- Methodology article
- Open Access
Data-driven assessment of eQTL mapping methods
© Michaelson et al; licensee BioMed Central Ltd. 2010
- Received: 13 April 2010
- Accepted: 17 September 2010
- Published: 17 September 2010
The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.
Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.
Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.
- Quantitative Trait Locus
- Random Forest
- Composite Interval Mapping
- Importance Measure
For decades scientists have used a variety of analytical techniques to relate allelic inheritance patterns in the genome to variation in continuous physical traits of interest. The goal of such analyses is often to locate quantitative trait loci (QTL), or genomic locations that exert an influence on the manifested trait. Understanding the genomic location of these genetic control points may provide insight into the genetic and molecular framework responsible for enabling the trait.
In the past decade, the advent of the DNA microarray and other high-throughput molecular technologies has updated the paradigm of the QTL. A QTL where mRNA expression is the complex trait of interest is generally referred to as an expression QTL or eQTL . By using DNA microarrays eQTL can be measured for basically all genes in the genome, rendering eQTL data information rich and potentially very powerful. eQTL have been studied in yeast, mouse, rat, human, and plants [2–6] and eQTL have proven to be useful for elucidating the molecular mechanisms of human diseases [7–10].
Although complex traits are by definition controlled by the coordination of multiple genes, the prevailing techniques for mapping them have been deeply rooted in univariate thinking - testing for genetic association to a trait one locus at a time, ignoring combinatorial effects and interactions. In contrast, Broman and Speed  defined the QTL problem as one of multivariate variable selection, where ideally all loci and their combinations are allowed to enter and exit the model as the data dictate. Viewing eQTL mapping as a variable selection problem opens the door to using a host of machine learning algorithms which have rarely, if at all, been applied to QTL and eQTL studies [12–15]. Such a fresh look at the QTL problem may help to uncover latent and meaningful information in otherwise underexploited data.
A systematic comparison of eQTL mapping approaches is necessary to inform the research community which methods work best and in which contexts. Toward that goal, the purpose of this work is twofold. First, we establish a framework for comparing available eQTL mapping methods based on the tendency of each method to map eQTL that are systematically supported by external biological data. This is important because methods papers proposing new (e)QTL mapping techniques often draw their conclusions either solely or largely on the basis of simulated data [11, 12, 14, 16–21]. This is perhaps understandable in the case of earlier work with QTL, where only a limited number of phenotypes were available and external knowledge about their context and probable genetic regulators was not available in a systematic form, making biology-based benchmarking difficult. However, this is not the case in the era of eQTL. Although some genes remain uncharacterized, there are rich sources of data for many genes that give insight about their role and context within the cell. Such knowledge is often contained in databases like the Kyoto Encyclopedia of Genes and Genomes (KEGG) , which makes using it as a basis for a benchmark easier. Our battery of knowledge-driven benchmarks consists of 1) assessing the proportion of cis-eQTL recovered by each method, 2) testing each method's high-scoring eQTL for enrichment of loci related to the target by KEGG pathway information, and 3) agreement of each method's high-scoring eQTL with systematic loss-of-function studies. In this framework we tested three variable importance measures from Random Forests (RF)  as well as sparse partial least squares (SPLS) , the lasso , the elastic net , Haley-Knott regression (HK) , and composite interval mapping (CIM) . We also performed simulations to complement the findings of the knowledge-driven benchmarking framework. We show that multi-locus methods in general (Random Forests, SPLS, lasso, elastic net) are better at recovering biologically meaningful loci than traditional QTL mapping methods such as HK and CIM. Second, we demonstrate that based on both simulations and the knowledge-driven benchmarks, RF shows superior performance as an eQTL mapping method. RF has previously been applied to genome-wide association studies (GWAS) and QTL studies [14, 15, 17, 26, 27]. The contribution of our work, however, lies in the discovery that the most naive measure of variable importance in RF, the variable selection frequency (RFSF), actually performs much better than the more popular permutation importance (RFPI) in this context. Since RFSF has been ignored in all previous works using RF in the QTL or GWAS context, its use here represents a novel eQTL mapping method with demonstrated superior performance.
In order to evaluate the performance of the eQTL mapping methods in a comprehensive way, we used both simulated data and a variety of published and previously unpublished experimental data from mouse and yeast. The mouse data sets include gene expression data from four tissues of recombinant inbred (RI) BXD mouse strains: regulatory T-cell (H. Chen, RA, and KS, unpublished data), lung (RA, L. Lu, R. Williams, and KS, unpublished data), hematopoietic stem cells , and hippocampus . The yeast data were taken from . Further details are available in the methods section.
We note here that one of the goals of this comparison is to determine how susceptible each method is to the effects of linkage disequilibrium. In light of this goal we used all genotype data as-is, without prefiltering or fusing markers, or assigning surrogate eQTL post-hoc. This enables a straightforward comparison across all mapping methods.
Mutant expression change enrichment
In this test, we collected the maximum absolute expression change observed for each target gene when genes co-localized with eQTL in the 99 th percentile are mutated. These values were aggregated over all target genes, forming a distribution for each eQTL mapping method. We compared these distributions to a null distribution (see methods for details) via the Kolmogorov-Smirnov test. We assert that the method that yields eQTL that are enriched for large changes in expression in the mutant study is the most desirable method.
High-throughput data make functional benchmarking of eQTL mapping methods possible
Augmenting eQTL with independent information has been done previously to strengthen hypotheses suggested by the eQTL data [33–36]. Although these applications demonstrate a certain degree of correspondence between eQTL data and external data sources, and imply that such correspondence is desirable in an eQTL mapping method, no benchmarks based on the systematic recovery of biological information have been proposed and applied to a wide variety of mapping methods and data sets. Validating the performance of mapping methods is important not only for those whose analysis ends with an eQTL map, but also for more sophisticated algorithms such as Lirnet  and Geronemo  which build on top of basic mapping concepts. Our analysis, combined with previously cited works that integrate eQTL with other data, show that there is indeed agreement among eQTL and data from different sources. Maximizing this agreement should be a core objective of future mapping techniques. We hope that this approach to benchmarking, in addition to traditional simulated benchmarks, will help practitioners find the appropriate method now, and lead to the development of better mapping methods in the future.
Multi-locus eQTL mapping methods outperform legacy methods
With few exceptions, the legacy methods -- HK and CIM -- stood out as the poor performers, particularly in the simulations, cis-eQTL proportions, and enrichment for KEGG pathway relationships. In preliminary analyses, we found related univariate mapping methods such as EM interval mapping  and ANOVA to have performance almost indistinguishable from HK (data not shown). This observation is important because even at the time of this writing there are still eQTL papers being published that use legacy mapping methods for their analysis [39–42], ostensibly because the more modern methods are not as accessible. In light of our results, we expect that these studies have not exploited the full potential of the collected data. This represents a challenge for the computational community of working to promote not just the development, but also the adoption of these more advanced methods.
There is a fundamental difference in how the legacy linear methods (HK, CIM) and the multi-locus linear methods (SPLS, lasso, elastic net) score loci. The univariate mapping methods rely on a LOD score (or a P value in the case of one-way ANOVA) that expresses the significance of the estimated correlation between a single marker and the trait, resulting in thousands of individual modeling attempts per expression trait. The multi-locus methods, in contrast, assign coefficients to multiple loci in a single final model. These coefficients are then used as locus scores. The disparity in performance between the two classes of methods is likely a result of scoring by contribution to the model (multi-locus approach), rather than scoring by significance (univariate approach).
RF offers a third paradigm for scoring that is conceptually similar to the coefficient approach of the multi-locus linear methods, though distinct in implementation. Each of the three importance measures derived from RF measures a locus' average contribution in an ensemble of models. This differs from the coefficient approach in that it is a summary of multiple models, each including multiple loci, rather than a summary of a single model including multiple loci. Additionally, the multi-locus linear methods do not implicitly allow for the inclusion of epistatic interactions in the locus scoring process, while RF does.
It should be noted that the benchmarking process described in this work did not focus on the methods' abilities for statistical inference, that is, determining whether a locus significantly explains an expression trait. Instead, our benchmarks focused on which methods prioritized the loci with the greatest degree of effectiveness over a large panel of data. If statistical inference is desired, appropriate permutation of the data can be performed to obtain a null distribution of scores for the chosen method, which can then be used to assess significance of the scores.
Random Forests selection frequency maps the most biologically consistent eQTL
Random Forests (RF)  is a classification and regression algorithm based on fitting an ensemble of trees. When mapping eQTL, RF fits decision trees by using markers as predictor variables, i.e., each node in a tree corresponds to a split of the population based on the genotype at the selected marker. By combining an ensemble of many diverse decision trees, RF guards against overfitting and also provides several measures of predictor variable importance. In this work, these measures of variable importance are used to map eQTL.
To further explore this idea, we performed simulations where the expression trait was a function of eight loci, two with strong effects, and six with small effects. As expected, the loci with the stronger effects were used in splits closer to the root node. The causal loci with weaker effects were used to split closer to the leaves. In these simulations, RFSF scored the weak causal loci in the 99 th percentile 18.3% of the time, while RFPI scored the same loci in the 99 th percentile only 10% of the time. These simulations also showed that RFPI is tightly coupled to a variable's proximity to the root node, while RFSF can give high scores even if the variable is not used close to the root node.
From these investigations we conclude that RFPI and RFRSS both essentially determine variable importance near the roots of the trees, and that biologically important splits further down the tree are not adequately reflected in the overall importance scores. RFSF on the other hand, recovers more biologically meaningful predictor variables (loci) when trees are grown deep, suggesting that even splits far down the tree can be reflected in this importance measure. Epistatic effects are an example of where this phenomenon is important -- often genetic interactions are weak and only present in a subset of the population. Such conditional effects are likely to manifest themselves deeper in the trees. RFSF is an attractive measure in these situations.
Because of its demonstrated performance advantages in finding biologically relevant loci, its ability to implicitly consider epistatic interactions, as well as its straightforward and readily available implementation, we recommend using Random Forests for eQTL mapping. We have prepared a short tutorial and example R code demonstrating mapping eQTL with the bias-corrected selection frequency at http://cellnet.biotec.tu-dresden.de/RFSF.
Marker density and analysis strategy
The state of computer hardware at the time of this writing makes the multi-locus methods presented here impractical for exhaustive evaluations of data sets with millions of SNPs and tens of thousands of expression traits. The current solution to this problem is to filter the SNPs to a more tractable number using univariate tests or expert knowledge [44–46]. Considering the joint effects of markers at this point is generally a fruitless effort, given the astronomical number of potential combinations and the problem of dealing with false positives.
As the number of markers considered falls into the tens of thousands, the problem transitions from filtering to mapping. Mapping is a combination of modeling and feature selection, and the methods we explored in this work address the mapping problem. Here the interplay between loci becomes important for accurately identifying the causal regions that should be included in an explicit model of the trait.
Once causal loci have been identified reliably and the relationships between them have been characterized (additive vs. dominant, epistatic vs. additive, etc.), one can construct a linear model, usually consisting of a handful of terms, that accurately describes the trait as a function of the genetic state of the organism. Such an explicit model, though desirable, is rarely attained.
Implications for related mapping problems
Most of the conclusions from our work have implications beyond eQTL mapping. Ideally, the concept of a knowledge-driven benchmark could be used for any physiological trait, but our approach depends on a fairly detailed knowledge of the molecular mechanisms underlying the mapped trait. Neither our notion of measuring the enrichment of regulator-target gene groups in common pathways, nor our counting of cis-eQTL is immediately extendible to physiological traits. Still, taken together, the evidence from this study indicates that QTL mapping - whatever the trait - should be performed using a multi-locus method. Using univariate methods such as HK will lead to severe underexploitation of the data.
Some of the more specific conclusions from our work will need further validation in other organisms and populations. For example, the study populations used here all had roughly a 50/50 distribution of two possible alleles at each marker. Human populations are characterized by very uneven distributions of SNPs, where minor alleles can be extremely rare in a given population. Such a change in the characteristics of the data could influence the ranking of the individual methods. However, such fluctuations in the individual rankings are still unlikely to affect the general conclusion that multi-locus methods produce more informative results than univariate methods, even in GWAS and linkage studies in outbred populations [47–51].
We have compared modern machine learning and regression eQTL mapping methods with more classical mapping approaches from statistical genetics, and evaluated the methods based on their ability to lead users to loci that are more readily supported by external information. We found that the modern methods, which freely allow the consideration of all loci simultaneously, generally outperform their classical counterparts in this regard. In particular, we found that Random Forests consistently mapped the most promising eQTL. Random Forests bias-corrected selection frequency, a novel importance measure, performed better in these tasks than the established permutation importance and RSS importance.
We used expression data from four eQTL studies in four different tissues in recombinant inbred BXD mouse strains: regulatory T-cell, lung, hematopoietic stem cells , and hippocampus . We used only probe sets that mapped unambiguously to Ensembl gene IDs with KEGG annotations . This resulted in a set of 6,121 probe sets for studies using the Affymetrix Mouse 430 2.0 array (lung, regulatory T-cell, and hippocampus) and 3,051 probe sets for the hematopoietic stem cell study, which used the Affymetrix U74Av2 array. Genotype data for the BXD recombinant inbred strains of mice used in these studies consisted of 3,794 markers and was downloaded from the GeneNetwork database . In addition to the mouse data, we used the yeast eQTL study previously published in . After filtering out probes with missing or otherwise ambiguous data, we were left with 4,501 gene expression measurements and 2,914 markers.
We used the reference implementation of Random Forests  in R for all mapping discussed in this work. We grew forests with 5,000 trees, the mtry parameter was set to the default (one third of the total number of markers) and the node size was also the default of 5, unless otherwise noted. We then extracted unscaled permutation importance measures (RFPI), residual sums of squares importance measures (RFRSS), and selection frequencies (RFSF) from the forests for use as the scores for each marker.
We estimated and accounted for bias in RFSF as follows. Using the actual genotype data as predictors, we fit 500 10-tree forests to independent draws from Gaussian noise. This resulted in 5,000 trees, equal in size to the forests used in this work. We collected the selection frequencies for each marker and subtracted the mean selection frequency to yield a vector of correction factors -- one value for each marker. Subtracting this vector of correction factors from the observed selection frequencies (from the observed data) gives bias-corrected selection frequencies (Figure 8). In the context of results in this work, all references to RFSF imply the bias-corrected RFSF, as described here.
Sparse partial least squares
Chun and Keles  recently introduced a method of eQTL mapping using sparse partial least squares, which included an R package and a thorough tutorial available online. We used the spls R package to map eQTL, performing cross-validation on every target to determine the optimal parameters for each fit. eta, the thresholding value, was allowed to vary between 0.3 and 0.7, to prevent both overfitting and a model that was too sparse to score multiple loci. The number of hidden components was allowed to vary from 1 to 5. A final fit was performed with the optimal parameters, and the absolute value of the coefficients was used as the score for each marker.
The lasso , a regression shrinkage method, has previously been applied to QTL mapping , but to our knowledge has never been tested against competing mapping methods in the context of an eQTL study. For this work, we used the lasso as implemented in the elasticnet package for R. The lasso is a special case of the elastic net with lambda equal to (or very near) 0. For each target gene examined, we took the absolute value of the lasso coefficients for a fit performed with the s parameter determined by 10-fold cross-validation, with an imposed minimum of 0.5. These coefficients were used as the score for each marker.
The elastic net
The use of the elastic net  was the same as above for the lasso, except that lambda was set to 1. We found this value of lambda to be optimal after testing a sample of target genes over a range of lambda values (0.5,1,10,100).
We used the implementation of Haley-Knott regression  available in the qtl package for R. LOD scores were calculated at the marker locations.
Composite interval mapping
To perform composite interval mapping  we used the implementation in the qtl package for R, with the method argument set to "EM", and all other arguments set to their default. LOD scores were calculated at the marker locations.
To simulate eQTL with known underlying models, we used the full BXD genotype matrix, available from the GeneNetwork . This matrix consists of 89 strains and 3,794 markers. Using this genotype data, we randomly selected one, two, or three markers (depending on the model to be simulated), and then simulated a trait by using a linear combination of the markers directly, or of logical operations on the markers (in the case of epistasis). All traits started with a baseline value of 9, before adding in the genetic effects. Genetic effects were added as follows: in the single locus model, a single marker was selected at random, and its vector of genotypes (where 1 = BB and 0 = DD) was multiplied by a coefficient, in this case 1. For the two-locus epistatic model, two marker vectors were selected at random, with each being multiplied by 0.25 and then summed. The epistatic component was added by applying the AND logical operation to the genotype vectors (where a 1 is a TRUE and a 0 is a FALSE) and then multiplying the result by a coefficient, in this case 1, and then adding to the additive component. Three locus additive and epistatic traits were constructed in a similar fashion. Gaussian noise with mean 0 was then added to the traits, over 8 levels of increasing standard deviation, which corresponded to 2.5, 5, 7.5, 10, 12.5, 15, 17.5, and 20% of the trait mean.
Each model type (i.e. single locus, two locus epistatic, etc.) was simulated independently 50 times, and each mapping method was applied to the same data. For each simulation and for each mapping method, the maximum (i.e. worst) rank among the set of causal markers was recorded in each noise level. The median of these values (over the 50 simulations) was used to reflect the performance of a given mapping method over the increasing levels of noise. Lower values represent the ability of a method to assign high scores to all causal loci.
Performance based on the proportion of recovered cis-eQTL was assessed by counting the number of expression traits where a marker within 500 kb (for mouse) or 50 kb (for yeast) of the midpoint of the target gene's genomic location had a score in the 99 th percentile of the scores for the respective target gene. These cutoffs, though arbitrary, reflect the difference in complexity between the yeast and mouse genomes - the conclusions drawn from the benchmark are not heavily influenced by this choice. This number was then divided by the number of total expression traits examined for the respective data set.
Each expression trait we tested mapped to at least one KEGG pathway, and each gene found in the KEGG pathway was mapped to the nearest marker. If no marker fell within 5 Mb of a gene, the gene was omitted. For each expression trait, the markers having scores in the 99 th percentile were selected for the enrichment test. The hypergeometric test was used to test this set for the enrichment of markers mapping to genes participating in the same KEGG pathway as the target gene. If multiple pathways existed for any expression trait, all were tested and the minimum P value was used as the representative P value.
In the case of the yeast eQTL data, we additionally assessed enrichment of pathways in which transcription factors binding to the target gene participate. As a basis for mapping transcription factors to their targets, we used . We did not attempt this test with the mouse data because of the lack of dense and reliable TF-target data for mouse.
Since in this test even randomly selected markers yield P values that deviate somewhat from the uniform distribution, we calculated an empirical null distribution of P values. To construct this distribution, we assigned scores to the markers, drawn randomly from a Gaussian distribution with mean 0 and standard deviation of 1. We then took the markers in the 99 th percentile and performed the proposed enrichment test. This was performed for an equivalent number of expression traits contained in the actual data sets. The actual enrichment P values were corrected against this empirical null distribution of enrichment P values.
We plotted the empirical cumulative distribution function (ECDF) of the corrected enrichment P values for each method. As a summary measure for each method's deviation from the uniform distribution, we used the D-statistic as given by the Kolmogorov-Smirnov test. The test was one-sided with the alternative hypothesis that the observed cumulative distribution function accumulated faster than the reference (i.e. uniform) distribution.
Mutant expression change enrichment
Systematic loss of function data in yeast [31, 32] was used to assess which eQTL mapping method tended to agree most with the regulatory relationships suggested by experimentally deactivating upstream regulators. We mapped each repressed gene to its nearest marker. Then, for each expression trait from the yeast eQTL study, we looked at markers in the 99 th percentile of scores for that target. For markers mapping to experimentally repressed regulator genes, we collected the maximum absolute log2 expression ratio (repressed expression divided by wild-type expression) for the appropriate target gene, aggregating them over the whole set of mapped expression traits. We then compared the distribution of the selected maximum absolute log2 ratios generated by each eQTL mapping method by the Kolmogorov-Smirnov (KS) test, collecting the associated P value and D statistic. As a reference distribution in the KS test, a null distribution was constructed by a similar aggregation of maximum absolute fold changes, only with the association between scores and markers randomized for each target gene. The test was one-sided with the alternative hypothesis that the observed cumulative distribution function accumulated slower than the reference distribution. Distributions with a tendency toward higher scores and deviating significantly from the reference distribution suggest an agreement between the eQTL and loss-of-function studies.
Variation of tree depth
To assess the impact of tree depth on each RF importance measure, we used the yeast eQTL data and recomputed eQTL maps for all expression traits, varying the nodesize argument to 5, 15, 29, 57, and 114.
The nodesize argument dictates whether or not a node may be split -- if the number of observations in the node under consideration is greater than nodesize, the node may be split. Otherwise the node is not split and is marked as a terminal node. The default value of nodesize is 5 -- this is the value used in the main body of the study. By selecting a nodesize of 114 (the number of samples in the yeast study), we ensure that splitting stops after the first split. The other values are intermediate steps, each about half the size of the last. We then assessed the improvement in the enrichment of KEGG pathway members and proportion of cis-eQTL identified when growing the trees deeper, using the forest with nodesize 114 as the baseline.
We gratefully thank Rupert Overall and Gerd Kempermann (both CRT, Dresden), as well as Rob Williams, University of Tennessee, Memphis, USA, for providing us eQTL data and for help with the data pre-processing. We thank the Center for High-Performance Computing, TU Dresden for providing computational resources. We also thank the anonymous reviewers for their constructive comments and suggestions. This work was funded by the Klaus Tschira Foundation, the European Commission FP7 (Grant ID 223539), and the Helmholtz Alliance on Systems Biology.
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