Many factors will affect the power of a QTL experiment to identify the loci that underlie phenotypic traits. These factors include experimental design, marker type, number and sample size. We pay particular attention to those issues affecting researchers who work outside model systems, how QTL analyses may address questions of specific concern to ecologists and evolutionary biologists, and suggest methods and future areas of development that may aid QTL analyses in studies of ecology and evolution.
Marker number, type and population sample size
The effects of marker number, type and sample size have been addressed in a number of fine reviews and books (Doerge et al. 1997; Liu 1998; Lynch & Walsh 1998; Patterson 1998; Doerge 2002). We briefly summarize some of the most salient points for the sake of context in the balance of this article. Essentially, the central issues in detecting QTL depend on the type of makers employed, their distribution (including coupling and repulsion phase), cross design and the magnitude of the QTL. In general, QTL studies employing traditional experimental designs and large sample sizes will readily identify QTL that are of large effect (i.e. where QTL effect exceeds 15%), however, identification of all or most loci contributing to a trait will be challenging at best. For the purpose of this discussion, we define QTL as chromosomal regions that are flanked by two markers delineating its position within the genome. Furthermore, we define QTL effect as the proportion of the genetic variance — as observed in a segregating population — that is explained by the QTL (alternatively, QTL effect can be defined in terms of the proportion of the difference between the parents). A rule of thumb in QTL experiments may be that experiments employing fewer than 300 individuals will have difficulty in estimating the true distribution of QTL effects. Under ideal conditions, a perfectly additive QTL exhibiting no dominance with an effect of 5% can be detected using 206 individuals in a F2 intercross, using codominant markers, at a spacing of 5 cm. However, because of G × E interactions, low heritability and incomplete accuracy in estimating both genotype and phenotype, it is suggested that 300 is a reasonable sample size to employ (Doerge et al. 1997). The type of experimental design (cross design) as well as the type of markers employed will affect this number and these issues have been addressed in some detail in Lynch & Walsh (1998) and Liu (1998). For example, under the conditions just mentioned, an experiment that employed a backcross rather than an F2 design would require double the sample size to infer QTL with equal precision (Lynch & Walsh 1998).
In terms of correctly estimating the magnitude of a QTL, a statistical problem associated with employing small sample sizes is exaggeration of the QTL effect, which has been termed the Beavis effect (Beavis 1994, 1998). When sample sizes fall far below 300, estimates of QTL effects will be exaggerated, and the power to identify small-effect QTL declines dramatically. The bias to inflate QTL effect is reduced as more and more small-effect QTL are identified (Xu 2003a), and is ultimately tied to increasing the overall power of the experiment. Thus an experiment with low power may not only fail to identify true QTL, it may also falsely suggest QTL or greatly exaggerate the effect of those QTL that are correctly identified as having an effect.
This statistical artefact is less important to plant and animal breeders and human health researchers (who are most interested in QTL of large effect), but is more important to ecologists and evolutionary biologists who may seek to investigate genetic architecture in terms of addressing predictions based upon evolutionary theory. Consequently, it may be more fruitful for experimental designs to maximize sample sizes employed at the expense of generating highly saturated linkage maps as a first approximation to infer QTL.
The types of genetic marker will also have some effect on QTL resolution. We can classify markers based on dominant vs. codominant markers (see Table 1). As an example, in order to generate the same inference of linkage between markers that are 10 cm apart, an experiment employing an F2 intercross design with dominant markers in the repulsion phase must include nearly 20 times as many individuals as would an F2 intercross using codominant markers (Liu 1998; Table 6.24). Dominant markers, such as AFLP, will produce two genotypic classes in an F2 population cross rather than three classes due to dominance, such that it is not possible to distinguish between the heterozygote and dominant homozygote dominant classes. Owing to masking of the genotypic state, there are fewer observable recombination events within the marker interval, resulting in lower information content when dominant markers are used (Liu 1998). AFLP markers do allow one to construct linkage maps with wide genome coverage without engaging in extensive sequencing or marker development programmes. Finally, AFLP are also faster than individual codominant marker types because a single polymerase chain reaction (PCR) can derive multiple loci simultaneously. Codominant markers, such as microsatellites, single nucleotide polymorphisms (SNP) and increasingly expressed sequence tags (EST) (Table 1), offer much greater power to infer recombination between adjacent markers and have much improved information content (Liu 1998). However, their greater expense in development and application are balanced by the greater power to resolve QTL effect and position.
Lastly, the distribution of markers on a chromosome will affect the power to resolve both QTL position and effect. Most QTL mapping programs make use of marker intervals, and in doing so help to define the location of a QTL within a pair of markers. As more markers are added to an experiment, a more precise estimate of both QTL position and effect can be generated. However a balance between marker number, or more correctly the size of the intervals between adjacent markers along a chromosome, with sample size need be established. The prior estimates of 300 individuals should be appropriate for 10–15 cm marker intervals in most experimental designs. If one has many more markers and hence smaller marker intervals, the number of recombination events between any pair of markers declines and the problem of un-replicated genotypes can arise. It has been suggested that QTL analyses can best be conducted in drafts. An initial draft would maximize sample size at the expense of marker density, and would identify broad intervals (~20 cm) containing putative QTL. More markers could then be included in the areas of interest, to refine QTL position effect issues in subsequent analyses. This approach can save time and money by avoiding the genotyping of areas where no QTL are suggested. The number of markers and sample sizes employed will depend upon the research questions, but many QTL experiments may provide the initial impetus to further explore quantitative genetic architecture.
The experimental design employed, i.e. the type of cross employed, will have a significant impact on the ability to detect QTL. There are a number of crossing designs employed in QTL analysis and we briefly review some of these and comment on their applicability. We specifically contrast inbred line cross designs with what may be termed ‘outbred’ QTL designs, the latter of which may be broadly applicable in ecological and evolutionary contexts where inbred line cross designs are not feasible.
Of the different designs, the inbred line cross is generally the most powerful method because it increases linkage disequilibrium between the genetic markers used and the QTL (Doerge et al. 1997; Liu 1998; MacKay 2001). This design employs two individuals that are highly differentiated for both the trait(s) of interest and the molecular markers used. One or more crosses between these individuals generate a hybrid F1 generation that may then be crossed to form a recombinant intercross generation (F2) or backcrossed to either parent, or both, to make a backcross (BC1). However, inbred line crossing designs do have a number of practical and experimental constraints. For many researchers, the creation of a recombinant F2 population derived from an inbred line cross may be impractical. Generation times and the ability to handle the organism in question may limit the ability to implement these designs. In addition, inbred line crosses necessarily limit the number of alleles present at any single QTL location. Thus, the populations from which individuals are derived may contain multiple alleles at each QTL location, but because only one, presumably inbred and hence homozygous, individual is chosen from each population, a maximum of two alleles at each QTL location is included in the experiment. If one wishes to detect multiple QTL that may reside within one or more populations, then an inbred line cross design may be the wrong method. Experiments based on outcrossed designs or sib-pair methods may be more appropriate for many questions and organisms, and we discuss these later.
There are a variety of derivations of the inbred line cross design, including recombinant inbred lines (RIL) or near isogenic lines (NIL). These are both fixed recombinant lines, in which after 6–7 generations of selfing (RIL) or backcrossing (NIL) each ‘line’ or individual is fixed for a different set of recombinant markers from each parent. Such fixed recombinant lines have some desirable properties, such as the ability to use progeny testing in multiple environments to test for genotype–environment interactions, as well as improved detection of epistasis, and, in some cases, more precise estimates of QTL location and effect (Doerge et al. 1997; Doerge 2002). Likewise, designs using both backcrossing and intercrossing (or selfing in plants) can create mixtures of recombinant genomes that may facilitate mapping of some quantitative traits (Doebley et al. 1995b; Rieseberg et al. 1996; Liu 1998). The power of these methods is to increase recombination and control for the genetic background into which putative QTL are placed. Researchers who have the time and ability to employ such designs should seriously consider them. For the rest of us, some developing alternatives provide hope to pursue QTL detection in less malleable study systems.
We consider two general classes of QTL design that reside outside the inbred line cross models — pedigree and sib-pair methods, respectively. We generally describe these as ‘outbred’ designs because the parents used in the cross are not inbred and may be heterozygous at both marker loci and QTL. The advantage of an outbred QTL design includes the ability to capture more than two alleles per QTL location, the high levels of recombination in the sample population, and its application to systems in which highly manipulated inbred line crosses are not possible. In addition, questions concerning whether QTL derived from inbred line crosses represent variation between or within lines should be considered. It is possible that most evolutionarily important variation occurs within lines, and although some work has addressed this directly (Nagamine et al. 2003), outbred designs may be able to more readily discern such variation.
A QTL mapping programme using a pedigree in structured outbred populations follows the methods of complex disease mapping in humans (Almasy & Blangero 1998; Almasy et al. 1999) and agricultural populations (Haley et al. 1994; Knott et al. 1998; Nagamine & Haley 2001), although it is considerably more difficult, because obtaining pedigrees from natural populations presents greater obstacles (Groover et al. 1994; Slate et al. 1999; George et al. 2000). The pedigrees must include many individuals, and thus may span multiple generations, otherwise sample sizes may be too small to detect any linkage among markers and QTL. Typically, a three-generation pedigree is the starting point, and is referred to as a ‘grandparent’ design (Williams 1998). The power of these methods is strongly affected by missing data, particularly at the grandparent or parent level. Methods that employ pedigrees in QTL detection estimate coefficients of identity by descent for marker loci calculated from the genotypes of the parents. Putative QTL are then inferred by identifying individuals with alleles identical by descent (IBD) that also share the same phenotype. However, ambiguity in estimating IBD and the confounding effect of missing genetic data reduce the power of these studies (Slate et al. 1999). For these reasons, very few studies on genetic architecture of fitness traits in wild, un-manipulated populations have been performed, although they have been employed with success in agricultural species such as wild boar and pigs (Knott et al. 1998). However, a method to map QTL in complex pedigrees has been described based on variance components analysis (George et al. 2000). Slate et al. (2002) used interval mapping and George et al.'s (2000) variance component analysis to map QTL for birth weight in wild, un-manipulated populations of red deer using a six-generation pedigree of > 350 animals. Evidence for segregating QTL was found on three linkage groups, one of which was significant at the genome-wide suggestive threshold. The authors argue that the QTL might be genuine, as two of the QTL were detected using alternate approaches making different assumptions in the underlying model, and also because birth weight QTL have been mapped at homologous sites in cattle (Davis et al. 1998; Stone et al. 1999; Grosz & MacNeil 2001). However, the QTL effects were likely upwardly biased, reflecting the limited sample sizes of specific families. Thus, application of these approaches employing organisms that have large family sizes may be most fruitful. Another way to improve the power of these methods is selective genotyping, in which individuals that are most highly differentiated are selected for inclusion in the study (Lynch & Walsh 1998). Lastly, methods that employ variance component or maximum likelihood models to detect QTL will require further analysis beyond identification of QTL to establish confidence intervals regarding the position of the QTL and the use of bootstrap or Markov chain-Monte Carlo (MCMC) simulations to estimate detection thresholds (Churchill & Doerge 1994; George et al. 2000). As with all the methods to search for QTL, a Bayesian methodology to search for QTL in pedigrees has been developed (Bink et al. 2002; Perez-Enciso 2003) which offers the advantage of accepting a wide array of experimental designs and marker information.
Sib-pair methods for QTL deduction have not, to our knowledge, been employed in an evolutionary or ecological context. However, the statistical underpinnings of these methods have been well investigated in the search for human QTL. The sib-pair method was first suggested by Haseman & Elston (1972), and employs the difference in trait value between pairs of relatives (typically sibs) in conjunction with estimation of IBD at sets of markers along a mapped chromosome. This approach uses the squared difference in phenotype between pairs of sibs in a regression onto the set of alleles that are IBD for that sib pair (Drigalenko 1998), and has been used extensively in QTL discovery in humans (Elston et al. 2000). The power of this method is that one can take advantage of the naturally occurring family structure, where there are many small families that show variation for the trait of interest. This method may be particularly useful in estimating QTL segregating within populations, or possibly within zones of hybrid contact. If a set of relatives differs for some trait, QTL affecting trait differentiation can be detected through regression of IBD against trait differentiation. The advantage of this type of method is that many plant and animal systems show the pattern of a large number of small nuclear families that can be identified. Although the method has some very real limitations with regard to power to detect QTL (Amos & Elston 1989; Lynch & Walsh 1998), its utility increases with the size and number of sibships employed, and it may serve as a robust alternative to the pedigree method when the reconstruction of a pedigree is too difficult or the size of the offspring class (essentially F2 population size) is too small. The general method of sib-pair analysis has been extended to incorporate elements of interval mapping (Fulker & Cardon 1994) and multipoint (multimarker) designs (Fulker et al. 1995; Cardon 2000) which offer promise in investigating QTL detection in natural populations. Knott & Haley (1998) further extended sib-pair methodology using a multipoint mapping method, which can account for differences in recombination between sexes. Nonmodel species, including many birds and mammals in which it is possible to sample many discrete families containing two or more sibs, or long-lived plants that produce very large half-sib arrays, may be good candidates for the sib-pair method of QTL deduction. In past reviews of sib-pair methods (Weller et al. 1990; Lynch & Walsh 1998), the limits in power of QTL detection have been highlighted. The increased efficiency of genotyping individuals offers promise to allow sufficient sample sizes to be employed in examination of QTL with these methods in an evolutionary context. These methods will never have the power of inbred line cross methods, because the degree of linkage disequilibrium between marker and QTL is relatively weak. Accordingly, a description of all loci contributing to quantitative variation using these designs is unrealistic. However, it is very possible to address questions of the role of major genes, the role of genetic–environment interactions and in some cases the mode of gene action at QTL affecting a trait. Furthermore, synteny — conservation of gene order — among species or genera may lead to the opportunity to complement initial QTL experiments with candidate gene approaches as QTL within an interval may be matched to genes of known function in homologous chromosomal locations identified in related model systems. Thus outbred systems of QTL detection do not offer the full power of inbred line designs to reveal a complete description of genetic architecture, but do offer the opportunity to ask some basic questions about gene number and effect, and may allow for further exploration though use of candidate genes or other evolving technologies.
Extension of QTL to specific questions of genetic architecture
Much of this review has considered some basic concepts of design that will affect resolution of QTL. However, there are a few considerations that must be mentioned directly to give a full account of the power of QTL to determine genetic architecture. These include distinguishing linkage from pleiotropy, measuring genotype–environment interaction (G × E), differentiating between dominant and overdominant gene action, and estimation of epistasis. In traditional quantitative genetics experiments, researchers often seek to distinguish the effect of the genetic variance from the effect of the environment on phenotypic variance, as well as the modes of gene action such as additive, dominance, and epistatic and pleiotropic effects.
Mode of gene expression. In addition to gene number and relative effect, the degree to which interactions play a role in phenotypic expression, at the level of alleles within loci (recessive, dominant, overdominant gene expression), with other loci (epistasis) or with the environment (genetic by environment interactions), has important implications for our understanding of a variety of evolutionary phenomenon. Furthermore, many relevant evolutionary questions focus on the relative role of pleiotropy vs. linkage in the co-expression of multiple traits. Marker-assisted approaches would appear to have the obvious advantage over previous biometrical methods by permitting description of a range of effects associated with individual marker or flanking regions vs. a sum or average effect across the genotype. The development of increasingly sophisticated analytical approaches is rapidly allowing much greater insight into quantifying modes of gene expression. Below we discuss, in turn, the ability of QTL methods to quantify the various modes of gene expression.
Dominance vs. overdominance basis of heterosis. Our understanding of both the maintenance and evolution of mating systems will be greatly enhanced by a thorough understanding of the genetic basis of heterosis and its converse, inbreeding depression (Charlesworth & Charlesworth 1987, 1999; Uyenoyama and Waller 1991). If the expression of heterosis is due to dominant gene action, then recessive deleterious alleles should be relatively efficiently purged from a population over the course of inbreeding, facilitating the evolution of inbreeding mating systems.
Carefully conducted biometric studies reveal that recessive deleterious alleles contribute to inbreeding depression (e.g. Dudash & Carr 1998; Willis 1999). However, a recent review of the QTL literature (Carr & Dudash 2003) indicates that overdominance-based heterosis is frequently initially observed, although later, more thorough analyses sometimes reveal dominance-based heterosis. The essential issue is whether QTL analysis can distinguish overdominance from pseudo-overdominance. Pseudo-overdominance is the situation in which two viability loci are closely linked in repulsion (++−−/−−++) and a cross between lines manifests apparent overdominance (i.e. the heterozygotes appear to have the highest fitness), when in fact the wild-type (+) alleles are simply dominant to the deleterious alleles at the closely linked loci. This can be easily seen by associating the two genotypes with flanking markers (e.g. M1M1++ −−M2M2 × M1′M1′ −− ++M2′M2′), resulting in individuals manifesting the heterozygote flanking region, M1M1′M2M2′, having highest fitness. Thus QTL analysis of inbreeding depression will be sensitive to map coverage and the number of loci within flanking regions. Given that QTL-based analyses of inbreeding depression have been mostly conducted using artificially selected varieties, e.g. maize (Stuber et al. 1992) and rice (Li et al. 2001; Luo et al. 2001) one would expect a high degree of repulsion linkage of viability loci associated with the Hill–Robertson effect (Hill & Robertson 1966). Indeed, pseudo-overdominance has been implicated in the maize results, following analyses that take into account multiple QTL per chromosome (Cockerham & Zeng 1996), and incorporate fine-scale mapping (Graham et al. 1997). The detection of a large contribution of epistasis to heterosis in the rice studies suggests that there are many loci within the flanking regions and thus pseudo-overdominance, especially considering the highly selfing mating system of rice. Studies with different cultivars in pine (Kuang et al. 1999; Remington & O'Malley 2000a,b), provide evidence for a mostly dominance basis of heterosis. QTL studies of the basis of inbreeding depression are clearly needed in wild populations, but will require extra effort to develop adequate coverage (Fu & Ritland 1994; Karkkainen et al. 1999). More sophisticated analytical approaches that allow for the greater detection of multiple QTL per linkage group (see Table 2), will greatly contribute to our understanding of the relative role of dominance and overdominance in the expression of heterosis.
Epistasis. The relevance of epistasis to questions in ecology and evolution is discussed in more detail in the applications section, and here we limit the discussion to methods for quantifying its contribution to phenotype through QTL analysis. Theory can be used to predict the existence of epistatic QTL that have no significant marginal effects (Culverhouse et al. 2002), but empirical demonstration of this fact has been rare. There have been a number of recent methods proposed to infer the contribution of interaction between markers which may help demonstrate epistasis in QTL studies: a one-dimensional scan that looks for the interaction of an allele with the genetic background (Jannink & Jansen 2001), simultaneous two-way searches at multiple, selected pairs of loci (Kao et al. 1999) and, most recently, a genome-wide method for the simultaneous mapping of all pairs of loci (Carlborg et al. 2003). These scans quantify the extent to which a QTL effect is dependent on the presence or absence of other QTL, i.e. the genetic background of the recombinant generation. However, because of the very many possible pair-wise interactions that must be considered [n * (n − 1)/2 possible pairs of markers where n = number of loci], very large sample sizes are necessary to detect interaction effects at even moderately significant levels of significance. Carlborg et al. (2003) employed a population of over 800 individuals, using a simultaneous genome-wide scan to detect high levels of interaction among markers that otherwise exhibited no marginal effects. Other studies that have identified a significant contribution of epistasis have either used large sample sizes (Li et al. 1997; Shook & Johnson 1999) or focus on specific candidate loci, or induced mutations to reduce the number of comparisons that must be made (Fijneman et al. 1996; Fedorowicz et al. 1998; Wade 2001). Because of the increased number of type 1 errors in estimating epistasis at many loci, higher thresholds of acceptance must be employed, and standard errors of 1 LOD score are inappropriate (Lander & Kruglyak 1995). However, the simultaneous methods for inferring epistasis reduce the problem of multiple tests and randomization tests can be used to estimate significance levels for interacting QTL (Carlborg & Andersson 2002).
G × E. Consideration of genetic interactions with the environment, or G × E interactions, is important in QTL studies not only to understand how the genes interact with the environment, but also to correctly document the relative effect of QTL. Several studies have documented the importance of G × E in shaping trait variability. Experiments that have identified QTL for resistance to a fungal pathogen (Ustilago myadis) in maize revealed that only a subset (~25) of the QTL is constant among all environments (Lubberstedt et al. 1998a). Similar experiments found that as many as 50% of the QTL were constant among experimental populations, but only for about half the populations compared (Lubberstedt et al. 1998b). In a study of QTL for date of bolting (the transition from vegetative to reproductive growth) in several natural field and laboratory environments, Weinig et al. (2002a) found substantial environmental-dependent expression of allelic variation in many QTL within Arabidopsis thaliana. This study used an RIL design, which allows for progeny testing of genetically identical individuals in multiple environments. They observed that most of the loci controlling variation in timing of bolting differed not only among populations, but also between spring and autumn generations in the same geographical locations. The authors hypothesize that if the genetic potential for response to natural selection on reproductive life histories differs among seasonal cohorts, then phenotypes expressed in autumn and spring may potentially evolve independently in response to divergent selection across seasons. Ungerer et al. (2003) similarly used an RIL design with A. thaliana to investigate G × E at QTL affecting inflorescence development. They found plasticity and G × E for the majority of 13 inflorescence traits, and this was associated with variable effects of specific QTL. Pooled across traits, 27% and 52% of QTL exhibited QTL–environment interactions in two recombinant inbred mapping populations. Interestingly, the observed interactions were attributable to changes in the magnitude of QTL effects rather than changes in rank order (sign) of effects. This is in contrast to associated reaction norms exhibiting frequent changes in rank order. This shows that changes in rank orders of reaction norms need not require congruent patterns of QTL effects. G × E at QTL has also been observed in Drosophila melanogaster and several crop species (see overview in Weinig et al. 2002), where the effects of QTL vary with the environment and the genetic background. It is important to take the possibility of G × E into consideration when designing QTL experiments aimed at identifying factors associated with natural variability in given traits. Artificial and unrealistic captive environments or growth conditions may yield phenotypic variance and associated QTL effects not necessarily present in natural environments of organisms.
Pleiotropy. Pleiotropy is invoked in a number of models of evolution, particularly with regard to mechanisms of speciation. For example, sympatric speciation in insects may be facilitated by pleiotropic effects contributing to both feeding site and mate choice (Hawthorne & Via 2001). However, suffice it to say that making a definitive determination of pleiotropy is challenging. Unless polymorphisms within actual genes are employed, interval mapping methods employing neutral genetic markers that outline 5 cm+ intervals (that may contain hundreds of genes) can only suggest the possibility of pleiotropy. Candidate gene approaches may be a powerful method to directly demonstrate pleiotropy, but even then, deletion mapping or complementation approaches would need to be employed to definitively demonstrate its effect. It is far easier to falsify the hypothesis of pleiotropy than to make a definitive determination of its action. Indeed, the search for pleiotropy in some ways encapsulates the search for QTL in general. While the power to resolve QTL into discrete intervals, with known effect on variance in phenotype can be achieved, there is a profound difference between identification of one or more QTL intervals and a complete description of the genetic architecture affecting phenotype. Although QTL represent a dramatic improvement over biometrical methods, and the technology is constantly advancing, prudence in interpretation of what the results of a QTL analysis mean is still the most important tool in estimating the contribution of QTL to phenotype.
Significance thresholds and the problem of linkage. We wish to briefly comment on two other issues in QTL mapping. First, how we decide on the appropriate threshold for accepting or rejecting a QTL as significant will have a profound effect on the estimation of genetic architecture. Historically, QTL were deemed significant if they exceeded a LOD score of 3.0, which is based upon assumptions of the distribution of QTL number and effect (Lynch & Walsh 1998). However, randomization or permutation methods are more robust for determining the threshold LOD score for acceptance of significant marginal QTL effects (Churchill & Doerge 1994; Doerge & Churchill 1996). These methods use data in a simulation to estimate the number and LOD scores of false positives. As mentioned previously, this threshold is even more important when epistatic QTL are considered. These methods do not rely on the assumptions of the number and distribution of QTL effect, and should provide LOD score thresholds that are more appropriate for each dataset used. Permutation tests have been incorporated into a number of QTL detection software packages (notably qtl cartographer) and should replace arbitrary estimates of QTL significance.
A second issue is the confounding relationship between QTL position and effect. Because hundreds of genes may reside within a 5 cm interval along a chromosome, mapping a QTL into such an interval leaves open the possibility that multiple linked QTL reside within that interval. Even in animal model systems of human disease, the issue of linked small-effect QTL limits the identification and cloning of important candidate loci (Mott & Flint 2002). At one level, it may not matter if one or more QTL reside in that interval, and we may wish to treat the linked QTL as a single integrated expression unit. Alternatively, if one wants to try to map to the gene or the nucleotide level, a candidate gene approach may help resolve the linkage/effect question. With the advance of genome sequencing, and the high degree of synteny among related groups of organisms, it may be possible to identify genes in model species at the approximate chromosomal location of your QTL (see below). With a bit of sequencing, it is possible to develop markers based on one or multiple candidate genes and then use them in a standard QTL analysis. Acceptance or rejection of these candidate loci, as well as comparison of their effect relative to the QTL effect of the entire interval may provide insight into contribution of linkage to QTL effect within a large marker interval.