The isolation, characterization, and application of WRKY genes as useful molecular markers in tropical trees

The improvement of tropical tree crops using conventional breeding methods faces challenges due to the length of time involved. Thus, like most crops, there is an effort to utilize molecular genetic markers in breeding programs to select for desirable agronomic traits. Known as marker assisted breeding or marker assisted selection, genetic markers associated with a phenotype of interest are used to screen and select material reducing the time necessary to evaluate candidates. As the focus of this research was improving disease resistance in tropical trees, the usefulness of the WRKY gene superfamily was investigated as candidates for generating useful molecular genetic markers. WRKY genes encode plant-specific transcriptional factors associated with regulating plants' responses to both biotic and abiotic stress. ^ One pair of degenerate primers amplified 48 WRKY gene fragments from three taxonomically distinct, economically important, tropical tree crop species: 18 from Theobroma cacao L., 21 from Cocos nucifera L. and 9 from Persea americana Mill. Several loci from each species were polymorphic because of single nucleotide substitutions present within a putative non-coding region of the loci. Capillary array electrophoresis-single strand conformational polymorphism (CAE-SSCP) mapped four WRKY loci onto a genetic linkage map of a T. cacao F2 population segregating for resistance to witches' broom disease. Additionally, PCR primers specific for four T. cacao loci successfully amplified WRKY loci from 15 members of the Byttneriae tribe. A method was devised to allow the reliable discrimination of alleles by CAE-SSCP using only the mobility assigned to the sample peaks. Once this method was validated, the diversity of three WRKY loci was evaluated in a germplasm collection of T. cacao . One locus displayed high diversity in the collection, with at least 18 alleles detected from mobility differences of the product peaks. The number of WRKY loci available within the genome, ease of isolation by degenerate PCR, codominant segregation demonstrated in the F2 population, and usefulness for screening germplasm collections and closely related wild species demonstrates that the WRKY superfamily of genes are excellent candidates for developing a number of genetic molecular markers for breeding purposes in tropical trees. ^

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.