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Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A maize ( Zea mays L.) association mapping panel including 527 inbred lines with tropical, subtropical and temperate backgrounds, representing the global maize diversity, was genotyped using 1,536 single nucleotide polymorphisms (SNPs). In total, 926 SNPs with minor allele frequencies of ≥0.1 were used to estimate the pattern of genetic diversity and relatedness among individuals. The analysis revealed broad phenotypic diversity and complex genetic relatedness in the maize panel. Two different Bayesian approaches identified three specific subpopulations, which were then reconfirmed by principal component analysis (PCA) and tree-based analyses. Marker-trait associations were performed to assess the suitability of different models for false-positive correction by population structure (Q matrix/PCA) and familial kinship (K matrix) alone or in combination in this panel. The K, Q + K and PCA + K models could reduce the false positives, and the Q + K model performed slightly better for flowering time, ear height and ear diameter. Our findings suggest that this maize panel is suitable for association mapping in order to understand the relationship between genotypic and phenotypic variations for agriculturally complex quantitative traits using optimal statistical methods. [ABSTRACT FROM AUTHOR]

Leaf collection from the field, labeling and tracking back to the source plants after genotyping are rate limiting steps in leaf DNA-based genotyping. In this study, an optimized genotyping method using endosperm DNA sampled from single maize seeds was developed, which can be used to replace leaf DNA-based genotyping for both genetic studies and breeding applications. A similar approach is likely to be suitable for all plants with relatively large seeds. Part of the endosperm was excised from imbibed maize seeds and DNA extracted in 96-tube plates using individuals from eight F2 populations and seven inbreds. The quality of the resultant DNA was functionally comparable to DNA extracted from leaf tissue. Extraction from 30 mg of endosperm yields 3–10 μg DNA, which is sufficient for analysis of 200–400 agarose-gel PCR-based markers, with the potential for several million chip-based SNP marker analyses. By comparing endosperm DNA and leaf DNA for individuals from an F2 population, genotyping errors caused by pericarp contamination and hetero-fertilization were found to average 3.8 and 0.6%, respectively. Endosperm sampling did not affect germination rates under controlled conditions, although under normal field conditions the germination rate, seedling establishment, and growth vigor were significantly lower than that of non-sampled controls for some genotypes. However, careful field management can compensate for these effects. Seed DNA-based genotyping lowered costs by 24.6% compared to leaf DNA-based genotyping due to reduced field plantings and labor costs. A substantial advantage of this approach is that it can be used to select desirable genotypes before planting. As such it provides an opportunity for dramatic improvements in the efficiency and selective gain of breeding systems based on optimum combinations of marker-assisted selection and phenotypic selection within and between generations. [ABSTRACT FROM AUTHOR]

Kernel weight in a unit volume is referred to as kernel test weight (KTW) that directly reflects maize (Zea mays L.) grain quality. In this study, an inter-mated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population and an association panel were used to identify loci responsible for KTW of maize across multiple environments. A total of 18 significant KTW-related single-nucleotide polymorphisms (SNPs) were identified using genome-wide association study (GWAS); they were closely linked to 12 candidate genes. In the IBM Syn10 DH population, linkage analysis detected 19 common quantitative trait loci (QTL), five of which were repeatedly detected among multiple environments. Several verified genes that regulate maize seed development were found in the confidence intervals of the mapped QTL and the LD regions of GWAS, such as ZmYUC1, BAP2, ZmTCRR-1, dek36 and ZmSWEET4c. Combined QTL mapping and GWAS identified one significant SNP that was co-identified in the both populations. Based on the co-localized SNP across the both populations, 17 candidate genes were identified. Of them, Zm00001d044075, Zm00001d044086, and Zm00001d044081 were further identified by candidate gene association study for KTW. Zm00001d044081 encodes homeobox-leucine zipper protein ATHB-4, which has been demonstrated to control apical embryo development in Arabidopsis. Our findings provided insights into the mechanism underlying maize KTW and contributed to the application of molecular-assisted selection of high KTW breeding in maize. [ABSTRACT FROM AUTHOR]

Large phenotypic variations in the lead (Pb) concentration were observed in grains and leaves of maize plants. A further understanding of inheritance of Pb accumulation may facilitate improvement of low-Pb-accumulating cultivars in maize. A genome-wide association study was conducted in a population of 269 maize accessions with 43,737 single-nucleotide polymorphisms (SNPs). The Pb concentrations in leaves and kernels of 269 accessions were collected in pot-culture and field experiments in years of 2015 and 2016. Significant differences in Pb accumulation were found among individuals under different environments. Using the structure and kinship model, a total of 21 SNPs significantly associated with the Pb accumulation were identified with P < 2.28 × 10−5 and FDR < 0.05 in the pot-culture and field experiments across 2 years. Three SNPs on chromosome 4 had significant associations simultaneously with the Pb concentrations of kernels and leaves and were co-localized with the previously detected quantitative trait loci. Through ridge regression best linear unbiased prediction Pb accumulation in the association population, the prediction accuracies by cross validation were 0.18-0.59 and 0.17-0.64, depending on the k-fold and the size of the training population. The results are helpful for genetic improvement and genomic prediction of Pb accumulation in maize. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is the most important macronutrient for plant growth and development. Hence, understanding genetic architectures and functional genes involved in the response to N deficiency can greatly facilitate the development of low-N-tolerant cultivars. In this study, we collected 212 quantitative trait loci (QTL) of agronomically important traits under low-N stress conditions in maize. We then identified 21 consensus QTL (cQTL) strongly induced for low-N tolerance after excluding overlapping cQTL containing QTL simultaneously identified in meta-analyses of studies performed under other environmental conditions. Among the 21 cQTL, 30 candidate maize genes were identified from maize large-scale differential expression data derived from analyses of low-N stress, and the 12 most important maize orthologs were identified using homologous BLAST analyses of genes with known functions in N use efficiency in model plants. Furthermore, maize orthologs associated with low-N tolerance and metabolism were also predicted using large-scale expression data from other model plants. The present genetic loci and candidate genes indicate the molecular mechanisms of low-N tolerance in maize and may provide information for QTL fine mapping and molecular marker-assisted selection. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is required for numerous developmental processes, such as flowering and reproduction, in maize (Zea mays L.). It is also an essential nutrient in fertilizers to sustain high yields in farmlands with insufficient available phosphate, particularly in acid soils in China. Flowering time is a key factor influencing the adaptation of maize to the local environment and is associated with high yields. However, few studies have focused on the genetic characteristics of flowering time under low phosphate conditions. In this study, a recombinant inbred line (RIL) population derived from a cross between X178 and 9782 was evaluated for three flowering time traits under two phosphates (Pi) levels at four locations. We found that low phosphate significantly delayed the flowering stage of maize and proposed the hypothesis of "Phosphorus Accumulation Effect". Mixed-model-based composite interval mapping was used to dissect the genetic basis of the flowering time traits. A total of 41 quantitative trait loci (QTL) was identified for the three flowering time traits through four procedures. Among these 41 QTL, 38 focused on four hot regions, including bin 2.02, bin 2.03, bin 5.04, and bin 6.01. These regions and linked markers should be the targets for fine mapping, map-based cloning, and marker-assisted selection of flowering time and phosphate utilization efficiency. [ABSTRACT FROM AUTHOR]