Biology subjects

Weisweiler, M.

Publications and source records attributed to Weisweiler, M..

3 recordsLinked to original sources

bioRxiv2023

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

Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Although their role as key traits, a comprehensive understanding of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Through a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as identify new genetic variants by single and multi-population quantitative trait loci (QTL) analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected one new QTL for flowering time and two new QTL for plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect or low frequency, genetic variants underlie the control of these two traits.

genetics↗

bioRxiv2022

The double round-robin population unravels the genetic architecture of grain size in barley

Grain number, size and weight primarily determine the yield of barley. Although the genes regulating grain number are well studied in barley, the genetic loci and the causal gene for sink capacity are poorly understood. Therefore, the primary objective of our work was to dissect the genetic architecture of grain size and weight in barley. We used a multi-parent population developed from a genetic cross between 23 diverse barley inbreds in a double round-robin design. Seed size-related parameters such as grain length, grain width, grain area and thousand-grain weight were evaluated in the HvDRR population comprising 45 recombinant inbred line sub-populations. We found significant genotypic variation for all seed size characters and observed 84 % or higher heritability across four environments. The results of the quantitative trait locus (QTL) detection indicate that the genetic architecture of grain size is more complex than reported previously. In addition, both cultivars and landraces contributed positive alleles at grain size QTLs. Candidate genes identified using genome-wide variant calling data for all parental inbred lines indicated overlapping and potential novel regulators of grain size in cereals. Furthermore, our results indicated that sink capacity was the primary determinant of grain weight in barley. HighlightMulti parent population uncovered the natural allelic series across quantitative loci associated with grain size and weight that will contribute to identifying causal genes and yield improvement in barley.

plant biology↗

bioRxiv2022

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

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 (SNV). 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 5kb 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.

genomics↗