The effects of selective genotyping on estimates of proportion of recombination between linked quantitative trait loci

Selective genotyping is the marker assay of only the more extreme phenotypes for a quantitative trait and is intended to increase the efficiency of quantitative trait loci (QTL) mapping. We show that selective genotyping can bias estimates of the recombination frequency between linked QTLs — upwardly when QTLs are in repulsion phase, and downwardly when QTLs are in coupling phase. We examined these biases under simple models involving two QTLs segregating in a backcross or F₂ population, using both analytical models and computer simulations. We found that bias is a function of the proportion selected, the magnitude of QTL effects, distance between QTLs and the dominance of QTLs. Selective genotyping thus may decrease the power of mapping multiple linked QTLs and bias the construction of a marker map. We suggest a large proportion than previously suggested (50%) or the entire population be genotyped if linked QTLs of large effects (explain > 10% phenotypic variance) are evident. New models need to be developed to explicitly incorporate selection into QTL map construction.     

Mendelian Factors Underlying Quantitative Traits in Tomato: Comparison across Species, Generations, and Environments

As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 in only a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii).(ABSTRACT TRUNCATED AT 400 WORDS)     

Transgressive segregation for very low and high levels of basal resistance to powdery mildew in barley

Basal resistance of barley to powdery mildew is a quantitatively inherited trait that limits the growth and sporulation of barley powdery mildew pathogen by a non-hypersensitive mechanism of defense. Two experimental barley lines were developed with a very high (ErBgh) and low (EsBgh) level of basal resistance to powdery mildew by cycles of convergent crossing and phenotypic selection between the most resistant and between the most susceptible lines, respectively, from four mapping populations of barley. Phenotypic selection in convergent crossing was highly effective in producing contrasting phenotypes for basal resistance and susceptibility. In ErBgh, almost 90% of infection units failed to form a primary haustorium in the epidermal cells in association with papilla formation, but in EsBgh only 33% of infection units failed to form a primary haustorium. The contrast between ErBgh and EsBgh for successful formation of secondary and subsequent haustoria was much less obvious (69% versus 79% successful secondary haustorium formation). In an earlier investigation, we determined seven QTLs for basal resistance in the four mapping populations. Checking the peak markers of these QTLs indicated that only four out of seven QTLs were confirmed to be present in the selected resistant lines and only four QTLs for susceptibility were confirmed to be present in the selected susceptible lines. Surprisingly, none of the expected QTLs could be detected in the resistant line ErBgh. We discuss some reasons why marker aided selection might be less efficient in raising levels of basal resistance than phenotypic selection. The very resistant and susceptible lines developed here are valuable material to be used in further experiments to characterize the molecular basis of basal resistance to powdery mildew.     

Towards the cloning of genes underlying murine QTLs

Many quantitative trait loci (QTLs), including those for ethanol-related traits, have been mapped in the mouse. In light of rapidly developing tools and resources, we briefly review the strategy for identifying the genes underlying these QTLs. We note that positional cloning will soon be a matter of testing candidate genes rather than discovering genes; therefore, we describe a ``congenic test' to support that a candidate gene is indeed a QTL. Considering the rapid development of congenics and mutants, we also identify four areas of investigation—phenotypes, ethanol specificity, environment, and gene interactions—that might be exploited during the course of positional cloning to gain insights into QTL pathways. In particular, we note that multiple mutants of nearly every major neurotransmitter pathway have now been made. These mutants are not only useful for phenotypic tests, but also could be used to conduct ``gene dependence' tests of QTLs. We also consider potential applications for the very recently developed ability to clone mice. Received: 15 September 1998 / Accepted: 8 October 1998     

Molecular diversity, structure and domestication of grasses

Map-based cloning has been considered problematic for isolating quantitative trait loci (QTLs) due to the confounding phenotypic effects of environment and other QTLs. However, five recent studies, all in plants, have succeeded in cloning QTLs using map-based methods. We review the important features of these studies and evaluate the prospects for broader application of the techniques. Successful map-based cloning requires that QTLs represent single genes that can be isolated in near-isogenic lines, and that genotypes can be unambiguously inferred by progeny testing. In plants or animals for which map-based cloning of genes with discrete phenotypes is feasible, the modified procedures required for QTLs should not be limiting in most cases. The choice between map-based cloning and alternative methods will depend on details of the species and traits being studied.     

THE DETECTION OF PLASTICITY GENES IN HETEROGENEOUS ENVIRONMENTS

The molecular genetic mechanisms for phenotypic plasticity across heterogeneous macro- and microenvironments were examined using the Populus genomic map constructed by DNA-based markers. Three hypotheses have been suggested to explain genetic variation in phenotypic response to varying environments (i.e., reaction norm): Lerner's homeostasis, allelic sensitivity, and gene regulation. The homeostasis hypothesis, which predicts that heterozygotes are less sensitive to the environment than homozygotes, was supported for phenotypic plasticity to unpredictable environments (microenvironmental plasticity) at the whole-genome level, but for phenotypic plasticity to predictable environments (macroenvironmental plasticity) the hypothesis was supported only at functioning quantitative trait loci (QTLs). For all growth traits studied, gene regulation was suggested to play a prevailing role in determining the norms of reaction to environments. Indirect evidence for gene regulation is that there tend to be more QTLs with larger effects on the phenotype in optimal growing conditions than suboptimal growing conditions because the expression of these QTLs identified is mediated by regulatory genes. Direct evidence for gene regulation is the identification of some loci that differ from QTLs for trait values within environments and exert an environmentally dependent control over structural gene expression. In this study, fewer environmentally sensitive QTLs were detected that display unparalleled allelic effects across environments. For stem height, there were more regulatory loci and more structural loci (whose expression is determined by gene regulation) affecting phenotypic plasticity than for basal area. It was found that microenvironmental plasticity was likely controlled by different genetic systems than those for macroenvironmental plasticity.     

Treatment- and population-dependent activity patterns of behavioral and expression QTLs

Genetic control of gene expression and higher-order phenotypes is almost invariably dependent on environment and experimental conditions. We use two families of recombinant inbred strains of mice (LXS and BXD) to study treatment- and genotype-dependent control of hippocampal gene expression and behavioral phenotypes. We analyzed responses to all combinations of two experimental perturbations, ethanol and restraint stress, in both families, allowing for comparisons across 8 combinations of treatment and population. We introduce the concept of QTL activity patterns to characterize how associations between genomic loci and traits vary across treatments. We identified several significant behavioral QTLs and many expression QTLs (eQTLs). The behavioral QTLs are highly dependent on treatment and population. We classified eQTLs into three groups: cis-eQTLs (expression variation that maps to within 5 Mb of the cognate gene), syntenic trans-eQTLs (the gene and the QTL are on the same chromosome but not within 5 Mb), and non-syntenic trans-eQTLs (the gene and the QTL are on different chromosomes). We found that most non-syntenic trans-eQTLs were treatment-specific whereas both classes of syntenic eQTLs were more conserved across treatments. We also found there was a correlation between regions along the genome enriched for eQTLs and SNPs that were conserved across the LXS and BXD families. Genes with eQTLs that co-localized with the behavioral QTLs and displayed similar QTL activity patterns were identified as potential candidate genes associated with the phenotypes, yielding identification of novel genes as well as genes that have been previously associated with responses to ethanol.     

A New Method to Infer Causal Phenotype Networks Using QTL and Phenotypic Information

In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs) to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO) algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG) algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request.     

Differences in DBA/1J and DBA/2J reveal lipid QTL genes

Recent advances in mouse genomics have revealed considerable variation in the form of single-nucleotide polymorphisms (SNPs) among common inbred strains. This has made it possible to characterize closely related strains and to identify genes that differ; such genes may be causal for quantitative phenotypes. The mouse strains DBA/1J and DBA/2J differ by just 5.6% at the SNP level. These strains exhibit differences in a number of metabolic and lipid phenotypes, such as plasma levels of triglycerides (TGs) and HDL. A cross between these strains revealed multiple quantitative trait loci (QTLs) in 294 progeny. We identified significant TG QTLs on chromosomes (Chrs) 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 16, and 19, and significant HDL QTLs on Chrs 3, 9, and 16. Some QTLs mapped to chromosomes with limited variability between the two strains, thus facilitating the identification of candidate genes. We suggest that Tshr is the QTL gene for Chr 12 TG and HDL levels and that Ihh may account for the TG QTL on Chr 1. This cross highlights the advantage of crossing closely related strains for subsequent identification of QTL genes.     

Diversification in flowering time due to tandem FT-like gene duplication, generating novel Mendelian factors in wild and cultivated rice

The complex structure of a single Mendelian factor widespread in the Asian cultivated rice ( Oryza sativa ) and its wild progenitor ( Oryza rufipogon ) that caused diverse phenotypes in the timing of flowering under natural field conditions was investigated in near isogenic lines. These near isogenic lines showed differences in flowering time despite all eight accessions collected from tropical regions possessing a recessive gene allelic to the se-pat gene. Fine mapping in two of these near-isogenic lines revealed that cultivated (Patpaku) and wild (W593) accessions had three and two linked quantitative trait loci (QTL) in the candidate regions, respectively, showing that Patpaku and W593 possessed linked QTLs with different effects in addition to the commonly-observed recessive gene ( se-pat ). Molecular dissection suggested that the tandemly duplicated FT-like genes ( Hd3a and RFT1 ) could be the candidate genes for these QTLs. Interestingly, the linked QTLs differed in their epistases, degree of dominance, and genotype × environment interactions. The nucleotide sequences showed that RFT1 has diverged more rapidly than Hd3a during rice evolution, suggesting phenotypic diversification of the two genes. Phylogenetic analysis implied that the se-pat ⁺ alleles might have emerged in different lineages within O. sativa . The present results strongly suggest that nucleotide divergence and shuffling of the linked QTLs by recombination might have created novel Mendelian factors that probably contribute to responding to local environments.     

Mapping quantitative trait loci controlling seed dormancy and heading date in rice, Oryza sativa L., using backcross inbred lines

 To detect quantitative trait loci (QTLs) controlling seed dormancy, 98 BC₁F₅ lines (backcross inbred lines) derived from a backcross of Nipponbare (japonica)/Kasalath (indica)//Nipponbare were analyzed genetically. We used 245 RFLP markers to construct a framework linkage map. Five putative QTLs affecting seed dormancy were detected on chromosomes 3, 5, 7 (two regions) and 8, respectively. Phenotypic variations explained by each QTL ranged from 6.7% to 22.5% and the five putative QTLs explained about 48% of the total phenotypic variation in the BC₁F₅ lines. Except for those of the QTLs on chromosome 8, the Nipponbare alleles increased the germination rate. Five putative QTLs controlling heading date were detected on chromosomes 2, 3, 4, 6 and 7, respectively. The phenotypic variation explained by each QTL for heading date ranged from 5.7% to 23.4% and the five putative QTLs explained about 52% of the total phenotypic variation. The Nipponbare alleles increased the number of days to heading, except for those of two QTLs on chromosomes 2 and 3. The map location of a putative QTL for heading date coincided with that of a major QTL for seed dormancy on chromosome 3, although two major heading-date QTLs did not coincide with any seed dormancy QTLs detected in this study. Received: 10 October 1997 / Accepted: 12 January 1998     

The Genetic Basis of Natural Variation in Oenological Traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae is the main microorganism responsible for wine alcoholic fermentation. The oenological phenotypes resulting from fermentation, such as the production of acetic acid, glycerol, and residual sugar concentration are regulated by multiple genes and vary quantitatively between different strain backgrounds. With the aim of identifying the quantitative trait loci (QTLs) that regulate oenological phenotypes, we performed linkage analysis using three crosses between highly diverged S. cerevisiae strains. Segregants from each cross were used as starter cultures for 20-day fermentations, in synthetic wine must, to simulate actual winemaking conditions. Linkage analysis on phenotypes of primary industrial importance resulted in the mapping of 18 QTLs. We tested 18 candidate genes, by reciprocal hemizygosity, for their contribution to the observed phenotypic variation, and validated five genes and the chromosome II right subtelomeric region. We observed that genes involved in mitochondrial metabolism, sugar transport, nitrogen metabolism, and the uncharacterized ORF YJR030W explained most of the phenotypic variation in oenological traits. Furthermore, we experimentally validated an exceptionally strong epistatic interaction resulting in high level of succinic acid between the Sake FLX1 allele and the Wine/European MDH2 allele. Overall, our work demonstrates the complex genetic basis underlying wine traits, including natural allelic variation, antagonistic linked QTLs and complex epistatic interactions between alleles from strains with different evolutionary histories.     

水稻品种USSR5早熟性的遗传分析

USSR5为极早熟的前苏联品种,以抽穗期近等基因系和抽穗期QTL近等基因系为测验品种,对USSR5的抽穗期基因型进行分析,表明USSR5携带了非感光基因e1、无感光功能的Se-1e基因、感光抑制基因i-Se-1和显性早熟基因Ef-1,从而使它表现极早熟的特性。此外,本研究调查了USSR5和N22的BC1F1和F2群体的抽穗期,利用WindowsQTLCartographer1.13a软件,采用复合区间作图法,在全基因组范围内,分析了南京夏季正常日照条件下2个群体的抽穗期QTL,在USSR5/N22//USSR5BC1F1群体,共检测到2个位点,分别位于第7、8染色体上,其LOD值分别是6.11和2.91,对表型总变异的解释率分别为27.38%和11.15%,2个位点上来自USSR5的等位基因均提早抽穗。在USSR5/N22F2群体,共检测到5个位点,分别位于第1、2、7、9、10染色体上。5个位点LOD值介于3.02～8.4,对表型总变异的解释率分别为4.07%和15.41%。除qHd-9外,其余控制抽穗期的4个基因位点上提早抽穗的等位基因均来源于USSR5。比较分析发现效应较大的qHd-7即是Hd4(E1),USSR5在该位点上携带非感光基因hd4(e1)。尽管本研究定位的其它抽穗期QTL和已知抽穗期基因之间尚不能一一对应,但在早熟性水稻品种选育中,USSR5将可作为良好的基因源加以利用。     

AFLP mapping and detection of quantitative trait loci (QTLs) for economically important traits in Pinus sylvestris: a preliminary study

We have applied a two-way pseudo-testcross strategy in an analysis of Pinus sylvestris for genetic mapping and detection of quantitative trait loci (QTLs) associated with economically important traits targeted in the Swedish tree-breeding programme. Based on 94 full-sib progeny of a cross between two plus-trees from northern Sweden we generated two parental maps using AFLP markers. The female map was comprised of 94 markers assigned to 15 linkage groups giving a size of 796 cM. On the male map 155 markers were assigned to 15 linkage groups, giving a total size of 1335 cM. The recombination frequency was found to be sex-dependent, being 29.3% higher in male than in female gametes. On the female map, 12 QTLs were detected (but none for branch diameter or wood density). Three QTLs for tree height accounted for 25.8% of the total phenotypic variation of this trait. When the QTLs detected for all the traits were taken independently, the percentages of phenotypic variance ranged from 9.3% to 22.7%. The highest value was observed for frost hardiness, an important trait in northern Sweden for which a major gene seemed to be involved. A cluster of QTLs for tree height, trunk diameter and volume was located on one linkage group. On the male map, four QTLs for trunk diameter and volume were detected. Due to the reduced number of individuals under study, the results are preliminary and have to be validated on more trees.     

Fine mapping of qPAA8, a gene controlling panicle apical development in rice

In rice, one detrimental factor influencing single panicle yield is the frequent occurrence of panicle apical abortion (PAA) under unfavorable climatic conditions. Until now, no detailed genetic information has been available to avoid PAA in rice breeding. Here, we show that the occurrence of PAA is associated with the accumulation of excess hydrogen peroxide. Quantitative trait loci (QTLs) mapping for PAA in an F(2) population derived from the cross of L-05261 (PAA line) × IRAT129 (non-PAA variety) identified seven QTLs over a logarithm of the odd (LOD) threshold of 2.5, explaining approximately 50.1% of phenotypic variance for PAA in total. Five of the QTLs with an increased effect from L-05261, were designated as qPAA3-1, qPAA3-2, qPAA4, qPAA5 and qPAA8, and accounted for 6.8%, 5.9%, 4.2%, 13.0% and 12.2% of phenotypic variance, respectively. We found that the PAA in the early heading plants was mainly controlled by qPAA8. Subsequently, using the sub-populations specific for qPAA8 based on marker-assisted selection, we further narrowed qPAA8 to a 37.6-kb interval delimited by markers RM22475 and 8-In112. These results are beneficial for PAA gene clone.     

A QTL analysis of female variation contributing to refractoriness and sperm competition in Drosophila melanogaster

Sperm competition is an important fitness component in many animal groups. Drosophila melanogaster males exhibit substantial genetic variation for sperm competitive ability and females show considerable genetic variation for first versus second male sperm use. Currently, the forces responsible for maintaining genetic variation in sperm competition related phenotypes are receiving much attention. While several candidate genes contributing to the variation seen in male competitive ability are known, genes involved in female sperm use remain largely undiscovered. Without knowledge of the underlying genes, it will be difficult to distinguish between different models of sexual selection such as cryptic female choice and sexual conflict. We used quantitative trait locus (QTL) mapping to identify regions of the genome contributing to female propensity to use first or second male sperm, female refractoriness to re-mating, and early-life fertility. The most well supported markers influencing the phenotypes include 33F/34A (P2), 57B (refractoriness) and 23F/24A (fertility). Between 10% and 15% of the phenotypic variance observed in these recombinant inbred lines was explained by these individual QTLs. More detailed investigation of the regions detected in this experiment may lead to the identification of genes responsible for the QTLs identified here.     

Correlation and Quantitative Trait Loci Analyses of Total Chlorophyll Content and Photosynthetic Rate of Rice (Oryza sativa) under Water Stress and Well-watered Conditions

In order to explore the relevant molecular genetic mechanisms of photosynthetic rate (PR) and chlorophyll content (CC) in rice ( Oryza sativa L.), we conducted a series of related experiments using a population of recombinant inbred lines (Zhenshan97B × IRAT109). We found a significant correlation between CC and PR ( R = 0.19**) in well-watered conditions, but no significant correlation during water stress ( r = 0.08). We detected 13 main quantitative trait loci (QTLs) located on chromosomes 1, 2, 3, 4, 5, 6, and 10, which were associated with CC, including six QTLs located on chromosomes 1, 2, 3, 4, and 5 during water stress, and seven QTLs located on chromosomes 2, 3, 4, 6, and 10 in well-watered conditions. These QTLs explained 47.39% of phenotypic variation during water stress and 56.19% in well-watered conditions. We detected four main QTLs associated with PR; three of them ( qPR2 , qPR10 , qPR11 ) were located on chromosomes 2, 10, and 11 during water stress, and one ( qPR10 ) was located on chromosome 10 in well-watered conditions. These QTLs explained 34.37% and 18.41% of the phenotypic variation in water stress and well-watered conditions, respectively. In total, CC was largely controlled by main QTLs, and PR was mainly controlled by epistatic QTL pairs.     

Mapping quantitative trait loci controlling oil content,oleic acid and linoleic acid content in sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

Sunflower oil with high oleic acid content is in great demand due to its nutritional as well as industrial benefits. The trait is mainly controlled by dominant alleles at a major gene, Ol, with other modifiers. The objectives of this research were to map the oil content, oleic acid and linoleic acid content in sunflower seeds. An F2 mapping population from cytoplasmic male-sterile line COSF 7A (33–35 % oleic acid) and high oleic acid inbred line HO 5–13 (88–90 % oleic acid) was developed and phenotyped for oil content, oleic acid and linoleic acid content at the F2 seed level. High phenotypic and genotypic coefficients of variation were recorded for oleic acid and linoleic acid content. High heritability and high genetic advance as percent of mean was recorded for oleic acid and linoleic acid content. This indicated the presence of the additive type of gene action controlling the traits oleic acid content and linoleic acid content. The Ol gene was mapped to linkage group (LG) 14 and tightly linked to the marker HO_Fsp_b. In addition, two more quantitative trait loci (QTLs) for oleic acid content were identified in LG8 and LG9. Two QTLs for oil content and two QTLs for linoleic acid content were also identified. All these QTLs explained over 10 % of phenotypic variation. A study was conducted with 13 genotypes differing in oil quality as well as quantity over three seasons to assess the reliability of the identified QTLs over seasons. It resulted in the identification of two potential QTLs for oleic acid as well as linoleic acid content with the markers ORS 762 and HO_Fsp_b. These markers explained more than 57.6–66.6 % of phenotypic variation. Hence it can be concluded that these markers/QTLs would be useful in the marker-assisted selection breeding programme to improve oil quality. The present study also indicated the presence of at least two other genomic regions controlling oleic and linoleic acid content in sunflower.     

QTL molecular marker location of powdery mildew resistance in cucumber (Cucumis sativus L.)

The cucumber lines, S94 (Northern China open-field type, powdery mildew (PM) susceptible) and S06 (European greenhouse type, PM resistant), and their F6:7 populations were used to investigate PM re-sistance under seedling spray inoculation in 2005/Autumn and 2006/Spring. QTL analysis was under-taken based on a constructed molecular linkage map of the corresponding F6 population using com-posite interval mapping. A total of four QTLs (pm1.1, pm2.1, pm4.1 and pm6.1) for PM resistance were identified and located on LG 1, 2, 4 and 6, respectively, explaining 5.2%-21.0% of the phenotypic variation. Three consistent QTLs (pm1.1, pm2.1 and pm4.1) were detected under the two test conditions. The QTL pm6.1 was only identified in 2005/Autumn. The total phenotypic variation explained by the QTLs was 52.0% and 42.0% in 2005/Autumn and 2006/Spring, respectively. Anchor markers tightly linked to those loci (<5 cM) could lay a basis for both molecular marker-assisted breeding and map-based gene cloning of the PM-resistance gene in cucumber.