Abstract
Unknown genetic architecture makes it difficult to characterize the genetic basis of traits and associated molecular markers because of the complexity of small effect quantitative trait loci (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 genome-wide association study (MT-GWAS) model and three single-trait GWAS (ST-GWAS) models. The ST-GWAS models included one single-locus model [mixed-linear model (MLM)] and two multi-locus models [fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK)]. We conducted GWAS using two populations. The first population consisted of 473 varieties from a diverse association mapping panel phenotyped from 2015 to 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. The FarmCPU and BLINK models, including covariates, were able to identify many small effect markers while identifying large effect markers on chromosome 5A. By using multi-locus model 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.
Highlights
Complex traits are controlled by many quantitative trait loci (QTLs) and are influenced by environmental conditions (Bernardo, 2020)
This study presents research to assess the genetic architecture of a complex trait by (1) comparing single-trait genome-wide association studies (GWASs) (ST-GWAS) and multi-trait genome-wide association study (MT-GWAS) models for correlated traits and (2) assessing the inclusion of fixed effects to improve the power to explore the genetic architecture of seedling emergence
Heritability for seedling emergence was moderately high in the breeding lines (BLs) and a few years in the diverse association mapping panel (DP), whereas the heritability decreased in combined years
Summary
Complex traits are controlled by many quantitative trait loci (QTLs) and are influenced by environmental conditions (Bernardo, 2020). Linkage mapping for complex traits can often result in inconsistent estimated QTL effects (Bernardo, 2020; Tibbs Cortes et al, 2021). Many of these complex traits are essential for selection in plant breeding programs, typically being associated with yield potential, end-use quality, and certain biotic and abiotic stress types of tolerance. Are complex traits influenced by the environment and multiple QTLs, but they interact with correlated traits that result in a complex genetic architecture. Using population structure or genetic relatedness controls p-value inflation for each marker and reduces false positives (Tibbs Cortes et al, 2021)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
