Abstract
Key messageAssociation mapping of drought-related traits in barley was used to increase the density of existing QTL maps without recreating mapping populations.We used 109 spring barley genotypes exhibiting high or low drought tolerance to elucidate the associations between diversity array technology sequencing (DArTseq) and single nucleotide polymorphism (SNP) markers and various physiological parameters related to plant responses to drought conditions. The study was performed in controlled conditions (growth chambers), drought tolerance was phenotyped in the four-leaf seedlings. We identified 58 associations including 34 new markers (i.e., 16 DArTseq and 18 SNP markers). The results for three markers were consistent with the data obtained in an earlier traditional biparental QTL mapping study. The regions neighboring markers on linkage group 2H contained the highest number of significant marker–trait associations. Five markers related to the photosynthetic activity of photosystem II were detected on chromosome 4H. The lowest number of associations were observed for the sequences neighboring DArT markers on linkage group 6H. A chromosome 3H region related to water use efficiency and net photosynthesis rate in both biparental QTL, and association study, may be particularly valuable, as these parameters correspond to the ability of plants to remain highly productive under water deficit stress. Our findings confirm that association mapping can increase the density of existing QTL maps without recreating mapping populations.
Highlights
Analyzing quantitative traits is challenging, which makes these traits difficult to improve via plant breeding
The diversity array technology sequencing (DArTseq) genotyping of 109 spring barley lines with diverse drought responses revealed 15,828 specific DArTseq markers, of which 10,652 sequences were localized in defined contigs of Morex, Barke, or Bosman barley varieties
The least effective polymorphism assessment based on the allelic readings and the ratio of the genotypes tested (i.e., 73–80%) was observed for 86 DArTseq sequences
Summary
Analyzing quantitative traits is challenging, which makes these traits difficult to improve via plant breeding. For many years, research tended to focus on the identification of single genes with the biggest effects on the overall phenotype until two methods enabling investigations of quantitative traits have been developed—genetic linkage-based biparental QTL mapping and association mapping. Both of these approaches rely on the analysis of the strength of the relationships between genetic markers and phenotypic traits (Korte and Farlow 2013). In contrast to QTL analyses, association mapping uses plant materials that are not closely related (i.e., natural populations) (Korte and Farlow 2013). Using LD to map quantitative traits is more challenging than the standard QTL analysis protocol
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