Abstract
BackgroundMeta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. The combination of meta-QTL intervals, significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants.ResultsIn our study, 884 QTLs associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1, as a result, 74 meta-QTLs were identified, including 19 meta-QTLs for fiber length; 18 meta-QTLs for fiber strength; 11 meta-QTLs for fiber uniformity; 11 meta-QTLs for fiber elongation; and 15 meta-QTLs for micronaire. Combined with 8 589 significant single nucleotide polymorphisms associated with fiber quality traits collected from 15 studies, 297 candidate genes were identified in the meta-QTL intervals, 20 of which showed high expression levels specifically in the developing fibers. According to the function annotations, some of the 20 key candidate genes are associated with the fiber development.ConclusionsThis study provides not only stable QTLs used for marker-assisted selection, but also candidate genes to uncover the molecular mechanisms for cotton fiber development.
Highlights
Meta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies
A total of 134 QTLs for fiber quality traits were detected using 231 F6:8 recombinant inbred lines (RIL), which were derived from an intraspecific cross between Xinluzao24 and Lumianyan 28 (Liu et al 2018b)
The combination of meta-QTL, significant SNP by genome-wide association analysis (GWAS), and spatiotemporal expression analysis provides stable QTLs used for Marker-assisted selection (MAS), and candidate genes to uncover the molecular mechanisms for cotton fiber development
Summary
Meta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. With the improvement of people’s living standard and advancements in techniques and diversified methods of spinning, demand for high quality cotton fiber is increasing. Cotton (2020) 3:34 as the most important traits affecting yarn quality, and FS is important for advanced spinning technologies in the textile industry (Yang et al 2016). A total of 104 QTLs for fiber quality traits were detected by using 180 recombinant inbred lines (RIL) derived from and Yumian 1, and 25 QTLs were detected in all three environments (Tan et al 2018). A total of 134 QTLs for fiber quality traits were detected using 231 F6:8 RILs, which were derived from an intraspecific cross between Xinluzao and Lumianyan 28 (Liu et al 2018b). One hundred and eighty-six additive QTLs were obtained for five fiber quality traits using 137 RILs (Jia et al 2018)
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