Screening of CIMMYT and South Asian Bread Wheat Germplasm Reveals Marker-Trait Associations for Seedling Resistance to Septoria Nodorum Blotch.

Wheat (Triticum aestivum L.) production is adversely impacted by Septoria nodorum blotch (SNB), a fungal disease caused by Parastagonospora nodorum. Wheat breeders are constantly up against this biotic challenge as they try to create resistant cultivars. The genome-wide association study (GWAS) has become an efficient tool for identifying molecular markers linked with SNB resistance. This technique is used to acquire an understanding of the genetic basis of resistance and to facilitate marker-assisted selection. In the current study, a total of 174 bread wheat accessions from South Asia and CIMMYT were assessed for SNB reactions at the seedling stage in three greenhouse experiments at CIMMYT, Mexico. The results indicated that 129 genotypes were resistant to SNB, 39 were moderately resistant, and only 6 were moderately susceptible. The Genotyping Illumina Infinium 15K Bead Chip was used, and 11,184 SNP markers were utilized to identify marker-trait associations (MTAs) after filtering. Multiple tests confirmed the existence of significant MTAs on chromosomes 5B, 5A, and 3D, and the ones at Tsn1 on 5B were the most stable and conferred the highest phenotypic variation. The resistant genotypes identified in this study could be cultivated in South Asian countries as a preventative measure against the spread of SNB. This work also identified molecular markers of SNB resistance that could be used in future wheat breeding projects.

Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight

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)

Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight

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.