ArticleBMC genomics2025
Large-scale integration of meta-QTL and genome-wide association study identifies genomic regions and candidate genes for photosynthetic efficiency traits in bread wheat.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Harnessing natural variation for photosynthetic improvement in next-generation crop breeding.Journal of integrative plant biology · 2026Review
- Genomic and Physiological Insights Into Heat-Drought Tolerance in Wheat Through GWAS and Phenotypic Evaluation.Plant, cell & environment · 2026Article
- Genomic dissection of iron toxicity tolerance in rice identifies key loci, candidate genes, and associated haplotypes.Scientific reports · 2026Article
- Uncover the genetic basis of processing quality related traits in common wheat (Frontiers in plant science · 2026Article
- Combining two high-density QTL maps with a reference genome to identify candidate genes for morphology, yield, and biotic resistance in faba bean (Frontiers in plant science · 2026Article
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12 authors.
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Abstract
backgroundImproving photosynthetic efficiency is an essential strategy for advancing wheat breeding progress. Integrating wheat genetic resources provides an opportunity to discover pivotal genomic regions and candidate genes (CGs) for photosynthetic efficiency traits in wheat.
resultsA large-scale meta-QTL (MQTL) analysis was performed with 1363 initial quantitative trait loci (QTLs) for photosynthetic efficiency traits extracted from 66 independent QTL mapping studies over the past decades. Consequently, 718 initial QTLs were refined into 74 MQTLs, which were distributed on all wheat chromosomes except 1D, 3 A, 4B, and 5B. Compared with the confidence interval (CI) of the initial QTL, the CI of the identified MQTL was 0.03 to 10.97 cM, with an average of 1.46 cM, which was 20.46 times narrower than that of the original QTL. The maximum explained phenotypic variance (PVE) of the MQTL ranged from 7.43 to 20.42, with an average of 11.97, which was 1.07 times higher than that of the original QTL. Of these, 54 MQTLs were validated using genome-wide association study (GWAS) data from different natural populations in previous research. A total of 3,102 CGs were identified within the MQTL intervals, where 342 CGs share homology with rice, and 1,043 CGs are highly expressed in leaves, spikes, and stems. These CGs were mainly involved in porphyrin metabolism, glyoxylate, dicarboxylate metabolism, carbon metabolism and photosynthesis antenna proteins metabolism pathways by the in silico transcriptome assessment. For the key CG TaGGR-6A (TraesCS6A02G307700) involved in the porphyrin metabolism pathway, a functional kompetitive allele-specific PCR (KASP) marker was developed at 2464 bp (A/G) position within the 3' untranslated region, successfully distinguishing two haplotypes: TaGGR-6A-Hap I (type AA) and TaGGR-6A-Hap II (type GG). Varieties with the TaGGR-6A-Hap II allele exhibited approximately 13.42% and 11.45% higher flag leaf chlorophyll content than those carrying the TaGGR-6A-Hap I allele. The elite haplotype TaGGR-6A-Hap II was positively selected during wheat breeding, as evidenced by the geographical and annual frequency distributions of the two TaGGR-6A haplotypes.
conclusionThe findings will give further insights into the genetic determinants of photosynthetic efficiency traits and provide some reliable MQTLs and putative CGs for the genetic improvement of photosynthetic efficiency in wheat.
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