Supplementary File for Capturing wheat phenotypes at the genome level

Supplementary S1: Yield and related traits in bread wheat. Table S1: Examples of genomic regions, candidate and cloned genes for yield and related traits in bread wheat. Supplementary S2: Drought tolerance. Table S2: Examples of genomic regions and candidate genes for drought tolerance. Supplementary S3: Heat tolerance. Table S3. Examples of genomic regions and candidate genes for heat tolerance. Supplementary S4: salinity tolerance in bread wheat. Table S4. Examples of genomic regions and candidate genes for salinity tolerance in bread wheat. Supplementary S5: Frost tolerance. Supplementary S6: Disease resistance. Table S5. Examples of genomic regions, candidate and cloned genes mapped for disease resistance in wheat species. Supplementary S7 insect and mite resistance. Table S6. Examples of genomic regions and candidate genes mapped for insect and mite resistance. Supplementary S8: Quality traits. Table S7. Examples of genomic regions, candidate and cloned genes for quality traits.Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.Peer reviewe

Assembly and Annotation of Transcriptome Provided Evidence of miRNA Mobility between Wheat and Wheat Stem Sawfly

Wheat Stem Sawfly (WSS), Cephus Cinctus Norton (Hymenoptera: Cephidae), is one of the most important pests, causing yield and economic losses in wheat and barley. The lack of information about molecular mechanisms of WSS for defeating plant’s resistance prevents application of effective pest control strategies therefore, it is essential to identify the genes and their regulators behind WSS infestations. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are recognized with their regulatory functions on gene expression, tuning protein production by controlling transcriptional and post-transcriptional activities. A transcriptome-guided approach was followed in order to identify miRNAs, lncRNAs, and mRNA of WSS, and their interaction networks. A total of 1,893 were presented here as differentially expressed between larva and adult WSS insects. There were 11 miRNA families detected in WSS transcriptome. Together with the annotation of 1,251 novel mRNAs, the amount of genetic information available for WSS was expanded. The network between WSS miRNAs, lncRNAs, and mRNAs suggested miRNA-mediated regulatory roles of lncRNAs as competing endogenous RNAs. In the light of the previous evidence that small RNA molecules of a pathogen could suppress the immune response of host plant, we analyzed the putative interactions between larvae and wheat at the miRNA level. Overall, this study provides a profile of larva and adult WSS life stages in terms of coding and non-coding elements. These findings also emphasize the potential roles of wheat and larval miRNAs in wheat resistance to infestation and in the suppression of resistance which is critical for the development of effective pest control strategies