## Figures

## Abstract

Studies in model organisms suggest that epistasis may play an important role in the etiology of complex diseases and traits in humans. With the era of large-scale genome-wide association studies fast approaching, it is important to quantify whether it will be possible to detect interacting loci using realistic sample sizes in humans and to what extent undetected epistasis will adversely affect power to detect association when single-locus approaches are employed. We therefore investigated the power to detect association for an extensive range of two-locus quantitative trait models that incorporated varying degrees of epistasis. We compared the power to detect association using a single-locus model that ignored interaction effects, a full two-locus model that allowed for interactions, and, most important, two two-stage strategies whereby a subset of loci initially identified using single-locus tests were analyzed using the full two-locus model. Despite the penalty introduced by multiple testing, fitting the full two-locus model performed better than single-locus tests for many of the situations considered, particularly when compared with attempts to detect both individual loci. Using a two-stage strategy reduced the computational burden associated with performing an exhaustive two-locus search across the genome but was not as powerful as the exhaustive search when loci interacted. Two-stage approaches also increased the risk of missing interacting loci that contributed little effect at the margins. Based on our extensive simulations, our results suggest that an exhaustive search involving all pairwise combinations of markers across the genome might provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

## Synopsis

Although there is growing appreciation that attempting to map genetic interactions in humans may be a fruitful endeavor, there is no consensus as to the best strategy for their detection, particularly in the case of genome-wide association where the number of potential comparisons is enormous. In this article, the authors compare the performance of four different search strategies to detect loci which interact in genome-wide association—a single-locus search, an exhaustive two-locus search, and two, two-stage procedures in which a subset of loci initially identified with single-locus tests are analyzed using a full two-locus model. Their results show that when loci interact, an exhaustive two-locus search across the genome is superior to a two-stage strategy, and in many situations can identify loci which would not have been identified solely using a single-locus search. Their findings suggest that an exhaustive search involving all pairwise combinations of markers across the genome may provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

**Citation: **Evans DM, Marchini J, Morris AP, Cardon LR (2006) Two-Stage Two-Locus Models in Genome-Wide Association. PLoS Genet 2(9):
e157.
https://doi.org/10.1371/journal.pgen.0020157

**Editor: **Trudy MacKay, North Carolina State University, United States of America

**Received: **March 8, 2006; **Accepted: **August 4, 2006; **Published: ** September 22, 2006

**Copyright: ** © 2006 Evans et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Funding: **This work was supported by the Wellcome Trust and the National Institutes of Health (EY-126562 [to LRC]).

**Competing interests: ** The authors have declared that no competing interests exist.

**Abbreviation:
**GWA,
genome-wide association

## Introduction

There is growing evidence supporting an important role for epistasis in the etiology of complex traits. Studies employing model organisms such as Drosophilla melanogaster and Saccharomyces cerevisiae (yeast) have suggested that epistasis occurs frequently, involves multiple loci, and in some cases produces effects as large as the main effects at the individual loci [1–4]. Although there is growing appreciation that searching for epistatic interactions in humans may be a fruitful endeavor, there is no consensus as to the best strategy for their detection, particularly in the case of genome-wide association (GWA) where the number of potential comparisons is enormous [5–7].

Recently, Marchini et al. [8] demonstrated that despite the considerable penalty introduced by multiple testing, it was possible to identify interacting loci that increased odds of disease using realistic sample sizes. Interestingly, searching over all possible pairs of loci was often more powerful than performing a single-locus scan across the genome in cases where the loci interacted epistatically. However, Marchini et al. examined three underlying disease models which represent only a small fraction of the entire parameter space of possible models. Additionally, Marchini et al. explicitly avoided models with little or no main effects at the margins. In these situations, single-locus searches are likely to fail and pairwise or higher-order searches will be necessary in order to detect loci [9–11]. A number of recent studies in humans and animals have identified loci that interact significantly but contribute little or no effect at the margins [12–29]. Such scenarios are challenging and worthy of further investigation since much of the current gene mapping methodology depends on the assumption of non-negligible main effects. If models that exhibit negligible marginal effects are common, then this will have significant consequences for how we go about searching for the genetic basis of complex phenotypes.

Even if testing all possible pairwise comparisons can be justified on theoretical grounds, there are still a number of practical difficulties associated with performing such a large number of statistical tests (i.e., storage requirements, computation time, etc.). For example, a smallish scan consisting of only 100,000 markers would entail ^{100,000}C_{2} (i.e., 100,000 “choose” 2), or approximately 4.9 × 10^{9} comparisons, which might take several days on a typical workstation. Thus, rather than testing all possible pairwise comparisons, a more practical strategy might be to examine a subset of loci which could influence the trait. An obvious method of selecting loci is to evaluate their performance first on a single-locus test of association. That is, loci that meet some low threshold in a single-locus test are subsequently followed up in a two-locus analysis [8]. Intuitively, such an approach might also provide an advantage in power since the penalty due to multiple testing would not be as great as in an exhaustive pairwise search. We therefore investigated the performance of two simple two-stage strategies. In the first strategy, only loci that met an initial threshold were included in subsequent testing. In the second strategy, any locus that met the first-stage threshold was subsequently tested with all other markers across the genome regardless of whether these other markers met the initial threshold.

In summary, our manuscript expands on earlier work by (a) examining the performance of a two-locus and single-locus search across the genome for an extensive range of genetic models, (b) investigating the performance of each of these strategies using quantitative rather than disease traits, and (c) characterizing the performance of two, two-stage strategies which reduce the computational and multiple testing burden associated with an exhaustive two-locus search across the genome. Based on our extensive simulations, we demonstrate that an exhaustive two-locus search is more powerful than a single-locus strategy when loci interact for many of the situations considered, and is capable of detecting interacting loci that contribute to moderate proportions of the phenotypic variance using realistic sample sizes. In addition, we also show that an exhaustive search involving all pairwise combinations of markers across the genome is preferable to analyzing the data using a two-stage procedure that first conditions on one or both of the loci meeting some marginal level of significance in a single-locus test. Our results suggest that an exhaustive pairwise search of markers across the genome may provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

## Results

Figure 1 presents an illustrative selection of results in order to highlight some general features of the data (a full list of results for all models and conditions can be found in Dataset S1). The first feature that should be apparent is that only 1,500 individuals are required in order to detect loci responsible for moderate proportions of the phenotypic variance (i.e., 5%) with appreciable power (approximately 80%) using an exhaustive pairwise search across the genome. As expected, when there was no interaction between the loci, the power to detect either locus using a single-locus search was greater than the power to detect both loci using the two-locus strategy. However, the power to detect both loci using a single-locus strategy was actually less than the power of the two-locus search—even when there was no epistasis, a result which also held with different numbers of markers across the genome (results not shown). Given this result, it is perhaps not surprising that when epistasis was present, the power to detect both loci was always greatest using the two-locus strategy. This reflects the usual situation in statistics where the most powerful test is the one that encompasses the true underlying model.

Power is displayed on the vertical axis, allele frequencies at both loci on the horizontal axes. Results are shown for an (A) additive model where there is no epistasis (MA), (B) a simple looking epistatic model (M27) in which an individual requires at least one copy of the increaser allele at both loci in order to increase the quantitative phenotype above baseline levels, and (C) a more exotic model where an individual has to be heterozygous at both loci in order to have a trait mean above baseline (M16).

However, much more interesting is the comparison between the power of the two-locus search and the power to detect *either* locus via single-locus scan (Figure 1). For many of the models, the power to detect *either* locus using a single-locus strategy was greater than the power of a two-locus search for the majority of allele frequencies considered. However, for a small to moderate proportion of the space of possible allele frequencies, a two-locus strategy actually performed better than a single-locus search. These situations represent cases where the combination of allele frequencies is such that the majority of the genetic variance resides in the epistatic variance component, and hence the loci cannot be identified via single-locus tests of association (see below). Interestingly, Figure 1 also shows that for a small number of models, the power to detect *either* locus using a single-locus strategy is actually less than the power of a two-locus search for the majority of the parameter space of allele frequency combinations. These models tended to be the more exotic looking ones (e.g., M170, which requires an individual to be heterozygous at one locus and homozygous at the other in order to display the increased phenotype). Models such as these are difficult to explain via simple additive and dominance effects and thus are not amenable to single-locus tests of association.

Figure 2 illustrates a partitioning of the variance for a simple quantitative trait model (M27). Under this model, an individual requires at least one copy of the increaser allele at both loci in order to increase the quantitative phenotype above baseline levels (one could imagine how this could occur via simple biological process). Notice in particular how the proportion of the genetic variance in each of the different components varies with changes in the allele frequencies. For example, when the “A” and “B” alleles are both common, the majority of variance resides in the epistatic component, and there is little effect at the margins. In this situation, single-locus tests of association might fail to detect the loci even though they clearly influence the quantitative trait. It is only when a two-locus model which explicitly models the interaction between both contributing loci is fit to the data that the true underlying relationship becomes apparent and both loci can be identified [8]. In fact, it is sobering to realize that many simple-looking models (e.g., M1, M3, etc.) also contain sizeable regions of the space of possible allele frequencies where the epistatic variance component is appreciable (see the bottom of Figure 2 for some more examples). The implication is that these situations might also be common in reality and therefore frustrate attempts to localize genes in human populations via simple single-locus approaches.

(A) Expected variance components at locus 1, locus 2, and the epistatic variance component for model M27 in which an individual requires at least one copy of the increaser allele at both loci in order to increase the quantitative phenotype above baseline levels. Note in particular that when alleles “A” and “B” are common at both loci, the majority of genetic variance resides in the epistatic component.

(B) The proportion of the total genetic variance which is due to statistical epistasis for three simple models. The vertical axis denotes the proportion of variance, whereas the horizontal axes denote allele frequencies at each locus. For each model, the epistatic variance component is appreciable for a sizeable portion of the space of possible allele frequencies.

Figure 3 illustrates the performance of each of the two-locus strategies for the same model depicted in Figure 2, which is also representative of many of the situations considered. In the case of the Both Significant Two-Stage Strategy (i.e., where both loci were required to meet a first-stage threshold in the single-locus scans in order to test the full interaction model), as the threshold became increasingly stringent, there was a decrease in power across the majority of the space of possible allele frequencies. However, for a small proportion of this space (i.e., when the allele frequencies at both loci were similar in the case of this model), there was a small increase in power, so long as the first-stage threshold was not too strict (the exact level varied across models). This pattern of results was similar across all of the models considered. In contrast, the power of the Either Significant Two-Stage Strategy (i.e., where only one locus was required to meet a first-stage threshold in order to test the full interaction model) was dependent on the type of model under which the data were simulated. For example, in the case of more exotic models (e.g., M170), there was a decrease in power across the majority of the space of possible allele frequencies even at liberal first-stage cutoff levels (i.e., *p* < 0.5). For other models, power remained relatively constant across the space of possible allele frequencies before decreasing at more stringent cutoffs (the exact threshold that this decline occurred depended on the particular model simulated).

Five different first-stage thresholds are shown. In the case of the Both Significant Two-Stage Strategy, there is a rapid decrease in power with increasingly stringent first-stage thresholds, except for a small proportion of the space of possible allele frequencies where there is a small gain in power. In the case of the Either Significant Strategy, power declines more slowly as the first-stage threshold becomes increasingly stringent.

## Discussion

Our results indicate that despite the considerable statistical penalty introduced by multiple testing, it is possible to detect interacting loci responsible for moderate proportions of the phenotypic variance (i.e., 5%) using realistic sample sizes (approximately 1,500 individuals). This result is important because it was widely assumed prior to this and our previous study [8] that testing for pairwise interactions in GWA was a waste of time, simply because the large number of comparisons would necessitate a severe statistical correction. In contrast, we have provided several examples, where not only is it possible to detect pairwise interacting loci in GWA analysis, but where performing an exhaustive pairwise search of the genome can actually increase power to detect interacting loci which might not be identified using single-locus searches.

A particularly novel aspect of our study concerns the evaluation of two simple two-stage procedures which could be used to decrease the multiple testing burden associated with an exhaustive two-locus scan of the genome. Our simulations surprisingly demonstrate that each of these two-stage strategies was associated with a decrease in power to detect both loci relative to an exhaustive two-stage search. Why was this the case? The key to understanding these results lies in the expected variance components underlying the model under which the data were simulated. In order for the Both Significant Two-Stage Strategy to succeed, *both* loci must pass the first-stage threshold in the single-locus test of association. For this to occur, some of the genetic variance must be present in *both* single-locus components. Using the example in Figures 2 and 3 as an illustration, this was not the case for the majority of the space of possible allele frequencies, and therefore the strategy tended to perform poorly (as was the case for all models except for the purely additive model MA). It was only when the frequency of the increaser alleles at both loci was low, that the genetic variance was divided equally between the two single-locus components. In this situation, both loci were correctly identified at stage 1 and there was a small increase in power due to the fewer tests performed.

In order for the Either Significant Two-Stage Strategy to succeed, some of the genetic variance must be present in *at least one* of the single-locus components. This is because a locus identified using this strategy is subsequently tested with all other markers across the genome regardless of whether they meet the first-stage threshold. In other words, the Either Significant Two-Stage Strategy will fail when the majority of the genetic variance is located in the epistatic variance component or when the first-stage threshold is so stringent that both of the single-locus components cannot be resolved with adequate power. Such situations occurred particularly under extreme scenarios (e.g., M170) but also for proportions of the space of possible allele frequencies in other simpler models (see, e.g., Figure 2 for examples). It is also important to note that although the Either Significant Two-Stage Strategy led to some gains in computation time, storage, etc., overall the method did not increase the statistical power to detect interacting loci. This was because the need to condition on one of the loci being significant in the first stage of testing offset any gains due to the decreased number of tests performed.

Our results imply that, practical considerations aside, it is preferable to perform an exhaustive two-locus search across the genome rather than either of the two-stage procedures that we examined. Otherwise, investigators risk discarding significant loci that only exhibit small effects at the margins. Although it is unclear to what extent multilocus interactions contribute to complex traits in humans, a number of recent studies employing computationally intensive methods have found evidence of statistical epistasis in the absence of significant marginal effects [27–29]. It has even been suggested that undetected epistasis may be one reason why single-locus tests of association fail to replicate across sample groups [8,30]. Since in the presence of epistasis any polymorphism must be considered in the context of other polymorphisms, if only single-locus tests are conducted, then a significant association may only be apparent when the allele frequencies in that sample are conducive to seeing a main effect [30].

Carlborg and colleagues reached similar conclusions but in the context of genome-wide linkage analysis of experimental line crosses [21,31–35]. Results from these designs are particularly relevant to our study since single-locus and epistatic effects can be modeled orthogonally [36–38] as they are in GWA. For example, Carlborg and Haley [32] advocate a two-locus search of the genome in preference to a forward selection procedure which conditions on significant marginal effects at the individual loci, the rationale being that loci which only contribute to epistatic effects might otherwise be missed [21,31–35]. Consistent with this view, a growing number of linkage studies in experimental animals have identified loci that interact significantly but contribute little or no effect at the margins [12–26].

We note that this situation is different from genome-wide linkage analysis in human (and other randomly mating) populations where single-locus and epistatic effects are partially confounded [39]. This is essentially because the allele sharing variables (identity by descent coefficients) that index epistatic and nonepistatic effects are correlated. As a consequence, a single-locus linkage model will absorb some of the variance due to epistatic effects, even though the power to detect epistasis formally through a multilocus linkage model might be low. Thus, a single-locus test of linkage might still be able to detect a locus that has little or no main effect but interacts epistatically with another (possibly) unmeasured locus [39]. In this vein, a sequential search for interacting loci seems like an ideal strategy in these situations. However, as we have shown in our simulations, a sequential search will not necessarily work in GWA as interacting loci that do not display large main effects may be missed.

Finally, we note that several groups are in the process of developing sophisticated algorithms which hold promise for the detection of even higher-order epistatic interactions [27,29–31,34,35,40]. For example, Millstein et al. [40] recently developed a strategy for detecting interacting loci in case control studies which they call a “focused interaction testing framework.” Their basic idea is to perform likelihood ratio tests in stages that increase in complexity with the order of the interaction considered. Joint tests of main effects and interactions are performed conditional on significant lower-order effects. The authors achieve a reduction in the total number of tests performed by first screening potential gene combinations with a goodness of fit chi-square statistic that depends on association among candidate genes in the pooled case control group. Through simulation they demonstrate that their focused interaction testing framework approach has good power to detect interacting loci and is more powerful than performing marginal tests of candidate genes under a range of situations [40]. We note that while their strategy holds great promise for the analysis of interacting candidate genes, the challenge remains to develop algorithms that will efficiently detect higher-order epistatic interactions in GWA analysis where the number of comparisons is much greater.

In conclusion, we have documented an extensive range of examples in which an exhaustive search involving all pairwise combinations of markers across the genome shows favorable performance for identifying interacting quantitative trait loci that contribute to moderate proportions of the phenotypic variance. Such a strategy can produce superior results to a simple single-locus search in situations where loci interact and is more powerful than a two-stage approach. With the era of GWA fast approaching, we believe that an exhaustive pairwise analysis may prove a useful complement to single-locus scans in shedding light on the genetic etiology of complex traits.

## Materials and Methods

### Simulations.

We considered an extensive range of quantitative trait models that incorporated varying degrees of epistasis (Figure 4). Our focus was on models with a specific set of phenotypic means (i.e., 0 or 1) in order to allow comparison with binary disease models. Although there are 2^{9} = 512 possible models, because of symmetries in the data, only 50 of these are unique [41]. These models include scenarios where it is easy to imagine a simple molecular basis for the interaction (e.g., M1), as well as more exotic situations which would require a more complicated biological explanation (e.g., M170). We also simulated under a purely additive model (“MA” in Figure 4) in order to contrast the case where there was no interaction between the loci. Data were simulated according to each model over a range of allele frequencies at both loci (i.e., 0.1, 0.2, …, 0.9). Generating the data in this way allowed the size of the effect at the margins to vary and also the relative importance of epistatic effects while maintaining the total genetic variance at a constant. We simulated cases where the two loci were responsible for 5% of the trait variance, and simulated 1,000, 1,200, or 1,500 unrelated individuals. We assumed perfect LD between trait and marker loci and, in order to correct for multiple testing, assumed the existence of an additional 100,000 markers across the genome (i.e., even though only two loci were in fact simulated in reality).

Each row indexes the genotype at the first locus and each column refers to the genotype at the second locus. The number in each cell is the trait mean for that genotype. Models range from the very simple (e.g., M1) to the more exotic (e.g., M170).

### QTL detection.

Initially we were most interested in whether it would be possible to detect interacting loci using realistic sample sizes, and whether there might be situations where undetected epistasis adversely affected power to detect association when only single-locus approaches were used. We therefore calculated the power to detect association using two different strategies: a locus-by-locus search using single-locus tests of association and an exhaustive two-locus search fitting two-locus models of association to all pairwise combinations of markers. For power comparisons, because the two-locus strategy considers two QTLs simultaneously, it is directly comparable to a single-locus approach that defines success on the basis of detecting either locus individually. We also compared the two-locus strategy with a more stringent single-locus search in which both of the interacting loci needed to be detected in order for the test to be declared significant.

Tests of association were fit to the data via maximum-likelihood assuming an underlying multivariate normal probability density function (we note that similar results were obtained using least squares regression). The log-likelihood *(l)* for individual *i* was calculated as:
where x* _{i}* is the trait value of the

*i*th individual,

*μ*is the maximum likelihood estimate of the mean of individual

_{i}*i*according to the underlying genetic model being fit, and

*σ*is the maximum likelihood estimate of the residual standard deviation not accounted for by the model. For example, in the case of the full two-locus model, nine different

*μ*values were estimated corresponding to each of the nine possible two-locus genotypic means. In the case of the single-locus models, three different

_{i}*μ*values were estimated corresponding to each of the three marginal genotypic means, and, similarly, in the case of the null model of no association,

_{i}*μ*was the overall mean of the trait values in the sample regardless of the genotypes at the two loci. We note that in all instances the maximum likelihood estimate of each parameter is simply its sample value (i.e., the sample genotypic mean or variance) which can be calculated quickly and efficiently in closed form without the need for optimization.

_{i}Significance was assessed by computing the difference in −2 log-likelihood χ^{2} between models, with the degrees of freedom for these tests equal to the difference in df between the full and submodels. For example, since ten parameters are estimated under the full two-locus model (i.e., nine genotypic means plus a single residual variance) and only two parameters are estimated under the null model (i.e., an overall mean and a residual variance), the full two-locus test involves eight degrees of freedom. Similarly, since four parameters are estimated under the single-locus model, the single-locus test of association involves two degrees of freedom. These numbers of course assume that all the relevant genotypic combinations are present in the data. In some circumstances, one or more genotypic combinations were not present in the simulated data in which case the degrees of freedom of the test were adjusted accordingly. In all cases, simple Bonferroni corrections were used to account for multiple testing (0.05/L in the case of the single-locus strategy and α/^{L}C_{2} in the case of the two-locus search where L = 100,000 is the number of markers). Power was computed via 10,000 replicates.

We also determined the power of two simple two-stage strategies. We refer to the first of these strategies as the “Both Significant Two-Stage Strategy” and the second as the “Either Significant Two-Stage Strategy.” In the first stage of both procedures, we identified all loci that were significant in single-locus tests (as above) at one of seven liberal thresholds (i.e., α_{1} = 0.5, 0.1, 0.05, 0.01, 0.001, 0.0005, 0.0001). We called this set of loci *I*_{1} ⊆ {1,…*L*}. We let *d _{1}* be the degrees of freedom of the single-locus model fitted at stage 1 for locus

*l*(maximum 2 degrees of freedom if all three genotypes are present) and defined

*k*such that for

_{l}*l*⊆

*I*

_{1}. In the case of the Both Significant Two-Stage Strategy, for each pair of loci

*l*and

*m*identified in stage 1 (

*l, m ∈ I*

_{1},

*l*≠

*m*), we calculated the log-likelihood ratio statistic

*R*for the full interaction model. For the Either Significant Two-Stage Strategy, we calculated the log-likelihood ratio statistic

_{(l,m)}*R*associated with the full interaction model for each locus identified in stage 1 with all other loci (regardless of whether these loci met the first-stage significance threshold). Because of the way in which loci

_{(l,m)}*l*and

*m*were identified,

*R*>

_{(l,m)}*k*+

_{l}*k*(where

_{m}*k*= 0 in the case of the Either Significant Two-Stage Strategy). We therefore defined a new statistic =

_{m}*R*– (

_{(l,m)}*k*+

_{l}*k*) and assessed the significance of this statistic against a distribution in which

_{m}*d*′ is the degrees of freedom of the full model fitted at the two loci. In the tests, the null hypothesis being tested is that both loci are not associated with the phenotype. We set the level of significance using a Bonferroni correction based on the expected number of tests to be performed, yielding the same overall error rate in each strategy, (α/

^{α1}

^{L}

*C*

_{2}) in the case of the Both Significant Two-Stage Strategy and [α/(

^{L}C_{2}−

^{(L − α1L)}

*C*

_{2})] in the case of the Either Significant Two-Stage Strategy. Through simulation we found this procedure to provide an accurate test of interaction between two loci [8].

### Partitioning the model into expected variance components.

In order to better understand the performance of the different strategies, it is instructive to consider how the total genetic variance for any two-locus model can be divided into three mutually exclusive components: the genetic variance at locus 1, the genetic variance at locus 2 and the epistatic (or interaction) variance (the derivation of these components is well known [42–46] and we refer the interested reader to these articles as well as several excellent texts which cover the subject [47,48]). The partitioning is important because it represents the amount of genetic variance (and hence statistical power) which can be captured by a single-locus test of association at locus 1, a single-locus test of association at locus 2, and the amount of genetic variance which cannot be captured by single-locus tests, respectively. The single-locus components represent the effects of a locus averaged over all other loci (i.e., the ‘marginal' effects), and are equivalent to the combined additive and dominance components of classical quantitative genetics [49]. The epistatic component arises because of nonadditive interactions between the loci and represents the remainder of the genetic variance which is not accounted for by the single-locus components. The sum of all three components is the amount of genetic variance which can be captured by a two-locus test of association.

To illustrate this partitioning formally, consider the 3 × 3 matrix of genotypic means and their frequencies in Table 1. Each row displays the genotype at the first locus, whereas each column indexes the genotype at the second. Each cell contains a genotypic mean and its respective frequency (under Hardy-Weinberg equilibrium). Given the genotypic means and frequencies at both loci, it is possible to calculate the mean (μ) and total genetic variance of the system (σ^{2}). The mean is the sum of all genotypic means weighted by their frequencies:
The total genetic variance is equal to each genotypic mean minus the overall mean (μ), squared, and then summed over all possible genotypes weighted by the appropriate genotypic frequency:
The amount of genetic variance at locus 1 (or locus 2) may be calculated by subtracting the mean for each marginal genotype from the overall mean, squaring and then summing over all possible genotypes weighted by the relevant genotypic frequency:
The epistatic variance is simply the amount of genetic variance not accounted for by the single-locus components:
We performed this partitioning for all 51 models in Figure 4 in order to gain a better insight into the relative performance of each of the different GWA strategies.

Genotypic Means and Frequencies for a Biallelic Two-Locus Model

## Author Contributions

DME conceived and designed the experiments. DME performed the experiments. DME analyzed the data. DME, JM, and APM contributed reagents/materials/analysis tools. DME and LRC wrote the paper.

## References

- 1. Brem RB, Kruglyak L (2005) The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc Natl Acad Sci U S A 102: 1572–1577.RB BremL. Kruglyak2005The landscape of genetic complexity across 5,700 gene expression traits in yeast.Proc Natl Acad Sci U S A10215721577
- 2. Brem RB, Storey JD, Whittle J, Kruglyak L (2005) Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436: 701–703.RB BremJD StoreyJ. WhittleL. Kruglyak2005Genetic interactions between polymorphisms that affect gene expression in yeast.Nature436701703
- 3. Storey JD, Akey JM, Kruglyak L (2005) Multiple locus linkage analysis of genomewide expression in yeast. PLoS Biol 3: e267.. JD StoreyJM AkeyL. Kruglyak2005Multiple locus linkage analysis of genomewide expression in yeast.PLoS Biol3e267.DOI: 10.1371/journal.pbio.0030267. DOI: 10.1371/journal.pbio.0030267.
- 4. Mackay TF (2001) The genetic architecture of quantitative traits. Annu Rev Genet 35: 303–339.TF Mackay2001The genetic architecture of quantitative traits.Annu Rev Genet35303339
- 5. Chesler EJ, Lu L, Shou S, Qu Y, Gu J, et al. (2005) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37: 233–242.EJ CheslerL. LuS. ShouY. QuJ. Gu2005Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function.Nat Genet37233242
- 6. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, et al. (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308: 385–389.RJ KleinC. ZeissEY ChewJY TsaiRS Sackler2005Complement factor H polymorphism in age-related macular degeneration.Science308385389
- 7. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, et al. (2002) Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet 32: 650–654.K. OzakiY. OhnishiA. IidaA. SekineR. Yamada2002Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction.Nat Genet32650654
- 8. Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37: 413–417.J. MarchiniP. DonnellyLR Cardon2005Genome-wide strategies for detecting multiple loci that influence complex diseases.Nat Genet37413417
- 9. Frankel WN, Schork NJ (1996) Who's afraid of epistasis? Nat Genet 14: 371–373.WN FrankelNJ Schork1996Who's afraid of epistasis?Nat Genet14371373
- 10. Culverhouse R, Klein T, Shannon W (2004) Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27: 141–152.R. CulverhouseT. KleinW. Shannon2004Detecting epistatic interactions contributing to quantitative traits.Genet Epidemiol27141152
- 11. Culverhouse R, Suarez BK, Lin J, Reich T (2002) A perspective on epistasis: Limits of models displaying no main effect. Am J Hum Genet 70: 461–471.R. CulverhouseBK SuarezJ. LinT. Reich2002A perspective on epistasis: Limits of models displaying no main effect.Am J Hum Genet70461471
- 12. van Wezel T, Ruivenkamp CA, Stassen AP, Moen CJ, Demant P (1999) Four new colon cancer susceptibility loci, Scc6 to Scc9 in the mouse. Cancer Res 59: 4216–4218.T. van WezelCA RuivenkampAP StassenCJ MoenP. Demant1999Four new colon cancer susceptibility loci, Scc6 to Scc9 in the mouse.Cancer Res5942164218
- 13. Kuida S, Beier DR (2000) Genetic localization of interacting modifiers affecting severity in a murine model of polycystic kidney disease. Genome Res 10: 49–54.S. KuidaDR Beier2000Genetic localization of interacting modifiers affecting severity in a murine model of polycystic kidney disease.Genome Res104954
- 14. Naber CK, Husing J, Wolfhard U, Erbel R, Siffert W (2000) Interaction of the ACE D allele and the GNB3 825T allele in myocardial infarction. Hypertension 36: 986–989.CK NaberJ. HusingU. WolfhardR. ErbelW. Siffert2000Interaction of the ACE D allele and the GNB3 825T allele in myocardial infarction.Hypertension36986989
- 15. Williams SM, Addy JH, Phillips JA 3rd, Dai M, Kpodonu J, et al. (2000) Combinations of variations in multiple genes are associated with hypertension. Hypertension 36: 2–6.SM WilliamsJH AddyJA Phillips 3rdM. DaiJ. Kpodonu2000Combinations of variations in multiple genes are associated with hypertension.Hypertension3626
- 16. Hsueh WC, Cole SA, Shuldiner AR, Beamer BA, Blangero J, et al. (2001) Interactions between variants in the beta3-adrenergic receptor and peroxisome proliferator-activated receptor-gamma2 genes and obesity. Diabetes Care 24: 672–677.WC HsuehSA ColeAR ShuldinerBA BeamerJ. Blangero2001Interactions between variants in the beta3-adrenergic receptor and peroxisome proliferator-activated receptor-gamma2 genes and obesity.Diabetes Care24672677
- 17. Kim JH, Sen S, Avery CS, Simpson E, Chandler P, et al. (2001) Genetic analysis of a new mouse model for non-insulin-dependent diabetes. Genomics 74: 273–286.JH KimS. SenCS AveryE. SimpsonP. Chandler2001Genetic analysis of a new mouse model for non-insulin-dependent diabetes.Genomics74273286
- 18. Tripodis N, Hart AA, Fijneman RJ, Demant P (2001) Complexity of lung cancer modifiers: Mapping of thirty genes and twenty-five interactions in half of the mouse genome. J Natl Cancer Inst 93: 1484–1491.N. TripodisAA HartRJ FijnemanP. Demant2001Complexity of lung cancer modifiers: Mapping of thirty genes and twenty-five interactions in half of the mouse genome.J Natl Cancer Inst9314841491
- 19. Ukkola O, Perusse L, Weisnagel SJ, Bergeron J, Despres JP, et al. (2001) Interactions among the glucocorticoid receptor, lipoprotein lipase, and adrenergic receptor genes and plasma insulin and lipid levels in the Quebec Family Study. Metabolism 50: 246–252.O. UkkolaL. PerusseSJ WeisnagelJ. BergeronJP Despres2001Interactions among the glucocorticoid receptor, lipoprotein lipase, and adrenergic receptor genes and plasma insulin and lipid levels in the Quebec Family Study.Metabolism50246252
- 20. Peripato AC, De Brito RA, Vaughn TT, Pletscher LS, Matioli SR, et al. (2002) Quantitative trait loci for maternal performance for offspring survival in mice. Genetics 162: 1341–1353.AC PeripatoRA De BritoTT VaughnLS PletscherSR Matioli2002Quantitative trait loci for maternal performance for offspring survival in mice.Genetics16213411353
- 21. Carlborg O, Kerje S, Schutz K, Jacobsson L, Jensen P, et al. (2003) A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res 13: 413–421.O. CarlborgS. KerjeK. SchutzL. JacobssonP. Jensen2003A global search reveals epistatic interaction between QTL for early growth in the chicken.Genome Res13413421
- 22. De Miglio MR, Pascale RM, Simile MM, Muroni MR, Virdis P, et al. (2004) Polygenic control of hepatocarcinogenesis in Copenhagen x F344 rats. Int J Cancer 111: 9–16.MR De MiglioRM PascaleMM SimileMR MuroniP. Virdis2004Polygenic control of hepatocarcinogenesis in Copenhagen x F344 rats.Int J Cancer111916
- 23. Yanchina ED, Ivchik TV, Shvarts EI, Kokosov AN, Khodzhayantz NE (2004) Gene-gene interactions between glutathione-S transferase M1 and matrix metalloproteinase 9 in the formation of hereditary predisposition to chronic obstructive pulmonary disease. Bull Exp Biol Med 137: 64–66.ED YanchinaTV IvchikEI ShvartsAN KokosovNE Khodzhayantz2004Gene-gene interactions between glutathione-S transferase M1 and matrix metalloproteinase 9 in the formation of hereditary predisposition to chronic obstructive pulmonary disease.Bull Exp Biol Med1376466
- 24. Yang P, Bamlet WR, Ebbert JO, Taylor WR, de Andrade M (2004) Glutathione pathway genes and lung cancer risk in young and old populations. Carcinogenesis 25: 1935–1944.P. YangWR BamletJO EbbertWR TaylorM. de Andrade2004Glutathione pathway genes and lung cancer risk in young and old populations.Carcinogenesis2519351944
- 25. Aston CE, Ralph DA, Lalo DP, Manjeshwar S, Gramling BA, et al. (2005) Oligogenic combinations associated with breast cancer risk in women under 53 years of age. Hum Genet 116: 208–221.CE AstonDA RalphDP LaloS. ManjeshwarBA Gramling2005Oligogenic combinations associated with breast cancer risk in women under 53 years of age.Hum Genet116208221
- 26.
Montooth KL, Marden JH, Clark AG (2003) Mapping determinants of variation in energy metabolism, respiration and flight in
*Drosophila*. Genetics 165: 623–635.KL MontoothJH MardenAG Clark2003Mapping determinants of variation in energy metabolism, respiration and flight in*Drosophila*.Genetics165623635 - 27. Moore JH, Lamb JM, Brown NJ, Vaughan DE (2002) A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 levels. Clin Genet 62: 74–79.JH MooreJM LambNJ BrownDE Vaughan2002A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 levels.Clin Genet627479
- 28. Nelson MR, Kardia SL, Ferrell RE, Sing CF (2001) A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 11: 458–470.MR NelsonSL KardiaRE FerrellCF Sing2001A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.Genome Res11458470
- 29. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, et al. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69: 138–147.MD RitchieLW HahnN. RoodiLR BaileyWD Dupont2001Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.Am J Hum Genet69138147
- 30. Moore JH (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56: 73–82.JH Moore2003The ubiquitous nature of epistasis in determining susceptibility to common human diseases.Hum Hered567382
- 31. Carlborg O, Andersson L, Kinghorn B (2000) The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci. Genetics 155: 2003–2010.O. CarlborgL. AnderssonB. Kinghorn2000The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci.Genetics15520032010
- 32. Carlborg O, Haley CS (2004) Epistasis: Too often neglected in complex trait studies? Nat Rev Genet 5: 618–625.O. CarlborgCS Haley2004Epistasis: Too often neglected in complex trait studies?Nat Rev Genet5618625
- 33. Carlborg O, Hocking PM, Burt DW, Haley CS (2004) Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth. Genet Res 83: 197–209.O. CarlborgPM HockingDW BurtCS Haley2004Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth.Genet Res83197209
- 34. Ljungberg K, Holmgren S, Carlborg O (2002) Efficient algorithms for quantitative trait loci mapping problems. J Comput Biol 9: 793–804.K. LjungbergS. HolmgrenO. Carlborg2002Efficient algorithms for quantitative trait loci mapping problems.J Comput Biol9793804
- 35. Ljungberg K, Holmgren S, Carlborg O (2004) Simultaneous search for multiple QTL using the global optimization algorithm DIRECT. Bioinformatics 20: 1887–1895.K. LjungbergS. HolmgrenO. Carlborg2004Simultaneous search for multiple QTL using the global optimization algorithm DIRECT.Bioinformatics2018871895
- 36. Kao CH, Zeng ZB (2002) Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics 160: 1243–1261.CH KaoZB Zeng2002Modeling epistasis of quantitative trait loci using Cockerham's model.Genetics2002. 16012431261 2002.
- 37. Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152: 1203–1216.CH KaoZB ZengRD Teasdale1999Multiple interval mapping for quantitative trait loci.Genetics15212031216
- 38. Zeng ZB, Kao CH, Basten CJ (1999) Estimating the genetic architecture of quantitative traits. Genet Res 74: 279–289.ZB ZengCH KaoCJ Basten1999Estimating the genetic architecture of quantitative traits.Genet Res74279289
- 39. Purcell S, Sham PC (2004) Epistasis in quantitative trait locus linkage analysis: Interaction or main effect? Behav Genet 34: 143–152.S. PurcellPC Sham2004Epistasis in quantitative trait locus linkage analysis: Interaction or main effect?Behav Genet34143152
- 40. Millstein J, Conti DV, Gilliland FD, Gauderman WJ (2006) A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 78: 15–27.J. MillsteinDV ContiFD GillilandWJ Gauderman2006A testing framework for identifying susceptibility genes in the presence of epistasis.Am J Hum Genet781527
- 41. Li W, Reich J (2000) A complete enumeration and classification of two-locus disease models. Hum Hered 50: 334–349.W. LiJ. Reich2000A complete enumeration and classification of two-locus disease models.Hum Hered50334349
- 42. Cockerham CC (1954) An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39: 859–882.CC Cockerham1954An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present.Genetics39859882
- 43. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinb 52: 399–433.RA Fisher1918The correlation between relatives on the supposition of Mendelian inheritance.Trans R Soc Edinb52399433
- 44. Kojima K (1959) Role of epistasis and overdominance in stability of equilibria with selection. Proc Natl Acad Sci U S A 1959 45: 984–989.K. Kojima1959Role of epistasis and overdominance in stability of equilibria with selection.Proc Natl Acad Sci U S A 195945984989
- 45. Tiwari HK, Elston RC (1997) Deriving components of genetic variance for multilocus models. Genet Epidemiol 14: 1131–1136.HK TiwariRC Elston1997Deriving components of genetic variance for multilocus models.Genet Epidemiol1411311136
- 46. Wright S (1935) The analysis of variance and the correlations between relatives with respect to deviations from an optimum. J Genet 30: 243–256.S. Wright1935The analysis of variance and the correlations between relatives with respect to deviations from an optimum.J Genet30243256
- 47.
Crow JF, Kimura M (1970) An introduction to population genetics theory. New York: Harper & Row. 591 p. JF CrowM. Kimura1970An introduction to population genetics theoryNew YorkHarper & Row591p.
- 48.
Sham PC (1997) Statistics in human genetics. New York: Wiley. 304 p. PC Sham1997Statistics in human geneticsNew YorkWiley304p.
- 49.
Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sunderland (Massachusetts): Sinauer Associates. 980 p. M. LynchB. Walsh1998Genetics and analysis of quantitative traitsSunderland (Massachusetts)Sinauer Associates980p.