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ANCILLARY SCIENTISTS SYMPOSIUM |
The Roslin Institute, Roslin Biocentre, Roslin, EH25 9PS, United Kingdom
2 Corresponding author: DJ.deKoning{at}BBSRC.AC.UK
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONTOWARD TARGETED AND INTEGRATED...TOWARD eQTL IN POULTRYCONCLUDING REMARKSREFERENCES |
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Key Words: experimental design • fine mapping • gene expression • quantitative trait locus
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONTOWARD TARGETED AND INTEGRATED...TOWARD eQTL IN POULTRYCONCLUDING REMARKSREFERENCES |
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An approach that has great promise to make a major contribution to the dissection of complex traits is genetical genomics, the combined study of gene-expression and marker genotypes in a segregating population (Jansen and Nap, 2001; Jansen, 2003).
Genetical genomics is aimed at detecting genomic loci that control variation in gene expression, so-called expression QTL (eQTL; to distinguish them from functional QTL that affect traits at the whole-organism level). The detected eQTL can represent a locus that lies close to the gene that is being controlled (cis-acting) or 1 or more loci that are unlinked to the gene that is being controlled (trans-acting; Jansen and Nap, 2001). A major promise of genetical genomics is that by examining the relationship between transcript location, location of eQTL, and pleiotropic effects of eQTL, it might be possible to reconstruct genetic pathways that underlie phenotypic variation (Jansen and Nap, 2001). Additional information to reconstruct pathways comes from the correlations between and among gene expression measurements and functional traits (Hitzemann et al., 2003) and the epistatic interactions between eQTL and functional QTL (Carlborg et al., 2004). Therefore, genetical genomics can be exploited as an additional tool to dissect phenotypic variation into its underlying components and elucidate how these components interact. If successful, genetical genomics will enhance and accelerate the characterization of functional QTL, which remains an arduous task, even in model species. At present, several studies have demonstrated the feasibility of eQTL studies, and some of these have successfully integrated eQTL and gene expression with data on traditional phenotypes. What all these studies have in common is that, in comparison to traditional QTL studies of functional traits, the sizes of the experiments are modest to small (de Koning and Haley, 2005). Consequently, the power of the studies is low, and many important QTL will not have been detected and interactions between QTL will have been missed. Thus the results to date have not been very successful at reconstructing genetic pathways or identifying genes underlying functional trait variation. Therefore, more powerful experiments addressing these issues are necessary to realize the full potential of eQTL mapping.
Following a case study of how a QTL experiment has been integrated with microarray analyses in poultry, we outline an experimental strategy to improve the efficiency of future eQTL studies. We subsequently introduce a targeted approach to study the gene expression effects of a marked QTL.
Integrating QTL and Gene Expression Studies
In a number of cases traditional QTL studies have been supplemented with microarray data in an attempt to move from a functional QTL to the underlying gene(s) (Wayne and McIntyre, 2002). Below, we outline a case study where detection of functional QTL was followed up by a gene expression analysis. In this example, microarray experiments were carried out on the founder lines of the study. The underlying idea was that genes that were differentially expressed between the founder lines AND were located in the areas of the QTL that were found in the cross resulting from these lines would be prime positional candidates for the functional genes underlying the QTL. In genetical genomics terms, this type of analysis explores whether the functional QTL is also a cis-acting eQTL. It would be much more difficult for such a study to determine the genetic basis of a QTL that had its functional effect through trans-acting regulation of expression of genes located outside the QTL region. This is because there are likely to be many differences in expression between lines for genes across the genome. This study provides no information on where in the genome the control of those expression differences lies and hence which of these genes are associated with the QTL region.
Resistance to Marek’s Disease in Chicken.
Marek’s disease (MD) is an infectious viral disease and a member of the herpes virus family. Marek’s disease costs the poultry industry about $1 billion per annum. To study the genetic control of MD susceptibility, an experimental cross was established between a resistant and a susceptible inbred line of chicken (Vallejo et al., 1998). The F2 offspring from this cross were experimentally challenged and genotyped, providing the data for a QTL analysis that resulted in 7 QTL for susceptibility to MD (Vallejo et al., 1998; Yonash et al., 1999). Subsequently, the founder lines of the F2 cross were used for a microarray study to identify genes that were differentially expressed between the 2 lines following artificial infection. Fifteen of these genes were mapped onto the chicken genome, and 2 of them mapped to a QTL region for Marek’s resistance (Liu et al., 2001a). At the same time, protein interaction studies between a viral protein (SORF2) and a chicken splenic cDNA library revealed an interaction with the chicken growth hormone (GH; Liu et al., 2001b). This led to the detection of a polymorphism in the GH gene that was associated with differences in the number of tumors between the susceptible and the resistant line (Liu et al., 2001b). The GH gene coincided with a QTL for resistance and also showed up as differentially expressed between the founder lines in the expression study (Liu et al., 2001a). More recently, the same group described detection of lymphocyte antigen 6 complex (LY6E) as a putative Marek’s disease resistance gene, again using the virus-host protein interaction screen (Liu et al., 2003). The LY6E had been demonstrated earlier to be differentially expressed between resistant and susceptible chickens, but its location was not near a MD QTL (Liu et al., 2001a). Hence, one could speculate that one of the MD QTL could act through trans-acting control of the expression of this locus.
This research has demonstrated nicely how integrating across research disciplines can be very profitable. A limiting factor in the further exploitation of the QTL is the lack of precision and power to detect QTL with only 272 chickens in the F2. The comparison of gene expression levels on the founder lines showed several potential candidate genes, but the link to the QTL regions is indirect. Scoring the gene expression levels on the F2 would have provided a more direct link between MD QTL and eQTL and may well have flagged LY6E and GH as targets for eQTL. The GH effect coincided with a functional QTL pointing toward a cis-effect, whereas the LY6E effect would appear to be trans regulated and therefore only traceable to its eQTL in a genetical genomics setting.
Status of eQTL Studies.
To date, actual eQTL studies have been published for mice (Schadt et al., 2003; Bystrykh et al., 2005; Chesler et al., 2005), rats (Hubner et al., 2005), corn (Schadt et al., 2003), yeast (Brem et al., 2002; Yvert et al., 2003; Brem and Kruglyak, 2005), eucalyptus (Kirst et al., 2005), and humans (Monks et al., 2004; Morley et al., 2004). Most of these studies are "proof of principle" or focus on the regulation of gene expression in itself.
The eQTL studies in yeast started out as a fairly straightforward proof of principle (Brem et al., 2002), which was followed up by exploring whether trans-regulating elements coincided with known transcription factors (Yvert et al., 2003). More recently, this work was extended to more general questions about the genetic regulation of gene expression in yeast (Brem and Kruglyak, 2005) and the relevance of epistasis (Brem et al., 2005). Two studies that used the same recombinant inbred (RI) lines of mice to study eQTL in forebrain (Chesler et al., 2005) and haematopoietic stem cells (Bystrykh et al., 2005), respectively, could relate their findings to a whole range of phenotypes that have been measured on these mice as part of other studies. These phenotypes, as well as the expression phenotypes, have been made available online at http://www.genenetwork.org, providing a very valuable resource for the research community. However, the phenotypes, including the expression phenotypes, are only provided for about 33 RI lines available so far, resulting in relatively low power to detect functional QTL and eQTL. With low power to detect QTL, only the largest of QTL effects are detected, and most moderate and small QTL will be missed. As a result, integration of eQTL results and functional trait QTL will only identify the largest effects.
A general conclusion from the published eQTL studies is that the most convincing evidence for eQTL is for the cis-acting effects (de Koning and Haley, 2005), whereas the reconstruction of genetic networks would require the identification of a larger proportion of trans-acting eQTL, including those with moderate effects. In short, current eQTL studies miss many important loci and fail to reconstruct genetic pathways underlying functional variation. At the same time, efforts to find the gene(s) underlying functional QTL via fine mapping or gene expression studies, or both, would be more effective if they were better integrated.
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TOWARD TARGETED AND INTEGRATED MAPPING |
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TOPABSTRACTINTRODUCTIONTOWARD TARGETED AND INTEGRATED...TOWARD eQTL IN POULTRYCONCLUDING REMARKSREFERENCES |
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In addition, combining larger studies with a more focused approach further improves the power of future eQTL studies. In Figure 1
we outline targeted and integrated mapping of marked phenotypic QTL for functional traits (functional QTL). The central idea is to focus the studies on a relevant functional trait for which QTL have been identified previously. Targeted and integrated mapping has 3 components (Figure 1): 1) from a large resource population, individuals that are nonrecombinant for markers flanking the QTL region(s) are selected for the eQTL experiment. 2) Individuals that are recombinant for the QTL region(s) are utilized for further fine mapping of the QTL. 3) Additional expression studies are carried out for some of the recombinant individuals to confirm or evaluate positional candidate genes underlying the QTL.
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The underlying assumption of targeted and integrated mapping is that QTL with major effects on the phenotype for a functional trait will often have major effects on expression of 1 or more genes. In some cases, the eQTL underlying a functional QTL may act in cis to control the expression that causes the phenotypic effect, as recently demonstrated for the IGF2 locus in pigs (Van Laere et al., 2003). Alternatively, the phenotypic effect of a QTL and effects on expression of 1 or more genes may be the downstream consequence of genetic variation acting within a pathway or complex (network) of pathways. In this case we might expect to map one or more trans-acting eQTL to the region of the functional QTL. Compared with an unspecific genome scan for eQTL, targeted and integrated mapping will have increased power to detect eQTL underlying functional QTL and to identify genetic networks and gene interactions for target QTL.
Step 1: The eQTL Study
Let us assume that we know from prior information (e.g., a QTL mapping study) that a selected genomic region affects a complex trait, usually because a functional QTL has been mapped there. Markers spaced through the putative functional QTL region (target region) are genotyped prior to phenotyping for fine-mapping or tissue collection for expression studies. This allows contrasting genotypes (e.g., alternative homozygotes in a F2 population) for 1 or more functional QTL to be selected for the expression study, whereas individuals that are recombinant in the QTL regions are diverted into the fine-mapping study. Selecting individuals that are homozygous for the target regions increases the power to detect eQTL for these regions and decreases genetic complexity.
This approach improves the power to detect eQTL in 3 ways: 1) When selecting n homozygous individuals from an F2, the power to detect the additive effect of an eQTL for the target regions equals that of an F2 of size 2n. 2) Because the contrast to estimate the putative eQTL effect is only between classes of homozygous individuals, the genetic test is simpler and uses fewer degrees of freedom. 3) A targeted study of 1 or several predetermined QTL regions involves substantially less multiple testing than does a complete genome scan, so the significance threshold for the identified regions could be less stringent than that for the remainder of the genome, increasing the power to detect eQTL even more.
The rest of the genome can also be studied for eQTL albeit with lower power than for the target regions. (For regions unlinked to selected regions, the power to detect eQTL should be equivalent to that of an unselected sample of the same size, so selection is not disadvantageous for eQTL mapping in these regions). Furthermore, interactions can be studied between target regions as well as between the target regions and the remainder of the genome (Carlborg and Haley, 2004).
Step 2: The Fine Mapping Study
Fine mapping strategies include those in which recombination in the QTL region is increased by targeted breeding [e.g., advanced intercross lines (Darvasi, 1998)] and those that exploit historical recombination events. Alternatively, a large pedigreed population should provide sufficient recombination to fine-map a QTL without the need for additional generations (Thaller and Hoeschele, 2000; Ronin et al., 2003). By typing all individuals of the population for markers flanking the QTL, all recombinant individuals are identified. These recombinant individuals are available for further focused study to fine map the functional QTL. To decrease the genotyping load of the fine mapping, a subset from these recombinant individuals could be chosen for further study based on their phenotypic values for the functional trait in question (Ronin et al., 2003). Rather than typing the selected individuals for all available markers in the QTL region, a further decrease of the genotyping load could be obtained by applying genotyping strategies like the half-section algorithm or the golden section algorithm (Ronin et al., 2003).
Step 3: Combining eQTL and Fine Mapping
Any eQTL that are identified in the target regions are potential candidates underlying the functional QTL effect. Given the increased power for the target regions, it is possible that eQTL that are detected in the target region have no direct relation with the functional QTL. The fine mapping study will reduce the confidence interval of the functional QTL, facilitating a more limited selection of positional candidate genes underlying the functional QTL effect.
For cis-acting eQTL, the position of the gene with the associated cis effect will be accurately known for species with good physical mapping or sequence data. Thus, it can be evaluated whether the gene with an associated cis effect still maps to the refined confidence interval of the functional QTL.
For trans effects that map to the candidate region, a simple comparison of location of eQTL and fine-mapped functional QTL is unlikely to be conclusive. In this case, additional expression studies using selected individuals from the fine mapping study may be required to resolve which eQTL are most likely correlated with the functional QTL and which are more likely to be linked effects. If the number of positional candidate genes is limited, such a study could evaluate a much smaller number of genes using methods like reverse transcription-PCR. The selection of genes that merit additional expression studies can be further limited by selecting those genes that give strong correlations with the phenotypic trait or have a known biological function related to the trait of interest.
Power, Precision, and Population Size
The successful implementation of the proposed strategy depends on the power to detect eQTL and the resolution of the fine mapping experiment. Selecting a required number of individuals that are homozygous for a functional QTL region determines the minimum size of the resource population. This in turn then determines the expected precision that can be achieved for fine mapping. Figure 2A shows the predicted statistical power to detect eQTL with different relative effects (on gene expression) and different numbers of F2 selected for eQTL mapping. As stated above, for the regions where all selected individuals are homozygous, the increase in power is equivalent to doubling the number of individuals. For instance, with 200 F2 the predicted power to detect an effect of 0.3 phenotypic SD is 0.34 for most of the genome, whereas for the target region it is 0.84 (Figure 2A).
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, we have calculated the required size of the resource population when 1, 2, or 3 functional QTL are targeted with an initial confidence interval of 20 cM (Figure 2B). These population sizes give a 95% probability of yielding the stated number of individuals homozygous for 1 or the other gametes through each of the selected regions (Jansen, 1992). When focusing on a single QTL, the required population size is 700 when aiming at 200 homozygous F2 for the expression study. The required population size is 1,650 when aiming at 500 homozygous F2 for expression studies. When targeting 2 (3) functional QTL, a population of 2,200 (6,850) is required to provide 200 homozygous F2 and 5,250 (16,400) to provide 500 homozygous F2 (Figure 2B). Selecting individuals that are homozygous for multiple functional QTL improves the ability to map interactions at the expression level between these QTL, but the required population size becomes prohibitive for most species when 3 or more QTL are considered. Assuming infinite map density, the expected confidence interval of a QTL study can be predicted based on the size of the experiment and the QTL effect (Darvasi, 1998). The predicted confidence interval for the functional QTL following the fine mapping exercise with 5,000 to 25,000 individuals in the resource population is shown in Figure 2C. For functional QTL of larger effect, subcM confidence intervals can be obtained when using a population exceeding 5,000 individuals (Figure 2C). Such an experiment could for instance accommodate eQTL mapping with 400 F2 that are selected to be homozygous for 2 QTL flanked by a 20-cM marker bracket (Figure 2B). Based on the numbers presented in Figure 2, targeting (multiple) functional QTL with more modest effects will prove very challenging.
Even though the size of the resource population may seem prohibitive, it is important to realize that not all individuals are fully genotyped. From a resource population of size N, all individuals will be typed for 2 markers flanking the m targeted functional QTL. The n selected F2 will be used for genome-wide marker analysis in the eQTL study. Linkage mapping does not require high density markers, and for most genomes anywhere between 200 and 400 markers should be sufficient for a medium density linkage analysis. The amount of genotyping required for the selective recombinant genotyping depends on the selected fraction, the genotyping strategy, and the size of the targeted interval (Ronin et al., 2003). Figure 2D summarizes the genotyping requirements for resource populations of 5,000 to 20,000 individuals, targeting 1, 2, or 3 functional QTL with an initial confidence interval of 20 cM using the combined golden section/half section algorithm (Ronin et al., 2003) and selecting the top and bottom 25% for the trait of interest.
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TOWARD eQTL IN POULTRY |
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TOPABSTRACTINTRODUCTIONTOWARD TARGETED AND INTEGRATED...TOWARD eQTL IN POULTRYCONCLUDING REMARKSREFERENCES |
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Limited Resources
With limited resources and a more focused objective, the principle of targeted eQTL mapping can still be applied. In the context of an F2 study or similar, increasing the power from a smaller study can be used if the main focus is the identification of cis and trans acting eQTL that underlie the QTL peak and a whole genome eQTL scan is not of interest. The increased power from selection of homozygous individuals and the less stringent significance threshold required in a focused study, as opposed to a genome scan, require a more modest-sized resource population and correspondingly fewer individuals to be assayed for gene expression. For an experiment with 200 F2 that are homozygous for the selected region(s), the power to detect eQTL is > 95% for any effect larger than 0.3 phenotypic SD (using a less stringent threshold of P < 0.01.) Recombinant individuals can be used to increase the mapping accuracy of the QTL, but the improved resolution will be more modest and correspond to the smaller overall size of the resource population. The smaller-sized study may mean that a genome scan is less worthwhile (although if genotyping costs are modest, a genome scan for the largest effect eQTL can be undertaken with little additional input because the expression data are already recorded).
Using Genetical Genomics for a Marked QTL
To illustrate potential of genetical genomics we describe a pilot study in chickens (Cabrera et al., 2006). The crucial part is the focused study of a particular putative QTL, in this case one affecting BW segregating in an intercross of broilers and layers. Our objective was to identify candidate genes through the effect of the QTL at the gene expression level: what genes are affected, where do they map, and in what kind of pathways are they involved?
We identified individuals that were homozygous for markers flanking a QTL region on chromosome 4 (GGA4) from the seventh generation of an advanced intercross between a single broiler and a single layer chicken. These were inferred to be QQ (broiler allele) or qq (layer allele) for the QTL, and matings were set up to provide birds with known QTL genotypes. From the resulting offspring, QQ males and qq males were slaughtered at 21 d of age, and a sample of the breast muscle was taken for RNA isolation and microarray studies. The microarrays design was a direct comparison of QQ vs. qq for 8 independent samples with a dye-swap (16 arrays used in total). The microarray was a chicken cDNA array with 12,877 functional features, spotted in duplicate (Ark-Genomics, http://www.ark-genomics.org). Using 5 alternative normalization procedures, we defined a consensus set of results consisting of 45 (895) differentially expressed genes when applying a false discovery rate (FDR) of 5% (20%; Cabrera et al., 2006). This implies that out of 45 (895) results we expect less than 3 (180) false positive results. The genes that are differentially expressed seem evenly distributed over the genome, and there appears to be no enrichment for affected genes in the QTL area on GGA4. However, among the differentially expressed genes (FDR < 20%) there are 12 genes that map to the QTL region and therefore should be considered positional candidate genes for the QTL. Among these, AADAT (FDR < 5%) is involved in lysine degradation, lysine biosynthesis, and tryptophan metabolism, making it a promising candidate gene. At present, we are performing pathway analyses to see what pathways are enriched for differentially expressed genes and thus providing further clues on the way in which the QTL affects body mass. Further annotation of the microarray and dedicated pathway databases for the chicken will further improve the characterization of this QTL. This demonstrates how a focused study can aid the dissection of a QTL using limited resources.
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CONCLUDING REMARKS |
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TOPABSTRACTINTRODUCTIONTOWARD TARGETED AND INTEGRATED...TOWARD eQTL IN POULTRYCONCLUDING REMARKSREFERENCES |
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Although the utility of inbred resources like RI lines for fine mapping and (e)QTL mapping has been demonstrated elsewhere (Bystrykh et al., 2005; Chesler et al., 2005; Hubner et al., 2005), we want to emphasize that genetical genomics should not be restricted to model species, and we make the case that poultry is very well placed among livestock species to pioneer these approaches.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Received for publication February 6, 2007. Accepted for publication February 10, 2007.
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