Combined Genomic and Genetic Data Integration of Major Agronomical Traits in Bread Wheat (Triticum aestivum L.)

Abstract

The high resolution integration of bread wheat genetic and genomic resources accumulated during the last decades offers the opportunity to unveil candidate genes driving major agronomical traits to an unprecedented scale. We combined 27 public quantitative genetic studies and four genetic maps to deliver an exhaustive consensus map consisting of 140,315 molecular markers hosting 221, 73, and 82 Quantitative Trait Loci (QTL) for respectively yield, baking quality, and grain protein content (GPC) related traits. Projection of the consensus genetic map and associated QTLs onto the wheat syntenome made of 99,386 genes ordered on the 21 chromosomes delivered a complete and non-redundant repertoire of 18, 8, 6 metaQTLs for respectively yield, baking quality and GPC, altogether associated to 15,772 genes (delivering 28,630 SNP-based makers) including 37 major candidates. Overall, this study illustrates a translational research approach in transferring information gained from grass relatives to dissect the genomic regions hosting major loci governing key agronomical traits in bread wheat, their flanking markers and associated candidate genes to be now considered as a key resource for breeding programs.

Uncovering the Genetic Architecture of Fruiting Efficiency in Bread Wheat: A Viable Alternative to Increase Yield Potential

“…A map showing the genomic regions significantly associated with yield-related traits was created with MapChart software version 2.3 (Voorrips, 2002). Finally, a comparison between the chromosomic location of SNP markers significantly associated with phenotypic traits in this study and those previously reported was performed using the released genome sequence of hexaploid wheat (IWGSC et al, 2018) and the integrated map of Quraishi et al (2017) for markers whose sequence was not available.…”

“…To compare the chromosome location of significant SNP markers found in this study with those previously reported, we used the annotated reference wheat genome and the integrated map reported by Quraishi et al (2017), which includes simple sequence repeats (SSRs), restriction fragment length polymorphisms (RFLPs), diversity arrays technology (DArT), genes, and SNP markers. Wheat grain yield is determined not only by the genes directly controlling yield and yield components, but also by the genes controlling plant development and maturity.…”

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat

“…However, despite the importance of genetic dissection of this complex trait, only a few QTL studies have been reported in wheat using bi-parental mapping populations (Guo et al, 2012;Kong et al, 2013;Zhao et al, 2014;Gong et al, 2015). Genetic maps used in these various studies can be integrated at one place to identify consensus genomic region called Meta-QTL (MQTL), independent of population type and genotype/ environment interaction (Quraishi et al, 2017). This approach of identifying MQTL by meta-analysis was first proposed by Goffinet and Gerber (2000), and has since been applied in many crops including wheat (Griffiths et al, 2009;Gegas et al, 2010;Quraishi et al, 2017).…”

Grain Protein Content QTLs Identified in a Durum × Wild Emmer Wheat Mapping Population Tested in Five Environments

“…GPC QTLs were reported previously for each chromosome of tetraploid and hexaploid wheat (Quraishi et al 2017). Similarly, we detected GPC QTLs in 10 out of 14 chromosomes of tetraploid wheat.…”

“…Further analysis, using high resolution SNP-based map, expanded the number of loci with contribution of WEW alleles from 8 to 14 (Fatiukha et al 2019b). The identification of common or meta-QTLs between independent populations can help to improve QTL interval confidence, as demonstrated by Quraishi et al (2017), and can be used for confirmation of detected effects in different genetic backgrounds. Nevertheless, the identification of co-located QTLs among the large number of publications is restricted by the absence of consensus positions for many of the published markers.…”