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Identification of consistent QTL and candidate genes associated with seed traits in common bean by combining GWAS and RNA-Seq.

María JuradoCarmen García-FernándezAna CampaJuan Jose Ferreira
Published in: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik (2024)
Association analysis, colocation study with previously reported QTL, and differential expression analyses allowed the identification of the consistent QTLs and main candidate genes controlling seed traits. Common beans show wide seed variations in shape, size, water uptake, and coat proportion. This study aimed to identify consistent genomic regions and candidate genes involved in the genetic control of seed traits by combining association and differential expression analyses. In total, 298 lines from the Spanish Diversity Panel were genotyped with 4,658 SNP and phenotyped for seven seed traits in three seasons. Thirty-eight significant SNP-trait associations were detected, which were grouped into 23 QTL genomic regions with 1,605 predicted genes. The positions of the five QTL regions associated with seed weight were consistent with previously reported QTL. HCPC analysis using the SNP that tagged these five QTL regions revealed three main clusters with significantly different seed weights. This analysis also separated groups that corresponded well with the two gene pools described: Andean and Mesoamerican. Expression analysis was performed on the seeds of the cultivar 'Xana' in three seed development stages, and 1,992 differentially expressed genes (DEGs) were detected, mainly when comparing the early and late seed development stages (1,934 DEGs). Overall, 91 DEGs related to cell growth, signaling pathways, and transcriptomic factors underlying these 23 QTL were identified. Twenty-two DEGs were located in the five QTL regions associated with seed weight, suggesting that they are the main set of candidate genes controlling this character. The results confirmed that seed weight is the sum of the effects of a complex network of loci, and contributed to the understanding of seed phenotype control.
Keyphrases
  • genome wide
  • high density
  • rna seq
  • dna methylation
  • copy number
  • single cell
  • physical activity
  • gene expression
  • weight loss
  • oxidative stress
  • genome wide identification
  • genome wide analysis
  • data analysis