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High-resolution bulked segregant analysis enables candidate gene identification for bacterial wilt resistance in Italian ryegrass (Lolium multiflorum Lam.).

Goettelmann FlorianYutang ChenVerena KnorstSteven YatesCopetti DarioStuder BrunoKölliker Roland
Published in: The Plant journal : for cell and molecular biology (2024)
Bacterial wilt, caused by Xanthomonas translucens pv. graminis (Xtg), is a serious disease of economically important forage grasses, including Italian ryegrass (Lolium multiflorum Lam.). A major QTL for resistance to Xtg was previously identified, but the precise location as well as the genetic factors underlying the resistance are yet to be determined. To this end, we applied a bulked segregant analysis (BSA) approach, using whole-genome deep sequencing of pools of the most resistant and most susceptible individuals of a large (n = 7484) biparental F 2 population segregating for resistance to Xtg. Using chromosome-level genome assemblies as references, we were able to define a ~300 kb region highly associated with resistance on pseudo-chromosome 4. Further investigation of this region revealed multiple genes with a known role in disease resistance, including genes encoding for Pik2-like disease resistance proteins, cysteine-rich kinases, and RGA4- and RGA5-like disease resistance proteins. Investigation of allele frequencies in the pools and comparative genome analysis in the grandparents of the F 2 population revealed that some of these genes contain variants with allele frequencies that correspond to the expected heterozygosity in the resistant grandparent. This study emphasizes the efficacy of combining BSA studies in very large populations with whole genome deep sequencing and high-quality genome assemblies to pinpoint regions associated with a binary trait of interest and accurately define a small set of candidate genes. Furthermore, markers identified in this region hold significant potential for marker-assisted breeding strategies to breed resistance to Xtg in Italian ryegrass cultivars more efficiently.
Keyphrases
  • genome wide
  • high resolution
  • copy number
  • single cell
  • dna methylation
  • risk assessment
  • gene expression
  • mass spectrometry
  • climate change
  • genome wide identification
  • human health