Deciphering genetic basis of developmental and agronomic traits by integrating high‐throughput optical phenotyping and genome‐wide association studies in wheat

Abstract

SummaryDissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield‐related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non‐invasive and high‐throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield‐related traits was further explored. Whole genome re‐sequencing of the panel provided 12.64 million markers for a high‐resolution genome‐wide association analysis using 190 image‐based traits and 17 agronomic traits. A total of 8327 marker‐trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield‐related traits are largely predictable using models developed based on i‐traits and provide possibility for high‐throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield‐related traits by combining high‐throughput phenotyping and genotyping, which further unravelled the complex and stage‐specific contributions of genetic loci to optimize growth and yield in wheat.

Genome-wide association study for seedling heat tolerance under two temperature conditions in bread wheat (Triticum aestivum L.)

“…From 253 re-sequenced wheat accessions, 13,116,299 high-quality SNP markers were filtered for subsequent analysis [ 38 , 39 ]. Out of that, 5,415,520 SNPs were detected on the A sub-genome, 7,121,654 SNPs on the B sub-genome, and 579,125 SNPs on the D sub-genome.…”

“…The genome sequencing data for the 253 wheat accessions have been deposited in the public database of the China National Genebank ( https://db.cngb.org/cnsa ) under the accession number CNP0004251 [ 38 , 39 ].…”

“…A threshold score of 5.5 (-lg ( P -value)) was determined to declare significant loci, considering the potential risk of type II error and the GWAS results in this study. The genomic interval 3-Mb upstream and downstream of the significant SNPs were examined as potential QTLs, and overlapping QTLs or QTLs close to each other (distance < 2 Mb) were considered as the same QTLs [ 38 – 41 ]. QTLs with spanned region larger than 50 Mb were excluded [ 38 ].…”

“…The genomic interval 3-Mb upstream and downstream of the significant SNPs were examined as potential QTLs, and overlapping QTLs or QTLs close to each other (distance < 2 Mb) were considered as the same QTLs [ 38 – 41 ]. QTLs with spanned region larger than 50 Mb were excluded [ 38 ]. The SNP with the minimum P -value was identified as the peak SNP of the QTL based on the P -value of the associated SNP [ 100 ].…”

Integrated VIS/NIR Spectrum and Genome-Wide Association Study for Genetic Dissection of Cellulose Crystallinity in Wheat Stems

“…Recently, genome-wide association studies (GWASs) have become a powerful tool for assessing complex traits [ 10 , 11 ]. Although GWASs face problems in explaining environmental effects, missing heritability, detecting false positives of significant loci, expanding the population size, increasing the number of markers, increasing the threshold, and collecting accurate phenotypes in different environments could make GWAS results more reliable.…”