A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits

Area of Science:

• Quantitative genetics
• Genomic prediction
• Agricultural biotechnology

Background:

• High-throughput genotyping and phenotyping enable simultaneous analysis of multiple genomic loci and traits.
• Current genotype-to-phenotype models often focus on either single markers across multiple traits or multiple loci for a single trait.

Purpose of the Study:

• To compare the performance of a novel multi-trait, multi-locus stepwise (MSTEP) model.
• To evaluate MSTEP against a common multi-trait single-locus model and a univariate multi-locus model.

Main Methods:

• Simulated multiple traits using real marker data from maize (Zea mays L.) and soybean (Glycine max L.).
• Investigated traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs).
• Assessed model performance under different genetic architectures, including traits with low heritability and numerous QTNs.

Main Results:

• Both multi-trait models generally outperformed the univariate multi-locus model, particularly for low heritability traits.
• The MSTEP model frequently surpassed at least one alternative model when traits involved complex QTN combinations or a large number of QTNs (e.g., 50).
• MSTEP successfully identified a known peak-associated marker for tocochromanol traits in maize grain.

Conclusions:

• The MSTEP model is a valuable statistical tool for understanding the genetic architecture of complex traits.
• This approach enhances the analysis of genotype-to-phenotype relationships in crop improvement.
• MSTEP offers improved insights compared to existing single-trait or single-locus methods.