Comparison of Single-Trait and Multi-Trait Genome-Wide Association Models and Inclusion of Correlated Traits in the Dissection of the Genetic Architecture of a Complex Trait in a Breeding Program

Abstract Traits with an unknown genetic architecture make it difficult to create a useful bi-parental mapping population to characterize the genetic basis of the trait due to a combination of complex and pleiotropic effects. Seedling emergence of wheat ( Triticum aestivum L.) from deep planting is a vital factor affecting stand establishment and grain yield, has a poorly understood genetic architecture, and is historically correlated with coleoptile length. The creation of bi-parental mapping populations can be overcome by using genome-wide association studies (GWAS). This study aimed to dissect the genetic architecture of seedling emergence while accounting for correlated traits using one multi-trait GWAS model (MT-GWAS) and three single-trait GWAS models (ST-GWAS) with the inclusion of covariates for correlated traits. The ST-GWAS models included one single locus model (MLM), and two multiple loci models (FarmCPU and BLINK). We conducted the GWAS using two populations, the first consisting of 473 varieties from a diverse association mapping panel (DP) phenotyped from 2015-2019, and the other population used as a validation population consisting of 279 breeding lines (BL) phenotyped in 2015 in Lind, WA, with 40,368 markers. We also compared the inclusion of coleoptile length and markers associated with reduced height as covariates in our ST-GWAS models for the DP. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. MT-GWAS models were able to identify large-effect markers on chromosome 5A. FarmCPU and BLINK models were able to identify many small effect markers, and the inclusion of covariates helped to identify the large effect markers on chromosome 5A. Therefore, by using multi-locus models combined with pleiotropic covariates, breeding programs can uncover the complex nature of traits to help identify candidate genes and the underlying architecture of a trait, such as seedling emergence of deep-sown winter wheat.