A multi‐trait multi‐locus stepwise approach for conducting GWAS on correlated traits. Issue 2 (20th March 2022)

• Record Type: Journal Article
• Authors: Fernandes, Samuel B.; Casstevens, Terry M.; Bradbury, Peter J.; Lipka, Alexander E.
• Is Part Of: œplant genome. Volume 15:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-20
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20200
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract: The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high‐throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype‐to‐phenotype relationships have focused on either multi‐trait models that test a single marker at a time or multi‐locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi‐trait, multi‐locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi‐trait single‐locus model and (b) a univariate multi‐locus model. We used real marker data in maize ( Zea mays L.) and soybean ( Glycine max L.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi‐trait models outperformed the univariate multi‐locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol‐related traits in maize grain, MSTEP identified the same peak‐associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits. Core Ideas: Multi‐trait multi‐locus GWAS is useful for traits controlled by both pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). Adding a correlated trait of high‐heritability in GWAS helps to detect QTN for a low‐heritability trait The optimum GWAS model varies with different trait genetic architectures.