Genome-wide association for grain morphology in synthetic hexaploid wheats using digital imaging analysis

Abstract

BackgroundGrain size and shape greatly influence grain weight which ultimately enhances grain yield in wheat. Digital imaging (DI) based phenomic characterization can capture the three dimensional variation in grain size and shape than has hitherto been possible. In this study, we report the results from using digital imaging of grain size and shape to understand the relationship among different components of this trait, their contribution to enhance grain weight, and to identify genomic regions (QTLs) controlling grain morphology using genome wide association mapping with high density diversity array technology (DArT) and allele-specific markers.ResultsSignificant positive correlations were observed between grain weight and grain size measurements such as grain length (r = 0.43), width, thickness (r = 0.64) and factor from density (FFD) (r = 0.69). A total of 231 synthetic hexaploid wheats (SHWs) were grouped into five different sub-clusters by Bayesian structure analysis using unlinked DArT markers. Linkage disequilibrium (LD) decay was observed among DArT loci > 10 cM distance and approximately 28% marker pairs were in significant LD. In total, 197 loci over 60 chromosomal regions and 79 loci over 31 chromosomal regions were associated with grain morphology by genome wide analysis using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. They were mainly distributed on homoeologous group 2, 3, 6 and 7 chromosomes. Twenty eight marker-trait associations (MTAs) on the D genome chromosomes 2D, 3D and 6D may carry novel alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Statistical simulations showed that favorable alleles for thousand kernel weight (TKW), grain length, width and thickness have additive genetic effects. Allelic variations for known genes controlling grain size and weight, viz. TaCwi-2A, TaSus-2B, TaCKX6-3D and TaGw2-6A, were also associated with TKW, grain width and thickness. In silico functional analysis predicted a range of biological functions for 32 DArT loci and receptor like kinase, known to affect plant development, appeared to be common protein family encoded by several loci responsible for grain size and shape.ConclusionConclusively, we demonstrated the application and integration of multiple approaches including high throughput phenotyping using DI, genome wide association studies (GWAS) and in silico functional analysis of candidate loci to analyze target traits, and identify candidate genomic regions underlying these traits. These approaches provided great opportunity to understand the breeding value of SHWs for improving grain weight and enhanced our deep understanding on molecular genetics of grain weight in wheat.

Association between Grain Size and Shape and Quality Traits, and Path Analysis of Thousand Grain Weight in Iranian Bread Wheat Landraces from Different Geographic Regions

“…In accordance with the results of Rasheed et al (2014), other important derived measurements like volume and perimeter on the contrary FFD were also positively correlated with length and width. So, with the contemporary increasing of grain length and width, volume and perimeter can creased.…”

“…Only few studies are available based on digital image analysis of grain size and shape in bread wheat (Williams et al, 2013;Gegas et al, 2010;Williams and Sorrells, 2014;Breseghello and Sorrells, 2007;Xiao et al, 2011;Rasheed et al, 2014;Zhang et al, 2015). Among these studies, Gegas et al, 2010;Williams et al (2013), Williams and Sorrells (2014) and Rasheed et al (2014), used shape variations as targeted traits influencing grain size and weight and results are comparable to our work. However, the most of them just used some grain morphology traits and didn't use any quality traits.…”

“…According to strong associations among these traits, it is possible with increasing of one of them, the other traits creased. Rasheed et al (2014) and Russo et al (2014), also reported similar correlations for mentioned traits in bread and durum wheat. respectively.…”

“…While, grain shape is generally estimated by length, width, vertical perimeter, sphericity and horizontal axes proportion (Breseghello and Sorrells, 2007). Many studies have been shown that wheat grain size and shape is positively correlated with TGW and they have affected flour yield, end-use quality and market price (Evers et al, 1990;Breseghello and Sorrells, 2006;Tsilo et al, 2010;Cui et al, 2011;Blanco et al, 2012;Williams et al, 2013;Rasheed et al, 2014). Theoretical models predict that milling yield could be increased by optimizing grain size and shape with large and spherical grains being the optimum grain morphology (Evers et al, 1990).…”

“…(Houle et al, 2010;Patil et al, 2013). More recently, a high-throughput method was used to capture grain size and shape variations in multiple mapping populations, elite varieties, and in a broad collection of ancestral wheat species (Gegas et al, 2010;Williams et al, 2013;Okamoto et al, 2013;Williams and Sorrells, 2014;Rasheed et al, 2014). The majority of these studies have been performed for only some grain morphology traits without quality traits.…”