 Methodology article
 Open Access
 Published:
Filterfree exhaustive odds ratiobased genomewide interaction approach pinpoints evidence for interaction in the HLA region in psoriasis
BMC Genetics volume 16, Article number: 11 (2015)
Abstract
Background
Deciphering the genetic architecture of complex traits is still a major challenge for human genetics. In most cases, genomewide association studies have only partially explained the heritability of traits and diseases. Epistasis, one potentially important cause of this missing heritability, is difficult to explore at the genomewide level. Here, we develop and assess a tool based on interactive odds ratios (I_{OR}), Fast Odds Ratiobased sCan for Epistasis (FORCE), as a novel approach for exhaustive genomewide epistasis search. I_{OR} is the ratio between the multiplicative term of the odds ratio (OR) of having each variant over the OR of having both of them. By definition, an I_{OR} that significantly deviates from 1 suggests the occurrence of an interaction (epistasis). As the I_{OR} is fast to calculate, we used the I_{OR} to rank and select pairs of interacting polymorphisms for P value estimation, which is more time consuming.
Results
FORCE displayed power and accuracy similar to existing parametric and nonparametric methods, and is fast enough to complete a filterfree genomewide epistasis search in a few days on a standard computer. Analysis of psoriasis data uncovered novel epistatic interactions in the HLA region, corroborating the known major and complex role of the HLA region in psoriasis susceptibility.
Conclusions
Our systematic study revealed the ability of FORCE to uncover novel interactions, highlighted the importance of exhaustiveness, as well as its specificity for certain types of interactions that were not detected by existing approaches. We therefore believe that FORCE is a valuable new tool for decoding the genetic basis of complex diseases.
Background
During the past decade, many genomewide association studies (GWAS) have aimed to identify new genetic factors determining susceptibility to a variety of diseases [1,2]. Although promising and sometimes successful, these largescale studies have only led to modest advances [3]. One explanation is that the underlying model that single SNPs contribute independently to the complex trait may frequently be too simple. Rather, complex traits are likely to result from a complex interplay between genes, notably epistatic geneenvironment and genegene interactions [4].
The principal obstacles in a genomewide search for epistasis are statistical power to overcome the limitations of multiple testing and the computational time of the search itself. Over the past decades, many tools have been developed for epistasis detection using various statistical methods [5,6], including those based on regression [711], linkage disequilibrium and haplotypes [12,13], and Bayesian approaches [14,15]. Alternative approaches are based on datafiltering, machinelearning and data mining [1619]. Here, we present an approach that detects pairwise epistasis on a genomewide scale based on the classical interaction odds ratio (I_{OR}). Introduced by Piegorsch et al. in 1994 [20], this approach has mainly been used for the detection of geneenvironment interactions in caseonly designs [21]. VanderWeele et al. [22] showed how the use of I_{OR} can help reveal mechanistic interactions in caseonly datasets.
Firstly, we report on the first efficient implementation of an approach for genomewide epistasis detection, which we call FORCE (Fast Odds Ratio sCan for Epistasis). Due to its mathematical simplicity, the approach is suitable for exhaustive unfiltered epistasis analysis; i.e., the exact value of the I_{OR} statistic can be evaluated for all pairs of genotyped SNPs in reasonable time on a standard computer. We introduce the mathematics to compute exact Pvalues for the most extreme values of I_{OR}.
Secondly, we describe the application of FORCE to the Welcome Trust Case Control Consortium (WTCCC) data on psoriasis, and analyze the previously unknown statistical interactions we found in the light of alreadyknown results.
Lastly we ask whether the statistical interactions detected by FORCE were found due to its exhaustiveness and/or its underlying genetic model, and we present evidence for both. We show that the restriction of FORCE to analyzing only certain SNPs selected according to their marginal effect on psoriasis (as previously described by Knight et al. [23]) strongly limits the statistical significance of the results. We then benchmark the performance of FORCE and other popular methods to detect simulated epistatic interactions, always using exhaustive search. Under different common models for interaction and noise, FORCE consistently detects certain types of interactions better than other approaches.
Methods
Definition of interaction odds ratio (I_{OR})
For any given pair of SNPs, the interaction oddsratio statistic I_{OR} is calculated from a pair of 2×2 contingency tables. These tables are derived from 3×3 tables of all allele combinations, by collapsing them according to a dominant or recessive model (see Table 1). Following preliminary evidence that the dominant model allowed more efficient detection of epistasis (Table 2), all analyses were performed using this dominant genetic model.
We define the following odds ratios:
Note that I_{OR} is undefined when the denominator of this expression becomes zero. For formal consistency, we therefore added a pseudocount of 1 to each cell of the two contingency tables.
Statistical significance: Empirical and exact Pvalues
Note that an I_{OR} of x equals an I_{OR} of 1/x after exchanging counts between cases and controls. We define universal I_{OR}, u(I_{OR}):
This definition allows us to express significant deviations of u(I_{OR}) from the expectation of 1 using a onetailed Pvalue.
Pairs with high u(I_{OR}) were identified by the straightforward algorithm that computes u(I_{OR}) for each pair of given SNPs. Our C implementation encodes, in a preprocessing step, all data related to any given SNP into a bit string, and then uses fast logical and bitcounting functions to compute u(I_{OR}) for all pairs.
Marginal empirical Pvalues for any given pair of SNPs were calculated as the proportion of u(I_{OR}) values from randomly generated permutations of case–control labels that were larger than or equal to the value of u(I_{OR}) obtained for the same pair in real data. The number of permutations performed (1000 for simulated data, 100,000 for real data) was adapted to the number of tests performed in these two scenarios.
Exact Pvalues were calculated using
and computed by the straightforward algorithm with four nested loops to cover all required parameter tuples (α’,γ’,ε’,η’). Each inner loop only visits those parameter values that correspond to possible tuples with α’ + γ’ + ε’ + η’ = α + γ + ε + η, given the parameter values in the outer loop. Summed are those terms with u(I_{OR}) ≥ x.
Application of FORCE to psoriasis data
To evaluate FORCE, we assessed its performance on the WTCCC psoriasis dataset. Initial GWAS and further analyses performed on these data are described in [24]. Following general practice for preprocessing, we excluded potentially lowquality SNP data from further analysis. Specifically, we discarded i) any individual whose total missing rate was above 0.05, ii) any SNP with a frequency of missing data above 0.05, and iii) any SNP with minor allele frequency below 0.05. After preprocessing, our dataset consisted of 2,618 cases, 2,737 controls and 491,191 SNPs, corresponding to approximately 1.2 × 10^{11} SNP pairs. We excluded pairs with a genomic distance of less than 100 kb to avoid pairs in linkage disequilibrium. In addition, we found that low row and cell counts in the contingency table (Table 1) can lead to extreme but frequently not significant values of u(I_{OR}). For the purposes of this study, we excluded 3,521,114 SNP pairs with a total count of less than 50 in any row, or less than 5 in any cell of the contingency table. In addition to FORCE, we performed PLINK (FastEpistasis mode) on the topranked 500 pairs to compare the results obtained with both methods.
Comparison of exhaustive FORCE with semiexhaustive and conditional search
To assess the utility of exhaustive search, we constructed a reference dataset of SNPs previously implicated in psoriasis. We started with a set of 34 SNPs from two previous reviews on psoriasis genetics [25,26] that were part of our psoriasis dataset. After applying quality control thresholds (described above), 18 SNPs remained.
Following general practice for genomewide approaches, for exhaustive and semiexhaustive searches, we used a genomewide significance threshold of \( \mathrm{p}=\frac{2\times 0.05}{{\left({10}^6\right)}^2}={10}^{13} \), which is based on a model of the human genome with 10^{6} independent SNPs [27].
Comparison of FORCE with other approaches on simulated datasets
We simulated datasets of 10 biallelic SNPs over 200 cases and 200 controls following the HardyWeinberg equilibrium model. Interactions were simulated according to six different previously described models without main effect [28] (Table 3). These models represent pure epistasis effects, and not confounding main effects. Model 1 is an interaction effect in which high risk of disease occurs when inheriting heterozygous genotypes at either locus (Aa or Bb) but not both. Model 2 represents high risk of disease when inheriting two highrisk alleles that could be A and/or B. Models 3–6 correspond to the epistasis model discovery method described by Moore et al. [29]. Each of these models represents an interaction effect without any main effects. Allele frequencies are p = 0.25 and q = 0.75 for model 3 and 4, p = 0.1 and q = 0.9 for model 5 and 6.
For each of the six models, we generated 100 datasets in each of the 16 conditions of the presence or absence of four of the most commonly encountered sources of noise: missing data (MS), genotyping errors (GE), genetic heterogeneity (GH), and phenocopy (PC).
For GH, two independent interactions were simulated instead of one, each interaction being riskassociated in half of the affected cases. When PC was simulated, interaction affected the trait for half of the cases, emulating an unknown environmental effect. GE and MS were simulated at 5%, as previously described [28].
An epistatic pair of SNPs was considered as detected if the empirical Pvalue was below 0.001, i.e., below 0.05 after Bonferroni correction. Power was estimated as n/100, where n is the number of datasets with detection(s). When two pairs (P_{1}, P_{2}) of SNPs were simulated, detection was counted under one of three different conditions: D1) when P_{1} and P_{2} were detected, D2) when P_{1} was detected, or D3) when P_{1} or P_{2} was detected. Familywise error rate (FWER) was calculated as m/100, where m is the number of datasets for which at least one pair other than the simulated pair was detected.
Results
FORCE enables exhaustive unfiltered epistasis analysis
The FORCE method for epistasis detection is based on the choice of a dominant or recessive model that collapses combinations of allele counts into two 2×2 incidence tables (see Methods). Interactions are then detected as extreme values of the I_{OR} statistic. We implemented the FORCE method for epistasis in C language [30]. Due to its mathematical simplicity and efficient implementation, the computation of I_{OR} could be performed rapidly, compared to other approaches (4.3 days on a single core of a standard computer). Table 4 shows running times of different methods selected for this study.
Identification of statistically strong interactions requires exhaustive search
To assess the value of exhaustive search, we first evaluated the performance of a conventional, nonexhaustive approach of constraining the analysis to pairs of SNPs that were previously shown to have main effects associated with the phenotype. We therefore performed a constrained analysis on all pairs of 18 highquality SNPs that had main effects on psoriasis in previous GWA studies (see Methods). Table 5 gives the best 25 hits obtained through this approach when evaluated on the WTCCC dataset on psoriasis [24] (the results of all pairs are shown in Additional file 1: Table S1). None of the 153 pairs reached a significant interaction Pvalue below a genomewide significance threshold of 10^{−13}.
A more comprehensive approach, to which we will here refer to as semiexhaustive, constrains only one of the SNPs in a pair to a set of previously identified SNPs [8]. Table 6 shows, for each of the 18 previously identified “main effects” SNPs, the highestscoring interactors, according to the FORCE and PLINK FastEpistasis statistics. Note that FORCE and PLINK identified a few genomewide significant interactions with Pvalues as low as 10^{−20}.
Finally, the relatively low computational complexity required for the FORCE statistic allowed us to perform exhaustive analysis of all SNP pairs in the psoriasis dataset. The results are shown in Table 7 (100 best hits shown in Additional file 1: Table S2). Strikingly, the best resulting Pvalues are another 20 orders of magnitude lower than the Pvalues identified by semiexhaustive search. This shows that a large number of the most significant interactions are missed by the semiexhaustive approach, and hence that the possibility of discovering the statistically bestsupported interactions requires an exhaustive approach. Interestingly, FORCE and PLINK identify distinct interactions.
FORCE pinpoints interactions beyond main effects in the HLA region
We also analyzed the exhaustive FORCE results with regard to previous studies, which have detected numerous main effects [2426], but only few weak statistical interactions [24,34,35]. We assessed the performance of FORCE using the WTCCC psoriasis dataset, which contains 2,618 cases, 2,737 controls and 491,191 SNPs. Table 7 shows the 25 best FORCE hits. Twentyone out of 25 SNP pairs involve SNPs located in the HLA region on chromosome 6, which is consistent with the known strong involvement of the HLA region in psoriasis. Interestingly, certain SNP pairs found to be statistically significant by FORCE did not reach genomewide significance when using PLINK FastEpistasis.
It is well known that SNPs with main effects may falsely appear to be interacting [36]. To avoid such artifacts in our analysis, we removed those SNPs that displayed a univariate statistical association Pvalue of 10^{−5} or less [24]. The results show three highly significant interactions involving SNPs from the HLA region that display no main effect (Table 8). In the absence of correlation between the SNPs we claim that these findings provide evidence of interactive effects involved in psoriasis susceptibility. This confirms that FORCE is able to uncover novel statistical interactions in the HLA region that have not been detected before using conventional approaches.
FORCE systematically detects interactions missed by other approaches
Besides its exhaustiveness, the other characteristic feature of the FORCE approach is the use of the I_{OR} statistic for genomewide epistasis analysis. To study the extent to which the choice of this statistic contributed to the identification of novel statistical interactions, we used datasets that contained different simulated epistatic interactions between SNPs without main effects, according one of six models of Ritchie [28], and none or one of the four sources of noise: Genotyping Error (GE), Missing Data (MS), Genetic Heterogeneity (GH), Phenocopy (PC) (see Methods for details). We then evaluated the power of FORCE and three other popular epistasis detection methods (PLINK Epistasis [7] and PLINK FastEpistasis [8] using default parameters, and MBMDR [16], using recommended parameters [37]) to detect the simulated interactions. We used a significance threshold of 0.001. Figure 1 shows the results for all epistatic models for the case of no noise.
Under all six models, FORCE and MBMDR consistently showed power close to 1. The situation became more interesting in the presence of noise. Figure 2 shows the power of the tested methods for all six models in the presence of one type of noise (numerical values for are given in Tables 9, 10, 11 and 12). While the results for Genotyping Errors (GE) and Missing Data (MS) were very similar to the nonoise scenario, the presence of Genetic Heterogeneity (GH, independent of the definition of “detection”) or Phenocopy (PC) revealed larger differences among the different approaches. Firstly, we noted that, with GH and PC, all approaches lose power. Secondly, we observed that different approaches worked consistently better than others, depending on the interaction model. For interaction models 1 and 2, MBMDR dominated all other approaches; FORCE dominated the other approaches for interaction models 3–6.
Discussion
This study introduces the FORCE approach for genomewide epistasis analysis. On the basis of the Interaction Odds Ratio (I_{OR}) statistic, it performs a genomewide search for epistatic interactions between pairs of SNPs in a reasonable time on a standard laptop computer. The search is exhaustive and filterfree; i.e., the result is guaranteed to reflect the most extreme I_{OR} values over all possible interactions. Exhaustive search using FORCE is possible because of the computational simplicity of the I_{OR} statistic.
Wu et al. [38] introduced a haplotypebased measure based on the following term:
where \( {OR}_{G_1{H}_1} \) is the odds ratio for both risk haplotypes when carried together, compared to the baseline haplotypes; \( {OR}_{G_1{H}_2} \) and \( {OR}_{G_2{H}_1} \) are the odds ratios for each risk haplotype, respectively, compared to the baseline haplotype.
Although both methods are based on odds ratios, the methods differ in several respects. First, and most significantly, Wu’s method uses haplotypes, which typically require the statistical inference of haplotypes. Even though this design was shown to be better powered than classical genotypebased statistics, the additional calculations are computationally costly. As a result, FORCE can perform an exhaustive genomewide epistasis search in a few days on a single compute core while, in practice, Wu’s method only allows a limited number of SNP pairs to be tested.
In addition to the different statistics themselves, the approaches to calculating significance differ. FORCE relies on an exact Pvalue that requires too much time to be calculated exhaustively for all SNP pairs. Instead, Pvalues are calculated only for pairs with the highest I_{OR}. Conversely, Wu et al. used an approximate, chisquare distributionbased, Pvalue which can be applied to each investigated pair of the search.
Our study on WTCCC psoriasis data suggests that the computational effort for exhaustive testing is currently not just a luxury. The popular class of conditional analyses focuses only on possible interactions of previously implicated SNPs – often the only option to perform largescale analysis in reasonable time. When comparing conditional and exhaustive FORCE analyses, we found that the conditional approach only detects interactions of vastly weaker statistical significance.
Our systematic study on small simulated datasets indicates that FORCE not only “goes farther” than existing approaches because of its exhaustive search, but also detects fundamentally different types of interactions, in particular in the biologically more relevant models 3–6. In two out of six models of epistatic interaction described by Ritchie [28], and across the different sources of noise in the data, FORCE consistently displayed a good power of detection compared to other approaches. Interestingly, each of the four approaches is always less efficient than another for at least one model associated with one type of noise.
Finally, by applying FORCE to WTCCC psoriasis data, we were able to detect statistical interactions between SNPs in the HLA region, even after the exclusion of all SNPs with main effects. To our knowledge this constitutes the first demonstration that the genetic structure of the HLA region cannot be understood by the analysis of main effects alone and that more than one interacting locus exists in that region.
Conclusions
Together, the different elements of our study suggest that FORCE represents a valuable new addition to the arsenal of genomewide epistasis detection approaches for case–control studies. As with other approaches, the additionally detected interactions are a priori of a statistical nature, and require detailed analysis and followup.
Beyond this, our study has provided an example for the need for exhaustive epistasis analysis. In the future, exhaustive analysis will be facilitated by the everincreasing computational power available to biological research. On one hand, this may enable the exhaustive calculation of FORCE Pvalues, which can be expected to lead to a potentially much enlarged set of statistically significant interactions. On the other hand, more computational power, as well as algorithmic improvements, may also render exhaustive analysis under those models of interactions feasible for which running times are prohibitive today. Finally, we believe that these improvements are necessary for the integration of different types of interactions and other types of largescale data, which may ultimately be key to understanding the genetic basis of complex diseases.
Abbreviations
 OR:

Odds ratio
 I_{OR} :

Interaction odds ratio
 HLA:

Human leukocyte antigen
 GWAS:

Genome wide association study
 SNP:

Single nucleotide polymorphism
 WTCCC:

The welcome trust case control consortium
 GE:

Genotyping error
 MS:

Missing data
 GH:

Genetic heterogeneity
 PC:

Phenocopy
 FWER:

Familywise error rate
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Acknowledgements
We would like to thank the WTCCC for permission to use the psoriasis genomewide data set. This article is linked to a project funded by “ANR11BSV102701”. LG is supported by Ministère de la Recherche of France, IN is supported by the French Government's Investissement d'Avenir program, Laboratoire d'Excellence “Integrative Biology of Emerging Infectious Diseases” (grant n°ANR10LABX62IBEID).
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Authors’ contributions
LG participated in the study design, carried out the analyses, interpreted results and drafted the manuscript. JFB participated in the study design, interpreted result and drafted the manuscript. IN contributed to the program, RP participated in interpreting results and was involved in drafting and revising the manuscript, KVS participated in the study design and revising the manuscript, BS conceived and implemented the FORCE method, analyzed and interpreted data, and was involved in drafting and revising the manuscript, AS conceived of the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
Additional file
Additional file 1: Table S1.
Epistasis analysis among GWAS hits: all 153 pairs of the conditional search. Table S2. FORCE Exhaustive search top 100 hits on psoriasis data.
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Grange, L., Bureau, J., Nikolayeva, I. et al. Filterfree exhaustive odds ratiobased genomewide interaction approach pinpoints evidence for interaction in the HLA region in psoriasis. BMC Genet 16, 11 (2015). https://doi.org/10.1186/s1286301501743
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Keywords
 Genomewide interaction studies
 Epistasis
 Plink
 MBMDR
 IOR