Coincidence in map positions between pathogen-induced defense-responsive genes and quantitative resistance loci in rice

Quantitative disease resistance conferred by quantitative trait loci (QTLs) is presumably of wider spectrum and durable. Forty-four cDNA clones, representing 44 defense-responsive genes, were fine mapped to 56 loci distributed on 9 of the 12 rice chromosomes. The locations of 32 loci detected by 27 cDNA clones were associated with previously identified resistance QTLs for different rice diseases, including blast, bacterial blight, sheath blight and yellow mottle virus. The loci detected by the same multiple-copy cDNA clones were frequently located on similar locations of different chromosomes. Some of the multiple loci detected by the same clones were all associated with resistance QTLs. These results suggest that some of the genes may be important components in regulation of defense responses against pathogen invasion and they may be the candidates for studying the mechanism of quantitative disease resistance in rice.     

Identification of genes contributing to quantitative disease resistance in rice

Despite the importance of quantitative disease resistance during a plant’s life, little is known about the molecular basis of this type of host-pathogen interaction, because most of the genes underlying resistance quantitative trait loci (QTLs) are unknown. To identify genes contributing to resistance QTLs in rice, we analyzed the colocalization of a set of characterized rice defense-responsive genes and resistance QTLs against different pathogens. We also examined the expression patterns of these genes in response to pathogen infection in the parents of the mapping populations, based on the strategy of validation and functional analysis of the QTLs. The results suggest that defense-responsive genes are important resources of resistance QTLs in rice. OsWRKY45-1 is the gene contributing to a major resistance QTL.NRR,OsGH3-1,and OsGLP members on chromosome 8 contribute alone or collectively to different minor resistance QTLs. These genes function in a basal resistance pathway or in major disease resistance gene-mediated race-specific pathways.     

Selection mapping of loci for quantitative disease resistance in a diverse maize population

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F(2) populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB.     

Association of transforming growth factor beta genes with quantitative trait loci for antibody response kinetics in hens

Antibody responses (primary and secondary phases) were measured in an F2 population. The resource population was derived from grandsires of two highly inbred major histocompatibility complex (MHC)-congenic Fayoumi chicken lines (named M5.1 and M15.2) mated with highly inbred Leghorn G-B1 hens. Secondary phase parameters of maximum titres (Ymax) and time required to achieve Ymax (Tmax) were estimated from post-secondary titres by using a non-linear regression model. Associations of single nucleotide polymorphisms (SNP) in transforming growth factor beta2 (TGFB2), 3, and 4 genes with antibody response parameters were evaluated. Multiple immune response parameters were significantly associated with the TGFB2 gene primarily in the lineage of the M5.1 grandsire, suggesting that TGFB2 or linked genes affect antibody response in hens. Significant main effects of the three genes were mostly found in the lineage of the M5.1 grandsire. Significant two-way interactions on antibody response were primarily detected between TGFB3 and TGFB4 genes, and in the lineage of the M15.2 grandsire. Effects preferentially detectable in only one of the MHC-congenic lineages suggest that there was interaction between the MHC and TGFB genes. The characterized TGFB SNPs might be applied in marker-assisted selection to improve antibody production.     

Inheritance of partial resistance against Colletotrichum lindemuthianum in Phaseolus vulgaris and co-localization of quantitative trait loci with genes involved in specific resistance

Anthracnose, one of the most important diseases of common bean (Phaseolus vulgaris), is caused by the fungus Colletotrichum lindemuthianum. A "candidate gene" approach was used to map anthracnose resistance quantitative trait loci (QTL). Candidate genes included genes for both pathogen recognition (resistance genes and resistance gene analogs [RGAs]) and general plant defense (defense response genes). Two strains of C. lindemuthianum, identified in a world collection of 177 strains, displayed a reproducible and differential aggressiveness toward BAT93 and JaloEEP558, two parental lines of P. vulgaris representing the two major gene pools of this crop. A reliable test was developed to score partial resistance in aerial organs of the plant (stem, leaf, petiole) under controlled growth chamber conditions. BAT93 was more resistant than JaloEEP558 regardless of the organ or strain tested. With a recombinant inbred line (RIL) population derived from a cross between these two parental lines, 10 QTL were located on a genetic map harboring 143 markers, including known defense response genes, anthracnose-specific resistance genes, and RGAs. Eight of the QTL displayed isolate specificity. Two were co-localized with known defense genes (phenylalanine ammonia-lyase and hydroxyproline-rich glycoprotein) and three with anthracnose-specific resistance genes and/or RGAs. Interestingly, two QTL, with different allelic contribution, mapped on linkage group B4 in a 5.0 cM interval containing Andean and Mesoamerican specific resistance genes against C. lindemuthianum and 11 polymorphic fragments revealed with a RGA probe. The possible relationship between genes underlying specific and partial resistance is discussed.     

Identification of quantitative trait loci controlling resistance to gray leaf spot disease in maize

Breeding maize for gray leaf spot (GLS) resistance has been hindered by the quantitative nature of the inheritance of GLS resistance and by the limitations of selection under less than optimumal disease pressure. In order to identify the quantitative trait loci (QTLs) controlling GLS resistance, a cross was made between B73 (susceptible) and Va14 (resistant) to generate a large F₂ population. Six GLS disease assessments were made throughout the disease season for over 1000 F₂ plants in 1989, and for 600 F₂-derived F₃ lines replicated in two blocks in 1990. RFLP analysis for78 marker loci representing all ten maize chromosomes was conducted in 239 F₂ individuals including those with the extreme GLS disease phenotypes. The GLS disease scores of the three field evaluations, each averaged over six ratings, were separately used for the interval mapping in order to determine the consistency of the QTL effects. The heavy GLS disease pressure, meticulous disease ratings, and large population size of this study afforded us the sensitivity for detecting QTL effects. QTLs located on three chromosomes (1, 4, and 8) had large effects on GLS resistance, each explaining 35.0–56.0%, 8.8–14.3%, and 7.7–11.0% of the variance, respectively. These three QTL effects were remarkably consistent across three disease evaluations over 2 years and two generations. Smaller QTL effects were also found on chromosomes 2 and 5, but the chromosome-5 effect might be a false positive because it was not repeatable even in the same location. The chromosome-1 QTLs had the largest effect or highest R² reported for any quantitative trait to-date. Except for the chromosome-4 gene, which was from the susceptible parent B73, the resistance alleles at all QTL were derived from Va14. The resistance QTLs on chromosomes 1 and 2 appear to have additive effects, but those on chromosomes 4 and 8 are dominant and recessive, respectively. Significant interaction between the QTLs on chromosomes 1 and 4 was detected in all three evaluations. Cumulatively, the four QTLs identified in this study explained 44, 60, and 68% of the variance in F₂, and in F₃ replications 1 and 2, respectively.     

An EM algorithm for mapping quantitative resistance loci

Many disease resistance traits in plants have a polygenic background and the disease phenotypes are modified by environmental factors. As a consequence, the phenotypic values usually show a quantitative variation. The phenotypes of such disease traits, however, are often measured in discrete but ordered categories. These traits are called ordinal traits. In terms of disease resistance, they are called quantitative resistance traits, as opposed to qualitative resistance traits, and are controlled by the quantitative resistance loci (QRL). Classical quantitative trait locus mapping methods are not optimal for ordinal trait analysis because the assumption of normal distribution is violated. Methods for mapping binary trait loci are not suitable either because there are more than two categories in ordinal traits. We developed a maximum likelihood method to map these QRL. The method is implemented via a multicycle expectation-conditional-maximization (ECM) algorithm under the threshold model, where we can estimate both the QRL effects and the thresholds that link the disease liability and the categorical phenotype. The method is verified in simulated data under various combinations of the parameters. An SAS program is available to implement the multicycle ECM algorithm. The program can be downloaded from our website at www.statgen.ucr.edu.     

Systems genetics approach reveals candidate genes for parasite resistance from quantitative trait loci studies in agricultural species

A systems genetics approach combining pathway analysis of quantitative trait loci (QTL) and gene expression information has provided strong evidence for common pathways associated with genetic resistance to internal parasites. Gene data, collected from published QTL regions in sheep, cattle, mice, rats and humans, and microarray data from sheep, were converted to human Entrez Gene IDs and compared to the KEGG pathway database. Selection of pathways from QTL data was based on a selection index that ensured that the selected pathways were in all species and the majority of the projects overall and within species. Pathways with either up- and down-regulated genes, primarily up-regulated genes or primarily down-regulated genes, were selected from gene expression data. After comparing the data sets independently, the pathways from each data set were compared and the common set of pathways and genes was identified. Comparisons within data sets identified 21 pathways from QTL data and 66 pathways from gene expression data. Both selected sets were enriched with pathways involved in immune functions, disease and cell responses to signals. The analysis identified 14 pathways that were common between QTL and gene expression data, and four directly associated with IFNγ or MHCII, with 31 common genes, including three MHCII genes. In conclusion, a systems genetics approach combining data from multiple QTL and gene expression projects led to the discovery of common pathways associated with genetic resistance to internal parasites. This systems genetics approach may prove significant for the discovery of candidate genes for many other multifactorial, economically important traits.     

A genome-screen experiment to detect quantitative trait loci affecting resistance to facial eczema disease in sheep

Facial eczema (FE) is a secondary photosensitization disease arising from liver cirrhosis caused by the mycotoxin sporidesmin. The disease affects sheep, cattle, deer and goats, and costs the New Zealand sheep industry alone an estimated NZ$63M annually. A long-term sustainable solution to this century-old FE problem is to breed for disease-resistant animals by marker-assisted selection. As a step towards finding a diagnostic DNA test for FE sensitivity, we have conducted a genome-scan experiment to screen for quantitative trait loci (QTL) affecting this trait in Romney sheep. Four F₁ sires, obtained from reciprocal matings of FE resistant and susceptible selection-line animals, were used to generate four outcross families. The resulting half-sib progeny were artificially challenged with sporidesmin to phenotype their FE traits measured in terms of their serum levels of liver-specific enzymes, namely gamma-glutamyl transferase and glutamate dehydrogenase. In a primary screen using selective genotyping on extreme progeny of each family, a total of 244 DNA markers uniformly distributed over all 26 ovine autosomes (with an autosomal genome coverage of 79–91%) were tested for linkage to the FE traits. Data were analysed using Haley–Knott regression. The primary screen detected one significant and one suggestive QTL on chromosomes 3 and 8 respectively. Both the significant and suggestive QTL were followed up in a secondary screen where all progeny were genotyped and analysed; the QTL on chromosome 3 was significant in this analysis.     

A genome-wide meta-analysis of rice blast resistance genes and quantitative trait loci provides new insights into partial and complete resistance

The completion of the genome sequences of both rice and Magnaporthe oryzae has strengthened the position of rice blast disease as a model to study plant-pathogen interactions in monocotyledons. Genetic studies of blast resistance in rice were established in Japan as early as 1917. Despite such long-term study, examples of cultivars with durable resistance are rare, partly due to our limited knowledge of resistance mechanisms. A rising number of blast resistance genes and quantitative trait loci (QTL) have been genetically described, and some have been characterized during the last 20 years. Using the rice genome sequence, can we now go a step further toward a better understanding of the genetics of blast resistance by combining all these results? Is such knowledge appropriate and sufficient to improve breeding for durable resistance? A review of bibliographic references identified 85 blast resistance genes and approximately 350 QTL, which we mapped on the rice genome. These data provide a useful update on blast resistance genes as well as new insights to help formulate hypotheses about the molecular function of blast QTL, with special emphasis on QTL for partial resistance. All these data are available from the OrygenesDB database.     

Identification of quantitative trait loci and candidate genes for specific cellular resistance responses against Didymella pinodes in pea

Key message

Phenotyping of specific cellular resistance responses and improvement of previous genetic map allowed the identification of novel genomic regions controlling cellular mechanisms involved in pea resistance to ascochyta blight and provided candidate genes suitable for MAS.

Abstract

Didymella pinodes, causing ascochyta blight, is a major pathogen of the pea crop and is responsible for serious damage and yield losses. Resistance is inherited polygenically and several quantitative trait loci (QTLs) have been already identified. However, the position of these QTLs should be further refined to identify molecular markers more closely linked to the resistance genes. In previous works, resistance was scored visually estimating the final disease symptoms; in this study, we have conducted a more precise phenotyping of resistance evaluating specific cellular resistance responses at the histological level to perform a more accurate QTL analysis. In addition, P665 × Messire genetic map used to identify the QTLs was improved by adding 117 SNP markers located in genes. This combined approach has allowed the identification, for the first time, of genomic regions controlling cellular mechanisms directly involved in pea resistance to ascochyta blight. Furthermore, the inclusion of the gene-based SNP markers has allowed the identification of candidate genes co-located with QTLs and has provided robust markers for marker-assisted selection.     

QTLs for tomato powdery mildew resistance (Oidium lycopersici) in Lycopersicon parviflorum G1.1601 co-localize with two qualitative powdery mildew resistance genes

Tomato (Lycopersicon esculentum) is susceptible to the powdery mildew Oidium lycopersici, but several wild relatives such as Lycopersicon parviflorum G1.1601 are completely resistant. An F2 population from a cross of Lycopersicon esculentum cv. Moneymaker x Lycopersicon parviflorum G1.1601 was used to map the O. lycopersici resistance by using amplified fragment length polymorphism markers. The resistance was controlled by three quantitative trait loci (QTLs). Ol-qtl1 is on chromosome 6 in the same region as the Ol-1 locus, which is involved in a hypersensitive resistance response to O. lycopersici. Ol-qtl2 and Ol-qtl3 are located on chromosome 12, separated by 25 cM, in the vicinity of the Lv locus conferring resistance to another powdery mildew species, Leveillula taurica. The three QTLs, jointly explaining 68% of the phenotypic variation, were confirmed by testing F3 progenies. A set of polymerase chain reaction-based cleaved amplified polymorphic sequence and sequence characterized amplified region markers was generated for efficient monitoring of the target QTL genomic regions in marker assisted selection. The possible relationship between genes underlying major and partial resistance for tomato powdery mildew is discussed.     

Disease resistance gene homologs correlate with disease resistance loci of Arabidopsis thaliana

The disease resistance genes RPS2 of Arabidopsis and N of tobacco, among other recently cloned resistance genes, share several conserved sequences. Degenerate oligonucleotide primers, based on conserved sequences in the nucleotide binding site (NBS) and a weak hydrophobic domain of RPS2 and N, were used to amplify homologous sequences from Arabidopsis thaliana. Amplification products were obtained that were similar in sequence to the disease resistance genes RPS2, RPM1, N and L6. The Arabidopsis CIC-YAC library was used to identify the position of the disease resistance homologs on the Arabidopsis genome. Their map positions could be correlated with the disease resistance loci RPS5, RAC1, RPP9, CAR1, RPP7, RPW2, RPP1, RPP10, RPP14, RPP5, RPP4, RPS2, RPW6, HRT, RPS4, RPP8, RPP21, RPP22, RPP23, RPP24 and TTR1. This method was therefore not only successful in the identification of sequences located within gene clusters that are involved in disease resistance, but could also contribute to the cloning of disease resistance genes from Arabidopsis.     

Mapping of quantitative trait loci affecting Drechslera teres resistance in barley with molecular markers

 Resistance loci for seedling-stage resistance to net blotch disease (Drechslera teres) in barley were mapped with molecular markers in an F₂ population derived from a cross between the susceptible barley cultivar ‘Arena’ and the resistant Ethiopian landrace ‘Hor 9088’. Disease reactions were scored with first and second leaves of 2-week-old plants 7 and 9 days after inoculation with a single spore-derived isolate. For linkage analysis, 22 RFLP markers and 284 AFLP markers were used. The seven linkage groups covered 1153.3 cM with an average marker interval of 3.76 cM. The resistance was determined to be inherited in a quantitative manner. Altogether, 12 QTLs were mapped with positions depending on the leaf used for testing and the time period after infection. Heritability in the broad sense ranged between 0.21 and 0.37. Received: 26 May 1998 / Accepted: 9 June 1998     

Detection of quantitative trait loci associated with leaf rust resistance in bread wheat.

Leaf rust, caused by Puccinia recondita Rob. ex Desm., is a common disease in wheat. The objective of this study was to develop molecular markers associated with the quantitative trait loci (QTLs) putatively conferring durable leaf rust resistance in Triticum aestivum L. em. Thell. A population of 77 recombinant inbred lines (RILs) developed from 'Parula' (resistant) and 'Siete Cerros' (moderately susceptible) was used. Bulked segregant analysis was done using random amplified polymorphic DNAs (RAPDs) with DNA enriched for low-copy sequences using hydroxyapatite chromatography. Out of 400 decamer primers screened, 3 RAPD markers were identified between the bulk of the most resistant and the bulk of the most susceptible lines. These were cloned and used as probes on the RILs in Southern hybridizations. Two probes revealed two tightly linked loci. One-way analysis of variance showed that these two loci, and another revealed by the third probe, were linked to QTLs controlling leaf rust resistance based on data taken from 2 years of replicated field trials. Cytogenetic analysis placed the two tightly linked loci on the long arm of chromosome 7B. The third probe detected loci located on the short arms of chromosomes 1B and 1D. It is suggested that the QTL detected on 7BL may well be homoeoallelic to Lr34.     

Evolving disease resistance genes

Defenses against most specialized plant pathogens are often initiated by a plant disease resistance gene. Plant genomes encode several classes of genes that can function as resistance genes. Many of the mechanisms that drive the molecular evolution of these genes are now becoming clear. The processes that contribute to the diversity of R genes include tandem and segmental gene duplications, recombination, unequal crossing-over, point mutations, and diversifying selection. Diversity within populations is maintained by balancing selection. Analyses of whole-genome sequences have and will continue to provide new insight into the dynamics of resistance gene evolution.     

Rust mite resistance in apple assessed by quantitative trait loci analysis

The aim of this study was to assess the genetic basis of rust mite (Aculus schlechtendali) resistance in apple (Malus × domestica). A. schlechtendali infestation of apple trees has increased as a consequence of reduced side effects of modern fungicides on rust mites. An analysis of quantitative trait loci (QTLs) was carried out using linkage map data available for F₁ progeny plants of the cultivars ‘Fiesta’ × ‘Discovery’. Apple trees representing 160 different genotypes were surveyed for rust mite infestation, each at three different sites in two consecutive years. The distribution of rust mites on the individual apple genotypes was aggregated and significantly affected by apple genotype and site. We identified two QTLs for A. schlechtendali resistance on linkage group 7 of ‘Fiesta’. The AFLP marker E35M42-0146 (20.2 cM) and the RAPD marker AE10-400 (45.8 cM) were closest positioned to the QTLs and explained between 11.0% and 16.6% of the phenotypic variability. Additionally, putative QTLs on the ‘Discovery’ chromosomes 4, 5 and 8 were detected. The SSR marker Hi03a10 identified to be associated to one of the QTLs (AFLP marker E35M42-0146) was traced back in the ‘Fiesta’ pedigree to the apple cultivar ‘Wagener’. This marker may facilitate the breeding of resistant apple cultivars by marker assisted selection. Furthermore, the genetic background of rust mite resistance in existing cultivars can be evaluated by testing them for the identified SSR marker. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Mapping quantitative trait loci for downy mildew resistance in pearl millet

Quantitative trait loci (QTLs) for resistance to pathogen populations of Scelerospora graminicola from India, Nigeria, Niger and Senegal were mapped using a resistant x susceptible pearl millet cross. An RFLP map constructed using F₂ plants was used to map QTLs for traits scored on F₄ families. QTL analysis was carried out using the interval mapping programme Mapmaker/QTL. Independent inheritance of resistance to pathogen populations from India, Senegal, and populations from Niger and Nigeria was shown. These results demonstrate the existence of differing virulences in the pathogen populations from within Africa and between Africa and India. QTLs of large effect, contributing towards a large porportion of the variation in resistance, were consistently detected in repeated screens. QTLs of smaller and more variable effect were also detected. There was no QTLs that were effective against all four pathogen populations, demonstrating that pathotype-specific resistance is a major mechanism of downy mildew resistance in this cross. For all but one of the QTLs, resistance was inherited from the resistant parent and the inheritance of resistance tended to be the result of dominance or over-dominance. The implications of this research for pearl millet breeding are discussed.