ABSTRACT
Phenomic characterization through digital imaging (DI) can capture the three dimensional variation in wheat grain size and shape using different image orientations. Digital imaging may help identifying genomic regions controlling grain morphology using association mapping with simple sequence repeats (SSRs) markers. Accordingly, seed shape phenotypic data of a core collection of 55 wheat genotypes, previously characterized for osmotic and drought tolerance, were produced using computer based Smart grain software. Measured dimensions included seed volume, area, perimeters, length, width, length to width ratio, circularity, horizontal deviation from ellipse (HDEV), vertical deviation from ellipse (VDEV), factor form density (FFD) etc. The thousand grain weight (TGW) was positively correlated with grain size direct measurements; however, VDEV, FFD and other derived grain attributes showed no or negative correlation with TGW. Digital imaging divided the genotypes correctly into well-defined clusters. The wheat genotypes studied were further grouped into two sub-clusters by the Bayesian structure analysis using unlinked SSR markers. A number of loci over various chromosomal regions were found associated to grain morphology by the genome wide analysis using mixed linear model (MLM) approach. A considerable number of marker-trait associations (MTAs) on chromosomes 1D and 2D may carry new alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Conclusively, we demonstrated the application of multiple approaches including high throughput phenotyping using DI complemented with genome wide association studies to identify candidate genomic regions underlying these traits, which allows a better understanding on molecular genetics of wheat grain weight.(AU)
Subject(s)
Triticum/genetics , Triticum/growth & development , GenomicsABSTRACT
Phenomic characterization through digital imaging (DI) can capture the three dimensional variation in wheat grain size and shape using different image orientations. Digital imaging may help identifying genomic regions controlling grain morphology using association mapping with simple sequence repeats (SSRs) markers. Accordingly, seed shape phenotypic data of a core collection of 55 wheat genotypes, previously characterized for osmotic and drought tolerance, were produced using computer based Smart grain software. Measured dimensions included seed volume, area, perimeters, length, width, length to width ratio, circularity, horizontal deviation from ellipse (HDEV), vertical deviation from ellipse (VDEV), factor form density (FFD) etc. The thousand grain weight (TGW) was positively correlated with grain size direct measurements; however, VDEV, FFD and other derived grain attributes showed no or negative correlation with TGW. Digital imaging divided the genotypes correctly into well-defined clusters. The wheat genotypes studied were further grouped into two sub-clusters by the Bayesian structure analysis using unlinked SSR markers. A number of loci over various chromosomal regions were found associated to grain morphology by the genome wide analysis using mixed linear model (MLM) approach. A considerable number of marker-trait associations (MTAs) on chromosomes 1D and 2D may carry new alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Conclusively, we demonstrated the application of multiple approaches including high throughput phenotyping using DI complemented with genome wide association studies to identify candidate genomic regions underlying these traits, which allows a better understanding on molecular genetics of wheat grain weight.