two years (2008/09 and 2009/10) in northern Italy and grain and straw yields recorded. In the first year of the trial, a severe attack of barley yellow mosaic virus (BaYMV) strongly influenced final performances with an average reduction of ~ 50% for grain and straw harvested in comparison to the second year. The genetic determination (GD) for grain yield was 0.49 and 0.70, for the two years respectively, and for straw yield GD was low in 2009 (0.09) and higher in 2010 (0.29). Cell wall polymers in culms were quantified by means of the monoclonal antibodies LM6, LM11, JIM13 and BS-400-3 and the carbohydrate-binding module CBM3a using the high-throughput CoMPP technique. Of these, LM6, which detects arabinan components, showed a relatively high GD in both years and a significantly negative correlation with grain yield (GYLD). Overall, heritability (H2) was calculated for GYLD, LM6 and JIM and resulted to be 0.42, 0.32 and 0.20, respectively. A total of 4,976 SNPs from the 9K iSelect array were used in the study for the analysis of population structure, linkage disequilibrium (LD) and genome-wide association study (GWAS). Marker-trait associations (MTA) were analyzed for grain yield and cell wall determination by LM6 and JIM13 as these were the traits showing significant correlations between the years. A single QTL for GYLD containing three MTAs was found on chromosome 3H located close to the Hv-eIF4E gene, which is known to regulate resistance to BaYMV. Subsequently the QTL was shown to be tightly linked to rym4, a locus for resistance to the virus. GWAs on arabinans quantified by LM6 resulted in the identification of major QTLs closely located on 3H and hypotheses regarding putative candidate genes were formulated through the study of gene expression levels based on bioinformatics tools.

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F 2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GE Control and GE Drought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.

A population of 239 perennial ryegrass (Lolium perenne L.) genotypes was analyzed to identify marker-trait associations for crown rust (Puccinia coronata f. sp. lolii) and brown rust (Puccinia graminis f. sp. loliina) resistance. Phenotypic data from field trials showed a low correlation (r = 0.17) between the two traits. Genotypes were resequenced, and a total of 14,538,978 SNPs were used to analyze population structure, linkage disequilibrium (LD), and for genome-wide association study. The SNP heritability (h2SNP) was 0.4 and 0.8 for crown and brown rust resistance, respectively. The high-density SNP dataset allowed us to estimate LD decay with the highest possible precision to date for perennial ryegrass. Results showed a low LD extension with a rapid decay of r2 value below 0.2 after 520 bp on average. Additionally, QTL regions for both traits were detected, as well as candidate genes by applying Genome Complex Trait Analysis and Multi-marker Analysis of GenoMic Annotation. Moreover, two significant genes, LpPc6 and LpPl6, were identified for crown and brown rust resistance, respectively, when SNPs were aggregated to the gene level. The two candidate genes encode proteins with phosphatase activity, which putatively can be induced by the host to perceive, amplify and transfer signals to downstream components, thus activating a plant defense response.