Foxtail millet [Setaria italica (L.) P. Beauv.], a crop of historical importance in China, has been adopted as a model crop for studying C-4 photosynthesis, stress biology and biofuel traits. Construction of a high density genetic map and identification of stable quantitative trait loci (QTL) lay the foundation for marker-assisted selection for agronomic traits and yield improvement. A total of 10598 SSR markers were developed according to the reference genome sequence of foxtail millet cultivar ‘Yugu1’. A total of 1013 SSR markers showing polymorphism between Yugu1 and Longgu7 were used to genotype 167 individuals from a Yugu1 × Longgu7 F2 population, and a high density genetic map was constructed. The genetic map contained 1035 loci and spanned 1318.8 cM with an average distance of 1.27 cM between adjacent markers. Based on agronomic and yield traits identified in 2 years, 29 QTL were identified for 11 traits with combined analysis and single environment analysis. These QTL explained from 7.0 to 14.3 % of phenotypic variation. Favorable QTL alleles for peduncle length originated from Longgu7 whereas favorable alleles for the other traits originated from Yugu1 except for qLMS6.1. New SSR markers, a high density genetic map and QTL identified for agronomic and yield traits lay the ground work for functional gene mapping, map-based cloning and marker-assisted selection in foxtail millet.

Cotton fiber quality traits are controlled by multiple quantitative trait loci (QTL), and the improvement of these traits requires extensive germplasm. Herein, an Upland cotton cultivar from America, Acala Maxxa, was crossed with a local high fiber quality cultivar, Yumian 1, and 180 recombinant inbred lines (RILs) were obtained. In order to dissect the genetic basis of fiber quality differences between these parents, a genetic map containing 12116 SNP markers was constructed using the CottonSNP80K assay, which covered 3741.81 cM with an average distance of 0.31 cM between markers. Based on the genetic map and growouts in three environments, we detected a total of 104 QTL controlling fiber quality traits. Among these QTL, 25 were detected in all three environments and 35 in two environments. Meanwhile, 19 QTL clusters were also identified, and 9 contained at least one stable QTL (detected in three environments for a given trait). These stable QTL or QTL clusters are priorities for fine mapping, identifying candidate genes, elaborating molecular mechanisms of fiber development, and application in cotton breeding programs by marker-assisted selection (MAS).