Multi-trait and multi-environment QTL analyses for resistance to wheat diseases

Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles

KEY MESSAGE

Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56 kb to 6.37 Mb, with a median of 921 kb, of which 27% had a CI lower than 500 kb and 53% had a CI lower than 1 Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.

Multivariate Genomic Hybrid Prediction with Kernels and Parental Information

Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait improvement, and enhancing adaptability to diverse environments. Leveraging genomic information empowers breeders to make informed decisions and significantly improve the efficiency and success rate of hybrid breeding programs. In order to improve the genomic ability performance, we explored the incorporation of parental phenotypic information as covariates under a multi-trait framework. Approach 1, referred to as Pmean, directly utilized parental phenotypic information without any preprocessing. While approach 2, denoted as BV, replaced the direct use of phenotypic values of both parents with their respective breeding values. While an improvement in prediction performance was observed in both approaches, with a minimum 4.24% reduction in the normalized root mean square error (NRMSE), the direct incorporation of parental phenotypic information in the Pmean approach slightly outperformed the BV approach. We also compared these two approaches using linear and nonlinear kernels, but no relevant gain was observed. Finally, our results increase empirical evidence confirming that the integration of parental phenotypic information helps increase the prediction performance of hybrids.

Genetics and breeding for resistance against four leaf spot diseases in wheat (Triticum aestivum L.)

In wheat, major yield losses are caused by a variety of diseases including rusts, spike diseases, leaf spot and root diseases. The genetics of resistance against all these diseases have been studied in great detail and utilized for breeding resistant cultivars. The resistance against leaf spot diseases caused by each individual necrotroph/hemi-biotroph involves a complex system involving resistance (R) genes, sensitivity (S) genes, small secreted protein (SSP) genes and quantitative resistance loci (QRLs). This review deals with resistance for the following four-leaf spot diseases: (i) Septoria nodorum blotch (SNB) caused by Parastagonospora nodorum; (ii) Tan spot (TS) caused by Pyrenophora tritici-repentis; (iii) Spot blotch (SB) caused by Bipolaris sorokiniana and (iv) Septoria tritici blotch (STB) caused by Zymoseptoria tritici.

Meta-QTLs for multiple disease resistance involving three rusts in common wheat (Triticum aestivum L.)

In wheat, multiple disease resistance meta-QTLs (MDR-MQTLs) and underlying candidate genes for the three rusts were identified which may prove useful for development of resistant cultivars. Rust diseases in wheat are a major threat to global food security. Therefore, development of multiple disease-resistant cultivars (resistant to all three rusts) is a major goal in all wheat breeding programs worldwide. In the present study, meta-QTLs and candidate genes for multiple disease resistance (MDR) involving all three rusts were identified using 152 individual QTL mapping studies for resistance to leaf rust (LR), stem rust (SR), and yellow rust (YR). From these 152 studies, a total of 1,146 QTLs for resistance to three rusts were retrieved, which included 368 QTLs for LR, 291 QTLs for SR, and 487 QTLs for YR. Of these 1,146 QTLs, only 718 QTLs could be projected onto the consensus map saturated with 2, 34,619 markers. Meta-analysis of the projected QTLs resulted in the identification of 86 MQTLs, which included 71 MDR-MQTLs. Ten of these MDR-MQTLs were referred to as the 'Breeders' MQTLs'. Seventy-eight of the 86 MQTLs could also be anchored to the physical map of the wheat genome, and 54 MQTLs were validated by marker-trait associations identified during earlier genome-wide association studies. Twenty MQTLs (including 17 MDR-MQTLs) identified in the present study were co-localized with 44 known R genes. In silico expression analysis allowed identification of several differentially expressed candidate genes (DECGs) encoding proteins carrying different domains including the following: NBS-LRR, WRKY domains, F-box domains, sugar transporters, transferases, etc. The introgression of these MDR loci into high-yielding cultivars should prove useful for developing high yielding cultivars with resistance to all the three rusts.

Genetic Inheritance of Stripe Rust (Puccinia Striiformis) Resistance in Bread Wheat Breeding Lines at Seedling and Maturity Stages

One hundred and five (105) bread wheat (Triticum aestivum L.) genotypes, including five commercial checks, were screened for stripe rust resistance at seedling and adult plant stages. Seedlings grown under controlled conditions were screened for disease resistance after 12 days concerning disease incidence percentage after inoculation. K-means cluster analysis divided the genotypes into five different classes according to the presence of virulence/avirulence profile, i.e., class 1, 2, 3, 4 and 5. The same set of genotypes was grown under field conditions for adult plant resistance. Data for disease scoring and different yield and yield-related parameters was recorded. A comparison of breeding lines indicated that all studied traits were negatively affected by disease incidence. Further cluster analysis ranked the genotypes into three distinct groups with Group I and III being the most diverse. Thirteen stripe rust resistance lines were identified using seedling and adult plant resistance strategies. Correlation analysis indicated a negative association between stripe rust incidence and yield and yield-related traits, particularly grains per spike, grain weight per spike, thousand-grain weight, and grain yield per plant. These findings suggested that stripe rust resistance negatively affects yield and yield related traits. The breeding programs aiming at the development of high yielding varieties must also focus on stripe rust resistance.

Identification and Validation of a Major Quantitative Trait Locus for Adult Plant Resistance Against Leaf Rust From the Chinese Wheat Landrace Bai Qimai

Leaf rust caused by Puccinia triticina Eriks. (Pt) is a common disease of wheat worldwide. The Chinese wheat landrace Bai Qimai (BQM) has shown high resistance to leaf rust for a prolonged period of time; the infected leaves of BQM displayed high infection types (ITs), but they showed low disease severities at the adult plant stage. To find quantitative trait loci (QTL) for resistance to leaf rust, 186 recombinant inbred lines from the cross Nugaines × BQM were phenotyped for leaf rust response in multiple field environments under natural Pt infections and genotyped using the 90K wheat single nucleotide polymorphism (SNP) chip and simple sequence repeat (SSR) markers. A total of 2,397 polymorphic markers were used for QTL mapping, and a novel major QTL (QLr.cau-6DL) was detected on chromosome 6DL from BQM. The effectiveness of QLr.cau-6DL was validated using the three additional wheat populations (RL6058 × BQM, Aikang58 × BQM, and Jimai22 × BQM). QLr.cau-6DL could significantly reduce leaf rust severities across all tested environments and different genetic backgrounds, and its resistance was more effective than that of Lr34. Moreover, QLr.cau-6DL acted synergistically with Lr34 to confer strong resistance to leaf rust. We believe that QLr.cau-6DL should have high potential value in the breeding of wheat cultivars with leaf rust resistance.

Meta-analysis reveals consensus genomic regions associated with multiple disease resistance in wheat (Triticum aestivum L.)

In wheat, meta-QTLs (MQTLs) and candidate genes (CGs) were identified for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the five diseases. As many as 493 QTLs were available from these studies, which were distributed in five diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103 QTLs; fusarium head blight (FHB), 184 QTLs; karnal bunt (KB), 66 QTLs; and loose smut (LS), 14 QTLs. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were verified using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identified which were further investigated for differential expression using data from five transcriptome studies, resulting in 194 differentially expressed candidate genes (DECGs). Among the DECGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for fine mapping and cloning of MDR genes and marker-assisted breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01282-z.

Centenary of Soil and Air Borne Wheat Karnal Bunt Disease Research: A Review

Karnal bunt (KB) of wheat (Triticum aestivum L.), known as partial bunt has its origin in Karnal, India and is caused by Tilletia indica (Ti). Its incidence had grown drastically since late 1960s from northwestern India to northern India in early 1970s. It is a seed, air and soil borne pathogen mainly affecting common wheat, durum wheat, triticale and other related species. The seeds become inedible, inviable and infertile with the precedence of trimethylamine secreted by teliospores in the infected seeds. Initially the causal pathogen was named Tilletia indica but was later renamed Neovossia indica. The black powdered smelly spores remain viable for years in soil, wheat straw and farmyard manure as primary sources of inoculum. The losses reported were as high as 40% in India and also the cumulative reduction of national farm income in USA was USD 5.3 billion due to KB. The present review utilizes information from literature of the past 100 years, since 1909, to provide a comprehensive and updated understanding of KB, its causal pathogen, biology, epidemiology, pathogenesis, etc. Next generation sequencing (NGS) is gaining popularity in revolutionizing KB genomics for understanding and improving agronomic traits like yield, disease tolerance and disease resistance. Genetic resistance is the best way to manage KB, which may be achieved through detection of genes/quantitative trait loci (QTLs). The genome-wide association studies can be applied to reveal the association mapping panel for understanding and obtaining the KB resistance locus on the wheat genome, which can be crossed with elite wheat cultivars globally for a diverse wheat breeding program. The review discusses the current NGS-based genomic studies, assembly, annotations, resistant QTLs, GWAS, technology landscape of diagnostics and management of KB. The compiled exhaustive information can be beneficial to the wheat breeders for better understanding of incidence of disease in endeavor of quality production of the crop.

Pre-emptive Breeding Against Karnal Bunt Infection in Common Wheat: Combining Genomic and Agronomic Information to Identify Suitable Parents

Wheat (Triticum aestivum L.) is the most widely grown cereal crop in the world and is staple food to half the world's population. The current world population is expected to reach 9.8 billion people by 2050, but food production is not expected to keep pace with demand in developing countries. Significant opportunities exist for traditional grain exporters to produce and export greater amounts of wheat to fill the gap. Karnal bunt, however, is a major threat, due to its use as a non-tariff trade barrier by several wheat-importing countries. The cultivation of resistant varieties remains the most cost-effective approach to manage the disease, but in countries that are free of the disease, genetic improvement is difficult due to quarantine restrictions. Here we report a study on pre-emptive breeding designed to identify linked molecular markers, evaluate the prospects of genomic selection as a tool, and prioritise wheat genotypes suitable for use as parents. In a genome-wide association (GWAS) study, we identified six DArTseq markers significantly linked to Karnal bunt resistance, which explained between 7.6 and 29.5% of the observed phenotypic variation. The accuracy of genomic prediction was estimated to vary between 0.53 and 0.56, depending on whether it is based solely on the identified Quantitative trait loci (QTL) markers or the use of genome-wide markers. As genotypes used as parents would be required to possess good yield and phenology, further research was conducted to assess the agronomic value of Karnal bunt resistant germplasm from the International Maize and Wheat Improvement Center (CIMMYT). We identified an ideal genotype, ZVS13_385, which possessed similar agronomic attributes to the highly successful Australian wheat variety, Mace. It is phenotypically resistant to Karnal bunt infection (<1% infection) and carried all the favourable alleles detected for resistance in this study. The identification of a genotype combining Karnal bunt resistance with adaptive agronomic traits overcomes the concerns of breeders regarding yield penalty in the absence of the disease.

GWAS revealed a novel resistance locus on chromosome 4D for the quarantine disease Karnal bunt in diverse wheat pre-breeding germplasm

This study was initiated to identify genomic regions conferring resistance to Karnal Bunt (KB) disease in wheat through a genome-wide association study (GWAS) on a set of 179 pre-breeding lines (PBLs). A GWAS of 6,382 high-quality DArTseq SNPs revealed 15 significant SNPs (P-value <10^-3) on chromosomes 2D, 3B, 4D and 7B that were associated with KB resistance in individual years. In particular, two SNPs (chromosome 4D) had the maximum R² values: SNP 1114200 | F | 0-63:T > C at 1.571 cM and R² of 12.49% and SNP 1103052 | F | 0-61:C > A at 1.574 cM and R² of 9.02%. These two SNPs displayed strong linkage disequilibrium (LD). An in silico analysis of SNPs on chromosome 4D identified two candidate gene hits, TraesCS4D02G352200 (TaNox8; an NADPH oxidase) and TraesCS4D02G350300 (a rhomboid-like protein belonging to family S54), with SNPs 1103052 | F | 0-61:C > A and 1101835 | F | 0-5:C > A, respectively, both of which function in biotic stress tolerance. The epistatic interaction analysis revealed significant interactions among 4D and 7B loci. A pedigree analysis of confirmed resistant PBLs revealed that Aegilops species is one of the parents and contributed the D genome in these resistant PBLs. These identified lines can be crossed with any elite cultivar across the globe to incorporate novel KB resistance identified on 4B.

Karnal Bunt: A Re-Emerging Old Foe of Wheat

Wheat (Triticum aestivum L.) crop health assumes unprecedented significance in being the second most important staple crop of the world. It is host to an array of fungal pathogens attacking the plant at different developmental stages and accrues various degrees of yield losses owing to these. Tilletia indica that causes Karnal bunt (KB) disease in wheat is one such fungal pathogen of high quarantine importance restricting the free global trade of wheat besides the loss of grain yield as well as quality. With global climate change, the disease appears to be shifting from its traditional areas of occurrence with reports of increased vulnerabilities of new areas across the continents. This KB vulnerability of new geographies is of serious concern because once established, the disease is extremely difficult to eradicate and no known instance of its complete eradication using any management strategy has been reported yet. The host resistance to KB is the most successful as well as preferred strategy for its mitigation and control. However, breeding of KB resistant wheat cultivars has proven to be not so easy, and the low success rate owes to the scarcity of resistance sources, extremely laborious and regulated field screening protocols delaying identification/validation of putative resistance sources, and complex quantitative nature of resistance with multiple genes conferring only partial resistance. Moreover, given a lack of comprehensive understanding of the KB disease epidemiology, host-pathogen interaction, and pathogen evolution. Here, in this review, we attempt to summarize the progress made and efforts underway toward a holistic understanding of the disease itself with a specific focus on the host-pathogen interaction between T. indica and wheat as key elements in the development of resistant germplasm. In this context, we emphasize the tools and techniques being utilized in development of KB resistant germplasm by illuminating upon the genetics concerning the host responses to the KB pathogen including a future course. As such, this article could act as a one stop information primer on this economically important and re-emerging old foe threatening to cause devastating impacts on food security and well-being of communities that rely on wheat.

Unravelling the Complex Genetics of Karnal Bunt (Tilletia indica) Resistance in Common Wheat (Triticum aestivum) by Genetic Linkage and Genome-Wide Association Analyses

Karnal bunt caused by Tilletia indica Mitra [syn. Neovossia indica (Mitra) Mundkur] is a significant biosecurity concern for wheat-exporting countries that are free of the disease. It is a seed-, soil-and air-borne disease with no effective chemical control measures. The current study used data from multi-year field experiments of two bi-parental populations and a genome-wide association (GWA) mapping panel to unravel the genetic basis for resistance in common wheat. Broad-sense heritability for Karnal bunt resistance in the populations varied from 0.52 in the WH542×HD29 population, to 0.61 in the WH542×W485 cross and 0.71 in a GWAS panel. Quantitative trait locus (QTL) analysis with seven years of phenotypic data identified a major locus on chromosome 3B (R² = 27.8%) and a minor locus on chromosome 1A (R² = 12.2%), in the WH542×HD29 population, with both parents contributing the high-value alleles. A major locus (R² = 27.8%) and seven minor loci (R² = 4.4–15.8%) were detected in the WH542×W485 population. GWA mapping validated QTL regions in the bi-parent populations, but also identified novel loci not previously associated with Karnal bunt resistance. Meta-QTL analysis aligned the results from this study with those reported in wheat over the last two decades. Two major clusters were detected, the first on chromosome 4B, which clustered with Qkb.ksu-4B, QKb.cimmyt-4BL, Qkb.cim-4BL, and the second on chromosome 3B, which clustered with Qkb.cnl-3B, QKb.cimmyt-3BS and Qkb.cim-3BS1. The results provide definitive chromosomal assignments for QTL/genes controlling Karnal bunt resistance in common wheat, and will be useful in pre-emptive breeding against the pathogen in wheat-producing areas that are free of the disease.

Loci on chromosomes 1A and 2A affect resistance to tan (yellow) spot in wheat populations not segregating for tsn1

KEY MESSAGE

QTL for tan spot resistance were mapped on wheat chromosomes 1A and 2A. Lines were developed with resistance alleles at these loci and at the tsn1 locus on chromosome 5B. These lines expressed significantly higher resistance than the parent with tsn1 only. Tan spot (syn. yellow spot and yellow leaf spot) caused by Pyrenophora tritici-repentis is an important foliar disease of wheat in Australia. Few resistance genes have been mapped in Australian germplasm and only one, known as tsn1 located on chromosome 5B, is known in Australian breeding programs. This gene confers insensitivity to the fungal effector ToxA. The main aim of this study was to map novel resistance loci in two populations: Calingiri/Wyalkatchem, which is fixed for the ToxA-insensitivity allele tsn1, and IGW2574/Annuello, which is fixed for the ToxA-sensitivity allele Tsn1. A second aim was to combine new loci with tsn1 to develop lines with improved resistance. Tan spot severity was evaluated at various growth stages and in multiple environments. Symptom severity traits exhibited quantitative variation. The most significant quantitative trait loci (QTL) were detected on chromosomes 2A and 1A. The QTL on 2A explained up to 29.2% of the genotypic variation in the Calingiri/Wyalkatchem population with the resistance allele contributed by Wyalkatchem. The QTL on 1A explained up to 28.1% of the genotypic variation in the IGW2574/Annuello population with the resistance allele contributed by Annuello. The resistance alleles at both QTL were successfully combined with tsn1 to develop lines that express significantly better resistance at both seedling and adult plant stages than Calingiri which has tsn1 only.

Reconstructing the Molecular Function of Genetic Variation in Regulatory Networks

Over the past decade, genetic studies have recognized hundreds of polymorphic DNA loci called response QTLs (reQTLs) as potential contributors to interindividual variation in transcriptional responses to stimulations. Such reQTLs commonly affect the transduction of signals along the regulatory network that controls gene transcription. Identifying the pathways through which reQTLs perturb the underlying network has been a major challenge. Here, we present GEVIN ("Genome-wide Embedding of Variation In Networks"), a methodology that simultaneously identifies a reQTL and the particular pathway in which the reQTL affects downstream signal transduction along the network. Using synthetic data, we show that this algorithm outperforms existing pathway identification and reQTL identification methods. We applied GEVIN to the analysis of murine and human dendritic cells in response to pathogenic components. These analyses revealed significant reQTLs together with their perturbed Toll-like receptor signaling pathways. GEVIN thus offers a powerful framework that renders a comprehensive picture of disease-related DNA loci and their molecular functions within regulatory networks.

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.

Identification of New Resistance Loci to African Stem Rust Race TTKSK in Tetraploid Wheats Based on Linkage and Genome-Wide Association Mapping

Stem rust, caused by Puccinia graminis Pers. f. sp. tritici Eriks. and E. Henn. (Pgt), is one of the most destructive diseases of wheat. Races of the pathogen in the "Ug99 lineage" are of international concern due to their virulence for widely used stem rust resistance genes and their spread throughout Africa. Disease resistant cultivars provide one of the best means for controlling stem rust. To identify quantitative trait loci (QTL) conferring resistance to African stem rust race TTKSK at the seedling stage, we evaluated an association mapping (AM) panel consisting of 230 tetraploid wheat accessions under greenhouse conditions. A high level of phenotypic variation was observed in response to race TTKSK in the AM panel, allowing for genome-wide association mapping of resistance QTL in wild, landrace, and cultivated tetraploid wheats. Thirty-five resistance QTL were identified on all chromosomes, and seventeen are of particular interest as identified by multiple associations. Many of the identified resistance loci were coincident with previously identified rust resistance genes; however, nine on chromosomes 1AL, 2AL, 4AL, 5BL, and 7BS may be novel. To validate AM results, a biparental population of 146 recombinant inbred lines was also considered, which derived from a cross between the resistant cultivar "Cirillo" and susceptible "Neodur." The stem rust resistance of Cirillo was conferred by a single gene on the distal region of chromosome arm 6AL in an interval map coincident with the resistance gene Sr13, and confirmed one of the resistance loci identified by AM. A search for candidate resistance genes was carried out in the regions where QTL were identified, and many of them corresponded to NBS-LRR genes and protein kinases with LRR domains. The results obtained in the present study are of great interest as a high level of genetic variability for resistance to race TTKSK was described in a germplasm panel comprising most of the tetraploid wheat sub-species.

A genome-wide association study of resistance to stripe rust (Puccinia striiformis f. sp. tritici) in a worldwide collection of hexaploid spring wheat (Triticum aestivum L.)

New races of Puccinia striiformis f. sp. tritici (Pst), the causal pathogen of wheat stripe rust, show high virulence to previously deployed resistance genes and are responsible for large yield losses worldwide. To identify new sources of resistance we performed a genome-wide association study (GWAS) using a worldwide collection of 1000 spring wheat accessions. Adult plants were evaluated under field conditions in six environments in the western United States, and seedlings were tested with four Pst races. A single-nucleotide polymorphism (SNP) Infinium 9K-assay provided 4585 SNPs suitable for GWAS. High correlations among environments and high heritabilities were observed for stripe rust infection type and severity. Greater levels of Pst resistance were observed in a subpopulation from Southern Asia than in other groups. GWAS identified 97 loci that were significant for at least three environments, including 10 with an experiment-wise adjusted Bonferroni probability < 0.10. These 10 quantitative trait loci (QTL) explained 15% of the phenotypic variation in infection type, a percentage that increased to 45% when all QTL were considered. Three of these 10 QTL were mapped far from previously identified Pst resistance genes and QTL, and likely represent new resistance loci. The other seven QTL mapped close to known resistance genes and allelism tests will be required to test their relationships. In summary, this study provides an integrated view of stripe rust resistance resources in spring wheat and identifies new resistance loci that will be useful to diversify the current set of resistance genes deployed to control this devastating disease.

Multi-trait multi-environment quantitative trait loci mapping for a sugarcane commercial cross provides insights on the inheritance of important traits

Breeding trials typically consist of phenotypic observations for various traits evaluated in multiple environments. For sugarcane in particular, repeated measures are obtained for plant crop and one or more ratoons, such that joint analysis through mixed models for modeling heterogeneous genetic (co)variances between traits, locations and harvests is appropriate. This modeling approach also enables us to include molecular marker information, aiding in understanding the genetic architecture of quantitative traits. Our work aims at detecting QTL and QTL by environment interactions by fitting mixed models with multiple QTLs, with appropriate modeling of multi-trait multi-environment data for outcrossing species. We evaluated 100 individuals from a biparental cross at two locations and three  years for fiber content, sugar content (POL) and tonnes of cane per hectare (TCH). We detected 13 QTLs exhibiting QTL by location, QTL by harvest or the three-way interaction. Overall, 11 of the 13 effects presented some degree of pleiotropy, affecting at least two traits. Furthermore, these QTLs always affected fiber and TCH in the same direction, whereas POL was affected in the opposite way. There was no evidence in favor of the linked QTL over the pleiotropic QTL hypothesis for any detected genome position. These results provide valuable insights into the genetic basis of quantitative variation in sugarcane and the genetic relation between traits.

Stripe rust and leaf rust resistance QTL mapping, epistatic interactions, and co-localization with stem rust resistance loci in spring wheat evaluated over three continents

In wheat, advantageous gene-rich or pleiotropic regions for stripe, leaf, and stem rust and epistatic interactions between rust resistance loci should be accounted for in plant breeding strategies. Leaf rust (Puccinia triticina Eriks.) and stripe rust (Puccinia striiformis f. tritici Eriks) contribute to major production losses in many regions worldwide. The objectives of this research were to identify and study epistatic interactions of quantitative trait loci (QTL) for stripe and leaf rust resistance in a doubled haploid (DH) population derived from the cross of Canadian wheat cultivars, AC Cadillac and Carberry. The relationship of leaf and stripe rust resistance QTL that co-located with stem rust resistance QTL previously mapped in this population was also investigated. The Carberry/AC Cadillac population was genotyped with DArT(®) and simple sequence repeat markers. The parents and population were phenotyped for stripe rust severity and infection response in field rust nurseries in Kenya (Njoro), Canada (Swift Current), and New Zealand (Lincoln); and for leaf rust severity and infection response in field nurseries in Canada (Swift Current) and New Zealand (Lincoln). AC Cadillac was a source of stripe rust resistance QTL on chromosomes 2A, 2B, 3A, 3B, 5B, and 7B; and Carberry was a source of resistance on chromosomes 2B, 4B, and 7A. AC Cadillac contributed QTL for resistance to leaf rust on chromosome 2A and Carberry contributed QTL on chromosomes 2B and 4B. Stripe rust resistance QTL co-localized with previously reported stem rust resistance QTL on 2B, 3B, and 7B, while leaf rust resistance QTL co-localized with 4B stem rust resistance QTL. Several epistatic interactions were identified both for stripe and leaf rust resistance QTL. We have identified useful combinations of genetic loci with main and epistatic effects. Multiple disease resistance regions identified on chromosomes 2A, 2B, 3B, 4B, 5B, and 7B are prime candidates for further investigation and validation of their broad resistance.

Genotype-environment interactions affecting preflowering physiological and morphological traits of Brassica rapa grown in two watering regimes

Plant growth and productivity are greatly affected by drought, which is likely to become more threatening with the predicted global temperature increase. Understanding the genetic architecture of complex quantitative traits and their interaction with water availability may lead to improved crop adaptation to a wide range of environments. Here, the genetic basis of 20 physiological and morphological traits is explored by describing plant performance and growth in a Brassica rapa recombinant inbred line (RIL) population grown on a sandy substrate supplemented with nutrient solution, under control and drought conditions. Altogether, 54 quantitative trait loci (QTL) were identified, of which many colocated in 11 QTL clusters. Seventeen QTL showed significant QTL-environment interaction (Q×E), indicating genetic variation for phenotypic plasticity. Of the measured traits, only hypocotyl length did not show significant genotype-environment interaction (G×E) in both environments in all experiments. Correlation analysis showed that, in the control environment, stomatal conductance was positively correlated with total leaf dry weight (DW) and aboveground DW, whereas in the drought environment, stomatal conductance showed a significant negative correlation with total leaf DW and aboveground DW. This correlation was explained by antagonistic fitness effects in the drought environment, controlled by a QTL cluster on chromosome A7. These results demonstrate that Q×E is an important component of the genetic variance and can play a great role in improving drought tolerance in future breeding programmes.

Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat

The accuracy of genomic selection depends on the relatedness between the members of the set in which marker effects are estimated based on evaluation data and the types for which performance is predicted. Here, we investigate the impact of relatedness on the performance of marker-assisted selection for fungal disease resistance in hybrid wheat. A large and diverse mapping population of 1739 elite European winter wheat inbred lines and hybrids was evaluated for powdery mildew, leaf rust and stripe rust resistance in multi-location field trials and fingerprinted with 9 k and 90 k SNP arrays. Comparison of the accuracies of prediction achieved with data sets from the two marker arrays revealed a crucial role for a sufficiently high marker density in genome-wide association mapping. Cross-validation studies using test sets with varying degrees of relationship to the corresponding estimation sets revealed that close relatedness leads to a substantial increase in the proportion of total genotypic variance explained by the identified QTL and consequently to an overoptimistic judgment of the precision of marker-assisted selection.

Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper

A mixed model framework was defined for QTL analysis of multiple traits across multiple environments for a RIL population in pepper. Detection power for QTLs increased considerably and detailed study of QTL by environment interactions and pleiotropy was facilitated. For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.

Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper

A mixed model framework was defined for QTL analysis of multiple traits across multiple environments for a RIL population in pepper. Detection power for QTLs increased considerably and detailed study of QTL by environment interactions and pleiotropy was facilitated. For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.

Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power

A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in the context of variable selection and develop a powerful implementation procedure of variable selection for genome scanning. We demonstrate the proposed method and selection procedure in a backcross population, using both simulated and real data. The extension to other experimental mapping populations is straightforward.