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.
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Claims 4
Claim · model-written summary
Dual-genome prediction models improved heritability estimates, error variances and model accuracy for maize Fusarium ear rot.
Zea maysNot reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Dual genome prediction models improved heritability estimates, error variances, and model accuracy”
t0“maize (Zea mays L.)”
Claim · model-written summary
Dual-genome models supplied host-by-pathogen interaction predictions for SNP interaction tests.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“providing predictions for host-by-pathogen interactions that may be used to test the significance of SNP-SNP interactions”
t0“maize (Zea mays L.)”
Claim · model-written summary
Independent maize and Fusarium GWAS identified significantly associated loci.
- Outcome
- significantly associated locie0
FusariumOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Independent GWAS for maize and Fusarium populations identified significantly associated loci”
Claim · model-written summary
The authors proposed protein interaction evaluation near candidate SNPs as an additional in silico way to identify interacting genes.
- Outcome
- evaluation of protein-protein interactions from candidate genes near the interacting SNPse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
t0“maize (Zea mays L.)”