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 - Merrick, Lance F.; Burke, Adrienne B.; Zhang, Zhiwu; Carter, Arron H. (Frontiers in Plant Science, 2022)
Unknown genetic architecture makes it difficult to characterize the genetic basis of traits and associated molecular markers due to the complexity of small effect QTLs, environmental effects, and difficulty in phenotyping. Seedling emergence of wheat (Triticum aestivum L.) from deep planting, has a poorly understood genetic architecture, is a vital factor affecting stand establishment and grain yield, and is historically correlated with coleoptile length. 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). The ST-GWAS models included one single-locus model (MLM), and two multi-locus models (FarmCPU and BLINK). We conducted GWAS using two populations. The first population consisted of 473 varieties from a diverse association mapping panel phenotyped from 2015-2019. The second population consisted of 279 breeding lines 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. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. FarmCPU and BLINK models, including covariates, were able to identify many small effect markers while identifying the large effect markers on chromosome 5A. By using multi-locus models 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.