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.

ABSTRACT. The accuracy of quantitative trait loci (QTLs) identified using different sample sizes and marker densities was evaluated in different genetic models. Model I assumed one additive QTL; Model II assumed three additive QTLs plus one pair of epistatic QTLs; and Model III assumed two additive QTLs with opposite genetic effects plus two pairs of epistatic QTLs. Recombinant inbred lines (RILs) (50-1500 samples) were simulated according to the Models to study the influence of different sample sizes under different genetic models on QTL mapping accuracy. RILs with 10-100 target chromosome markers were simulated according to Models I and II to evaluate the influence of marker density on QTL mapping accuracy. Different marker densities did not significantly influence accurate estimation of genetic effects with simple additive models, but influenced QTL mapping accuracy in the additive and epistatic models. The optimum marker density was approximately 20 markers when the recombination fraction between two adjacent markers was 0.056 in the additive and epistatic models. A sample size of 150 was sufficient for detecting simple additive QTLs. Thus, a sample size of approximately 450 is needed to detect QTLs with additive and epistatic models. Sample size must be approximately 750 to detect QTLs with additive, epistatic, and combined effects between QTLs. The sample size should be increased to >750 if the genetic models of the data set become more complicated than Model III. Our results provide a theoretical basis for marker-assisted selection breeding and molecular design breeding.