Using genome-wide associations and host-by-pathogen predictions to identify allelic interactions that control disease resistance.

Abstract

Characterizing the molecular mechanisms underlying disease symptom expression has been used to improve human health and disease resistance in crops and animal breeds. Quantitative trait loci and genome-wide association studies (GWAS) are widely used to identify genomic regions that are involved in disease progression. This study extends traditional GWAS significance tests of host and pathogen marker main effects by utilizing dual-genome reaction norm models to evaluate the importance of host-single nucleotide polymorphism (SNP) by pathogen-SNP interactions. Disease symptom severity data from Fusarium ear rot (FER) on maize (Zea mays L.) is used to demonstrate the use of both genomes in genomic selection models for breeding and the identification of loci that interact across organisms to impact FER disease development. Dual genome prediction models improved heritability estimates, error variances, and model accuracy while providing predictions for host-by-pathogen interactions that may be used to test the significance of SNP-SNP interactions. Independent GWAS for maize and Fusarium populations identified significantly associated loci and predictions that were used to evaluate the importance of interactions using two different association tests. Predictions from dual genome models were used to evaluate the significance of the SNP-SNP interactions that may be associated with population structure or polygenic effects. As well, association tests incorporating host and pathogen markers in models that also included genomic relationship matrices were used to account for population structure. Subsequent evaluation of protein-protein interactions from candidate genes near the interacting SNPs provides a further in silico evaluation method to expedite the identification of interacting genes.