Genome-wide association and validation of key loci for yield-related traits in wheat founder parent Xiaoyan 6

Xiaoyan 6, one of the most important founder parents in wheat, possesses many superior agronomic traits and has played a crucial role in Chinese wheat breeding programs. In this study, a panel of 66 elite wheat accessions derived from Xiaoyan 6 was planted in four growing seasons; genome-wide association study (GWAS) was performed for six yield-related traits using the wheat 90K genotyping assay. A total of 803 significant marker-trait associations (MTAs) that explained up to 35.0% of the phenotypic variation were detected. Of these, the locus QTkw-5B which contains 19 MTAs for thousand kernel weight (TKW) was consistently detected in three growing seasons and confirmed in a recombinant inbred line (RIL) population by developing simple sequence repeats (SSR) and kompetitive allele-specific PCR (KASP) markers. The locus QPh-3A containing eight repetitive MTAs for plant height (PH) was consistently identified in all the four growing seasons and validated in a RIL population by developing SSR markers. The transmission of Xiaoyan 6 allele indicated that the favorite allele of QPh-3A was strongly selected in breeding programs. Comparing with previous studies, QTkw-5B and QPh-3A should be novel QTL. The locus QFss-2D for fertile spikelet number per spike (FSS) was identified and then validated in three bi-parental populations. This locus controlled various spike-related traits and may be a key spike polymorphic locus. This study could provide insight into dissecting yield-related traits in the breeding population and reliable molecular markers that might be valuable for marker-assisted selection in wheat high-yield breeding programs.     

Single nucleotide polymorphism tightly linked to a major QTL on chromosome 7A for both kernel length and kernel weight in wheat

Thousand-kernel weight (TKW) is one of the major components of grain yield in wheat (Triticum aestivum). Identifying major quantitative trait loci (QTLs) for TKW and developing effective markers are prerequisite for success in marker-assisted selection (MAS) to improve wheat yield through breeding. This study mapped a major QTL, designated as TaTKW-7AL, for increasing TKW on the long arm of chromosome 7A of ‘Clark’ to a 1.3-cM interval between single nucleotide polymorphism (SNP) markers IWB13913 and IWA5913. This QTL explained 19.7 % of the phenotypic variation for TKW. A QTL for increasing kernel length (KL), one of the major components of TKW, was mapped in the same interval as TaTKW-7AL, suggesting that increased TKW by the QTL in ‘Clark’ is most likely due to the increased KL. Association analysis on a diversity panel of 200 US winter wheat accessions also identified a haplotype of three SNP markers (IWB13913, IWB6693 and IWA5913) that were tightly associated with the both KL and TKW. The analysis of allele frequencies of the haplotype in the diversity panel suggested that the favorable allele of TaTKW-7AL has not been strongly selected for in practice and has potential to be used to improve grain yield in US hard winter wheat breeding. Two user-friendly flanking KASPar markers, IWB13913 and IWA5913, were developed for MAS of TaTKW-7AL.     

Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations

Key message

A novel high-density consensus wheat genetic map was obtained based on three related RIL populations, and the important chromosomal regions affecting yield and related traits were specified.

Abstract

A prerequisite for mapping quantitative trait locus (QTL) is to build a genetic linkage map. In this study, three recombinant inbred line populations (represented by WL, WY, and WJ) sharing one common parental line were used for map construction and subsequently for QTL detection of yield-related traits. PCR-based and diversity arrays technology markers were screened in the three populations. The integrated genetic map contains 1,127 marker loci, which span 2,976.75 cM for the whole genome, 985.93 cM for the A genome, 922.16 cM for the B genome, and 1,068.65 cM for the D genome. Phenotypic values were evaluated in four environments for populations WY and WJ, but three environments for population WL. Individual and combined phenotypic values across environments were used for QTL detection. A total of 165 putative additive QTL were identified, 22 of which showed significant additive-by-environment interaction effects. A total of 65 QTL (51.5 %) were stable across environments, and 23 of these (35.4 %) were common stable QTL that were identified in at least two populations. Notably, QTkw-5B.1, QTkw-6A.2, and QTkw-7B.1 were common major stable QTL in at least two populations, exhibiting 11.28–16.06, 5.64–18.69, and 6.76–21.16 % of the phenotypic variance, respectively. Genetic relationships between kernel dimensions and kernel weight and between yield components and yield were evaluated. Moreover, QTL or regions that commonly interact across genetic backgrounds were discussed by comparing the results of the present study with those of previous similar studies. The present study provides useful information for marker-assisted selection in breeding wheat varieties with high yield.     

Comparative genome-wide mapping versus extreme pool-genotyping and development of diagnostic SNP markers linked to QTL for adult plant resistance to stripe rust in common wheat

Key message

A major stripe rust resistance QTL on chromosome 4BL was localized to a 4.5-Mb interval using comparative QTL mapping methods and validated in 276 wheat genotypes by haplotype analysis.

Abstract

CYMMIT-derived wheat line P10103 was previously identified to have adult plant resistance (APR) to stripe rust in the greenhouse and field. The conventional approach for QTL mapping in common wheat is laborious. Here, we performed QTL detection of APR using a combination of genome-wide scanning and extreme pool-genotyping. SNP-based genetic maps were constructed using the Wheat55 K SNP array to genotype a recombinant inbred line (RIL) population derived from the cross Mingxian 169?×?P10103. Five stable QTL were detected across multiple environments. After comparing SNP profiles from contrasting, extreme DNA pools of RILs six putative QTL were located to approximate chromosome positions. A major QTL on chromosome 4B was identified in F_2:4 contrasting pools from cross Zhengmai 9023?×?P10103. A consensus QTL (LOD?=?26–40, PVE?=?42–55%), named QYr.nwafu-4BL, was defined and localized to a 4.5-Mb interval flanked by SNP markers AX-110963704 and AX-110519862 in chromosome arm 4BL. Based on stripe rust response, marker genotypes, pedigree analysis and mapping data, QYr.nwafu-4BL is likely to be a new APR QTL. The applicability of the SNP-based markers flanking QYr.nwafu-4BL was validated on a diversity panel of 276 wheat lines. The additional minor QTL on chromosomes 4A, 5A, 5B and 6A enhanced the level of resistance conferred by QYr.nwafu-4BL. Marker-assisted pyramiding of QYr.nwafu-4BL and other favorable minor QTL in new wheat cultivars should improve the level of APR to stripe rust.

     

Identification of QTL for flag leaf length in common wheat and their pleiotropic effects

Leaf size is an important factor contributing to the photosynthetic capability of wheat plants. It also significantly affects various agronomic traits. In particular, the flag leaves contribute significantly to grain yield in wheat. A recombinant inbred line (RIL) population developed between varieties with significant differences in flag leaf traits was used to map quantitative trait loci (QTL) of flag leaf length (FLL) and to evaluate its pleiotropic effects on five yield-related traits, including spike length (SL), spikelet number per spike (SPN), kernel number per spike (KN), kernel length (KL), and thousand-kernel weight (TKW). Two additional RIL populations were used to validate the detected QTL and reveal the relationships in different genetic backgrounds. Using the diversity arrays technology (DArT) genetic linkage map, three major QTL for FLL were detected, with single QTL in different environments explaining 8.6–23.3% of the phenotypic variation. All the QTL were detected in at least four environments, and validated in two related populations based on the designed primers. These QTL and the newly developed primers are expected to be valuable for fine mapping and marker-assisted selection in wheat breeding programs.     

Identification of QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2-236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.     

Identification and validation of QTL and their associated genes for pre-emergent metribuzin tolerance in hexaploid wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Background

Herbicide tolerance is an important trait that allows effective weed management in wheat crops. Genetic knowledge of metribuzin tolerance in wheat is needed to develop new cultivars for the industry. Here, we evaluated metribuzin tolerance in a recombinant inbred line (RIL) mapping population derived from Synthetic W7984 and Opata 85 over two consecutive years to identify quantitative trait loci (QTL) contributing to the trait. Herbicide tolerance was measured by two chlorophyll traits, SPAD chlorophyll content index (CCI) and visual senescence score (SNS). The markers associated with major QTL from Synthetic W7984, positively contributing to reduced phytotoxic effects under herbicide treatment were validated in two F_3/4 recombinant inbred populations developed from crosses of Synthetic W7984?×?Westonia and Synthetic W7984?×?Lang.

Results

Composite interval mapping (CIM) identified four QTL, two on chromosome 4A and one each on chromosomes 2D and 1A. The chromosomal position of the two QTL mapped on 4A within 10 cM intervals was refined and validated by multiple interval mapping (MIM). The major QTL affecting both measures of tolerance jointly explained 42 and 45% of the phenotypic variation by percentage CCI reduction and SNS, respectively. The identified QTL have a pure additive effect. The metribuzin tolerant allele of markers, Xgwm33 and Xbarc343, conferred lower phytotoxicity and explained the maximum phenotypic variation of 28.8 and 24.5%, respectively. The approximate physical localization of the QTL revealed the presence of five candidate genes (ribulose-bisphosphate carboxylase, oxidoreductase (rbcS), glycosyltransferase, serine/threonine-specific protein kinase and phosphotransferase) with a direct role in photosynthesis and/or metabolic detoxification pathways.

Conclusion

Metribuzin causes photo-inhibition by interrupting electron flow in PSII. Consequently, chlorophyll traits enabled the measure of high proportion of genetic variability in the mapping population. The validated molecular markers associated with metribuzin tolerance mediating QTL may be used in marker-assisted breeding to select metribuzin tolerant lines. Alternatively, validated favourable alleles could be introgressed into elite wheat cultivars to enhance metribuzin tolerance and improve grain yield in dryland farming for sustainable wheat production.

     

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015–2016 and 2016–2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents (‘SeriM82’ and ‘Babax’). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL?×?environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL?×?environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.     

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P _N, and C _i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.     

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F(8:9) recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.     

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.     

Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments

Key message

We identified 27 stable loci associated with agronomic traits in spring wheat using genome-wide association analysis, some of which confirmed previously reported studies. GWAS peaks identified in regions where no QTL for grain yield per se has been mapped to date, provide new opportunities for gene discovery and creation of new cultivars with desirable alleles for improving yield and yield stability in wheat.

Abstract

We undertook large-scale genetic analysis to determine marker-trait associations (MTAs) underlying agronomic and physiological performance in spring wheat using genome-wide association studies (GWAS). Field trials were conducted at seven sites in three countries (Sudan, Egypt, and Syria) over 2–3 years in each country. Twenty-five agronomic and physiological traits were measured on 188 wheat genotypes. After correcting for population structure and relatedness, a total of 245 MTAs distributed over 66 loci were associated with agronomic traits in individual and mean performance across environments respectively; some of which confirmed previously reported loci. Of these, 27 loci were significantly associated with days to heading, thousand kernel weight, grain yield, spike length, and leaf rolling for mean performance across environments. Despite strong QTL by environment interactions, eight of the loci on chromosomes 1A, 1D, 5A, 5D, 6B, 7A, and 7B had pleiotropic effects on days to heading and yield components (TKW, SM^?2, and SNS). The winter-type alleles at the homoeologous VRN1 loci significantly increased days to heading and grain yield in optimal environments, but decreased grain yield in heat prone environments. Top 20 high-yielding genotypes, ranked by additive main effects and multiplicative interaction (AMMI), had low kinship relationship and possessed 4–5 favorable alleles for GY MTAs except two genotypes, Shadi-4 and Qafzah-11/Bashiq-1–2. This indicated different yield stability mechanisms due to potentially favorable rare alleles that are uncharacterized. Our results will enable wheat breeders to effectively introgress several desirable alleles into locally adapted germplasm in developing wheat varieties with high yield stability and enhanced heat tolerance.

     

Identification of plant architecture and yield-related QTL in <Emphasis Type="Italic">Vicia faba</Emphasis> L.

Low and unstable yields across seasons and environments are among the main reasons which make profitability for farmers too low. Along with resistance/tolerance to biotic and abiotic stresses, plant architecture and yield-related traits are the main determinants of yield stability. The current study was conducted to identify and validate quantitative trait loci (QTL) for plant architecture and yield-related traits in faba bean; our results provide novel information about the genetics of plant architecture traits in this crop. An equina × paucijuga recombinant inbred line population was derived and submitted to field experiments at Córdoba (Spain) over a period of four seasons. Stable QTL were identified for eight of the traits evaluated. QTL clusters were identified on almost each chromosome. The high inter-trait correlations between some of the traits controlled by a cluster of QTL might reflect either a set of closely linked loci or, more likely, pleiotropic effects. The stability of many of these major QTL in different years offers the possibility of exploiting them via marker-assisted selection. Further fine mapping of these target regions will help to identify potential candidate genes using synteny.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

QTL mapping of pre-harvest sprouting resistance in a white wheat cultivar Danby

Key message

One major and three minor QTLs for resistance to pre-harvest sprouting (PHS) were identified from a white wheat variety “Danby.” The major QTL on chromosome 3A is TaPHS1, and the sequence variation in its promoter region was responsible for the PHS resistance. Additive?×?additive effects were detected between two minor QTLs on chromosomes 3B and 5A, which can greatly enhance the PHS resistance.

Abstract

Pre-harvest sprouting (PHS) causes significant losses in yield and quality in wheat. White wheat is usually more susceptible to PHS than red wheat. Therefore, the use of none grain color-related PHS resistance quantitative trait loci (QTLs) is essential for the improvement in PHS resistance in white wheat. To identify PHS resistance QTLs in the white wheat cultivar “Danby” and determine their effects, a doubled haploid population derived from a cross of Danby?×?“Tiger” was genotyped using genotyping-by-sequencing markers and phenotyped for PHS resistance in two greenhouse and one field experiments. One major QTL corresponding to a previously cloned gene, TaPHS1, was consistently detected on the chromosome arm 3AS in all three experiments and explained 21.6–41.0% of the phenotypic variations. A SNP (SNP?222) in the promoter of TaPHS1 co-segregated with PHS in this mapping population and was also significantly associated with PHS in an association panel. Gene sequence comparison and gene expression analysis further confirmed that SNP?222 is most likely the causal mutation in TaPHS1 for PHS resistance in Danby in this study. In addition, two stable minor QTLs on chromosome arms 3BS and 5AL were detected in two experiments with allele effects consistently contributed by Danby, while one minor QTL on 2AS was detected in two environments with contradicted allelic effects. The two stable minor QTLs showed significant additive?×?additive effects. The results demonstrated that pyramiding those three QTLs using breeder-friendly KASP markers developed in this study could greatly improve PHS resistance in white wheat.

     

QTL mapping identifies a major locus for resistance in wheat to Sunn pest (<Emphasis Type="Italic">Eurygaster</Emphasis><Emphasis Type="Italic">integriceps</Emphasis>) feeding at the vegetative growth stage

Key message

This research provides the first report of a major locus controlling wheat resistance to Sunn pest. It developed and validated SNP markers that will be useful for marker-assisted selection.

Abstract

Sunn pest (Eurygaster integriceps Puton) is the most destructive insect pest of bread wheat and durum wheat in West and Central Asia and East Europe. Breeding for resistance at the vegetative stage of growth is vital in reducing the damage caused by overwintered adult populations that feed on shoot and leaves of seedlings, and in reducing the next generation of pest populations (nymphs and adults), which can cause damage to grain quality by feeding on spikes. In the present study, two doubled haploid (DH) populations involving resistant landraces from Afghanistan were genotyped with the 90k SNP iSelect assay and candidate gene-based KASP markers. The DH lines and parents were phenotyped for resistance to Sunn pest feeding, using artificial infestation cages at Terbol station, in Lebanon, over three years. Quantitative trait locus (QTL) analysis identified a single major locus on chromosome 4BS in the two populations, with the resistance allele derived from the landrace accessions, IG139431 and IG139883. The QTL explained a maximum of 42 % of the phenotypic variation in the Cham6 × IG139431 and 56 % in the Cham6 × IG139883 populations. SNP markers closest to the QTL showed high similarity to rice genes that putatively encode proteins for defense response to herbivory and wounding. The markers were validated in a large, unrelated population of parental wheat genotypes. All wheat lines carrying the ‘C–G’ haplotype at the identified SNPs were resistant, suggesting that selection based on a haplotype of favourable alleles would be effective in predicting resistance status of unknown genotypes.

     

Development of an integrated linkage map of einkorn wheat and its application for QTL mapping and genome sequence anchoring

Key message

An integrated genetic map was constructed for einkorn wheat A genome and provided valuable information for QTL mapping and genome sequence anchoring.

Abstract

Wheat is one of the most widely grown food grain crops in the world. The construction of a genetic map is a key step to organize biologically or agronomically important traits along the chromosomes. In the present study, an integrated linkage map of einkorn wheat was developed using 109 recombinant inbred lines (RILs) derived from an inter sub-specific cross, KT1-1 (T. monococcum ssp. boeoticum) × KT3-5 (T. monococcum ssp. monococcum). The map contains 926 molecular markers assigned to seven linkage groups, and covers 1,377 cM with an average marker interval of 1.5 cM. A quantitative trait locus (QTL) analysis of five agronomic traits identified 16 stable QTL on all seven chromosomes, except 6A. The total phenotypic variance explained by these stable QTL using multiple regressions varied across environments from 8.8 to 87.1 % for days to heading, 24.4–63.0 % for spike length, 48.2–79.6 % for spikelet number per spike, 13.1–48.1 % for plant architecture, and 12.2–26.5 % for plant height, revealing that much of the RIL phenotypic variation had been genetically dissected. Co-localizations of closely linked QTL for different traits were frequently observed, especially on 3A and 7A. The QTL on 3A, 5A and 7A were closely associated with Eps-A ^m 3, Vrn1 and Vrn3 loci, respectively. Furthermore, this genetic map facilitated the anchoring of 237 T. urartu scaffolds onto seven chromosomes with a physical length of 26.15 Mb. This map and the QTL data provide valuable genetic information to dissect important agronomic and developmental traits in diploid wheat and contribute to the genetic ordering of the genome assembly.

     

Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number

Key message

GWAS on multi-environment data identified genomic regions associated with trade-offs for grain weight and grain number.

Abstract

Grain yield (GY) can be dissected into its components thousand grain weight (TGW) and grain number (GN), but little has been achieved in assessing the trade-off between them in spring wheat. In the present study, the Wheat Association Mapping Initiative (WAMI) panel of 287 elite spring bread wheat lines was phenotyped for GY, GN, and TGW in ten environments across different wheat growing regions in Mexico, South Asia, and North Africa. The panel genotyped with the 90 K Illumina Infinitum SNP array resulted in 26,814 SNPs for genome-wide association study (GWAS). Statistical analysis of the multi-environmental data for GY, GN, and TGW observed repeatability estimates of 0.76, 0.62, and 0.95, respectively. GWAS on BLUPs of combined environment analysis identified 38 loci associated with the traits. Among them four loci—6A (85 cM), 5A (98 cM), 3B (99 cM), and 2B (96 cM)—were associated with multiple traits. The study identified two loci that showed positive association between GY and TGW, with allelic substitution effects of 4% (GY) and 1.7% (TGW) for 6A locus and 0.2% (GY) and 7.2% (TGW) for 2B locus. The locus in chromosome 6A (79–85 cM) harbored a gene TaGW2-6A. We also identified that a combination of markers associated with GY, TGW, and GN together explained higher variation for GY (32%), than the markers associated with GY alone (27%). The marker-trait associations from the present study can be used for marker-assisted selection (MAS) and to discover the underlying genes for these traits in spring wheat.

     

Mapping of new quantitative trait loci for sudden death syndrome and soybean cyst nematode resistance in two soybean populations

Key message

Novel QTL conferring resistance to both the SDS and SCN was detected in two RIL populations. Dual resistant RILs could be used in breeding programs for developing resistant soybean cultivars.

Abstract

Soybean cultivars, susceptible to the fungus Fusarium virguliforme, which causes sudden death syndrome (SDS), and to the soybean cyst nematode (SCN) (Heterodera glycines), suffer yield losses valued over a billion dollars annually. Both pathogens may occur in the same production fields. Planting of cultivars genetically resistant to both pathogens is considered one of the most effective means to control the two pathogens. The objective of the study was to map quantitative trait loci (QTL) underlying SDS and SCN resistances. Two recombinant inbred line (RIL) populations were developed by crossing ‘A95-684043’, a high-yielding maturity group (MG) II line resistant to SCN, with ‘LS94-3207’ and ‘LS98-0582’ of MG IV, resistant to both F. virguliforme and SCN. Two hundred F₇ derived recombinant inbred lines from each population AX19286 (A95-684043 × LS94-3207) and AX19287 (A95-684043 × LS98-0582) were screened for resistance to each pathogen under greenhouse conditions. Five hundred and eighty and 371 SNP markers were used for mapping resistance QTL in each population. In AX19286, one novel SCN resistance QTL was mapped to chromosome 8. In AX19287, one novel SDS resistance QTL was mapped to chromosome 17 and one novel SCN resistance QTL was mapped to chromosome 11. Previously identified additional SDS and SCN resistance QTL were also detected in the study. Lines possessing superior resistance to both pathogens were also identified and could be used as germplasm sources for breeding SDS- and SCN-resistant soybean cultivars.

     

Genotyping-by-sequencing to remap QTL for type II Fusarium head blight and leaf rust resistance in a wheat–tall wheatgrass introgression recombinant inbred population

Fusarium graminearum Schwabe (Fusarium head blight, FHB) and Puccinia triticina Eriks (leaf rust) are two major fungal pathogens posing a continuous threat to the wheat crop; consequently, identifying resistance genes from various sources is always of importance to wheat breeders. We identified tightly linked single nucleotide polymorphism (SNP) markers for the FHB resistance quantitative trait locus (QTL) Qfhs.pur-7EL and the leaf rust resistance locus Lr19 using genotyping-by-sequencing (GBS) in a wheat–tall wheatgrass introgression-derived recombinant inbred line (RIL) population. One thousand and seven hundred high-confidence SNPs were used to conduct the linkage and QTL analysis. Qfhs.pur-7EL was mapped to a 2.9 cM region containing four markers within a 43.6 cM segment of wheatgrass chromosome 7el₂ that was translocated onto wheat chromosome 7DL. Lr19 from 7el₁ was mapped to a 1.21 cM region containing two markers in the same area, in repulsion. Five lines were identified with the resistance-associated SNP alleles for Qfhs.pur-7EL and Lr19 in coupling. Two SNP markers in the Qfhs.pur-7EL region were converted into PCR-based KASP markers. Investigation of the genetic characteristics of the parental lines of this RIL population indicated that they are translocation lines in two different wheat cultivar genetic backgrounds instead of 7E–7D substitution lines in Thatcher wheat background, as previously reported in the literature.