Genome based meta-QTL analysis of grain weight in tetraploid wheat identifies rare alleles ofGRF4associated with larger grains

Abstract

Wheat domestication and subsequent improvement formed a wide phenotypic variation in Grain Weight (GW) between the domesticated wheat species and their wild progenitors. GW continues to be an important goal of many wheat-breeding programs and yet, although studies found many quantitative trait loci (QTLs) for GW, not many genes that underlay these loci were identified. Here we performed QTL analysis for GW using a Recombinant Inbred Line (RIL) population based of a cross between wild emmer wheat accession 'Zavitan' and durum wheat variety 'Svevo'. Using the recent Zavitan genome assembly, we anchored the identified QTLs to the reference sequence and added the positions of previously published QTLs for GW in tetraploid wheat. This genome based meta-QTL analysis enabled us to identify a locus on chromosome 6A with a positive effect on GW that was contributed by wild wheat in a few studies. This locus was validated using an introgression line that contains the 6A GW QTL from Zavitan in the background of Svevo with higher grain weight. Using the reference sequence and genes associated with GW from rice, we were able to identify a wheat ortholog in the 6A QTL region to rice gene, OsGRF4. The coding sequence of this gene, TtGRF4-A, showed four SNPs between Zavitan and Svevo. Molecular marker developed for the first SNP showed that the Zavitan allele of TtGRF4-A is rare in a core collection of wild emmer and absent in domesticated emmer genepool. We suggest that TtGRF4-A is a candidate underlying the 6A GW QTL and breeding with its natural Zavitan allele may have the potential to increase wheat yields. . Faris, J. D.; Zhang, Q.; Chao, S.; Zhang, Z.; Xu, S. S. Analysis of agronomic and domestication traits in a durum × cultivated emmer wheat population using a high-density single nucleotide

Molecular Mapping and Genomics of Grain Yield in Durum Wheat: A Review

“…Thus, the genetic basis of GY has been investigated through the three main yield components which will be explored in this review: kernel weight, kernels number and kernels number per unit area [ 28 , 31 ]. A total of 665 QTLs (100 with a pleiotropic effect) were identified and associated with grain yield and its components ( Table S1 ) [ 7 , 14 , 15 , 17 , 18 , 30 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. Specifically, nine additional yield components were considered: kernel length (KL), kernel width (KW), thousand-kernel weight (TKW) associated with the kernel weight, kernels number per spike (KNS), kernels number per spikelet (KNSL), spikelets number per spike (SLNS) associated with the kernels number, grain yield (GY), kernels number per square meter (KNM) and spike number per square meter (SNM) related to the kernels number per unit area ( Figure 1 ).…”

“…For example, the QTL for TKW detected on chromosome 2A was stable in five different environments and explained between 1.4% and 12.9% of the phenotypic variation in each environment [ 39 ]. In addition, the moderate QTL located at 43 cM on chromosome 1B and linked to the “IWB20542” marker had an effect of 20.9% of the phenotypic variance for TKW in an RIL population (Zavitan × Svevo) [ 40 ]. However, in the same region of chromosome 1B, minor QTLs were identified in independent populations, whose effects were of 2.6% [ 41 ] and 3.4–6.2% [ 39 ] of the TKW variance.…”

Identification of Candidate Genes Controlling Fiber Quality Traits in Upland Cotton Through Integration of Meta-QTL, Significant SNP and Transcriptomic Data

“…3; Table S7). In the biological process category, protein modi cation process (30, 15%), cellular protein modi cation process (30, 15%), protein metabolic process (46, 23%), macromolecule modi cation (30, 15%), macromolecule metabolic process (69, 34.5%), cellular protein metabolic process (37, 18.5%), cellular macromolecule metabolic process (60, 30%), and proteolysis (11,5.5%) were the major subcategories (Fig. 3, Table S7).…”

“…In this regard, meta-analysis of QTL has been proven as an e cient approach, and has been constantly improved (Go net and Gerber 2000; Salvi and Tuberosa 2015), which aims to verify whether QTL in isolated studies actually represent a common position or not on the genetic map of the studied species, and stablish the occurrence of QTL "hotspots" in a consensus map. This approach was already applied to various crops and complex traits, such as fusarium head blight resistance in bread wheat (Venske et al 2019), grain weight in tetraploid wheat (Avni et al 2018), cist nematode resistance in soybean (Guo et al 2006a), resistance to white mold in common bean (Vasconcellos et al 2017), yield under drought in rice (Swamy et al 2011), yield in maize (Martinez et al, 2016), and multiple traits in cotton (Said et al 2013). (Zhang et al 2019a).…”

Allele mining of TaGRF-2D gene 5’-UTR in Triticum aestivum and Aegilops tauschii genotypes

“…), associated with 1000 grain weight, was colocalized with the OsGRF4 homolog gene on 6A chromosome. The GRF4-Az allele was identified, the presence of which is associated with an increased 1000 grain weight in durum wheat compared with other alleles of this gene [18]. Haplotypes that increase the nitrogen uptake in both rice and durum wheat are very rare in the germplasm collections of these crops.…”