Unravelling consensus genomic regions conferring leaf rust resistance in wheat via meta-QTL analysis

Abstract

Leaf rust, caused by the fungus Puccinia triticina Erikss (Pt), is a destructive disease affecting wheat and a threat to food security. Developing resistant varieties 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 (QTLs) was performed, and 393 QTLs were collected from 50 QTL mapping studies. Afterwards, a consensus map with a total length of 4567 cM consisting of different types of markers (SSR, DArT, Chip-based SNP markers and SNP markers from GBS) was used for QTL projection, and meta-QTL analysis was performed on 320 QTLs. A total of 75 genetic map positions (gmQTLs) were discovered and refined to 15 high confidence mQTLs (hcmQTLs). 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 varieties by incorporating the most important hcmQTLs.

Meta-QTLs, ortho-MQTLs and candidate genes for thermotolerance in wheat (Triticum aestivum L.)

“…Overall, around 90% of the total MQTLs were reliable in the present study since they were veri ed using GWAS results. These ndings are in sharp contrast to recent studies from Aduragbemi and Soriano (2021) and Yang et al (2021), where only 38.66% and 62.68% MQTLs, respectively were veri ed with MTAs obtained from GWA studies. This might be due to the availability of a comparatively large number of MTAs for comparison in the present study.…”

“…In the recent past, MQTL analysis in wheat has been carried out successfully to detect robust and stable genomic regions for a variety of traits, including the following: (i) grain yield and associated traits (Gegas et al 2010;Bilgrami et al 2020;Saini et al 2021;Yang et al 2021a), (ii) root morphology traits (Darzi-Ramandi et al 2017;Soriano and Alvaro 2019), (iii) potassium use e ciency (Safdar et al 2020), (iv) nutritional and quality traits (Quraishi et al 2011b;Quraishi et al 2017;Shariatipour et al 2021), (v) fusarium head blight resistance (Liu et al 2009;Mao et al 2010;Cai et al 2019;Venske et al 2019;Zheng et al 2020), (vi) leaf rust resistance (Soriano and Royo 2015;Aduragbemi and Soriano 2021), (vii) yellow rust resistance (Jan et al 2021), (viii) pre-harvest sprouting tolerance (Tyagi and Gupta 2012;Tai et al 2021), and (ix) drought tolerance . Two of the studies involving meta-QTL analysis for tolerance against heat stress (Acun˜a-Galindo et al 2015;Liu et al 2020); however gave limited information.…”

Meta-QTLs and candidate genes for stripe rust resistance in wheat

“…This probably indicated either a tight linkage of genes for different traits, or occurrence of pleiotropic genes. This may also be attributed to a bias due to the use of related traits for identification of stripe rust resistance as also reported earlier in case of MQTL analysis for leaf rust resistance in wheat 35 , 36 .…”

“…Perhaps this was due to relatively fewer QTL studies (19) available for meta-analysis in case of leaf rust resistance, so that in a recent report, the number of MQTLs for leaf rust resistance has risen to 75 MQTLs. In this recent study MQTLs are described as genetic map positions or gmQTLs (based on genetic map), since these were named on the basis of genetic map positions that were later converted into sequence mapped meta-QTLs or smQTL 36 . This new terminology for naming meta-QTLs as gmQTL and smQTL may be more widely used in future.…”

“…Meta-QTL (MQTL) analysis involving known QTLs for resistance against stripe rust in wheat has never been conducted, although it has been conducted for a number of other traits. These other traits earlier used for MQTL analysis included the following: tolerance to abiotic stresses such as drought and heat 33 , 34 , resistance to a number of diseases including leaf rust 35 , 36 , tan spot 37 , Fusarium head blight 38 and powdery mildew 39 . The objective of the present study was to conduct MQTL analysis for resistance against stripe rust in wheat, followed by identification of candidate genes (CGs) for the MQTLs thus identified.…”