Computational methods to identify ionomic candidate genes in plants
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High throughput phenotyping and quantitative genetics have enabled researchers to identify genetic regions associated with changes in phenotype. However, going from GWAS loci to candidate genes is still challenging. When selecting candidate genes for ionomic GWAS loci, we curated a collection of well-known ionomic genes (KIG) experimentally shown to alter plant elemental uptake and their orthologs in 10 crop species. Yet when compared to ionomic GWAS markers, over 90 percent of significant markers were not linked to a KIG - indicating the list is incomplete, and many causal genes are unknown. We propose an unbiased computational approach that compares analogous GWAS markers from multiple species and searches for conserved genes linked to trait markers. Like the KIG list, we expect many of these unknown candidate genes to have orthologs in other species. By leveraging the evolutionary relationship of these conserved genes, rather than prior knowledge and gene annotations, this method: 1) finds more candidate genes than we expect from random chance, 2) selects and prioritizes candidates in poorly annotated species, and 3) includes unknown genes in the results. We used this approach with comparable seed weight GWAS and preliminarily confirmed phenotypes for 9 of 12 Arabidopsis thaliana candidate genes. Future efforts will solidify confirmations and improve parameters for candidate selection.
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Ph. D.