Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GBS) for large-scale SNP discovery and simultaneous genotyping of all F2 individuals from a cross between two varieties of maize that are in clear contrast in yield and related traits. A set of 199 F2 progeny derived from the cross of varieties SG-5 and SG-7 were generated and genotyped by GBS. A total of 1,046,524,604 reads with an average of 5,258,918 reads per F2 individual were generated. This number of reads represents an approximately 0.36-fold coverage of the maize reference genome Zea_mays.AGPv3.29 for each F2 individual. A total of 68,882 raw SNPs were discovered in the F2 population, which, after stringent filtering, led to a total of 29,927 high quality SNPs. Comparative analysis using these physically mapped marker loci revealed a higher degree of synteny with the reference genome. The SNP genotype data were utilized to construct an intra-specific genetic linkage map of maize consisting of 3,305 bins on 10 linkage groups spanning 2,236.66 cM at an average distance of 0.68 cM between consecutive markers. From this map, we identified 28 QTLs associated with yield traits (100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, corn grains per row, ear weight, and grain weight per plant) using the composite interval mapping (CIM) method and 29 QTLs using the least absolute shrinkage selection operator (LASSO) method. QTLs identified by the CIM method account for 6.4% to 19.7% of the phenotypic variation. Small intervals of three QTLs (qCGR-1, qKW-2, and qGWP-4) contain several genes, including one gene (GRMZM2G139872) encoding the F-box protein, three genes (GRMZM2G180811, GRMZM5G828139, and GRMZM5G873194) encoding the WD40-repeat protein, and one gene (GRMZM2G019183) encoding the UDP-Glycosyltransferase. The work will not only help to understand the mechanisms that control yield traits of maize, but also provide a basis for marker-assisted selection and map-based cloning in further studies.

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The results of QTL mapping based on a primary mapping population should be further verified and refined for its real utilization in marker-assisted selection or map-based cloning. The primary mapping population contains 114 BC 1 F 1 plants of the backcross between Essex (maturity group V, MG V) as the donor parent and ZDD2315 (MG II) as the recurrent parent. In this study, a genetic linkage map with 250 SSR markers spanning a total length of 2963.5 cM on 25 linkage groups (LG) was constructed using software MAPMAKER3.0. Six kinds of genetic statistical models of 4 softwares, i.e. WinQTL Cartographer Version 2.5, IciMapping Version 2.0, MapQTL Version 5.0 and QTLnetwork Version 2.0, were used to map QTLs conferring days to flowering of the BC 1 F 3 lines. Nine QTLs were mapped on 6 different linkage groups (LG). Of those, 6 QTLs were detected by at least two different genetic statistical models, while the other three were detected by only one procedure. Among the three QTLs, Flwdt7 was mapped between Sat_213 and Satt643 on LG C2 with only 11.0% contribution rate. For confirmation of Flwdt7, 5 RHL populations were developed through selfing eight BC 1 F 5 plants heterozygous at seven markers around the locus. The RHL populations with the same segregating loci were bulked and used to construct a secondary linkage map of the specific segment using software JoinMap ® 3.0. The genetic distances among the markers on the specific segment became shorter than those of the whole genome map. On the secondary map, Flwdt7 was mapped between Satt277 and Satt489, next to its primary interval Sat_213-Satt643, with distance 1.40 cM to Satt277 and 0.45 cM to Satt489, confidence interval narrowed to 2.7 cM, and contribution rate increased to 36.8%. The results were confirmed with significance analysis among marker genotypes on individual loci and comparison analysis of target marker intervals among near isogenic lines (plants). Thus the strategy by using residual heterozygous lines for QTL fine-mapping on target segments based on primary whole genome scanning with multiple mapping models was demonstrated to be effective. soybean, backcross line, RHL (residual heterozygous line ), SSR (simple sequence repeat) marker, flowering date, QTL fine-mapping Citation: Su C F, Lu W G, Zhao T J, et al. Verification and fine-mapping of QTLs conferring days to flowering in soybean using residual heterozygous lines.

Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

Kernel size is an important agronomic trait for grain yield in maize. The purpose of this study is to map QTLs and predict candidate genes for kernel size in maize. A total of 199 F2 and its F2:3 lines from the cross between SG5/SG7 were developed. A composite interval mapping (CIM) method was used to detect QTLs in three environments of F2 and F2:3 populations. The result showed that a total of 10 QTLs for kernel size were detected, among which were five QTLs for kernel length (KL) and five QTLs for kernel width (KW). Two stable QTLs, qKW-1, and qKL-2, were mapped in all three environments. Three QTLs, qKL-1, qKW-1, and qKW-2, were overlapped with the QTLs identified from previous studies. In order to validate and fine map qKL-2, near-isogenic lines (NILs) were developed by continuous backcrossing between SG5 as the donor parent and SG7 as the recurrent parent. Marker-assisted selection was conducted from BC2F1 generation with molecular markers near qKL-2. A secondary linkage map with six markers around the qKL-2 region was developed and used for fine mapping of qKL-2. Finally, qKL-2 was confirmed in a 1.95 Mb physical interval with selected overlapping recombinant chromosomes on maize chromosome 9 by blasting with the Zea_Mays_B73 v4 genome. Transcriptome analysis showed that a total of 11 out of 40 protein-coding genes differently expressed between the two parents were detected in the identified qKL-2 interval. GRMZM2G006080 encoding a receptor-like protein kinase FERONIA, was predicted as a candidate gene to control kernel size. The work will not only help to understand the genetic mechanisms of kernel size of maize but also lay a foundation for further fine mapping and even cloning of the promising loci.