A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species

Abstract

Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to candidate genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, a mere 5% of loci are associated with a known ionomic gene - indicating that many causal genes are still unknown. To select candidates for the remaining 95% of loci, we developed a method to identify conserved genes underlying GWAS loci in multiple species. For 19 ionomic traits, we identified 14,336 candidates across Arabidopsis, soybean, rice, maize, and sorghum. We calculated the likelihood of candidates with random permutations of the data and determined that most of the top 10% of candidates were orthologous genes linked to GWAS loci across all five species. The candidate list also includes orthologous genes with previously established ionomic functions in Arabidopsis and rice. Our methods highlight the conserved nature of ionomic genetic regulators and enable the identification of previously unknown ionomic genes.Author summaryIdentifying the genes contributing to changes in a given trait is challenging. Many genes can be near the region of interest, but proximity does not always translate to causality. We use other methods to narrow our focus to the genes most likely involved in our trait of interest before confirming their involvement through confirmation experiments. However, these other methods are often time, labor, and resource-consuming. We developed an approach to narrow these gene lists before these laborious methods are required. Through testing with genetic markers for elemental (i.e., calcium, iron, zinc) uptake, we found that comparing markers across multiple species for nearby evolutionarily conserved genes is a successful approach. We’ve produced a list of candidate genes likely to be involved in elemental uptake traits, including previously known elemental uptake genes and genes whose potential elemental uptake function has yet to be observed. Some of these genes would not have been considered according to the significance threshold within a single GWAS, but combining comparable datasets across species has collectively boosted their signal. Methods like our approach are useful for reducing candidate lists to conserve resources spent in functional characterization experiments and encouraging the discovery of new functional roles of these genes.