Pathogens hitting the plant cell wall is the first impetus that triggers the phenylpropanoid pathway for plant defense. The phenylpropanoid pathway bifurcates into the production of an enormous array of compounds based on the few intermediates of the shikimate pathway in response to cell wall breaches by pathogens. The whole metabolomic pathway is a complex network regulated by multiple gene families and it exhibits refined regulatory mechanisms at the transcriptional, post-transcriptional, and post-translational levels. The pathway genes are involved in the production of anti-microbial compounds as well as signaling molecules. The engineering in the metabolic pathway has led to a new plant defense system of which various mechanisms have been proposed including salicylic acid and antimicrobial mediated compounds. In recent years, some key players like phenylalanine ammonia lyases (PALs) from the phenylpropanoid pathway are proposed to have broad spectrum disease resistance (BSR) without yield penalties. Now we have more evidence than ever, yet little understanding about the pathway-based genes that orchestrate rapid, coordinated induction of phenylpropanoid defenses in response to microbial attack. It is not astonishing that mutants of pathway regulator genes can show conflicting results. Therefore, precise engineering of the pathway is an interesting strategy to aim at profitably tailored plants. Here, this review portrays the current progress and challenges for phenylpropanoid pathway-based resistance from the current prospective to provide a deeper understanding.

Improving yield and yield-related traits are key goals in wheat breeding program. The integration of accumulated wheat genetic resources provides an opportunity to uncover important genomic regions and candidate genes that affect wheat yield. Here, a comprehensive Meta-QTL analysis was conducted on 2230 QTLs of yield-related traits obtained from 119 QTL studies. These QTLs were re ned into 145 Meta-QTLs (MQTLs), and 89 MQTLs were veri ed by GWAS with different natural populations. The average con dence interval (CI) of these MQTLs was 2.92 times less than that of the initial QTLs. Furthermore, 76 core MQTL regions with a physical distance less than 25 Mb were detected. Based on the homology analysis and expression patterns, 237 candidate genes in the MQTLs involved in photoperiod response, grain development, multiple plant growth regulator pathways, carbon and nitrogen metabolism, and spike and ower organ development were determined. A novel candidate gene TaKAO-4A was con rmed to be signi cantly associated with grain size, and a CAPS marker was developed based on its dominant haplotype. In summary, this study clari ed a method based on the integration of Meta-QTL, GWAS and homology comparison to reveal the genomic regions and candidate genes that affect important yield-related traits in wheat. This work will help to lay a foundation for the identi cation, transfer and aggregation of these important QTLs or candidate genes in wheat high-yield breeding. Key MessageBased on the large-scale integration of Meta-QTL and Genome-Wide Association Study, 76 high-con dence MQTL regions and 237 candidate genes that affected wheat yield and yield-related traits were discovered.

Leaf rust, caused by the fungus Puccinia triticina Erikss (Pt), is a destructive disease affecting wheat (Triticum aestivum L.) and a threat to food security. Developing resistant cultivars represents a useful method of disease control, and thus, understanding the genetic basis for leaf rust resistance is required. To this end, a comprehensive bibliographic search for leaf rust resistance quantitative trait loci (QTL) was performed, and 393 QTL were collected from 50 QTL mapping studies. Afterward, a consensus map with a total length of 4,567 cM consisting of different types of markers (simple sequence repeat [SSR], diversity arrays technology [DArT], chip‐based single‐nucleotide polymorphism [SNP] markers, and SNP markers from genotyping‐by‐sequencing) was used for QTL projection, and meta‐QTL (MQTL) analysis was performed on 320 QTL. A total of 75 MQTL were discovered and refined to 15 high‐confidence MQTL (hcmQTL). The candidate genes discovered within the hcmQTL interval were then checked for differential expression using data from three transcriptome studies, resulting in 92 differentially expressed genes (DEGs). The expression of these genes in various leaf tissues during wheat development was explored. This study provides insight into leaf rust resistance in wheat and thereby provides an avenue for developing resistant cultivars by incorporating the most important hcmQTL.

Improving yield and yield-related traits are key goals in wheat breeding program. The integration of accumulated wheat genetic resources provides an opportunity to uncover important genomic regions and candidate genes that affect wheat yield. Here, a comprehensive Meta-QTL analysis was conducted on 2230 QTLs of yield-related traits obtained from 119 QTL studies. These QTLs were refined into 145 Meta-QTLs (MQTLs), and 89 MQTLs were verified by GWAS with different natural populations. The average confidence interval (CI) of these MQTLs was 2.92 times less than that of the initial QTLs. Furthermore, 76 core MQTL regions with a physical distance less than 25 Mb were detected. Based on the homology analysis and expression patterns, 237 candidate genes in the MQTLs involved in photoperiod response, grain development, multiple plant growth regulator pathways, carbon and nitrogen metabolism, and spike and flower organ development were determined. A novel candidate gene TaKAO-4A was confirmed to be significantly associated with grain size, and a CAPS marker was developed based on its dominant haplotype. In summary, this study clarified a method based on the integration of Meta-QTL, GWAS and homology comparison to reveal the genomic regions and candidate genes that affect important yield-related traits in wheat. This work will help to lay a foundation for the identification, transfer and aggregation of these important QTLs or candidate genes in wheat high-yield breeding.