High throughput phenotyping and quantitative genetics have enabled
researchers to identify genetic regions, or markers, associated with
changes in phenotype. However, going from GWAS markers to candidate
genes is still challenging. When selecting candidate genes for ionomic
GWAS markers, we curated a collection of well-known ionomic genes (KIG)
experimentally shown to alter plant elemental uptake and their orthologs
in 10 crop species: 2066 genes total. Yet when compared to ionomic GWAS
markers, over 90% of significant markers were not linked to a KIG -
indicating the list is incomplete and many causal genes are unknown.
Continuing to use only functional annotations as candidate selection
criteria will keep efforts biased toward well-known genes and hinder the
characterization of unknown genes. 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. With this approach, we now
have an unbiased list of gene candidates across 19 ionomic traits in
model species and crop species to verify in future experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.