AAQSP increases mapping resolution of stable QTLs through applying NGS-BSA in multiple genetic backgrounds.

KEY MESSAGE: In this study, we present AAQSP as an extension of existing NGS-BSA applications for identifying stable QTLs at high resolution. GhPAP16 and GhIQD14 fine mapped on chromosome D09 of upland cotton are identified as important candidate genes for lint percentage (LP). Bulked segregant analysis combined with next generation sequencing (NGS-BSA) allows rapid identification of genome sequence differences responsible for phenotypic variation. The NGS-BSA approach applied to crops mainly depends on comparing two bulked DNA samples of individuals from an F2 population. Since some F2 individuals still maintain high heterozygosity, heterosis will exert complications in pursuing NGS-BSA in such populations. In addition, the genetic background influences the stability of gene expression in crops, so some QTLs mapped in one segregating population may not be widely applied in crop improvement. The AAQSP (Association Analysis of QTL-seq on Semi-homologous Populations) reported in our study combines the optimized scheme of constructing BSA bulks with NGS-BSA analysis in two (or more) different parental genetic backgrounds for isolating the stable QTLs. With application of AAQSP strategy and construction of a high-density linkage map, we have successfully identified a QTL significantly related to lint percentage (LP) in cultivated upland cotton, followed by map-based cloning to dissect two candidate genes, GhPAP16 and GhIQD14. This study demonstrated that AAQSP can efficiently identify stable QTLs for complex traits of interest, and thus accelerate the genetic improvement of upland cotton and other crop plants.

Linkage and association analyses reveal that hub genes in energy-flow and lipid biosynthesis pathways form a cluster in upland cotton.

Upland cotton is an important allotetraploid crop that provides both natural fiber for the textile industry and edible vegetable oil for the food or feed industry. To better understand the genetic mechanism that regulates the biosynthesis of storage oil in cottonseed, we identified the genes harbored in the major quantitative trait loci/nucleotides (QTLs/QTNs) of kernel oil content (KOC) in cottonseed via both multiple linkage analyses and genome-wide association studies (GWAS). In 'CCRI70' RILs, six stable QTLs were simultaneously identified by linkage analysis of CHIP and SLAF-seq strategies. In '0-153' RILs, eight stable QTLs were detected by consensus linkage analysis integrating multiple strategies. In the natural panel, thirteen and eight loci were associated across multiple environments with two algorithms of GWAS. Within the confidence interval of a major common QTL on chromosome 3, six genes were identified as participating in the interaction network highly correlated with cottonseed KOC. Further observations of gene differential expression showed that four of the genes, LtnD, PGK, LPLAT1, and PAH2, formed hub genes and two of them, FER and RAV1, formed the key genes in the interaction network. Sequence variations in the coding regions of LtnD, FER, PGK, LPLAT1, and PAH2 genes may support their regulatory effects on oil accumulation in mature cottonseed. Taken together, clustering of the hub genes in the lipid biosynthesis interaction network provides new insights to understanding the mechanism of fatty acid biosynthesis and TAG assembly and to further genetic improvement projects for the KOC in cottonseeds.