Validating Genome-Wide Association Candidates Controlling Quantitative Variation in Nodulation. Issue 2 (5th January 2017) - Curtin, Shaun J.; Tiffin, Peter; Guhlin, Joseph; Trujillo, Diana I.; Burghardt, Liana T.; Atkins, Paul; Baltes, Nicholas J.; Denny, Roxanne; Voytas, Daniel F.; Stupar, Robert M.; Young, Nevin D.
Abstract : GWA studies combined with multiple mutagenesis technologies, including CRISPR/Cas9, were used to functionally validate novel candidate genes contributing to phenotypic variation in symbiosis between legume plants and rhizobial bacteria. Abstract: Genome-wide association (GWA ) studies offer the opportunity to identify genes that contribute to naturally occurring variation in quantitative traits. However, GWA relies exclusively on statistical association, so functional validation is necessary to make strong claims about gene function. We used a combination of gene-disruption platforms (Tnt1 retrotransposons, hairpin RNA-interference constructs, and CRISPR/Cas9 nucleases) together with randomized, well-replicated experiments to evaluate the function of genes that an earlier GWA study in Medicago truncatula had identified as candidates contributing to variation in the symbiosis between legumes and rhizobia. We evaluated ten candidate genes found in six clusters of strongly associated single nucleotide polymorphisms, selected on the basis of their strength of statistical association, proximity to annotated gene models, and root or nodule expression. We found statistically significant effects on nodule production for three candidate genes, each validated in two independent mutants. Annotated functions of these three genes suggest their contributions to quantitative variation in nodule production occur through processes not previously connected to nodulation, including phosphorous supply and salicylic acid-related defense response. These results demonstrate the utility of GWA combined with reverse mutagenesis technologies to discover and validate genes contributing to naturally occurring variation in quantitative traits. The results highlight the potential for GWA to complement forward genetics in identifying the genetic basis of ecologically and economically important traits.