Location: Plant Science ResearchTitle: Genome-wide association study of Fusarium ear rot disease in the U.S.A. maize inbred line collection
|ZILA, CHARLES - North Carolina State University|
|OGUT, FUNDA - North Carolina State University|
|ROMAY, M - Cornell University - New York|
|Buckler, Edward - Ed|
|Holland, Jim - Jim|
Submitted to: BMC Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/8/2014
Publication Date: 12/30/2014
Citation: Zila, C., Ogut, F., Romay, M.C., Gardner, C.A., Buckler IV, E.S., Holland, J.B. 2014. Genome-wide association study of Fusarium ear rot disease in the U.S.A. maize inbred line collection. Biomed Central (BMC) Plant Biology. 14:372.
Interpretive Summary: Fusarium ear rot disease is common worldwide and can reduce yield. The causal fungus also can contaminate corn grain with a mycotoxin hazardous to human and animal health. The disease cannot be controlled with fungicide, so breeding for resistance is needed. Using extensive field phenotyping and genotyping of the USDA-ARS maize seed bank collection, we identified seven DNA sequence variations associated with resistance. We show that the DNA sequence variants that enhance resistance come from unadapted maize and so could be useful selection targets for commercial USA corn breeders. We also show that combining two distinct methods for analysis of large genomic data sets provides substantially better prediction of genetic value of inbred lines for this trait.
Technical Abstract: Resistance to Fusarium ear rot of maize is a highly quantitative and complex trait. Marker-trait associations to date have had small additive effects and were inconsistent between previous studies, likely due to the combined effects of genetic heterogeneity and low power of detection of many small effect variants. The complexity of inheritance of resistance hinders the use marker-assisted selection for ear rot resistance. Genomic selection (GS) offers an alternative approach that is optimal for predicting values of traits with highly polygenic additive architecture. However, GS methods (even those that model variable marker effects) are not geared to identify and estimate effects of specific variants, or to select specific favorable variants with larger effects that are in repulsion phase disequilibrium with desired genomic backgrounds. We conducted a genome-wide association study (GWAS) for Fusarium ear rot resistance in a panel of 1689 diverse inbred lines from the USDA maize gene bank with 200,978 SNPs while controlling for background genetic relationships with a mixed model and identified seven SNPs associated with disease resistance. The three most significantly associated SNPs were then included as linear fixed effects in a genomic best linear unbiased prediction (G-BLUP) model and compared to a traditional G-BLUP model using cross-validation to see if the inclusion of GWAS associations along with a GS polygenic background model can improve predictive ability across a diverse panel with low (21%) heritability. Although the three SNPs had small effects, their inclusion increased G-BLUP prediction accuracies from 11% to 15%.