Association mapping based on the linkage disequilibrium provides a promising tool to identify genes responsible for quantitative variations underlying complex traits. Presented here is a maize association mapping panel consisting of 155 inbred lines with mainly temperate germplasm, which was phenotyped for 34 traits and genotyped using 82 SSRs and 1,536 SNPs. Abundant phenotypic and genetic diversities were observed within the panel based on the phenotypic and genotypic analysis. A model-based analysis using 82 SSRs assigned all inbred lines to two groups with eight subgroups. The relative kinship matrix was calculated using 884 SNPs with minor allele frequency > or = 20% indicating that no or weak relationships were identified for most individual pairs. Three traits (total tocopherol content in maize kernel, plant height and kernel length) and 1,414 SNPs with missing data < 20% were used to evaluate the performance of four models for association mapping analysis. For all traits, the model controlling relative kinship (K) performed better than the model controlling population structure (Q), and similarly to the model controlling both population structure and relative kinship (Q + K) in this panel. Our results suggest this maize panel can be used for association mapping analysis targeting multiple agronomic and quality traits with optimal association model.

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Among cereals, only maize has not only a high amount of carotenoids, tocopherols, and oil content but also is rich in starch and protein content compared with other major food crops, such as rice and wheat. The present investigation was made primarily to assess the genetic variability for nutritionally important traits in 87 elite maize inbreds representing major heterotic groups in China. Carotenoid and tocopherol fractions were measured by high-performance liquid chromatography (HPLC), whereas oil, starch, and protein contents were detected by a VECTER22/N near-infrared analyzer. Significant interactions between genotypes and years were observed for all the traits. The pooled mean values of beta-carotene, beta-cryptoxanthin, alpha-carotene, lutein, zeaxanthin, and total carotenoids were 0.449, 0.876, 0.121, 5.803, 3.048, and 10.298 microg g (-1), respectively, whereas the combined mean performance of alpha-tocopherol, gamma-tocopherol, delta-tocopherol, and total tocopherols were 23.98, 32.90, 2.189, and 59.55 microg g (-1), respectively. The average protein, starch, and oil contents were observed to be 12.28, 64.51, and 3.55%, respectively. High level of heritability estimates were observed for all the traits and ranged from 65.6% (protein content) to 92.5% (alpha/gamma-tocopherol ratio). Most of the traits studied in this experiment were either significantly positive correlated or independent. The present finding exhibits substantial opportunities to the breeders for improvement of these traits in maize cultivars and also suggests further exploration of a new source of elite breeding stocks containing a high level of these nutritionally important compounds. Finally, these findings may also help in biofortification of maize.

Background: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. Results: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. Conclusions: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

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