<p>Phenotypic data for <i>D13-1/+</i> FOAM population.</p>

Deciphering the molecular basis of complex traits requires understanding how natural genetic variation interacts with underlying biological pathways. In this study, we explored how natural genetic variation influences traits in maize affected by a semi-dominant maize dwarfing allele, Dwarf13–1 (D13-1) which encodes a defective ionotropic glutamate receptor (GLR). This allowed us to investigate natural genetic variation in the genome affecting GLR signaling in maize. We implemented an F1 association mapping (FOAM) approach, where heterozygous mutants carrying the semi-dominant D13-1 allele were crossed with a maize association panel. The resulting F1 families segregated 1:1 for mutant and wild-type phenotypes allowing comparisons between the congenic F1 hybrid siblings to identify and map natural alleles that interact with the D13-1 mutant allele. FOAM mapping detected two loci that modify the expression of the D13-1/+ mutant phenotype. The phenotypic impacts of both loci were epistatically controlled by D13-1, and only affected the phenotypes of mutant F1 hybrids. One, tropotriskaideka1 (tod1), encoded a maize homolog of the GLR-interacting cornichon gene and modified D13-1/+ mutant severity. A second, encoded by the d13 locus itself, affected the severity of the D13-1/+ phenotype via variation in the wild-type allele in the heterozygous mutants. By integrating gene expression analyses, these epistatic interactions, and SNP linkage information we identified multiple, unlinked, alleles affecting expression of the wild-type D13 transcript that modify mutant trait expression. Greater expression of the wild-type D13 allele increased plant height and suppressed D13-1/+ mutant severity, consistent with a multi-subunit complex GLR structure and complex-poisoning mode-of-action for the semi-dominant D13-1 allele. This approach identifies natural alleles affecting the GLR pathway in maize and establishes GLRs and their interactors as dose-dependent regulators of plant architecture. Our pathway-focused framework and epistasis testing of natural variants provides greater confidence in identifying genes contributing to complex traits.