Controlling the Type I and Type II Errors in Mapping Quantitative Trait Loci

Although the interval mapping method is widely used for mapping quantitative trait loci (QTLs), it is not very well suited for mapping multiple QTLs. Here, we present the results of a computer simulation to study the application of exact and approximate models for multiple QTLs. In particular, we focus on an automatic two-stage procedure in which in the first stage ``important' markers are selected in multiple regression on markers. In the second stage a QTL is moved along the chromosomes by using the pre-selected markers as cofactors, except for the markers flanking the interval under study. A refined procedure for cases with large numbers of marker cofactors is described. Our approach will be called MQM mapping, where MQM is an acronym for ``multiple-QTL models' as well as for ``marker-QTL-marker.' Our simulation work demonstrates the great advantage of MQM mapping compared to interval mapping in reducing the chance of a type I error (i.e., a QTL is indicated at a location where actually no QTL is present) and in reducing the chance of a type II error (i.e., a QTL is not detected).     

Study of strategies for selecting quantitative trait locus mapping procedures by computer simulation

Along with the development and integration of molecular genetics and quantitative genetics, many quantitative trait locus (QTL) mapping studies have been conducted using different mapping populations in various crop species. Existing QTLs can be used for marker-assisted breeding and map-based cloning, whereas the false-positive QTLs are no use. The purpose of this study is to evaluate the suitability of different mapping procedures for data from different genetic models. In this study, four types of recombinant inbred lines (RILs) with different genetic models, viz. additive QTLs (Model I), additive and epistatic QTLs (Model II), additive QTLs and QTL × environment interaction (Model III), additive, epistatic QTLs and QTL × environment interaction (Model IV), were simulated by computer. Six types of QTL mapping procedures, viz. CIM, MIMF, MIMR, ICIM, MQM and NWIM, on four kinds of QTL mapping software, viz. WinQTL Cartographer Version 2.5, IciMapping Version 2.0, MapQTL Version 5.0 and QTLnetwork Version 2.0, were used for screening QTLs of the simulated RILs. The results showed that different mapping procedures have different suitability for different genetic models. CIM and MQM can only screen Model I data. MIMR, MIMF and ICIM can only screen Model I and Model II data. NWIM can screen all four models’ data. It can be concluded that different genetic models’ data have different most suitable mapping procedures. In practical experiments where the genetic model of the data is unknown, a multiple model mapping strategy should be used, that is a full model scanning with complex model procedure followed by verification with other procedures corresponding to the scanning results.     

Interval Mapping of Multiple Quantitative Trait Loci

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.     

Mapping of quantitative trait loci for fire blight resistance in the apple cultivars 'Florina' and 'Nova Easygro'

Fire blight is a devastating bacterial disease of rosaceous plants. Its damage to apple production is a major concern, since no existing control option has proven to be completely effective. Some commercial apple varieties, such as 'Florina' and 'Nova Easygro', exhibit a consistent level of resistance to fire blight. In this study, we used an F1 progeny of 'Florina'?× 'Nova Easygro' to build parental genetic maps and identify quantitative trait loci (QTLs) related to fire blight resistance. Linkage maps were constructed using a set of microsatellites and enriched with amplified fragment length polymorphism (AFLP) markers. In parallel, progeny plants were artificially inoculated with Erwinia amylovora strain CFBP 1430 in a quarantine glasshouse. Shoot length measured 7?days after inoculation (DAI) and lesion length measured 7 and 14 DAI were used to calculate the lesion length as a percentage of the shoot length (PLL1 and PLL2, respectively). Percent lesion length data were log10-transformed (log10(PLL)) and used to perform the Kruskal-Wallis test, interval mapping (IM), and multiple QTL mapping (MQM). Two significant fire blight resistance QTLs were detected in 'Florina'. One QTL was mapped on linkage group 10 by IM and MQM; it explained 17.9% and 15.3% of the phenotypic variation by MQM with log10(PLL1) and log10(PLL2) data, respectively. A second QTL was identified on linkage group 5 by MQM with log10(PLL2) data; it explained 10.1% of the phenotypic variation. Genotyping the plants of 'Florina' pedigree with the microsatellites flanking the QTLs showed that the QTLs on linkage groups 5 and 10 were inherited from 'Jonathan' and 'Starking' (a 'Red Delicious' sport mutation), respectively. Other putative QTLs (defined as QTLs with LOD scores above the chromosomal threshold and below the genome-wide threshold) were detected by IM on linkage groups 5 and 9 of 'Nova Easygro'.     

Molecular mapping of soybean aphid resistance genes in PI 567541B

The soybean aphid (Aphis glycines Matsumura) is an important pest of soybean [Glycine max (L.) Merr.] in North America since it was first reported in 2000. PI 567541B is a newly discovered aphid resistance germplasm with early maturity characteristics. The objectives of this study were to map and validate the aphid resistance genes in PI 567541B using molecular markers. A mapping population of 228 F₃ derived lines was investigated for the aphid resistance in both field and greenhouse trials. Two quantitative trait loci (QTLs) controlling the aphid resistance were found using the composite interval mapping method. These two QTLs were localized on linkage groups (LGs) F and M. PI 567541B conferred resistant alleles at both loci. An additive × additive interaction between these two QTLs was identified using the multiple interval mapping method. These two QTLs combined with their interaction explained most of the phenotypic variation in both field and greenhouse trials. In general, the QTL on LG F had less effect than the one on LG M, especially in the greenhouse trial. These two QTLs were further validated using an independent population. The effects of these two QTLs were also confirmed using 50 advanced breeding lines, which were all derived from PI 567541B and had various genetic backgrounds. Hence, these two QTLs identified and validated in this study could be useful in improving soybean aphid resistance by marker-assisted selection.     

Precision Mapping of Quantitative Trait Loci

Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.     

Identification of QTLs associated with Fusarium head blight resistance in Zhedar 2 barley

Fusarium head blight (FHB) in barley and wheat, caused by Fusarium graminearum, is a continual problem worldwide. Primarily, FHB reduces yield and quality, and results in the production of the toxin deoxynivalenol (DON), which can affect food safety. Identification of QTLs for FHB severity, DON level and related traits heading-date (HD) and plant-height (HT) with consistent effects across a set of environments, would provide the basis for marker-assisted selection (MAS) and potentially increase the efficiency of selection for resistance. A segregating population of 75 double-haploid lines, developed from the three-way cross Zhedar 2/ND9712//Foster, was used for genome mapping and FHB severity evaluation. A linkage map of 214 RFLP, SSR and AFLP markers was constructed. Phenotypic data were collected in replicated field trials from five environments in two growing seasons. The data were analyzed using MQTL software to detect quantitative trait locus (QTL) × environment (E) interactions. Because of the presence of QTL × E, the MQM procedure in MAPQTL was applied to identify QTLs in single environments. We identified nine QTLs for FHB severity and five for low DON. Many of the disease-related QTLs identified were coincident with FHB QTLs identified in previous studies. Only two of the QTLs identified in this study were consistent across all five environments, and both were Zhedar 2 specific. Five of the FHB QTLs were associated with HD, and two were associated with HT. Regions that appear to be promising candidates for MAS and further genetic analysis include the two FHB QTLs on chromosome 2H and one on 6H, which were also associated with low DON and later heading-date in multiple environments. This study provides a starting point for manipulating Zhedar 2-derived resistance by MAS in barley to develop cultivars that will show effective resistance under disease pressure.Communicated by H.F. Linskens     

Comparative mapping of QTLs for agronomic traits of rice across environments by using a doubled-haploid population

We report here the RFLP mapping of quantitative trait loci (QTLs) which affect some important agronomic traits in cultivated rice. An anther culture-derived doubled-haploid (DH) population was established from a cross between indica and japonica rice varieties. A molecular linkage map comprising 137 markers was constructed based on this population which covered the rice genome at intervals of 14.8 cM on average. The linkage map was used to locate QTLs for such important agronomic traits as heading date, plant height, number of spikelets per panicle, number of grains per panicle, 1 000-grain weight and the percentage of seed set, by interval mapping. Evidence of genotype-by-environment interaction was found by comparing QTL maps of the same population grown in three diverse environments. A total of 22 QTLs for six agronomic traits was detected which were significant in at least one environment, but only seven were significant in all three environments; seven were significant in two environments and eight could only be detected in a single environment. However, QTLs-by-environment interaction was trait dependent. QTLs for spikelets and grains per panicle were common across environments while traits like heading date and plant height were more sensitive to environment. Received: 22 February 1996 / Accepted: 10 May 1996     

Mapping multiple QTL of different effects: comparison of a simple sequential testing strategy and multiple QTL mapping

The aim of this study was to explore, by computer simulation, the mapping of QTLs in a realistic but complex situation of many (linked) QTLs with different effects, and to compare two QTL mapping methods. A novel method to dissect genetic variation on multiple chromosomes using molecular markers in backcross and F₂ populations derived from inbred lines was suggested, and its properties tested using simulations. The rationale for this sequential testing method was to explicitly test for alternative genetic models. The method consists of a series of four basic statistical tests to decide whether variance was due to a single QTL, two QTLs, multiple QTLs, or polygenes, starting with a test to detect genetic variance associated with a particular chromosome. The method was able to distinguish between different QTL configurations, in that the probability to `detect' the correct model was high, varying from 0.75 to 1. For example, for a backcross population of 200 and an overall heritability of 50%, in 78% of replicates a polygenic model was detected when that was the underlying true model. To test the method for multiple chromosomes, QTLs were simulated on 10 chromosomes, following a geometric series of allele effects, assuming positive alleles were in coupling in the founder lines For these simulations, the sequential testing method was compared to the established Multiple QTL Mapping (MQM) method. For a backcross population of 400 individuals, power to detect genetic variance was low with both methods when the heritability was 0.40. For example, the power to detect genetic variation on a chromosome on which 6 QTLs explained 12.6% of the genetic variance, was less than 60% for both methods. For a large heritability (0.90), the power of MQM to detect genetic variance and to dissect QTL configurations was generally better, due to the simultaneous fitting of markers on all chromosomes. It is concluded that when testing different QTL configurations on a single chromosome using the sequential testing procedure, regions of other chromosomes which explain a significant amount of variation should be fitted in the model of analysis. This study reinforces the need for large experiments in plants and other species if the aim of a genome scan is to dissect quantitative genetic variation.     

Detection of quantitative trait loci for body weight at 10 weeks from Philippine wild mice

A genome-wide scan for quantitative trait loci (QTLs) controlling body weight at 10 weeks after birth was carried out in a population of 387 intersubspecific backcross mice derived from a cross between C57BL/6J inbred mice (Mus musculus domesticus) and wild mice (M. m. castaneus) captured in the Philippines, in order to discover novel QTLs from the wild mice that have about 60% lower body weight than C57BL/6J. By interval mapping, we detected four QTLs: a highly significant QTL on Chromosome (Chr) 2, which was common in both sexes; two significant QTLs on Chr 13, one male-specific and the other female-specific; and a suggestive male-specific QTL on X Chr. By composite interval mapping, we confirmed the presence of the three QTLs on Chrs 2 and 13, but not of the male-specific X-linked QTL. The composite interval mapping analysis newly identified three QTLs: a significant male-specific QTL on Chr 11 and two highly significant female-specific QTLs on Chrs 9 and X. Individual QTLs explained 3.8–11.6% of the phenotypic variance, and all the QTL alleles derived from the wild mice decreased body weight. A two-way analysis of variance revealed a significant epistatic interaction between the Chr 2 QTL and the background marker locus D12Mit4 on Chr 12 only in males. The interaction effect unexpectedly increased body weight. The chromosomal region containing the Chr 2 QTL did not coincide with those of growth or fatness QTLs mapped in previous studies. These results suggest that a population of wild mice may play an important role as new sources of valuable QTLs. Received: 14 January 2000 / Accepted: 14 April 2000     

Detection of quantitative trait loci influencing growth trajectories of adventitious roots in Populus using functional mapping

The capacity to root from cuttings is a key factor for the mass deployment of superior genotypes in clonal forestry. We studied the genetic basis of rooting capacity by mapping quantitative trait loci (QTLs) that control growth rate and form of root traits in a full-sib family of 93 hybrids derived from an interspecific cross between two Populus species, P. deltoides and P. euramericana. The hybrid family was typed for different marker systems (including SSRs, AFLPs, RAPDs, ISSRs, and SNPs), leading to the construction of two linkage maps based on the female P. deltoides (D map) and male P. euramericana (E map) with a pseudotestcross mapping strategy. The two maps were scanned by functional mapping to detect QTLs that control early growth trajectories of two rooting traits, maximal single-root length and the total number of roots per cutting, measured at five time points in water culture. Of the six QTLs detected for these two growth traits, only one is segregating in P. deltoides with poor rooting capacity, while the other five are segregating in P. euramericana showing good rooting capacity. Tests with functional mapping suggest different developmental patterns of the genetic effects of these root QTLs in time course. Five QTLs were detected to change their effects on root growth trajectories with time, whereas one detected to affect root growth consistently in time course. Knowledge about the genetic and developmental control mechanisms of root QTLs will have important implications for the genetic improvement of vegetative propagation traits in Populus.     

Mapping Quantitative Trait Loci Affecting Variation in Drosophila Triacylglycerol Storage

Objective: Recent genetic studies indicate that Drosophila melanogaster could be a powerful model to identify genes involved in mammalian adipocyte differentiation and fat storage. The objective of our study was to identify quantitative trait loci (QTLs) that contribute to variation in triacylglycerol (TAG) storage in two D. melanogaster laboratory strains. Research Methods and Procedures: We used two genetic mapping procedures to identify loci with main and epistatic effects on TAG storage. First, using 68 recombinant inbred lines derived from the unrelated Oregon R and Russian 2b strains, we mapped the location of QTLs affecting TAG storage using both composite interval mapping and Bayesian epistatic methods. Second, we used the quantitative deficiency mapping procedure to identify candidate genes affecting this trait within one of the QTLs identified on the second chromosome. For both mapping experiments, flies were cultured in standard conditions. TAG content of 4‐ to 5‐day‐old flies, adjusted for live body mass and total proteins, was used as the phenotypic measure. Results: Multiple QTLs associated with variation in TAG storage were identified by the genome‐wide recombination mapping method, and some of them were sex‐specific. The QTLs had main effects, but a male‐specific epistatic interaction between two QTLs was also found. Finally, two closely linked QTLs were detected by deficiency mapping at 57E1‐57E3 and 57E4‐57F1 on chromosome 2, the first of which causes female‐specific variation in TAG between the Oregon R and 2b strains. Discussion: Our results suggest that variation in TAG storage in D. melanogaster is controlled by different genetic mechanisms and different sets of QTLs in male and female flies.     

R/qtl: high-throughput multiple QTL mapping

MOTIVATION: R/qtl is free and powerful software for mapping and exploring quantitative trait loci (QTL). R/qtl provides a fully comprehensive range of methods for a wide range of experimental cross types. We recently added multiple QTL mapping (MQM) to R/qtl. MQM adds higher statistical power to detect and disentangle the effects of multiple linked and unlinked QTL compared with many other methods. MQM for R/qtl adds many new features including improved handling of missing data, analysis of 10,000 s of molecular traits, permutation for determining significance thresholds for QTL and QTL hot spots, and visualizations for cis-trans and QTL interaction effects. MQM for R/qtl is the first free and open source implementation of MQM that is multi-platform, scalable and suitable for automated procedures and large genetical genomics datasets. AVAILABILITY: R/qtl is free and open source multi-platform software for the statistical language R, and is made available under the GPLv3 license. R/qtl can be installed from http://www.rqtl.org/. R/qtl queries should be directed at the mailing list, see http://www.rqtl.org/list/. CONTACT: kbroman@biostat.wisc.edu.     

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions. Received: 23 October 1998 / Accepted: 11 May 1999     

Multi-environment mapping and meta-analysis of 100-seed weight in soybean

100-Seed weight (100-SW) of soybean is an important but complicated quantitative trait to yield. This study was focus on the quantitative trait loci (QTLs) of soybean 100-SW from 2006 to 2010, using recombination inbred lines population that was derived from a cross between Charleston and Dongnong 594. A total of 23 QTLs for 100-SW were detected in the linkage group C2, D1a, F, G and O. Nine QTLs were identified by composite interval mapping including one QTL with the minimum confidence interval (CI) of 1.3?cM, while 14 QTLs by multiple interval mapping. Furthermore, 94 reported QTLs of 100-SW were integrated with our QTL mapping results using BioMercator. As a result, 15 consensus QTLs and their corresponding markers were identified. The minimum CI was reduced to 1.52?cM by the combination of meta-analysis. These findings may merit fine-mapping of these QTL in soybean.     

QTL mapping in A-genome diploid Asiatic cotton and their congruence analysis with AD-genome tetraploid cotton in genus Gossypium

Asiatic cotton(Gossypium arboreum L.) is an Old World cultivated cotton species.The sinense race was planted extensively in China.Due to the advances in spinning technology during the last century,the species was replaced by the New World allotetraploid cotton G.hirsutum L.Gossypium arboreum is still grown in India and Pakistan and also used as an elite in current cotton breeding programs.In addition,G.arboreum serves as a model for genomic research in Gossypium.In the present study,we generated an A-genome diploid cotton intraspecific genetic map including 264 SSR loci with three morphological markers mapped to 1 3 linkage groups.The map spans 2,508.71 cM with an average distance of 9.4 cM between adjacent loci.A population containing 1 76 F2:3 families was used to perform quantitative trait loci(QTL)mapping for 17 phenotypes using Multiple QTL Model(MQM)of MapQTL ver 5.0.Overall,108 QTLs were detected on 13 chromosomes.Thirty-one QTLs for yield and its components were detected in the F2 population.Forty-one QTLs for yield and its components were detected in the F2:3 families with a total of 43 QTLs for fiber qualities.Two QTLs for seed cotton weight/plant and lint index and three QTLs for seed index were consistently detected both in F2 and F2:3.Most QTLs for fiber qualities and yields were located at the same interval or neighboring intervals.These results indicated that the negative correlation between fiber qualities and yield traits may result from either pleiotropic effect of one gene or linkage effects of multiple closely linked genes.     

Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions

In the rainfed lowlands, rice ( Oryza sativa L.) develops roots under anaerobic soil conditions with ponded water, prior to exposure to water stress and aerobic soil conditions that arise later in the season. Constitutive root system development in anaerobic soil conditions has been reported to have a positive effect on subsequent expression of adaptive root traits and water extraction during progressive water stress in aerobic soil conditions. We examined quantitative trait loci (QTLs) for constitutive root morphology traits using a mapping population derived from a cross between two rice lines which were well-adapted to rainfed lowland conditions. The effects of phenotyping environment and genetic background on QTLs identification were examined by comparing the experimental data with published results from four other populations. One hundred and eighty-four recombinant inbred lines (RILs) from a lowland indica cross (IR58821/IR52561) were grown under anaerobic conditions in two experiments. Seven traits, categorized into three groups (shoot biomass, deep root morphology, root thickness) were measured during the tillering stage. Though parental lines showed consistent differences in shoot biomass and root morphology traits across the two seasons, genotype-by-environment interaction (GxE) and QTL-by-environment interaction were significant among the progeny. Two, twelve, and eight QTLs for shoot biomass, deep root morphology, and root thickness, respectively, were identified, with LOD scores ranging from 2.0 to 12.8. Phenotypic variation explained by a single QTL ranged from 6% to 30%. Only two QTLs for deep root morphology, in RG256-RG151 in chromosome 2 and in PC75M3-PC11M4 in chromosome 4, were identified in both experiments. Comparison of positions of QTLs across five mapping populations (the current population plus populations from four other studies) revealed that these two QTLs for deep root morphology were only identified in populations that were phenotyped under anaerobic conditions. Fourteen and nine chromosome regions overlapped across different populations as putative QTLs for deep root morphology and root thickness, respectively. PC41M2-PC173M5 in chromosome 2 was identified as an interval that had QTLs for deep root morphology in four mapping populations. The PC75M3-PC11M4 interval in chromosome 4 was identified as a QTL for root thickness in three mapping populations with phenotypic variation explained by a single QTL consistently as large as 20-30%. Three QTLs for deep root morphology were found only in japonica/indica populations but not in IR58821/IR52561. The results identifying chromosome regions that had putative QTLs for deep root morphology and root thickness over different mapping populations indicate potential for marker-assisted selection for these traits.     

A complete genetic linkage map and QTL analyses for bast fibre quality traits, yield and yield components in jute (Corchorus olitorius L.)

We report the first complete microsatellite genetic map of jute (Corchorus olitorius L.; 2n = 2 × = 14) using an F₆ recombinant inbred population. Of the 403 microsatellite markers screened, 82 were mapped on the seven linkage groups (LGs) that covered a total genetic distance of 799.9 cM, with an average marker interval of 10.7 cM. LG5 had the longest and LG7 the shortest genetic lengths, whereas LG1 had the maximum and LG7 the minimum number of markers. Segregation distortion of microsatellite loci was high (61%), with the majority of them (76%) skewed towards the female parent. Genomewide non-parametric single-marker analysis in combination with multiple quantitative trait loci (QTL)-models (MQM) mapping detected 26 definitive QTLs for bast fibre quality, yield and yield-related traits. These were unevenly distributed on six LGs, as co-localized clusters, at genomic sectors marked by 15 microsatellite loci. LG1 was the QTL-richest map sector, with the densest co-localized clusters of QTLs governing fibre yield, yield-related traits and tensile strength. Expectedly, favorable QTLs were derived from the desirable parents, except for nearly all of those for fibre fineness, which might be due to the creation of new gene combinations. Our results will be a good starting point for further genome analyses in jute.     

Limitations of a two-step moment method for mapping linked multiple QTL

As an alternative to multiple-interval mapping a two-step moment method was recently proposed to map linked multiple quantitative trait loci (QTLs). The advantage of this moment method was supposed to be its simplicity and computational efficiency, especially in detecting closely linked QTLs within a marker bracket, but also in mapping QTLs in different marker intervals. Using simulations it is shown that the two-step moment method may give poor results compared with multiple-interval mapping, irrespective of whether the QTLs are in the same or in different marker intervals, especially if linked QTLs are in repulsion. The criteria of comparison are number of identified QTLs, likelihood ratio test statistics, means and empirical standard errors of the QTL position and QTL effects estimates, and the accuracy of the residual variance estimates. Further, the joint conditional probabilities of QTL genotypes for two putative QTLs within a marker interval were derived and compared with the unmodified approach ignoring the non-independence of the conditional probabilities.     

Impact of epistasis and QTL×environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.)

QTLs with epistatic effects and environmental interaction effects for the developmental behavior of plant height in rice were studied by conventional and conditional methods for quantitative trait loci (QTLs) by mapping with a doubled-haploid population of 123 lines from IR64/Azucena in three environments. The results showed that epistatic effects were important and most epistasis could be detected only by conditional QTL mapping, while most non–epistatic QTLs could be detected by both conventional and conditional methods. Many modificative QTLs showed only epistatic effects without their own additive effects at some stages. QTL×environment (QE) interaction effects were detected more often than QTL main effects for plant-height behavior, which might indicate that gene expression could be greatly affected by the environment. No QTLs had effects during the whole of ontogeny. Conditional QTL mapping might be a valid way to reveal dynamic gene expression for the development of quantitative traits, especially for epistatic effects. Received: 19 May 2000 / Accepted: 27 October 2000