Inconsistent reports exist on the relationships between key agronomic traits and maize yield. We performed a multivariate analysis of yield and 10 agronomic traits in 59 hybrids to explore maize yields in mountainous areas. The yield per plant (YP) was significantly and positively correlated with kernel weight (KW), growth period (GP), and kernel row number (KRN). KW and KRN had positive effects on YP, whereas kernel rows per ear (KRE) had a negative effect. GP indirectly affected YP. GP, KW, KRN, and ear length (EL) showed the highest grey relational degree with YP. The first four principal components cumulatively accounted for 73.36% of variation. EL, KW, plant height (PH), ear height (EH), GP, KRN, and YP contributed positively to the variation, whereas KRE, shelling percentage (SP), bald-tip length (BTL), and ear girth (EG) contributed negatively. Based on trait similarity, the 59 maize hybrids were classified into two clusters, Clusters I and II. A total of 11 traits were grouped into four clusters, Clusters A–D. Cluster D included KW, GP, KRN, EL, EH, PH, and YP, and the 22 maize hybrids in Cluster I performed better in these traits. These results provide a theoretical basis for the breeding of high-yield maize varieties in mountainous areas.

The ability of immature embryos to induce embryogenic callus (EC) is crucial for genetic transformation in maize, which is highly genotype-dependent. To dissect the genetic basis of maize EC induction, we conducted QTL mapping for four EC induction-related traits, the rate of embryogenic callus induction (REC), rate of shoot formation (RSF), length of shoot (LS), and diameter of callus (DC) under three environments by using an IBM Syn10 DH population derived from a cross of B73 and Mo17. These EC induction traits showed high broad-sense heritability (>80%), and significantly negative correlations were observed between REC and each of the other traits across multiple environments. A total of 41 QTLs for EC induction were identified, among which 13, 12, 10, and 6 QTLs were responsible for DC, RSF, LS, and REC, respectively. Among them, three major QTLs accounted for >10% of the phenotypic variation, including qLS1-1 (11.54%), qLS1-3 (10.68%), and qREC4-1 (11.45%). Based on the expression data of the 215 candidate genes located in these QTL intervals, we performed a weighted gene co-expression network analysis (WGCNA). A combined use of KEGG pathway enrichment and eigengene-based connectivity (KME) values identified the EC induction-associated module and four hub genes (Zm00001d028477, Zm00001d047896, Zm00001d034388, and Zm00001d022542). Gene-based association analyses validated that the variations in Zm00001d028477 and Zm00001d034388, which were involved in tryptophan biosynthesis and metabolism, respectively, significantly affected EC induction ability among different inbred lines. Our study brings novel insights into the genetic and molecular mechanisms of EC induction and helps to promote marker-assisted selection of high-REC varieties in maize.