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The integration of target capture systems with next-generation sequencing has emerged as an efficient tool for exploring specific genetic regions with a high resolution and facilitating the rapid discovery of novel alleles. Despite these advancements, the application of targeted sequencing methodologies, such as the myBaits technology, in polyploid oat species remains relatively unexplored. In this study, we utilized the myBaits target capture method offered by Daicel Arbor Biosciences to detect variants and assess their reliability for variant detection in oat genomics and breeding. Ten oat genotypes were carefully chosen for targeted sequencing, focusing on specific regions on chromosome 2A to detect variants. The selected region harbors 98 genes. Precisely designed baits targeting the genes within these regions were employed for the target capture sequencing. We employed various mappers and variant callers to identify variants. After the identification of variants, we focused on the variants identified via all variants callers to assess the applicability of the myBaits sequencing methodology in oat breeding. In our efforts to validate the identified variants, we focused on two SNPs, one deletion and one insertion identified via all variant callers in the genotypes KF-318 and NOS 819111-70 but absent in the remaining eight genotypes. The Sanger sequencing of targeted SNPs failed to reproduce target capture data obtained through the myBaits technology. Similarly, the validation of deletion and insertion variants via high-resolution melting (HRM) curve analysis also failed to reproduce target capture data, again suggesting limitations in the reliability of the myBaits target capture sequencing using short-read sequencing for variant detection in the oat genome. This study shed light on the importance of exercising caution when employing the myBaits target capture strategy for variant detection in oats. This study provides valuable insights for breeders seeking to advance oat breeding efforts and marker development using myBaits target capture sequencing, emphasizing the significance of methodological sequencing considerations in oat genomics research.

Blumeria graminis f. sp. hordei ( Bgh ), the causal agent of barley powdery mildew (PM), is one of the most important barley leaf diseases and is prevalent in most barley growing regions. Infection decreases grain quality and yields on average by 30%. Multi-parent advanced generation inter-cross (MAGIC) populations combine the advantages of bi-parental and association panels and offer the opportunity to incorporate exotic alleles into adapted material. Here, four barley MAGIC populations consisting of six to eight founders were tested for PM resistance in field trials in Denmark. Principle component and STRUCTURE analysis showed the populations were unstructured and genome-wide linkage disequilibrium (LD) decay varied between 14 and 38 Mbp. Genome-wide association studies (GWAS) identified 11 regions associated with PM resistance located on chromosomes 1H, 2H, 3H, 4H, 5H and 7H, of which three regions are putatively novel resistance quantitative trait locus/loci (QTL). For all regions high-confidence candidate genes were identified that are predicted to be involved in pathogen defense. Haplotype analysis of the significant SNPs revealed new allele combinations not present in the founders and associated with high resistance levels.

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F 6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype-environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.