Mapping quantitative trait loci (QTLs) associated with dough quality in a soft×hard bread wheat progeny

Genome-Wide Association Mapping of Processing Quality Traits in Common Wheat (Triticum aestivum L.)

Processing quality is an important economic wheat trait. The marker-assisted selection (MAS) method plays a vital role in accelerating genetic improvement of processing quality. In the present study, processing quality in a panel of 165 cultivars grown in four environments was evaluated by mixograph. An association mapping analysis using 90 K and 660 K single nucleotide polymorphism (SNP) arrays identified 24 loci in chromosomes 1A, 1B (4), 1D, 2A, 2B (2), 3A, 3B, 3D (2), 4A (3), 4B, 5D (2), 6A, 7B (2) and 7D (2), explaining 10.2-42.5% of the phenotypic variances. Totally, 15 loci were stably detected in two or more environments. Nine loci coincided with known genes or QTL, whereas the other fifteen were novel loci. Seven candidate genes encoded 3-ketoacyl-CoA synthase, lipoxygenase, pyridoxal phosphate-dependent decarboxylase, sucrose synthase 3 and a plant lipid transfer protein/Par allergen. SNPs significantly associated with processing quality and accessions with more favorable alleles can be used for marker-assisted selection.

Identification and Validation of Stable Quantitative Trait Loci for SDS-Sedimentation Volume in Common Wheat (Triticum aestivum L.)

Sodium dodecyl sulfate-sedimentation volume is an important index to evaluate the gluten strength of common wheat and is closely related to baking quality. In this study, a total of 15 quantitative trait locus (QTL) for sodium dodecyl sulfate (SDS)-sedimentation volume (SSV) were identified by using a high-density genetic map including 2,474 single-nucleotide polymorphism (SNP) markers, which was constructed with a doubled haploid (DH) population derived from the cross between Non-gda3753 (ND3753) and Liangxing99 (LX99). Importantly, four environmentally stable QTLs were detected on chromosomes 1A, 2D, and 5D, respectively. Among them, the one with the largest effect was identified on chromosome 1A (designated as QSsv.cau-1A.1) explaining up to 39.67% of the phenotypic variance. Subsequently, QSsv.cau-1A.1 was dissected into two QTLs named as QSsv.cau-1A.1.1 and QSsv.cau-1A.1.2 by saturating the genetic linkage map of the chromosome 1A. Interestedly, favorable alleles of these two loci were from different parents. Due to the favorable allele of QSsv.cau-1A.1.1 was from the high-value parents ND3753 and revealed higher genetic effect, which explained 25.07% of the phenotypic variation, mapping of this locus was conducted by using BC3F1 and BC3F2 populations. By comparing the CS reference sequence, the physical interval of QSsv.cau-1A.1.1 was delimited into 14.9 Mb, with 89 putative high-confidence annotated genes. SSVs of different recombinants between QSsv.cau-1A.1.1 and QSsv.cau-1A.1 detected from DH and BC3F2 populations showed that these two loci had an obvious additive effect, of which the combination of two favorable loci had the high SSV, whereas recombinants with unfavorable loci had the lowest. These results provide further insight into the genetic basis of SSV and QSsv.cau-1A.1.1 will be an ideal target for positional cloning and wheat breeding programs.

Quantitative Trait Locus (QTLs) Mapping for Quality Traits of Wheat Based on High Density Genetic Map Combined With Bulked Segregant Analysis RNA-seq (BSR-Seq) Indicates That the Basic 7S Globulin Gene Is Related to Falling Number

Numerous quantitative trait loci (QTLs) have been identified for wheat quality; however, most are confined to low-density genetic maps. In this study, based on specific-locus amplified fragment sequencing (SLAF-seq), a high-density genetic map was constructed with 193 recombinant inbred lines derived from Chuanmai 42 and Chuanmai 39. In total, 30 QTLs with phenotypic variance explained (PVE) up to 47.99% were identified for falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting properties across three environments. Five NAM genes closely adjacent to QGPC.cib-4A probably have effects on GPC. QGH.cib-5D was the only one detected for GH with high PVE of 33.31-47.99% across the three environments and was assumed to be related to the nearest pina-D1 and pinb-D1genes. Three QTLs were identified for FN in at least two environments, of which QFN.cib-3D had relatively higher PVE of 16.58-25.74%. The positive effect of QFN.cib-3D for high FN was verified in a double-haploid population derived from Chuanmai 42 × Kechengmai 4. The combination of these QTLs has a considerable effect on increasing FN. The transcript levels of Basic 7S globulin and Basic 7S globulin 2 in QFN.cib-3D were significantly different between low FN and high FN bulks, as observed through bulk segregant RNA-seq (BSR). These QTLs and candidate genes based on the high-density genetic map would be beneficial for further understanding of the genetic mechanism of quality traits and molecular breeding of wheat.

Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F₆ winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype–environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

Genetic and transcriptional variations in NRAMP-2 and OPAQUE1 genes are associated with salt stress response in wheat

SNP alleles on chromosomes 4BL and 6AL are associated with sensitivity to salt tolerance in wheat and upon validation can be exploited in the development of salt-tolerant wheat varieties. The dissection of the genetic and molecular components of salt stress response offers strong opportunities toward understanding and improving salt tolerance in crops. In this study, GWAS was employed to identify a total of 106 SNP loci (R² = 0.12-63.44%) linked to salt stress response in wheat using leaf chlorophyll fluorescence, grain quality and shoot ionic (Na⁺ and K⁺ ions) attributes. Among them, 14 SNP loci individually conferred pleiotropic effects on multiple independent salinity tolerance traits including loci at 99.04 cM (R² ≥ 14.7%) and 68.45 cM (R² ≥ 4.10%) on chromosomes 6AL and 4BL, respectively, that influenced shoot Na⁺-uptake, shoot K⁺/Na⁺ ratio, and specific energy fluxes for absorption (ABS/RC) and dissipation (DIo/RC). Analysis of the open reading frame (ORF) containing the SNP markers revealed that they are orthologous to genes involved in photosynthesis and plant stress (salt) response. Further transcript abundance and qRT-PCR analyses indicated that the genes are mostly up-regulated in salt-tolerant and down-regulated in salt-sensitive wheat genotypes including NRAMP-2 and OPAQUE1 genes on 4BL and 6AL, respectively. Both genes showed highest differential expression between contrasting genotypes when expressions of all the genes within their genetic intervals were analyzed. Possible cis-acting regulatory elements and coding sequence variation that may be involved in salt stress response were also identified in both genes. This study identified genetic and molecular components of salt stress response that are associated with Na⁺-uptake, shoot Na⁺/K⁺ ratio, ABS/RC, DIo/RC, and grain quality traits and upon functional validation would facilitate the development of gene-specific markers that could be deployed to improve salinity tolerance in wheat.

Interactions between Glu-1 and Glu-3 loci and associations of selected molecular markers with quality traits in winter wheat (Triticum aestivum L.) DH lines

The quality of wheat depends on a large complex of genes and environmental factors. The objective of this study was to identify quantitative trait loci controlling technological quality traits and their stability across environments, and to assess the impact of interaction between alleles at loci Glu-1 and Glu-3 on grain quality. DH lines were evaluated in field experiments over a period of 4 years, and genotyped using simple sequence repeat markers. Lines were analysed for grain yield (GY), thousand grain weight (TGW), protein content (PC), starch content (SC), wet gluten content (WG), Zeleny sedimentation value (ZS), alveograph parameter W (APW), hectolitre weight (HW), and grain hardness (GH). A number of QTLs for these traits were identified in all chromosome groups. The Glu-D1 locus influenced TGW, PC, SC, WG, ZS, APW, GH, while locus Glu-B1 affected only PC, ZS, and WG. Most important marker-trait associations were found on chromosomes 1D and 5D. Significant effects of interaction between Glu-1 and Glu-3 loci on technological properties were recorded, and in all types of this interaction positive effects of Glu-D1 locus on grain quality were observed, whereas effects of Glu-B1 locus depended on alleles at Glu-3 loci. Effects of Glu-A3 and Glu-D3 loci per se were not significant, while their interaction with alleles present at other loci encoding HMW and LMW were important. These results indicate that selection of wheat genotypes with predicted good bread-making properties should be based on the allelic composition both in Glu-1 and Glu-3 loci, and confirm the predominant effect of Glu-D1d allele on technological properties of wheat grains.

Genome-Wide Association Mapping of Yield and Grain Quality Traits in Winter Wheat Genotypes

The main goal of this study was to investigate the genetic basis of yield and grain quality traits in winter wheat genotypes using association mapping approach, and identify linked molecular markers for marker assisted selection. A total of 120 elite facultative/winter wheat genotypes were evaluated for yield, quality and other agronomic traits under rain-fed and irrigated conditions for two years (2011–2012) at the Tel Hadya station of ICARDA, Syria. The same genotypes were genotyped using 3,051 Diversity Array Technologies (DArT) markers, of which 1,586 were of known chromosome positions. The grain yield performance of the genotypes was highly significant both in rain-fed and irrigated sites. Average yield of the genotypes ranged from 2295 to 4038 kg/ha and 4268 to 7102 kg/ha under rain-fed and irrigated conditions, respectively. Protein content and alveograph strength (W) ranged from 13.6–16.1% and 217.6–375 Jx10-4, respectively. DArT markers wPt731910 (3B), wPt4680 (4A), wPt3509 (5A), wPt8183 (6B), and wPt0298 (2D) were significantly associated with yield under rain-fed conditions. Under irrigated condition, tPt4125 on chromosome 2B was significantly associated with yield explaining about 13% of the variation. Markers wPt2607 and wPt1482 on 5B were highly associated with protein content and alveograph strength explaining 16 and 14% of the variations, respectively. The elite genotypes have been distributed to many countries using ICARDA’s International system for potential direct release and/or use as parents after local adaptation trials by the NARSs of respective countries. The QTLs identified in this study are recommended to be used for marker assisted selection after through validation using bi-parental populations.

Genetic dissection reveals effects of interaction between high-molecular-weight glutenin subunits and waxy alleles on dough-mixing properties in common wheat

The glutenin and waxy loci of wheat are important determinants of dough quality. This study was conducted to evaluate the effects of high-molecular-weight glutenin (HMW-GS) and waxy alleles on dough-mixing properties. Molecular mapping was used to investigate these effects on Mixograph properties in a population of 290 (Nuomai1 x Gaocheng8901) recombinant inbred lines (RILs) from three environments in the harvest years 2008, 2009 and 2011. The results indicated the following: (i) the Glu-A1 and Glu-D1 loci have greater impacts on Mixograph properties compared to the Wx-1 loci and the effects of Glu-D1d and Glu-D1h on dough mixing are better than those of Glu-D1f and Glu-D1new1 in this population; (ii) the interactions between the Glu-1 and Wx-1 loci affected some traits, especially the midline peak value (MPV), and the lack of Wx-B1 or Wx-D1 led to increased MPV for all types of Glu-1 loci; and (iii) 30 quantitative-trait loci (QTL) over nine wheat chromosomes were identified with ICIM analysis based on the genetic map of 498 loci. Eight major QTL and 16 QTL in the Glu-1 loci from the three environments were found. The major QTL clusters were associated with the Glu-1 loci, and also were found in two regions on chromosome 3B and one region on chromosome 6A, which is one of the novel chromosome regions influencing dough-mixing strength. The two QTL for MPV are located around Wx-B1 on chromosome 4A. QMPT-1D.1, QMPI-1D.1 and Q8MW-1D.1 were stable in different environments and could potentially be used in molecular marker-assisted breeding.

Mapping of main and epistatic effect QTLs associated to grain protein and gluten strength using a RIL population of durum wheat

Quality, specifically protein content and gluten strength are among the main objectives of a durum wheat breeding program. The aim of this work was to validate quantitative trait loci (QTLs) associated with grain protein content (GPC) and gluten strength measured by SDS sedimentation volume (SV) and to find additional QTLs expressed in Argentinean environments. Also, epistatic QTL and QTL x environmental interactions were analyzed. A mapping population of 93 RILs derived from the cross UC1113 x Kofa showing extreme values in gluten quality was used. Phenotypic data were collected along six environments (three locations, two years). Main effect QTLs associated with GPC were found in equivalent positions in two environments on chromosomes 3BS (R(2)=21.0-21.6%) and 7BL (R(2)=12.1-13%), and in one environment on chromosomes 1BS, 2AL, 2BS, 3BL, 4AL, 5AS, 5BL and 7AS. The most important and stable QTL affecting SV was located on chromosome 1BL (Glu-B1) consistently detected over the six environments (R(2)=20.9- 54.2%). Additional QTLs were found in three environments on chromosomes 6AL (R(2)=6.4-12.5%), and in two environments on chromosomes 6BL (R(2)=11.5-12.1%), 7AS (R(2)=8.2-10.2%) and 4BS (R(2)=11-16.4%). In addition, pleiotropic effects were found affecting grain yield, test weight, thousand-kernel-weight and days to heading in some of these QTLs. Epistatic QTLs and QTL x environment interactions were found for both quality traits, mostly for GPC. The flanking markers of the QTLs detected in this work could be efficient tools to select superior genotypes for the mentioned traits.