A comprehensive meta-QTL analysis for yield-related traits of durum wheat (Triticum turgidum L. var. durum) grown under different water regimes

Abstract

Abiotic stress strongly affects yield-related traits in durum wheat, in particular drought is one of the main environmental factors that have effect on grain yield and plant architecture. In order to obtain new genotypes well adapted to stress conditions, the highest number of desirable traits needs to be combined in the same genotype. In this context, hundreds of quantitative trait loci (QTL) have been identified for yield-related traits in different genetic backgrounds and environments. Meta-QTL (MQTL) analysis is a useful approach to combine data sets and for creating consensus positions for the QTL detected in independent studies for the reliability of their location and effects. MQTL analysis is a useful method to dissect the genetic architecture of complex traits, which provide an extensive allelic coverage, a higher mapping resolution and allow the identification of putative molecular markers useful for marker-assisted selection (MAS). In the present study, a complete and comprehensive MQTL analysis was carried out to identify genomic regions associated with grain-yield related traits in durum wheat under different water regimes. A total of 724 QTL on all 14 chromosomes (genomes A and B) were collected for the 19 yield-related traits selected, of which 468 were reported under rainfed conditions, and 256 under irrigated conditions. Out of the 590 QTL projected on the consensus map, 421 were grouped into 76 MQTL associated with yield components under both irrigated and rainfed conditions, 12 genomic regions containing stable MQTL on all chromosomes except 1A, 4A, 5A, and 6B. Candidate genes associated to MQTL were identified and an in-silico expression analysis was carried out for 15 genes selected among those that were differentially expressed under drought. These results can be used to increase durum wheat grain yields under different water regimes and to obtain new genotypes adapted to climate change.

Discovering Loci for Breeding Prospective and Phenology in Wheat Mediterranean Landraces by Environmental and eigenGWAS

“…Climatic data are highly correlated with the adaptation of the crop. As it is shown by different authors [ 22 , 29 ], PCoA using molecular data followed the climatic classification reported by these authors when genotypes were grouped by their country of origin. However, the high level of admixture and genetic exchange in the Mediterranean landraces according to Rufo et al [ 12 ] revealed an unclear pattern when comparing PCoA using structure or climatic data.…”

“…The dissection of genetic architecture for phenology in Mediterranean wheat landraces revealed the presence of 26 QTL hotspots (57%) with significant associations. According to the position of functional genes described by Liu et al [ 29 ], major genes affecting photoperiod sensitivity and vernalization were in QTL hotspots 2A.1 ( Ppd-A1 ) and 5A.3 ( Vrn-A1 ), respectively. However, no MTAs were found in the homologous regions corresponding to the Ppd and Vrn genes on chromosomes 2B and 2D, and 5B and 5D, respectively.…”

Meta-QTL Analysis for Yield Components in Common Bean (Phaseolus vulgaris L.)

“…The main approach to augmenting crop yield is to increase the number and the weight of grains [ 64 ]. In this sense, the traits that have been studied to increase seed yield in common bean are pods per plant, seeds per plant, seed weight, and pod harvest index [ 65 ].…”

“…The best model of MQTLs was chosen according to Akaike Information Criterion (AIC), corrected Akaike Information Criterion (AICc and AIC3), Bayesian Information Criterion (BIC), and Average Weight of Evidence (AWE) criteria. The number of MQTLs present on each chromosome was determined when values of the model selection criteria were the lowest in at least three of the five models [ 64 ]. Given that MQTL analysis is a method to combine QTL results from different independent analyses, a MQTL with a confidence interval of 95% was reported when it contained QTL from at least two different studies [ 28 ].…”

Whole-exome sequencing of selected bread wheat recombinant inbred lines as a useful resource for allele mining and bulked segregant analysis

“…Polyploidy, in fact, on the one hand confers a high degree of plasticity to the organism, on the other hand it makes genetic analysis more difficult for scientists and breeders aimed at dissecting the molecular basis underlying quantitative and qualitative traits ( Dubcovsky et al, 2007 ; Bevan et al, 2017 ). So far, most efforts to understand the genetic basis of key traits related to yield, grain quality and adaptability have been made through map-based cloning approaches (summarized by Colasuonno et al, 2021 ; Soriano et al, 2021 ; Arrigada et al, 2021 ). For example, genes involved in vernalization and photoperiod response ( Yan et al, 2003 , 2004 ; Díaz et al, 2012 ), grain protein content ( Uauy et al, 2006 ), grain quality ( Jin et al, 2018 ; Goel et al, 2019 ; Semagn et al, 2021 ) stem solidness ( Nilsen et al, 2020 ), male sterility ( Ni et al, 2017 ; Xia et al, 2017 ), and resistance to fungal diseases ( Fu et al, 2009 ) have been identified and cloned.…”