Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Abstract

Key message Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. Abstract In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

Genetic mapping reveals new loci and alleles for flowering time and plant height using the double round-robin population of barley

“…All five sub-populations had HOR1842 or IG128104 as parental inbreds ( Supplementary Table S1 ). From the whole genome sequencing data of the parental inbreds ( Weisweiler et al , 2022 ), followed by Sanger sequencing, we identified a not previously reported polymorphism, SNP 1945, that is unique to HOR1842 and IG128104 ( Fig. 7 ).…”

“…For the QTLs that fulfilled these criteria, all the genes within the confidence interval were extracted using the Morex v3 reference sequence ( Mascher et al , 2021 ). Next, variant calling data of SNPs, causing tolerated and deleterious mutations, insertion and deletions (INDELs), and predicted structural variants, obtained as described by Weisweiler et al . (2022) , were used to identify genes that were polymorphic between the two parental inbreds of the sub-population in which the QTL was detected.…”

“…The data for membership of genes to gene modules used in the WGCNA are available at Zenodo repository: https://zenodo.org/record/7525604#.Y7_VgxXMLIW ( Cosenza, 2023 ). Genetic maps and variant calling data can be obtained from Casale et al (2022) and Weisweiler et al (2022) . Seeds of the RILs of the HvDRR population can be requested from the corresponding author.…”

Structural genomic variations and their effects on phenotypes inPopulus

“…Domesticated crops such as soybean, rice, tomato, barley, or wheat were among the first plants to attract interest in SVs (cited in Hämälä et al, 2021). Recently, cocoa ( Theobroma cacao ) and barley ( Hordeum vulgare ) were evaluated for direct effects of SVs on plant fitness and crop yield (Hämälä et al, 2021; Weisweiler et al, 2022). Both studies, although using different technologies to detect SVs - haplotype-resolved genome assembly vs. short-read sequencing - showed that SVs affect transcript abundance, and some SVs even have positive effects on fitness (including pathogen resistance).…”

“…While most SVs in wild accessions, as shown for cocoa, are subject to purifying selection – that is, these SVs have been selected against and are rare in the population -, several SVs in domesticated crops are associated with important traits for plant improvement, such as abiotic stress tolerance (Hämälä et al, 2021). The use of SV clusters (especially insertions) determined from short-read sequencing data in phenotypic prediction models yielded, on average, higher predictive ability than SNPs for a range of yield and plant architecture related quantitative traits in barley (Weisweiler et al, 2022). This is because (1) gene-associated SV clusters (i.e., SVs clustering within 50 bp and found within and 5 kb upstream/downstream around genes) are more likely to underlie causal genes than SNPs, a significant proportion of SVs (c.10%) is directly associated with gene-specific gene expression, and also because (3) SV clusters have less linkage disequilibrium between them, thus, collectively, making SV clusters more informative than SNPs for phenotypic prediction and genomic selection.…”