A Highly Contiguous Genome Assembly of Arthrinium puccinoides

The phylogenetic relationship of the Arthrinium genus has changed throughout the years. For many years, the Arthrinium genus included the Apiospora genus as well. New evidence has now showed that these two genera in fact are phylogenetically different and belong to two different clades. Here, we present the first genome draft within the Arthrinium genus. This genome was sequenced using the MinION platform from Oxford Nanopore Technologies and the assembly was contiguous. The assembly comprises ten contigs totaling 39.8 Mb with an N50 length of 7.9. In the assembly, 11,602 genes were predicted whereof 10,784 were functionally annotated. A total of 37 rRNA genes were observed in the assembly and repeat elements spanning 7.39% of the genome were found. A total of 99 secondary metabolite gene clusters were predicted, showing a high potential of novel secondary metabolites. This genome sequence will not only be useful for further investigation of the Arthrinium clade, but also for discovery of novel secondary metabolite compounds that could be of high interest for the food, agricultural, or pharmaceutical industry

The effect of marker types and density on genomic prediction and GWAS of key performance traits in tetraploid potato

Genomic prediction and genome-wide association studies are becoming widely employed in potato key performance trait QTL identifications and to support potato breeding using genomic selection. Elite cultivars are tetraploid and highly heterozygous but also share many common ancestors and generation-spanning inbreeding events, resulting from the clonal propagation of potatoes through seed potatoes. Consequentially, many SNP markers are not in a 1:1 relationship with a single allele variant but shared over several alleles that might exert varying effects on a given trait. The impact of such redundant “diluted” predictors on the statistical models underpinning genome-wide association studies (GWAS) and genomic prediction has scarcely been evaluated despite the potential impact on model accuracy and performance. We evaluated the impact of marker location, marker type, and marker density on the genomic prediction and GWAS of five key performance traits in tetraploid potato (chipping quality, dry matter content, length/width ratio, senescence, and yield). A 762-offspring panel of a diallel cross of 18 elite cultivars was genotyped by sequencing, and markers were annotated according to a reference genome. Genomic prediction models (GBLUP) were trained on four marker subsets [non-synonymous (29,553 SNPs), synonymous (31,229), non-coding (32,388), and a combination], and robustness to marker reduction was investigated. Single-marker regression GWAS was performed for each trait and marker subset. The best cross-validated prediction correlation coefficients of 0.54, 0.75, 0.49, 0.35, and 0.28 were obtained for chipping quality, dry matter content, length/width ratio, senescence, and yield, respectively. The trait prediction abilities were similar across all marker types, with only non-synonymous variants improving yield predictive ability by 16%. Marker reduction response did not depend on marker type but rather on trait. Traits with high predictive abilities, e.g., dry matter content, reached a plateau using fewer markers than traits with intermediate-low correlations, such as yield. The predictions were unbiased across all traits, marker types, and all marker densities >100 SNPs. Our results suggest that using non-synonymous variants does not enhance the performance of genomic prediction of most traits. The major known QTLs were identified by GWAS and were reproducible across exonic and whole-genome variant sets for dry matter content, length/width ratio, and senescence. In contrast, minor QTL detection was marker type dependent

Applying the GBS technique for the genomic characterization of a Danish population of European hedgehogs (Erinaceus europaeus)

The objective of the study was to establish and refine a method for the genomic characterization of European hedgehogs in Denmark using the second-generation genotyping technique, genotyping by sequencing (GBS). Single nucleotide polymorphisms (SNPs) were filtered with a read coverage between 20 - 100 and a maximum number of missing data of 25 %. Individuals with > 25 % missing data were removed yielding a total of 2.4 million SNPs, and after filtering for Minor allele frequency (MAF) >1 %, 2902 SNPs remained. Approximately half of the individuals analysed contained less than 75% of the selected SNPs, and were removed, resulting in a sample size of 30. We estimated inbreeding coefficients (F), observed (HO), expected (HE) and unbiased expected (uHE) heterozygosity and the percent of polymorphic loci (P%). We tested for deviations from Hardy-Weinberg equilibrium (HWE) and patterns of isolation by distance (IBD). We assessed the genetic structure of the sampled individuals based on a Bayesian clustering method, and tested for recent population expansion or decline. We found a P% = 94.5%, a uHE and HE of mean ± SE; 0.31 ± 0.04 and 0.30 ± 0.02, respectively and an HO of 0.290 ± 0.03. The heterozygosity deficiency was reflected in a positive F-value; 0.1 ± 0.01 and a significant deviation for HWE (p < 0.05). The Mantel test for association between the genetical and geographical distances of populations was not significant (b = 0.007, R = 0.145, p > 0.05). The significant and positive F-value found, was explained by inbreeding, genetic substructure and low effective population size (Ne) which are all consequences of habitat fragmentation. We failed to detect recent signs of a population bottleneck or expansion. Further studies on a larger scale are needed to obtain a general view of the conservation status of the Danish hedgehog populatio

Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

peer-reviewedGenomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato. Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30-0.31 and 0.42-0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.The Danish Council of Strategic Researc