Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding

Genotyping-by-Sequencing (GBS) is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73 × Mo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: ~ 25% missing data for 96-plex and ~ 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.

PedigreeNet: a web-based pedigree viewer for biological databases

Motivation Plant breeding aims to improve current germplasm that can tolerate a wide range of biotic and abiotic stresses. To accomplish this goal, breeders rely on developing a deeper understanding of genetic makeup and relationships between plant varieties to make informed plant selections. Although rapid advances in genotyping technology generated a large amount of data for breeders, tools that facilitate pedigree analysis and visualization are scant, leaving breeders to use classical, but inherently limited, hierarchical pedigree diagrams for a handful of plant varieties. To answer this need, we developed a simple web-based tool that can be easily implemented at biological databases, called PedigreeNet, to create and visualize customizable pedigree relationships in a network context, displaying pre- and user-uploaded data. Results As a proof-of-concept, we implemented PedigreeNet at the maize model organism database, MaizeGDB. The PedigreeNet viewer at MaizeGDB has a dynamically-generated pedigree network of 4706 maize lines and 5487 relationships that are currently available as both a stand-alone web-based tool and integrated directly on the MaizeGDB Stock Pages. The tool allows the user to apply a number of filters, select or upload their own breeding relationships, center a pedigree network on a plant variety, identify the common ancestor between two varieties, and display the shortest path(s) between two varieties on the pedigree network. The PedigreeNet code layer is written as a JavaScript wrapper around Cytoscape Web. PedigreeNet fills a great need for breeders to have access to an online tool to represent and visually customize pedigree relationships. Availability and implementation PedigreeNet is accessible at https://www.maizegdb.org/breeders_toolbox. The open source code is publically and freely available at GitHub: https://github.com/Maize-Genetics-and-Genomics-Database/PedigreeNet. Supplementary information Supplementary data are available at Bioinformatics online.

Molecular Mechanisms Governing Shade Responses in Maize

Light is one of the most important environmental factors affecting plant growth and development. Plants use shade avoidance and shade tolerance strategies to adjust their growth and development thus increase their success in the competition for incoming light. To investigate the mechanism of shade responses in maize (Zea mays), we examined the anatomical and transcriptional dynamics of the early shade response in seedlings of the B73 inbred line. Transcriptome analysis identified 912 differentially expressed genes, including genes involved in light signaling, auxin responses, and cell elongation pathways. Grouping transcription factor family genes and performing enrichment analysis identified multiple types of transcription factors that are differentially regulated by shade and predicted putative core genes responsible for regulating shade avoidance syndrome. For functional tests, we ectopically over-expressed ZmHB53, a type II HD-ZIP transcription factor gene significantly induced by shade, in Arabidopsis thaliana. Transgenic Arabidopsis plants overexpressing ZmHB53 exhibited narrower leaves, earlier flowering, and enhanced expression of shade-responsive genes, suggesting that ZmHB53 participates in the regulation of shade responses in maize. This study increases our understanding of the regulatory network of the shade response in maize and provides a useful resource for maize genetics and breeding.HighlightOur findings not only increase the understanding of the regulatory network of the shade avoidance in maize, and also provide a useful resource for maize genetics and breeding.

Medical subject heading (MeSH) annotations illuminate maize genetics and evolution

High-density marker panels and/or whole-genome sequencing,coupled with advanced phenotyping pipelines and sophisticated statistical methods, have dramatically increased our ability to generate lists of candidate genes or regions that are putatively associated with phenotypes or processes of interest. However, the speed with which we can validate genes, or even make reasonable biological interpretations about the principles underlying them, has not kept pace. A promising approach that runs parallel to explicitly validating individual genes is analyzing a set of genes together and assessing the biological similarities among them. This is often achieved via gene ontology (GO) analysis, a powerful tool that involves evaluating publicly available gene annotations. However, additional tools such as Medical Subject Headings (MeSH terms) can also be used to evaluate sets of genes to make biological interpretations. In this manuscript, wedescribe utilizing MeSH terms to make biological interpretations in maize. MeSH terms are assigned to PubMed-indexed manuscripts by the National Library of Medicine, and can be directly mapped to genes to develop gene annotations. Once mapped, these terms can be evaluated for enrichment in sets of genes or similarity between gene sets to provide biological insights. Here, we implement MeSH analyses in five maize datasets to demonstrate how MeSH can be leveraged by the maize and broader crop-genomics community.