Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Abstract

Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.Agronomy 2020, 10, 368 2 of 15 the strength of linkage disequilibrium (i.e., the non-random association of alleles at multiple loci) between markers and functional polymorphism across diverse germplasm [10]. In wheat, GWAS has been conducted in different traits such as grain yield, yield components (thousand kernel weight and kernel number), Fusarium head blight resistance, snow mold tolerance, plant height, and heading date [11][12][13][14][15][16]. Compared with biparental mapping, GWAS results in a higher mapping resolution as it exploits the majority of the recombination histories of the individuals belonging to the diverse population used [17,18]. The main drawback of GWAS, nonetheless, is that it might not able to capture the causative and rare variants (loci) with small effects, and therefore would be a special case of missing heritability, i.e., the portion of genetic variance that cannot be explained by all significant loci [19]. In this case, genomic selection (GS) is seen as a complementary approach to GWAS.GS uses genomewide markers to estimate the marker effects and calculate genomic estimated breeding values (GEBV), which represents the genetic "worth" or merit of an individual. A high GEBV for grain yield, for example, would mean that a line is predicted to have better grain yield compared with others. In contrast to GWAS, GS does not test for significance but rather uses genomewide marker data to calculate the GEBV. More genetic variation could be captured in GS compared with marker-assisted selection as all markers are used in estimating breeding values [20]. In GS, a prediction model ...

Genomic Selection in Winter Wheat Breeding Using a Recommender Approach

“…Selecting stable lines with minimal G x E effects to ensure better correlation of phenotypes across years would therefore be crucial in the success of an IBCF recommender system for predicting future performance of lines in breeding programs. Recently, analyses of G x E interactions for this population of US Pacific Northwest winter wheat lines was conducted and stable lines based on AMMI and Finlay-Wilkinson regression coefficients were identified [20]. Furthermore, genetic mapping for yield and yield stability identified loci controlling both sets of traits demonstrating the potential of simultaneously selecting stable and higher-yielding lines [20].…”

“…An association mapping panel consisting of 456 winter wheat lines adapted to the Pacific Northwest region of the US was evaluated for different grain yield, agronomic, spectral reflectance, and disease resistance traits between the 2015 and 2019 growing seasons across various locations in the state of Washington, US, namely, Lind (LND), Pullman (PUL), Waterville (WAT), and Mansfield (MAN), as described previously [ 17 , 18 , 19 ]. Grain yield (GY), plant height (PH), and heading date (HD) data collection and analyses for an augmented design were previously described in Lozada and Carter [ 20 ]. Briefly, grain yield (in t ha −1 ) was collected by harvesting whole plots using a Zurn 150 Plot combine (Waldenburg, Germany), whereas plant height (in cm) was reported as the measurement from the ground to the tip of the spike, excluding the awn when present.…”

Comparing a Mixed Model Approach to Traditional Stability Estimators for Mapping Genotype by Environment Interactions and Yield Stability in Soybean [Glycine max (L.) Merr.]

“…GxE interactions often heavily influence the per environment rankings of quantitative phenotypes such as grain yield, complicating the breeders’ task of developing a stable variety. As a result, the modeling of phenotypic stability and identification of the involved genes has been the focus of many recent scientific studies ( Bouchet et al, 2016 ; Xavier et al, 2018 ; Lozada and Carter, 2020 ). QTL affecting stability have been discovered using either a direct approach to modeling GxE interactions, or first calculating a yield stability “value” from the phenotypic data to then be used a phenotype in the GWAS.…”

“…An increased knowledge of the genetic basis of GxE interactions opens avenues for breeders to manipulate stability through exploiting or minimizing the response to environmental aspects. Several stability measures have recently been used as phenotypes in genome wide association studies (GWAS) to identify novel genomic loci associated with GxE interactions ( Bouchet et al, 2016 ; Xavier et al, 2018 ; Lozada and Carter, 2020 ). Explicit mapping of GxE as a marker by environment effect has also been explored, but less considered in stability analyses due to the logistical and computational demands needed to apply the methodology appropriately ( Piepho and Pillen, 2004 ; van Eeuwijk et al, 2010 ; Malosetti et al, 2013 ).…”

Breeding with Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

“…The accuracy was reduced when the DP predicted the BL, but eventually increased as more BL lines were introduced into the training population. The decrease in validation sets can also be attributed to GEI (Michel et al, 2016; Huang et al, 2018; Lozada and Carter, 2019, 2020; Haile et al, 2020).…”