A comparison of interval mapping procedures including the QTL-cluster mapping

Using the deterministic sampling, patterns of the log-likelihood surfaces expected in some interval mapping procedures for estimating the position of, and the effect for, QTL(s) were investigated for the situations where a single QTL or closely linked QTLs are contained in a chromosome segment bracketed with two markers. The mapping procedures compared were the conventional, likelihood-based interval mapping (IM), the regression interval mapping (RIM), and the QTL-cluster mapping (CM) in which the conditional probabilities of transmission of the whole segment marked by the flanking markers were taken into consideration. The half-sib design was used here, and several cases of the true genetic model were considered, differing in the number of QTLs contained in the marker interval, the linkage phase for the sire, and the magnitude of the QTL(s) effect. For the true genetic models where a single QTL or closely linked QTLs being in coupling phase are contained in the interval, with (R)IM, clear global maxima of the log-likelihood were observed within the range of the marker interval. It was shown that the estimates of the QTL(s) effect at the marked segment level are expected to be unbiased. On the other hand, in a setting where the linkage phase for the linked QTLs located in the interval was different from coupling and repulsion, there was found a ridge along the interval for the log-likelihood surface with (R)IM, indicating the dependency between the estimates of the position of, and the effect for, the putative QTL. For this case, it was found that the position of the putative QTL could be estimated as that of one of the flanking markers, and the estimate of the QTL effect be biased. In contrast, it was revealed that CM is expected to provide the unbiased estimate of the QTL(s)-effect at the segment level for any case of the true genetic models considered here. If the aim is for marker-assisted selection rather than mapping closely linked QTLs individually, then the CM approach is considered to be useful.     

A modified algorithm for the improvement of composite interval mapping

Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.     

The application of high-density genetic maps of rye for the detection of QTLs controlling morphological traits

The development of genetic maps is, nowadays, one of the most intensive research activities of plant geneticists. One of the major goals of genome mapping is the localisation of quantitative trait loci (QTLs). This study was aimed at the identification of QTLs controlling morphological traits of rye and comparison of their localisation on genetic maps constructed with the use of genetically different germplasms. For QTL analyses, two high-density consensus maps of two populations (RIL-S and RIL-M) of recombinant inbred lines (RIL) were applied. Plant height (Ph), length of spikes (Sl) and the number of spikelets per spike (Sps) were studied in both populations. Additionally, the number of kernels per spike under isolation (Kps), the weight of kernels per spike (Kw) and thousand kernel weight (Tkw) were assessed in the RIL-M population. Except for Tkw, the majority of the traits were correlated to each other. The non-parametric Kruskal–Wallis (K-W) test and composite interval mapping (CIM) revealed 18/48 and 24/18 regions of rye chromosomes engaged in the determination of Ph, Sl and Sps in the RIL-S and RIL-M populations, respectively. An additional 18/15 QTLs controlling Kps, Kw and Tkw were detected on a map of the RIL-M population. A numerous group of QTLs detected via CIM remained in agreement with the genomic regions found when the K-W test was applied. Frequently, the intervals indicated by CIM were narrower.     

Approaches to interval mapping of QTL in a multigeneration pedigree: the example of porcine chromosome 4

Quantitative trait loci (QTLs) have been mapped in many studies of F2 populations derived from crosses between diverse lines. One approach to confirming these effects and improving the mapping resolution is genetic chromosome dissection through a backcrossing programme. Analysis by interval mapping of the data generated is likely to provide additional power and resolution compared with treating data marker by marker. However, interval mapping approaches for such a programme are not well developed, especially where the founder lines were outbred. We explore alternative approaches to analysis using, as an example, data from chromosome 4 in an intercross between wild boar and Large White pigs where QTLs have been previously identified. A least squares interval mapping procedure was used to study growth rate and carcass traits in a subsequent second backcross generation (BC2). This procedure requires the probability of inheriting a wild boar allele for each BC2 animal for locations throughout the chromosome. Two methods for obtaining these probabilities were compared: stochastic or deterministic. The two methods gave similar probabilities for inheriting wild boar alleles and, hence, gave very similar results from the QTL analysis. The deterministic approach has the advantage of being much faster to run but requires specialized software. A QTL for fatness and for growth were confirmed and, in addition, a QTL for piglet growth from weaning at 5 weeks up to 7 weeks of age and another for carcass length were detected.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

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.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

构建数量性状基因图谱的几种统计方法

以线性数学模型为线索,概述了用于构建数量性状基因图谱的几种主要统计方法,包括方差分析法、标记回归法、区间作图法、复合区间作图法、Jansen的复合区间作图法、双侧标记回归法以及新近发展的多区间作图法和多亲本作图法等.讨论了各种方法的优缺点. Abstract:Statistical methods for mapping QTLs were summarized, including one marker analysis, arker regression analysis,interval mapping (IM),composite interval mapping (CIM),Jansen's composite interval mapping, flanking marker regression analysis,multiple interval mapping (MIM) and multiple families mapping.Their advantages and disadvantages were discussed.     

QTL analysis of variation in male courtship song characters in Drosophila virilis

We have used a quantitative trait locus (QTL) mapping approach to study the genetic basis of differences between two Drosophila virilis strains representing extreme phenotypes in two song characters, the number of pulses in a pulse train (PN) and the length of a pulse train (PTL). Variation in these characters among 520 F2 males was studied by single-marker analysis and composite interval mapping (CIM) using a recombination linkage map constructed for 26 microsatellite markers. In single-marker analysis, two adjacent microsatellite markers on the third chromosome, msat19 and vir84 explained 13.8 and 12.4% of the variation in PN and 9.9 and 6.5% of the variation in PTL, respectively. CIM analysis revealed significant QTLs affecting PN, located on the X and the second, third and fourth chromosome of D. virilis, while variation in PTL was attributable to QTLs located only on the third chromosome.     

On the efficiency of composite interval mapping in an outbred population

Composite interval mapping (CIM) has been successfully applied to the detection of QTL in experimental animals and plants. However, practical analyses based on CIM have been reported mainly for populations derived from cross between inbred lines. There are few studies on QTL analyses with CIM in outbred populations. To evaluate the applicability of CIM to outbred populations is prerequisite for the fine mapping of QTL in industrial animals such as pig and chicken. Some markers are usually not fully informative in outbred populations. In application of CIM to outbred populations, the influence of inclusion of such uninformative markers used as covariates on the efficiency of CIM should be investigated. In this paper a least-squares method for CIM was formalized in an F(2) population derived by crossing two outbred lines. The efficiencies of CIM were evaluated for outbred populations in comparison with simple interval mapping (SIM) for several cases of marker informativeness using simulations. By incorporating markers linked to a tested position as well as those unlinked, CIM showed a higher efficiency to separate two linked QTL over SIM. The efficiency of dissection was enhanced as the marker informativeness was increased. The power of CIM to detect an isolated QTL was improved by excluding markers linked to a tested position from covariates and higher than SIM regardless of marker informativeness. In conclusion, CIM is a useful procedure for the analysis of QTL in outbred populations even under low marker informativeness.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

Quantitative trait loci for low aflatoxin production in two related maize populations

Aflatoxin B(1) formed by Aspergillus flavus Fr:Link has been associated with animal disease and liver cancer in humans. We performed genetic studies in progenies derived from maize inbred Tex6, associated with relatively low levels of aflatoxin production, crossed with the historically important inbred B73. (Tex6 x B73) x B73 BC(1)S(1) and Tex6 x B73 F(2:3) mapping populations were produced and evaluated in 1996 and 1997 in Champaign, Ill. Ears were inoculated 20 to 24 days after midsilk using a pinboard method and a mixture of conidia of A. flavus Link:Fr. isolates. Aflatoxin B(1) levels in harvested ears were determined using an indirect competitive ELISA. Molecular markers were assayed on the populations and used to generate maps. Molecular marker - QTL associations for lower levels of aflatoxin production were determined using multiple regression (MR) and composite interval analysis with multiple regression (CIM MR). MR revealed sets of markers associated with lower aflatoxin production in 1996 and 1997, and CIM MR detected a smaller subset of loci significant in 1997. QTLs for lower aflatoxin were attributed to both Tex6 and B73 parental sources. Environment strongly influenced the detection of QTLs for lower aflatoxin production in different years. There were very few chromosome regions associated with QTLs in more than 1 year or population with MR analysis, and none with CIM MR analysis. In 1997, QTLs for lower aflatoxin were detected with CIM MR in bins 5.01-2 and 5.04-5 in the BC(1)S(1) population, and in bins 3.05-6, 4.07-8 and 10.05-10.07 in the F(2:3) population. These QTL associations appear the most promising for further study.     

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.     

RFLP mapping of QTLs for yield and related characters in rice (Oryza sativa L.)

Quantitative triat loci (QTLs) for yield and related traits in rice were mapped based on RFLP maps from two indica/indica F₂ populations, Tesanai 2/CB and Waiyin 2/CB. In Tesanai 2/CB, 14 intervals carrying QTLs for eight traits were detected, including 3 for grain weight per plant (GWT), 2 for number of panicles per plant (NP), 2 for number of grains per panicle (NG), 1 for total number of spikelets per panicle (TNS), 1 for spikelet fertility (SF), 3 for 1000-grain weight (TGWT), 1 for spikelet density (SD), and 1 for number of first branches per main panicle. The 3 QTLs for GWT were located on chromosomes 1, 2, and 4, with 1 in each chromosome. The additive effect of the single locus ranged from 2.0 g to 9.1 g. A major gene (np4) for NP was detected on chromosome 4 within the interval of RG143–RG214, about 4cM for RG143, and this locus explained 26.1% of the observed phenotypic variance for NP. The paternal allele of this locus was responsible for reduced panicles per plant (3 panicles per plant). In another population, Waiyin 2/CB, 12 intervals containing QTLs for six of the above-mentioned traits were detected, including 3 for GWT, 2 for each of NP, TNS, TGWT and SD, 1 for SF. Three QTLs for GWT were located on chromosome 1, 4, and 5, respectively. The additive effect of the single locus for GWT ranged from 6.7 g to 8.8 g, while the dominance effect was 1.7–11.5 g. QTL mapping in two populations with a common male parent is compared and discussed.     

An intervarietal genetic linkage map of Indian bread wheat (Triticum aestivum L.) and QTL maps for some metric traits

Bread wheat (Triticum aestivum L.) exhibits very narrow genetic diversity and hence there is high relatedness among cultivated varieties. However, a population generated from an intervarietal cross, with the parents differing in a large number of traits, could lead to the generation of QTL maps which will be useful in practice. In this report a genetic linkage map of wheat is constructed using a cross between two Indian bread wheat varieties: Sonalika and Kalyansona. The linkage map consisted of 236 markers and spanned a distance of 3639 cM, with 1211.2 cM for the A genome, 1669.2 cM for the B genome, 192.4 cM for the D genome and 566.2 cM for unassigned groups. Linkage analysis defined 37 linkage groups of which 24 were assigned to 17 chromosomes. The genetic map was used to identify QTLs by composite internal mapping (CIM) for three metric traits, viz. culm length (CL), flag leaf length (FLL) and flag leaf breadth (FLB). Of 25 QTLs identified in this study, 15 have not been reported previously. Multitrait CIM (MCIM) analysis was carried out for traits that were significantly correlated such as FLB-FLL and CL-FLB-FLL. Detection of a large number of QTLs for the three traits analysed suggests that in parent cultivars that are not too diverse, the differences at genetic level detected as polymorphisms may be mostly associated with QTLs for the observed differences.     

Mapping QTLs and meta-QTLs for two inflorescence architecture traits in multiple maize populations under different watering environments

Drought significantly affects the architectural development of maize inflorescence, which leads to massive losses in grain yield. However, the genetic mechanism for traits involved in inflorescence architecture in different watering environments, remains poorly understood in maize. In this study, 19 QTLs for tassel primary branch number (TBN) and ear number per plant (EN) were detected in 2 F_2:3 populations under both well-watered and water-stressed environments by single environment mapping with composite interval mapping (CIM); 11/19 QTLs were detected under water-stressed environments. Moreover, 21 QTLs were identified in the 2 F_2:3 populations by joint analysis of all environments with a mixed linear model based on composite interval mapping (MCIM), 11 QTLs were involved in QTL × environment interactions, seven epistatic interactions were identified with additive by additive/dominance effects. Remarkably, 12 stable QTLs (sQTLs) were simultaneously detected by single environment mapping with CIM and joint analysis through MCIM, which were concentrated in ten bins across the chromosomes: 1.05_1.07, 1.08_1.10, 2.01_2.04, 3.01, 4.06, 4.09, 5.06_5.07, 6.05, 7.00, and 7.04 regions. Twenty meta-QTLs (mQTLs) were detected across 19 populations under 51 watering environments using a meta-analysis, and 34 candidate genes were predicted in corresponding mQTLs regions to be involved in the regulation of inflorescence development and drought resistance. Therefore, these results provide valuable information for finding quantitative trait genes and to reveal the genetic mechanisms responsible for TBN and EN under different watering environments. Furthermore, alleles for TBN and EN provide useful targets for marker-assisted selection to generate high-yielding maize varieties.     

Mapping Mendelian Factors Underlying Quantitative Traits Using RFLP Linkage Maps

The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.     

A wheat intervarietal genetic linkage map based on microsatellite and target region amplified polymorphism markers and its utility for detecting quantitative trait loci

Efficient user-friendly methods for mapping plant genomes are highly desirable for the identification of quantitative trait loci (QTLs), genotypic profiling, genomic studies, and marker-assisted selection. SSR (microsatellite) markers are user-friendly and efficient in detecting polymorphism, but they detect few loci. Target region amplification polymorphism (TRAP) is a relatively new PCR-based technique that detects a large number of loci from a single reaction without extensive pre-PCR processing of samples. In the investigation reported here, we used both SSRs and TRAPs to generate over 700 markers for the construction of a genetic linkage map in a hard red spring wheat intervarietal recombinant inbred population. A framework map consisting of 352 markers accounted for 3,045 cM with an average density of one marker per 8.7 cM. On average, SSRs detected 1.9 polymorphic loci per reaction, while TRAPs detected 24. Both marker systems were suitable for assigning linkage groups to chromosomes using wheat aneuploid stocks. We demonstrated the utility of the maps by identifying major QTLs for days to heading and reduced plant height on chromosomes 5A and 4B, respectively. Our results indicate that TRAPs are highly efficient for genetic mapping in wheat. The maps developed will be useful for the identification of quality and disease resistance QTLs that segregate in this population.     

The effect of an imprecise map on interval mapping QTLs

The statistical analysis of quantitative trait locus (QTL) experiments relies on the use of a linkage map of the markers genotyped. Such a map is, at best, a good estimate of the true map. Resources might be diverted into developing better marker maps or improved maps become available after the analysis, raising concerns over the original analysis. It is therefore important to understand the sensitivity of QTL analysis to map inaccuracy. We have used simulation methods to investigate the consequences of an incorrect map on the results of a QTL analysis using interval mapping. Backcross data sets were generated with a particular map and then analysed with both the correct map and incorrect maps. If the incorrect maps maintained the true linkage groups (i.e. no markers were incorrectly assigned to another linkage group), the accuracy of the map had little or no impact on the ability to detect QTLs, the true significance levels of the tests or the relative placement of QTLs. When a marker was incorrectly placed on another linkage group, there was a small increase in the level of the test. After adjusting for this increase, there was a decrease in power to detect a QTL near the misplaced marker. This decrease was of a similar magnitude to that found when using a single-marker analysis compared with interval mapping. These results mean that QTL analyses can proceed without the need for very accurate marker maps, and that estimated QTL positions can be translated onto updated maps without the need for reanalysis.     

Mapping quantitative trait loci for milling quality, protein content and color characteristics of rice using a recombinant inbred line population derived from an elite rice hybrid

Milling properties, protein content, and flour color are important factors in rice. A marker-based genetic analysis of these traits was carried out in this study using recombinant inbred lines (RILs) derived from an elite hybrid cross ’Shanyou 63’, the most-widely grown rice hybrid in production in China. Correlation analysis shows that the traits were inter-correlated, though the coefficients were generally small. Quantitative trait locus (QTL) analysis with both interval mapping (IM) and composite interval mapping (CIM) revealed that the milling properties were controlled by the same few loci that are responsible for grain shape. The QTL located in the interval of RM42-C734b was the major locus for brown rice yield, and the QTL located in the interval of C1087-RZ403 was the major locus for head rice yield. These two QTLs are the loci for grain width and length, respectively. The Wx gene plays a major role in determining protein content and flour color, and is modified by several QTLs with minor effect. The implications of the results in rice breeding were discussed. Received: 15 September 2000 / Accepted: 31 March 2001