QTLs Associated with Agronomic Traits in the Cutler × AC Barrie Spring Wheat Mapping Population Using Single Nucleotide Polymorphic Markers

Identification of genetic loci for early maturity in spring bread wheat using the association analysis and gene dissection

Background

Early maturity in spring bread wheat is highly desirable in the regions where it enables the plants to evade high temperatures and plant pathogens at the end of the growing season.

Methods

To reveal the genetic loci responsible for the maturity time association analysis was carried out based on phenotyping for an 11-year period and high-throughput SNP genotyping of a panel of the varieties contrasting for this trait. The expression of candidate genes was verified using qPCR. The association between the SNP markers and the trait was validated using the biparental F2:3 population.

Results

Our data showed that under long-day conditions, the period from seedling to maturity is mostly influenced by the time from heading to maturity, rather than the heading time. The QTLs associated with the trait were located on 2A, 3B, 4A, 5B, 7A and 7B chromosomes with the 7BL locus being the most significant and promising for its SNPs accelerated the maturity time by about 9 days. Gene dissection in this locus detected a number of candidates, the best being TraesCS7B02G391800 (bZIP9) and TraesCS7B02G412200 (photosystem II reaction center). The two genes are predominantly expressed in the flag leaf while flowering. The effect of the SNPs was verified in F2:3 population and confirmed the association of the 4A, 5B and 7BL loci with the maturity time.

FHB resistance conferred by Fhb1 is under inhibitory regulation of two genetic loci in wheat (Triticum aestivum L.)

KEY MESSAGE

Two loci inhibiting Fhb1 resistance to Fusarium head blight were identified through genome-wide association mapping and validated in biparental populations. Fhb1 confers Fusarium head blight (FHB) resistance by limiting fungal spread within spikes in wheat (type II resistance). However, not all lines with Fhb1 display the expected resistance. To identify genetic factors regulating Fhb1 effect, a genome-wide association study for type II resistance was first performed with 72 Fhb1-carrying lines using the Illumina 90 K iSelect SNP chip. Of 84 significant marker-trait associations detected, more than half were repeatedly detected in at least two environments, with the SNPs distributed in one region on chromosome 5B and one on chromosome 6A. This result was validated in a collection of 111 lines with Fhb1 and 301 lines without Fhb1. We found that these two loci caused significant resistance variations solely among lines with Fhb1 by compromising the resistance. In1, the inhibitory gene on chromosome 5B, was in close linkage with Xwgrb3860 in a recombinant inbred line population derived from Nanda2419 × Wangshuibai and a double haploid (DH) population derived from R-43 (Fhb1 near isogenic line) × Biansui7 (with Fhb1 and In1); and In2, the inhibitory gene on chromosome 6A, was mapped to the Xwgrb4113-Xwgrb4034 interval using a DH population derived from R-43 × PH8901 (with Fhb1 and In2). In1 and In2 are present in all wheat-growing areas worldwide. Their frequencies in China's modern cultivars are high but have significantly decreased in comparison with landraces. These findings are of great significance for FHB resistance breeding using Fhb1.

Identification of quantitative trait loci of agronomic traits in bread wheat using a Pamyati Azieva × Paragon mapping population harvested in three regions of Kazakhstan

Background

Although genome-wide association studies (GWAS) are an increasingly informative tool in the mining of new quantitative trait loci (QTLs), a classical biparental mapping approach is still a powerful, widely used method to search the unique genetic factors associated with important agronomic traits in bread wheat.

Methods

In this study, a newly constructed mapping population of Pamyati Azieva (Russian Federation) × Paragon (UK), consisting of 94 recombinant inbred lines (RILs), was tested in three different regions of Kazakhstan with the purpose of QTL identification for key agronomic traits. The RILs were tested in 11 environments of two northern breeding stations (Petropavlovsk, North Kazakhstan region, and Shortandy, Aqmola region) and one southeastern station (Almalybak, Almaty region). The following eight agronomic traits were studied: heading days, seed maturation days, plant height, spike length, number of productive spikes, number of kernels per spike, thousand kernel weight, and yield per square meter. The 94 RILs of the PAxP cross were genotyped using Illumina's iSelect 20K single nucleotide polymorphism (SNP) array and resulted in the identification of 4595 polymorphic SNP markers.

Results

The application of the QTL Cartographer statistical package allowed the identification of 53 stable QTLs for the studied traits. A survey of published studies related to common wheat QTL identification suggested that 28 of those 53 QTLs were presumably novel genetic factors. The SNP markers for the identified QTLs of the analyzed agronomic traits of common wheat can be efficiently applied in ongoing breeding activities in the wheat breeding community using a marker-assisted selection approach.

High-Density Linkage Mapping of Agronomic Trait QTLs in Wheat under Water Deficit Condition using Genotyping by Sequencing (GBS)

Improvement of grain yield is the ultimate goal for wheat breeding under water-limited environments. In the present study, a high-density linkage map was developed by using genotyping-by-sequencing (GBS) of a recombinant inbred line (RIL) population derived from the cross between Iranian landrace #49 and cultivar Yecora Rojo. The population was evaluated in three locations in Iran during two years under irrigated and water deficit conditions for the agronomic traits grain yield (GY), plant height (PH), spike number per square meter (SM), 1000 kernel weight (TKW), grain number per spike (GNS), spike length (SL), biomass (BIO) and harvest index (HI). A linkage map was constructed using 5831 SNPs assigned to 21 chromosomes, spanning 3642.14 cM of the hexaploid wheat genome with an average marker density of 0.62 (markers/cM). In total, 85 QTLs were identified on 19 chromosomes (all except 5D and 6D) explaining 6.06–19.25% of the traits phenotypic variance. We could identify 20 novel QTLs explaining 8.87–19.18% of phenotypic variance on chromosomes 1A, 1B, 1D, 2B, 3A, 3B, 6A, 6B and 7A. For 35 out of 85 mapped QTLs functionally annotated genes were identified which could be related to a potential role in drought stress.

Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat

This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.

Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat

Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2-6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54-0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat.

Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems

Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0_M) or environments (CV0), a subset of lines that have been evaluated in either management (CV2_M) or some environments (CV2), and the performance of newly developed lines in either management (CV1_M) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.

A Combination of Leaf Rust Resistance Genes, Including Lr34 and Lr46, Is the Key to the Durable Resistance of the Canadian Wheat Cultivar, Carberry

The hexaploid spring wheat cultivar, Carberry, was registered in Canada in 2009, and has since been grown over an extensive area on the Canadian Prairies. Carberry has maintained a very high level of leaf rust (Puccinia triticina Eriks.) resistance since its release. To understand the genetic basis of Carberry's leaf rust resistance, Carberry was crossed with the susceptible cultivar, Thatcher, and a doubled haploid (DH) population of 297 lines was generated. The DH population was evaluated for leaf rust in seven field environments at the adult plant stage. Seedling and adult plant resistance (APR) to multiple virulence phenotypes of P. triticina was evaluated on the parents and the progeny population in controlled greenhouse studies. The population was genotyped with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, and quantitative trait loci (QTL) analysis was performed. The analysis using field leaf rust response indicated that Carberry contributed nine QTL located on chromosomes 1B, 2B (2 loci), 2D, 4A, 4B, 5A, 5B, and 7D. The QTL located on 1B, 2B, 5B, and 7D chromosomes were observed in two or more environments, whereas the remainder were detected in single environments. The resistance on 1B, detected in five environments, was attributed to Lr46 and on 7D, detected in seven environments to Lr34. The first 2B QTL corresponded with the adult plant gene, Lr13, while the second QTL corresponded with Lr16. The seedling analysis showed that Carberry carries Lr2a, Lr16, and Lr23. Five epistatic effects were identified in the population, with synergistic interactions being observed for Lr34 with Lr46, Lr16, and Lr2a. The durable rust resistance of Carberry is attributed to Lr34 and Lr46 in combination with these other resistance genes, because the resistance has remained effective even though the P. triticina population has evolved virulent to Lr2a, Lr13, Lr16, and Lr23.

Physical mapping of QTL associated with agronomic and end-use quality traits in spring wheat under conventional and organic management systems

Using phenotypic data of four biparental spring wheat populations evaluated at multiple environments under two management systems, we discovered 152 QTL and 22 QTL hotspots, of which two QTL accounted for up to 37% and 58% of the phenotypic variance, consistently detected in all environments, and fell within genomic regions harboring known genes. Identification of the physical positions of quantitative trait loci (QTL) would be highly useful for developing functional markers and comparing QTL results across multiple independent studies. The objectives of the present study were to map and characterize QTL associated with nine agronomic and end-use quality traits (tillering ability, plant height, lodging, grain yield, grain protein content, thousand kernel weight, test weight, sedimentation volume, and falling number) in hard red spring wheat recombinant inbred lines (RILs) using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. We evaluated a total of 698 RILs from four populations derived from crosses involving seven parents at 3-8 conventionally (high N) and organically (low N) managed field environments. Using the phenotypic data combined across all environments per management, and the physical map between 1058 and 6526 markers per population, we identified 152 QTL associated with the nine traits, of which 29 had moderate and 2 with major effects. Forty-nine of the 152 QTL mapped across 22 QTL hotspot regions with each region coincident to 2-6 traits. Some of the QTL hotspots were physically located close to known genes. QSv.dms-1A and QPht.dms-4B.1 individually explained up to 37% and 58% of the variation in sedimentation volume and plant height, respectively, and had very large LOD scores that varied from 19.0 to 35.7 and from 16.7 to 55.9, respectively. We consistently detected both QTL in the combined and all individual environments, laying solid ground for further characterization and possibly for cloning.

Mapping QTL associated with partial resistance to Aphanomyces root rot in pea (Pisum sativum L.) using a 13.2 K SNP array and SSR markers

KEY MESSAGE

A stable and major QTL, which mapped to an approximately 20.0 cM region on pea chromosome 4, was identified as the most consistent region conferring partial resistance to Aphanomyces euteiches. Aphanomyces root rot (ARR), caused by Aphanomyces euteiches Drechs., is a destructive soilborne disease of field pea (Pisum Sativum L.). No completely resistant pea germplasm is available, and current ARR management strategies rely on partial resistance and fungicidal seed treatments. In this study, an F8 recombinant inbred line population of 135 individuals from the cross 'Reward' (susceptible) × '00-2067' (tolerant) was evaluated for reaction to ARR under greenhouse conditions with the A. euteiches isolate Ae-MDCR1 and over 2 years in a field nursery in Morden, Manitoba. Root rot severity, foliar weight, plant vigor and height were used as estimates of tolerance to ARR. Genotyping was conducted with a 13.2 K single-nucleotide polymorphism (SNP) array and 222 simple sequence repeat (SSR) markers. Statistical analyses of the phenotypic data indicated significant (P < 0.001) genotypic effects and significant G × E interactions (P < 0.05) in all experiments. After filtering, 3050 (23.1%) of the SNP and 30 (13.5%) of the SSR markers were retained for linkage analysis, which distributed 2999 (2978 SNP + 21 SSR) of the markers onto nine linkage groups representing the seven chromosomes of pea. Mapping of quantitative trait loci (QTL) identified 8 major-effect (R2 > 20%), 13 moderate-effect (10% < R2 < 20%) effect and 6 minor-effect (R2 < 10%) QTL. A genomic region on chromosome 4, delimited by the SNP markers PsCam037549_22628_1642 and PsCam026054_14999_2864, was identified as the most consistent region responsible for partial resistance to A. euteiches isolate Ae-MDCR1. Other genomic regions important for resistance were of the order chromosome 5, 6 and 7.

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the 'Cutler' × 'AC Barrie' population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the 'Attila' × 'CDC Go' population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the 'Peace' × 'Carberry' population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the 'Peace' × 'CDC Stanley' population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.

Genetic Mapping by Integration of 55K SNP Array and KASP Markers Reveals Candidate Genes for Important Agronomic Traits in Hexaploid Wheat

Agronomic traits such as heading date (HD), plant height (PH), thousand grain weight (TGW), and spike length (SL) are important factors affecting wheat yield. In this study, we constructed a high-density genetic linkage map using the Wheat55K SNP Array to map quantitative trait loci (QTLs) for these traits in 207 recombinant inbred lines (RILs). A total of 37 QTLs were identified, including 9 QTLs for HD, 7 QTLs for PH, 12 QTLs for TGW, and 9 QTLs for SL, which explained 3.0-48.8% of the phenotypic variation. Kompetitive Allele Specific PCR (KASP) markers were developed based on sequencing data and used for validation of the stably detected QTLs on chromosomes 3A, 4B and 6A using 400 RILs. A QTL cluster on chromosome 4B for PH and TGW was delimited to a 0.8 Mb physical interval explaining 12.2-22.8% of the phenotypic variation. Gene annotations and analyses of SNP effects suggested that a gene encoding protein Photosynthesis Affected Mutant 68, which is essential for photosystem II assembly, is a candidate gene affecting PH and TGW. In addition, the QTL for HD on chromosome 3A was narrowed down to a 2.5 Mb interval, and a gene encoding an R3H domain-containing protein was speculated to be the causal gene influencing HD. The linked KASP markers developed in this study will be useful for marker-assisted selection in wheat breeding, and the candidate genes provide new insight into genetic study for those traits in wheat.

Genetic analyses of native Fusarium head blight resistance in two spring wheat populations identifies QTL near the B1, Ppd-D1, Rht-1, Vrn-1, Fhb1, Fhb2, and Fhb5 loci

QTL analyses of two bi-parental mapping populations with AC Barrie as a parent revealed numerous FHB-resistance QTL unique to each population and uncovered novel variation near Fhb1. Fusarium head blight (FHB) is a destructive disease of wheat worldwide, leading to severe yield and quality losses. The genetic basis of native FHB resistance was examined in two populations: a recombinant inbred line population from the cross Cutler/AC Barrie and a doubled haploid (DH) population from the cross AC Barrie/Reeder. Numerous QTL were detected among the two mapping populations with many being cross-specific. Photoperiod insensitivity at Ppd-D1 and dwarfing at Rht-B1 and Rht-D1 was associated with increased FHB susceptibility. Anthesis date QTL at or near the Vrn-A1 and Vrn-B1 loci co-located with major FHB-resistance QTL in the AC Barrie/Reeder population. The loci were epistatic for both traits, such that DH lines with both late alleles were considerably later to anthesis and had reduced FHB symptoms (i.e., responsible for the epistatic interaction). Interestingly, AC Barrie contributed FHB resistance near the Fhb1 locus in the Cutler population and susceptibility in the Reeder population. Analyses of the Fhb1 candidate genes PFT and TaHRC confirmed that AC Barrie, Cutler, and Reeder do not carry the Sumai-3 Fhb1 gene. Resistance QTL were also detected at the expected locations of Fhb2 and Fhb5. The native FHB-resistance QTL detected near Fhb1, Fhb2, and Fhb5 do not appear to be as effective as Fhb1, Fhb2, and Fhb5 from Sumai-3. The presence of awns segregated at the B1 awn inhibitor locus in both populations, but was only associated with FHB resistance in the Cutler/AC Barrie population suggesting linkage caused the association rather than pleiotropy.

Identification of the vernalization gene VRN-B1 responsible for heading date variation by QTL mapping using a RIL population in wheat

BACKGROUND

Heading time is one of the most important agronomic traits in wheat, as it largely affects both adaptation to different agro-ecological conditions and yield potential. Identification of genes underlying the regulation of wheat heading and the development of diagnostic markers could facilitate our understanding of genetic control of this process.

RESULTS

In this study, we developed 400 recombinant inbred lines (RILs) by crossing a γ-ray-induced early heading mutant (eh1) with the late heading cultivar, Lunxuan987. Bulked Segregant Analysis (BSA) of both RNA and DNA pools consisting of various RILs detected a quantitative trait loci (QTL) for heading date located on chromosomes 5B, and further genetic linkage analysis limited the QTL to a 3.31 cM region. We then identified a large deletion in the first intron of the vernalization gene VRN-B1 in eh1, and showed it was associated with the heading phenotype in the RIL population. However, it is not the mutation loci that resulted in early heading phonotype in the mutant compared to that of wildtype. RNA-seq analysis suggested that Vrn-B3 and several newly discovered genes, including beta-amylase 1 (BMY1) and anther-specific protein (RTS), were highly expressed in both the mutant and early heading pool with the dominant Vrn-B1 genotype compared to that of Lunxuan987 and late heading pool. Enrichment analysis of differentially expressed genes (DEGs) identified several key pathways previously reported to be associated with flowering, including fatty acid elongation, starch and sucrose metabolism, and flavonoid biosynthesis.

CONCLUSION

The development of new markers for Vrn-B1 in this study supplies an alternative solution for marker-assisted breeding to optimize heading time in wheat and the DEGs analysis provides basic information for VRN-B1 regulation study.

Mapping quantitative trait loci associated with leaf rust resistance in five spring wheat populations using single nucleotide polymorphism markers

Growing resistant wheat (Triticum aestivum L) varieties is an important strategy for the control of leaf rust, caused by Puccinia triticina Eriks. This study sought to identify the chromosomal location and effects of leaf rust resistance loci in five Canadian spring wheat cultivars. The parents and doubled haploid lines of crosses Carberry/AC Cadillac, Carberry/Vesper, Vesper/Lillian, Vesper/Stettler and Stettler/Red Fife were assessed for leaf rust severity and infection response in field nurseries in Canada near Swift Current, SK from 2013 to 2015, Morden, MB from 2015 to 2017 and Brandon, MB in 2016, and in New Zealand near Lincoln in 2014. The populations were genotyped with the 90K Infinium iSelect assay and quantitative trait loci (QTL) analysis was performed. A high density consensus map generated based on 14 doubled haploid populations and integrating SNP and SSR markers was used to compare QTL identified in different populations. AC Cadillac contributed QTL on chromosomes 2A, 3B and 7B (2 loci), Carberry on 1A, 2B (2 loci), 2D, 4B (2 loci), 5A, 6A, 7A and 7D, Lillian on 4A and 7D, Stettler on 2D and 6B, Vesper on 1B, 1D, 2A, 6B and 7B (2 loci), and Red Fife on 7A and 7B. Lillian contributed to a novel locus QLr.spa-4A, and similarly Carberry at QLr.spa-5A. The discovery of novel leaf rust resistance QTL QLr.spa-4A and QLr.spa-5A, and several others in contemporary Canada Western Red Spring wheat varieties is a tremendous addition to our present knowledge of resistance gene deployment in breeding. Carberry demonstrated substantial stacking of genes which could be supplemented with the genes identified in other cultivars with the expectation of increasing efficacy of resistance to leaf rust and longevity with little risk of linkage drag.

Agronomic and Physiological Traits, and Associated Quantitative Trait Loci (QTL) Affecting Yield Response in Wheat (Triticum aestivum L.): A Review

Enhanced grain yield has been achieved in bread wheat (Triticum aestivum L.) through development and cultivation of superior genotypes incorporating yield-related agronomic and physiological traits derived from genetically diverse and complementary genetic pool. Despite significant breeding progress, yield levels in wheat have remained relatively low and stagnant under marginal growing environments. There is a need for genetic improvement of wheat using yield-promoting morpho-physiological attributes and desired genotypes under the target production environments to meet the demand for food and feed. This review presents breeding progress in wheat for yield gains using agronomic and physiological traits. Further, the paper discusses globally available wheat genetic resources to identify and select promising genotypes possessing useful agronomic and physiological traits to enhance water, nutrient-, and radiation-use efficiency to improve grain yield potential and tolerance to abiotic stresses (i.e. elevated CO2, high temperature, and drought stresses). Finally, the paper highlights quantitative trait loci (QTL) linked to agronomic and physiological traits to aid breeding of high-performing wheat genotypes.

Quantitative trait loci for resistance to stripe rust of wheat revealed using global field nurseries and opportunities for stacking resistance genes

Quantitative trait loci controlling stripe rust resistance were identified in adapted Canadian spring wheat cultivars providing opportunity for breeders to stack loci using marker-assisted breeding. Stripe rust or yellow rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss., is a devastating disease of common wheat (Triticum aestivum L.) in many regions of the world. The objectives of this research were to identify and map quantitative trait loci (QTL) associated with stripe rust resistance in adapted Canadian spring wheat cultivars that are effective globally, and investigate opportunities for stacking resistance. Doubled haploid (DH) populations from the crosses Vesper/Lillian, Vesper/Stettler, Carberry/Vesper, Stettler/Red Fife and Carberry/AC Cadillac were phenotyped for stripe rust severity and infection response in field nurseries in Canada (Lethbridge and Swift Current), New Zealand (Lincoln), Mexico (Toluca) and Kenya (Njoro), and genotyped with SNP markers. Six QTL for stripe rust resistance in the population of Vesper/Lillian, five in Vesper/Stettler, seven in Stettler/Red Fife, four in Carberry/Vesper and nine in Carberry/AC Cadillac were identified. Lillian contributed stripe rust resistance QTL on chromosomes 4B, 5A, 6B and 7D, AC Cadillac on 2A, 2B, 3B and 5B, Carberry on 1A, 1B, 4A, 4B, 7A and 7D, Stettler on 1A, 2A, 3D, 4A, 5B and 6A, Red Fife on 2D, 3B and 4B, and Vesper on 1B, 2B and 7A. QTL on 1A, 1B, 2A, 2B, 3B, 4A, 4B, 5B, 7A and 7D were observed in multiple parents. The populations are compelling sources of recombination of many stripe rust resistance QTL for stacking disease resistance. Gene pyramiding should be possible with little chance of linkage drag of detrimental genes as the source parents were mostly adapted cultivars widely grown in Canada.

Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms

Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP) markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called 'large p, small n' problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers). While there are a number of related papers, none have addressed the impact of co-segregating markers on genetic maps. In the present study, we investigated the effects of co-segregating markers on high-density genetic map length and marker order using empirical data from two populations of wheat, Mohawk × Cocorit (durum wheat) and Norstar × Cappelle Desprez (bread wheat). The maps of both populations consisted of 85% co-segregating markers. Our study clearly showed that excess of co-segregating markers can lead to map expansion, but has little effect on markers order. To estimate the inflation factor (IF), we generated a total of 24,473 linkage maps (8,203 maps for Mohawk × Cocorit and 16,270 maps for Norstar × Cappelle Desprez). Using seven machine learning algorithms, we were able to predict with an accuracy of 0.7 the map expansion due to the proportion of co-segregating markers. For example in Mohawk × Cocorit, with 10 and 80% co-segregating markers the length of the map inflated by 4.5 and 16.6%, respectively. Similarly, the map of Norstar × Cappelle Desprez expanded by 3.8 and 11.7% with 10 and 80% co-segregating markers. With the increasing number of markers on SNP-chips, the proportion of co-segregating markers in high-density maps will continue to increase making map expansion unavoidable. Therefore, we suggest developers improve linkage mapping algorithms for efficient analysis of high-throughput data. This study outlines a practical strategy to estimate the IF due to the proportion of co-segregating markers and outlines a method to scale the length of the map accordingly.

QTLs associated with agronomic traits in the Attila × CDC Go spring wheat population evaluated under conventional management

Recently, we investigated the effect of the wheat 90K single nucleotide polymorphic (SNP) array and three gene-specific (Ppd-D1, Vrn-A1 and Rht-B1) markers on quantitative trait loci (QTL) detection in a recombinant inbred lines (RILs) population derived from a cross between two spring wheat (Triticum aestivum L.) cultivars, 'Attila' and 'CDC Go', and evaluated for eight agronomic traits at three environments under organic management. The objectives of the present study were to investigate the effect of conventional management on QTL detection in the same mapping population using the same set of markers as the organic management and compare the results with organic management. Here, we evaluated 167 RILs for number of tillers (tillering), flowering time, maturity, plant height, test weight (grain volume weight), 1000 kernel weight, grain yield, and grain protein content at seven conventionally managed environments from 2008 to 2014. Using inclusive composite interval mapping (ICIM) on phenotypic data averaged across seven environments and a subset of 1203 informative markers (1200 SNPs and 3 gene specific markers), we identified a total of 14 QTLs associated with flowering time (1), maturity (2), plant height (1), grain yield (1), test weight (2), kernel weight (4), tillering (1) and grain protein content (2). Each QTL individually explained from 6.1 to 18.4% of the phenotypic variance. Overall, the QTLs associated with each trait explained from 9.7 to 35.4% of the phenotypic and from 22.1 to 90.8% of the genetic variance. Three chromosomal regions on chromosomes 2D (61-66 cM), 4B (80-82 cM) and 5A (296-297 cM) harbored clusters of QTLs associated with two to three traits. The coincidental region on chromosome 5A harbored QTL clusters for both flowering and maturity time, and mapped about 2 cM proximal to the Vrn-A1 gene, which was in high linkage disequilibrium (0.70 ≤ r2 ≤ 0.75) with SNP markers that mapped within the QTL confidence interval. Six of the 14 QTLs (one for flowering time and plant height each, and two for maturity and kernel weight each) were common between the conventional and organic management systems, which suggests issues in directly utilizing gene discovery results based on conventional management to make in detail selection (decision) for organic management.

QTL Analysis of Spike Morphological Traits and Plant Height in Winter Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map

Wheat yield can be enhanced by modifying the spike morphology and the plant height. In this study, a population of 191 F₉ recombinant inbred lines (RILs) was developed from a cross between two winter cultivars Yumai 8679 and Jing 411. A dense genetic linkage map with 10,816 markers was constructed by incorporating single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) marker information. Five spike morphological traits and plant height were evaluated under nine environments for the RILs and parental lines, and the number of detected environmentally stable QTLs were 18 and three, respectively. The 1RS/1BL (rye) translocation increased both spike length and spikelet number with constant spikelet compactness. The QPht.cau-2D.1 was identical to gene Rht8, which decreased spike length without modifying spikelet number. Notably, four novel QTLs locating on chromosomes 1AS (QSc.cau-1A.1), 2DS (QSc.cau-2D.1), and 7BS (QSl.cau-7B.1 and QSl.cau-7B.2) were firstly identified in this study, which provide further insights into the genetic factors that shaped the spike morphology in wheat. Moreover, SNP markers tightly linked to previously reported QTLs will eventually facilitate future studies including their positional cloning or marker-assisted selection.