Presentation Title

Determining Fusarium Head Blight Resistances in Bread Wheat Using Genome-Wide Association Studies

Format of Presentation

Poster to be presented the Friday of the conference

Abstract

Wheat is a staple crop and an integral part of the world economy; however, these crops are frequently threatened by FHB, a fungal disease. Genome-Wide Association Studies (GWAS) is a powerful computational approach to identify the causal relationship between genetic single nucleotide polymorphisms (SNPs) within a species and the phenotypic differences between individuals of the same species. In this study, we apply different GWAS models to identify the linkage between wheat SNPs and Fusarium Head Blight (FHB) resistance and to identify the best GWAS models.

The Genome Association and Prediction Integration Tool is used in this study. Associations are predicted with three statistical models, general linear model, mixed linear effects model (MLM), and the multiple locus mixed effects model (MLMM), taking into account the covariance between individuals by feeding in a k-matrix. Shell scripts were then developed to obtain the most common and significant SNPs for each phenotype with multi-year data and across the statistical tests for each phenotype.

Some of the resultant QQ-Plots do not show that the model fits the data well; however, this is due to the fact that the 90k SNP array only detects SNPs in "preselected" genomic regions that have a higher likelihood to be associated with traits of interest without including any irrelevant positions. Overall, the MLM and the MLMM fit the data best.

In the future, we will incorporate phenotype data that is being evaluated from greenhouses for the current season and will use other GWAS programs in order to verify and compare significant SNPs found.

Department

Mathematics and Statistics

Faculty Advisor

Lingling Jin

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Determining Fusarium Head Blight Resistances in Bread Wheat Using Genome-Wide Association Studies

Wheat is a staple crop and an integral part of the world economy; however, these crops are frequently threatened by FHB, a fungal disease. Genome-Wide Association Studies (GWAS) is a powerful computational approach to identify the causal relationship between genetic single nucleotide polymorphisms (SNPs) within a species and the phenotypic differences between individuals of the same species. In this study, we apply different GWAS models to identify the linkage between wheat SNPs and Fusarium Head Blight (FHB) resistance and to identify the best GWAS models.

The Genome Association and Prediction Integration Tool is used in this study. Associations are predicted with three statistical models, general linear model, mixed linear effects model (MLM), and the multiple locus mixed effects model (MLMM), taking into account the covariance between individuals by feeding in a k-matrix. Shell scripts were then developed to obtain the most common and significant SNPs for each phenotype with multi-year data and across the statistical tests for each phenotype.

Some of the resultant QQ-Plots do not show that the model fits the data well; however, this is due to the fact that the 90k SNP array only detects SNPs in "preselected" genomic regions that have a higher likelihood to be associated with traits of interest without including any irrelevant positions. Overall, the MLM and the MLMM fit the data best.

In the future, we will incorporate phenotype data that is being evaluated from greenhouses for the current season and will use other GWAS programs in order to verify and compare significant SNPs found.