Predicting heterosis via genetic distance and the number of SNPs in selected segments of chromosomes in maize.

A reliable method is needed for predicting heterosis to help maize (Zea mays L.) breeders develop new hybrids more efficiently. The objectives of this study were to 1) investigate if the numbers of selected PEUS SNPs (the SNP in the Promoters (1 kb upstream of the start codon), Exons, Untranslated region (UTR), and Stop codons) could be used for predicting MPH or BPH of GY; 2) if the number of PEUS SNPs is a better predictor of MPH and/or BPH of GY than genetic distance (GD). A line × tester experiment was conducted with 19 elite maize inbreds from three heterotic groups, which were crossed with five testers. The multi-location trial data on GY were recorded. Whole-genome resequencing of the 24 inbreds was carried out. After filtration, a total of 58,986,791 SNPs were called with high confidence. Selected SNPs in the promoters, exons, untranslated region (UTRs), and stop codons (PEUS SNPs) were counted, and the GD was calculated. The correlation between heterozygous PEUS SNPs/GD and mean MPH, BPH of GY revealed that 1) both the number of heterozygous PEUS SNP and the GD were highly correlated to both MPH_GY and BPH_GY at p<0.01 with correlation coefficients for the number of heterozygous PEUS SNP being higher than that for GD; 2) the mean number of heterozygous PEUS SNPs was also highly correlated with mean BPH_GY or mean MPH_GY (p<0.05) in the 95 crosses grouped by either male or female parents, implying that inbreds can be selected before making the actual crosses in the field. We concluded that the number of heterozygous PEUS SNPs would be a better predictor of MPH_GY and BPH_GY than GD. Hence, maize breeders could use heterozygous PEUS SNPs to select inbreds with high heterosis potential before actually making the crosses, thus improving the breeding efficiency.

Fine mapping of candidate quantitative trait loci for plant and ear height in a maize nested-association mapping population.

Plant height (PH) and ear height (EH) are two important traits in maize (Zea mays L.), as they are closely related to lodging resistance and planting density. Our objectives were to (1) investigate single-nucleotide polymorphisms (SNPs) that are associated with PH and EH for detecting quantitative trait loci (QTL) and new gene that determines PH and EH, (2) explore the value of the QTL in maize breeding, and (3) investigate whether the "triangle heterotic group" theory is applicable for lowering PH and EH to increase yield. Seven inbred female parents were crossed with a common founder male parent Ye 107 to create a nested association mapping (NAM) population. The analysis of phenotypic data on PH and EH revealed wide variation among the parents of the NAM population. Genome-wide association study (GWAS) and high-resolution linkage mapping were conducted using the NAM population, which generated 264,694 SNPs by genotyping-by-sequencing. A total of 105 SNPs and 22 QTL were identified by GWAS and found to be significantly associated with PH and EH. A high-confidence QTL for PH, Qtl-chr1-EP, was identified on chromosome 1 via GWAS and confirmed by linkage analysis in two recombinant inbred line (RIL) populations. Results revealed that the SNP variation in the promoter region of the candidate gene Zm00001d031938, located at Qtl-chr1-EP, which encoded UDP-N-acetylglucosamine-peptide N-acetyl-glucosaminyl-transferase, might decrease PH and EH. Furthermore, the triangle heterotic pattern adopted in maize breeding programs by our team is practicable in selecting high-yield crosses based on the low ratio of EH/PH (EP).

High-density mapping for gray leaf spot resistance using two related tropical maize recombinant inbred line populations.

Gray leaf spot (GLS) caused by Cercospora zeae-maydis or Cercospora zeina is one of the devastating maize foliar diseases worldwide. Identification of GLS-resistant quantitative trait loci (QTL)/genes plays an urgent role in improving GLS resistance in maize breeding practice. Two groups of recombinant inbred line (RIL) populations derived from CML373 × Ye107 and Chang7-2 × Ye107 were generated and subjected to genotyping-by-sequencing (GBS). A total of 1,929,222,287 reads in CML373 × Ye107 (RIL-YCML) and 2,585,728,312 reads in Chang7-2 × Ye107 (RIL-YChang), with an average of 10,961,490 (RIL-YCML) and 13,609,096 (RIL-YChang) reads per individual, were got, which was roughly equal to 0.70-fold and 0.87-fold coverage of the maize B73 RefGen_V4 genome for each F7 individual, respectively. 6418 and 5139 SNP markers were extracted to construct two high-density genetic maps. Comparative analysis using these physically mapped marker loci demonstrated a satisfactory colinear relationship with the reference genome. 11 GLS-resistant QTL have been detected. The individual QTL accounted for 1.53-24.00% of the phenotypic variance explained (PVE). The new consensus QTL (qYCM-DS3-3/qYCM-LT3-1/qYCM-LT3-2) with the largest effect was located in chromosome bin 3.05, with an interval of 2.7 Mb, representing 13.08 to 24.00% of the PVE. Further gene annotation indicated that there were four candidate genes (GRMZM2G032384, GRMZM2G041415, GRMZM2G041544, and GRMZM2G035992) for qYCM-LT3-1, which may be related to GLS resistance. Combining RIL populations and GBS-based high-density genetic maps, a new larger effect QTL was delimited to a narrow genomic interval, which will provide a new resistance source for maize breeding programs.

Introgression of the crtRB1 gene into quality protein maize inbred lines using molecular markers.

Quality protein maize (QPM; Zea mays L.) has effectively enhanced levels of the amino acids, lysine, and tryptophan, over normal maize and provided balanced dietary protein for the health and development of monogastric animals and humans. However, as in normal maize, QPM varieties are low in provitamin A (ProVA), a precursor of vitamin A, which can lead to vitamin A deficiency in humans when maize is a significant part of their diet. In this study, maize inbred Hp321-1 carrying the favorable alleles crtRB1-5'TE-2 and crtRB1-3'TE-1 that can enhance levels of ProVA, was used as donor for improving ProVA in QPM inbred lines CML161 and CML171. Functional markers for identifying the favorable alleles crtRB1-5'TE-2 and crtRB1-3'TE-1 were used in foreground selection, and simple sequence repeat markers were used in background selection for the BC1F1, BC2F1, and BC2F2 generations. The background recovery rates were 77.4 and 84.5 % for CML161 and CML171 populations, respectively, in the BC1F1 generation, and 89.9 and 92.1 % in the BC2F2 generation. With foreground and background selection, the mean ProVA concentration has been improved from 1.60 µg g-1 in the parent of CML161 to 5.25 µg g-1 in its BC2F3 offspring, from 1.80 µg g-1 in the parent of CML171 to 8.14 µg g-1 in its BC2F3 offspring while maintaining similar QPM characteristics of the recurrent parents. The success from this study offers maize breeders a procedure for increasing ProVA in QPM lines, which will greatly mitigate vitamin A deficiency and protein-energy malnutrition in developing countries.

Gene Expression Browser: large-scale and cross-experiment microarray data integration, management, search & visualization.

BACKGROUND: In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for exploring gene functions, gene networks and biological pathways. Effectively utilizing these invaluable microarray data remains challenging due to a lack of powerful tools to integrate large-scale gene-expression information across diverse experiments and to search and visualize a large number of gene-expression data points. RESULTS: Gene Expression Browser is a microarray data integration, management and processing system with web-based search and visualization functions. An innovative method has been developed to define a treatment over a control for every microarray experiment to standardize and make microarray data from different experiments homogeneous. In the browser, data are pre-processed offline and the resulting data points are visualized online with a 2-layer dynamic web display. Users can view all treatments over control that affect the expression of a selected gene via Gene View, and view all genes that change in a selected treatment over control via treatment over control View. Users can also check the changes of expression profiles of a set of either the treatments over control or genes via Slide View. In addition, the relationships between genes and treatments over control are computed according to gene expression ratio and are shown as co-responsive genes and co-regulation treatments over control. CONCLUSION: Gene Expression Browser is composed of a set of software tools, including a data extraction tool, a microarray data-management system, a data-annotation tool, a microarray data-processing pipeline, and a data search & visualization tool. The browser is deployed as a free public web service (http://www.ExpressionBrowser.com) that integrates 301 ATH1 gene microarray experiments from public data repositories (viz. the Gene Expression Omnibus repository at the National Center for Biotechnology Information and Nottingham Arabidopsis Stock Center). The set of Gene Expression Browser software tools can be easily applied to the large-scale expression data generated by other platforms and in other species.