The cytosine methylation landscape of spring barley revealed by a new reduced representation bisulfite sequencing pipeline, WellMeth.

Patterns and level of cytosine methylation vary widely among plant species and are associated with genome size as well as the proportion of transposons and other repetitive elements in the genome. We explored epigenetic patterns and diversity in a representative proportion of the spring barley (Hordeum vulgare L.) genome across several commercial and historical cultivars. This study adapted a genotyping-by-sequencing (GBS) approach for the detection of methylated cytosines in genomic DNA. To analyze the data, we developed WellMeth, a complete pipeline for analysis of reduced representation bisulfite sequencing. WellMeth enabled quantification of context-specific DNA methylation at the single-base resolution as well as identification of differentially methylated sites (DMCs) and regions (DMRs). On average, DNA methylation levels were significantly higher than what is commonly observed in many plants species, reaching over 10-fold higher levels than those in Arabidopsis thaliana (L.) Heynh. in the CHH methylation. Preferential methylation was observed within and at the edges of long-terminal repeats (LTR) retrotransposons Gypsy and Copia. From a pairwise comparison of cultivars, numerous DMRs could be identified of which more than 5,000 were conserved within the analyzed set of barley cultivars. The subset of regions overlapping with genes showed enrichment in gene ontology (GO) categories associated with chromatin and cellular structure and organization. A significant correlation between genetic and epigenetic distances suggests that a considerable portion of methylated regions is under strict genetic control in barley. The data presented herein represents the first step in efforts toward a better understanding of genome-level structural and functional aspects of methylation in barley.

Breeding for dual-purpose wheat varieties using marker-trait associations for biomass yield and quality traits.

KEY MESSAGE: This study demonstrates that an active breeding nursery with rotation can be used to identify marker-trait associations for biomass yield and quality parameters that are important for biorefinery purposes. Wheat straw is a valuable feedstock for bioethanol production, but due to the recalcitrant nature of lignocellulose, its efficient use in biorefineries is limited by its low digestibility and difficult conversion of structural carbohydrates into free sugars. A genome-wide association study (GWAS) was conducted to search for significant SNP markers that could be used in a breeding programme to improve the value of wheat straw in a biorefinery setting. As part of a 3-year breeding programme (2013-2016), 190 winter wheat lines were phenotyped for traits that affect the yield and quality of the harvested biomass. These traits included straw yield, plant height, lodging at three growth stages and Septoria tritici blotch (STB) susceptibility. Release of glucose, xylose and arabinose was determined after hydrothermal pretreatment and enzymatic hydrolysis of the straw. The lines were genotyped using 15 K SNP markers and 5552 SNP markers could be used after filtering. Heritability for all traits ranged from 0.02 to 0.74. GWASs were conducted using CMLM, SUPER and FarmCPU algorithms, to analyse which algorithm could detect the highest number of marker-trait associations (MTAs). Comparable tendencies were obtained from CMLM and FarmCPU, but FarmCPU produced the most significant results. MTAs were obtained for lodging, harvest index, plant height, STB, glucose, xylose and arabinose at a significance level of p < 9.01 × 10-6. MTAs in chromosome 6A were observed for glucose, xylose and arabinose, and could be of importance for increasing sugar release for bioethanol production.

Identification of Ideal Allele Combinations for the Adaptation of Spring Barley to Northern Latitudes.

The northwards expansion of barley production requires adaptation to longer days, lower temperatures and stronger winds during the growing season. We have screened 169 lines of the current barley breeding gene pool in the Nordic region with regards to heading, maturity, height, and lodging under different environmental conditions in nineteen field trials over 3 years at eight locations in northern and central Europe. Through a genome-wide association scan we have linked phenotypic differences observed in multi-environment field trials (MET) to single nucleotide polymorphisms (SNP). We have identified an allele combination, only occurring among a few Icelandic lines, that affects heat sum to maturity and requires 214 growing degree days (GDD) less heat sum to maturity than the most common allele combination in the Nordic spring barley gene pool. This allele combination is beneficial in a cold environment, where autumn frost can destroy a late maturing harvest. Despite decades of intense breeding efforts relying heavily on the same germplasm, our results show that there still exists considerable variation within the current breeding gene pool and we identify ideal allele combinations for regional adaptation, which can facilitate the expansion of cereal cultivation even further northwards.

Analysis of pregerminated barley using hyperspectral image analysis.

Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel's lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI(95%): 29-35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI(95%): 0-15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.