Introduction

Rare variants have increasingly been cited as major contributors to the disease etiology of several complex disorders. A variety of approaches for analyzing rare variants have been proposed [1]–[4]. Morris and Zeggini [4] have shown that tests based on single variants have limited power compared with tests based on summing or collapsing rare variants. Several tests based on collapsing rare variants have been proposed. Originally, Morgenthaler and Thilly [3] proposed the Cohort Allelic Sum Test (CAST) based on the difference in the number of mutant alleles in cases and controls. The Combined Multivariate and Collapsing (CMC) test extends CAST by collapsing all variants below a minor allele frequency threshold but variants above this minor allele frequency threshold are not collapsed and incorporated into multiple regression model [1]. Furthermore, the CMC uses the Hotelling test statistic, which is more robust than the proportion-based test used in the CAST. In both the CMC and CAST, all variants are assumed to have an equal effect on the phenotype. Therefore, Madsen and Browning [2] proposed a Weighted Sum Statistic (WSS), which weighs the variants based on the inverse of the estimated standard deviation of the total number of rare variants in the sample. This strategy assumes that rarer variants have a greater impact on the phenotype. Price et al. [5] proposed a variable allele-frequency threshold method (VT) for selecting rare variants based on the assumption that variants with minor allele frequency below an unknown allele frequency threshold are more likely to be functional. Han and Pan [6] proposed the Sum of Squared Score (SSU) test and its weighted version, which is robust to the direction of the effect of rare variants. Hoffmann et al. [7] used the general regression framework and model the weights as the product of three variables. The multiplicative model allows for weighting, direction of effect, and variant selection to be incorporated into the framework. They also proposed selecting variants based on functional significance and a data-driven method of variable selection called “step-up,” which is based on the standard forward selection algorithm. Recently, Wu et al. [8] proposed a regression approach, sequence kernel association test (SKAT), based on the score-based variance components test.

However, none of these approaches consider information about linkage disequilibrium (LD) among rare variants. In this article, we propose a new approach, based on the LD among rare variants, for selecting a subset of variants to include in the analysis. Using simulations, we compared our subset selection approach to the step-up method of Hoffmann et al. [7], three related variations: the step-down, step-up-down, and step-down-up methods, the VT method, and SKAT. The variants selected using VT approach were analyzed using the logistic regression framework. We considered three different simulation scenarios: (1) a simulation based on the LD structure of the DRD2 gene, and CHRNA3/A5/B4 gene cluster from the HapMap3 dataset [9], (2) a simulation scenario involving LD block simulation, and (3) a scenario which involved simulating the rare variants without any LD structure, that is, all the variants were independent of each other. The power comparisons of the methods in the three scenarios showed that our approach to selecting rare variants based on their LD is an attractive alternative method for selecting rare variants for association studies.

Methods

We assumed that the data were case-control data from N individuals with a binary phenotype that indicates whether the individuals are affected. Let N₁ be the number of affected individuals (cases) and N₀ be the number of unaffected individuals (controls). A locus is a contiguous sequence of common and rare variants in the genome. Our goal was to detect the association of a particular binary phenotype with rare variants at a particular locus X.

Let us assume that there are p rare variants in the locus of interest. Let denote the genotype for individual i. We choose the collapsing model as detailed in [7]:where is the intercept, is the regression coefficient of association of the locus with the disease, and can be modeled as a multiplicative weight, as in

where is a continuous weight used to up-weigh the effect of rare alleles based on the minor allele frequency (MAF), is a binary weight with a value of 1 or −1, depending on the direction of the effect of the variant on the disease (i.e., protective or deleterious), and is the binary variable selection weight (0 or 1), which represents whether or not the particular variant k is included in the model. Many approaches have been proposed to model and [2], [7]. We concentrated our efforts on selecting the subset of variants that best explain the phenotype based on the available data. Therefore, without loss of generality, we assume and , as we are not focusing on optimizing these weights in our model and instead they can be modeled in the most optimal way suggested in the literature [2], [7], [8].

Simulations

We simulated 1000 replicate samples, each with 1000 cases and 1000 controls, for each of the three scenarios listed below and performed type 1 error and power comparisons based on the simulated data. For the type 1 error comparisons, we generated data from a null model in which none of the variants were causal. The LD structure between the variants was assumed to be from the DRD2 gene of scenario 1. The permutation strategy involved permuting the case-control status and performing the analysis on the permuted data. The permutation-based p value can be estimated by counting the proportion of p-values from the permuted data sets that are less than or equal to the observed p-value for the original dataset. For the type 1 error and power comparisons, 1000 permutations were used for each dataset to obtain the permutation-based p-value.

Scenario 1: Simulation from HapMap3 LD structure for the DRD2 gene.

In this scenario, we simulated rare variants using the LD structure of the DRD2 gene from the HapMap3 data (HapMap3 Genome Browser, release #2 [Phase 3 - genotypes, frequencies, & LD]). The DRD2 gene spans 112,785,528 bp to 112,851,091 bp on chromosome 11. In total, the HapMap3 database identified 49 variants located in the gene region. However, two of the variants were not polymorphic and so we removed them from further consideration. The pairwise LD pattern () and the MAF information were downloaded from the database. HapSim [11] is a simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients. We used HapSim to generate haplotypes for each of the two strands of the DNA based on the LD pattern for the DRD2 gene. The base pairs at each locus are combined without reference to the strand to get the genotype data.

A total of 47 rare variants were simulated. The MAFs of the rare variants were randomly generated from a uniform distribution between 0.25% and 0.5%. Of the 47 variants, 5 variants were randomly designated to be causal variants. A logistic regression model was used to simulate the case-control status.where , if the variant is causal, and zero otherwise, is the log odds of the population disease risk, and is the log odds ratio for the causal variants.

Scenario 1: Simulation from HapMap3 LD structure for the CHRNA3/A5/B4 gene cluster.

In order to further evaluate the proposed methodology, we also considered CHRNA3/A5/B4, a gene cluster encompassing multiple genes. The CHRNA3/A5/B4 cluster spans 76,490,686 bp to 76,899,993 bp on chromosome 15. In total, the HapMap3 database identified 186 variants located in the gene region. However, 22 of the variants were not polymorphic and so we removed them from further consideration. The pairwise LD pattern () and the MAF information were downloaded from the database. A total of 154 rare variants were simulated. The MAFs of the rare variants were randomly generated from a uniform distribution between 0.25% and 0.5%. Of the 154 variants, 7 variants were randomly designated to be causal variants. A logistic regression model was used to simulate the case-control status as in the previous case.

Scenario 2: Simulation from block LD structure.

The simulations for the block LD scenario involved modeling the LD structure of the rare variants as blocks. As in the previous scenario, the MAFs of the rare variants were randomly generated from a uniform distribution between 0.25% and 0.5%. The LD structure for rare variants was simulated from a three-block diagonal structure, as shown in Figure 1. Each LD block had 10 variants that were in LD. Within each block, two variants were randomly designated to be causal. Because of LD, the other 8 variants within each LD block were also associated with the disease. In addition to the 30 associated variants within the three blocks, we simulated 20, 70, or 170 independent variants (i.e., not in LD with any other variants) outside the three LD blocks. These three different numbers (20, 70 and 170) of non-causal and non-associated variants were simulated to assess the performances of the different methods over a range of signal-to-noise ratios. The disease model was the same as in the previous simulation scenario.

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Figure 1. LD structure for block LD simulated data in scenario 2.

The orange blocks contain 10 variants each, which are in LD with one another. Two red variants in each block were randomly designated as the causal variants. The rest of the variants are independent of all the other variants.

https://doi.org/10.1371/journal.pone.0069226.g001

Scenario 3: Simulation from No LD structure.

The no LD scenario involved simulating 50 rare variants that were in linkage equilibrium. Once again, the MAF of each rare variant was randomly generated from a uniform distribution between 0.25% and 0.5%. Of the 50 rare variants, 5 were randomly designated to be causal. The disease model was the same as in the previous simulations.

Results

We analyzed the 1000 null replicates using the step-up, full model, step-down, step-up-down, step-down-up, LDSEL, SKAT, and VT methods. Table 1 shows the type 1 error values at the 5% and 1% levels of significance for each of these methods. The uncorrected type 1 errors were liberal for all methods except the full model and SKAT. The step-based methods over-fit the data, as they are driven by minimizing the p-value. Therefore, the type 1 error must be controlled using a permutation strategy. As we can see from the second panel of Table 1, the permutation-based corrected type 1 errors were well controlled at the 5% and 1% significance levels for all the methods. For example, the step-up approach had uncorrected type 1 errors of 0.996 and 0.946 at the 5% and 1% levels of significance, respectively, but after permutation-based correction, the type 1 errors were well controlled at 0.052 and 0.009, respectively. The proposed LDSEL method had uncorrected type 1 errors of 0.353 and 0.183 at the 5% and 1% levels of significance, and the corrected type 1 errors for LDSEL were 0.049 and 0.010 for the 5% and 1% levels of significance, which are well controlled. This shows that the permutation-based strategy accurately accounts for over-fitting of the data that results from the subset selection procedure.

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Table 1. Uncorrected and permutation-based corrected type 1 errors for different methods at the 5% and 1% level of significance.

https://doi.org/10.1371/journal.pone.0069226.t001

Power comparison for different approaches at the 5% and 1% levels of significance for scenario 1 are presented in Table 2. The first panel corresponds to simulation scenario 1 using the LD structure of the DRD2 gene and the second panel corresponds to simulation scenario 1 using the LD structure of the CHRNA3/A5/B4 gene cluster. All the step-based methods had very similar power. In the first panel, the full model had a power of 0.538 at the 5% level of significance, which was slightly higher than the power for the step-based methods. This is due to the inability of the step-based approaches to select variants in LD with the associated variants which is also reflected in the second panel with a larger cluster of genes. In the first panel, the LDSEL approach had a power of 0.552 at the 5% level of significance, which was higher than SKAT, VT, and the step-based methods. In the second panel, which corresponds to a larger gene cluster, the LDSEL approach had higher power than the step-based methods and VT, but had similar power as the full model and SKAT. The VT method had slightly lower power than the step-based methods in the first panel whereas in the second panel it had higher power than the step-based methods.

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Table 2. Power comparison for different approaches at the 5% and 1% levels of significance.

https://doi.org/10.1371/journal.pone.0069226.t002

Table 3 reports the power comparisons for 3 types of datasets simulated using the block LD structure as presented in scenario 2; the three panels in Table 3 correspond to three datasets simulated using 20 non-associated variants, 70 non-associated variants, and 170 non-associated variants. As in scenario 1, all of the step-based methods had similar powers. In all three datasets, the power of the full model was higher than that of the step-based methods. For example, at 5% level of significance, the power for full model was 0.872 compared to power of 0.797 for the step-down-up method when the number of non-associated variants was 20 (see panel 1). In the first panel the proposed LDSEL method had higher power than all the step-based methods, VT, SKAT and also slightly better power than the full model. Once again, the step-based methods had lower power because of their inability to select variants in LD with their associated variants. The power for VT method was lower than the full model but higher than the step-based methods.

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Table 3. Power comparison for different approaches at the 5% and 1% levels of significance.

https://doi.org/10.1371/journal.pone.0069226.t003

In panel 2 and 3 of Table 3, we provide power comparisons when the number of non-associated variants (i.e. number of noisy variants) is 70 and 170, respectively. Once again, all the step-based methods had very similar power. In panel 2, the full model had a power of 0.733 which was higher than all the step-based methods. However, now the power of our proposed LDSEL method was higher than the full model, SKAT, and VT. Similar results were noted in panel 3.

Table 4 shows the power comparison results at the 5% and 1% levels of significance for the third scenario, in which the variants were simulated to be in linkage equilibrium with each other. The step-based methods had maximum powers of 0.469 and 0.237 at the 5% and 1% levels of significance, respectively. The full model had powers of 0.331 and 0.128 at the 5% and 1% levels of significance, respectively, which was considerably lower than that of the step-based methods. The VT method had significantly lower power than all the methods in this scenario. The LDSEL method had powers of 0.442 and 0.216 at 5% and 1% levels of significance, respectively, which was significantly higher than the full model but marginally lower than the step-based methods and SKAT.

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Table 4. Power comparison for different approaches at the 5% and 1% levels of significance for simulation scenario 3, where all the variants were in linkage equilibrium.

https://doi.org/10.1371/journal.pone.0069226.t004

Discussion

In this paper, we proposed a novel methodology for detecting rare variants. Our motivation was to use the information about linkage disequilibrium between variants to select the best subset of rare variants to improve the power of association. The proposed subset selection algorithm can be incorporated seamlessly into existing methodologies that up-weigh the effect of rare alleles based on MAFs and also use binary weights, 1 or −1, depending upon the direction of the effect of the variant on the disease (i.e., deleterious or protective)[1]–[3], [6]–[8].

We simulated a range of scenarios for LD between the variants. We found that in most of the scenarios, our proposed LDSEL approach had powers higher than or comparable to those of the existing variable selection approaches. LDSEL is a flexible method and, depending on the structure of LD between the variants, it converts to a full model when all of the rare variants are in LD or to a step-based approach when all the variants are in linkage equilibrium. Applying LDSEL when the variants were in LD led to a gain in the power of association for the rare variants. The step-based subset selection methods had the highest power when the variants were not in LD.

The proposed method utilizes LD among variants to select the best subset of variants. There are several measures to assess linkage disequilibrium (e.g. , ). We used the measure because it is commonly used in the context of genetic association studies [12]. It is possible that, for rare variants, the true LD may be over or under estimated using In our LDSEL approach, overestimation (underestimation) of LD would result in selecting more (less) rare variants in the model. In general the effect of error in estimation of LD on power depends on various factors, the true LD structure between the variants, the number of causal variants, and the minor allele frequencies of the variants.

The proposed LDSEL method utilized threshold of 0.1 to select the best subset. The threshold was selected based on assessing its impact on power in our simulation study. We performed power comparisons for different values of Specifically, we compared the results for three LD thresholds: and . In general as the threshold decreases the proposed method is closer to the full model and as the threshold increases it is closer to the step-based methods. In the LDSEL method, we proposed a threshold of because it led to increased power in most of the scenarios considered in this manuscript.

As the number of non-associated variants being pooled increased, substantial power was gained by the LDSEL method compared with the full model or the step-based selection methods. We also simulated a range of odds ratios from 1.4 to 2 for the causal variants under scenario 2 with similar conclusions (data not shown). In general, no single method is uniformly powerful over all the scenarios and all the ranges of odds ratios. The computational burden for LDSEL is heavy, as is the case with the step-based selection methods and any permutation-based test. However, with increasing computation power, the time it takes to complete analyses using LDSEL is not prohibitive. Also, the computational burden can be alleviated by use of adaptive permutation methods. There is a need to leverage any possibility of gaining power for association, and this is even more important with rare variants, which are very hard to detect because of the limitations of sample size.

There is the possibility that some biologically important variants may be removed from methods that select rare variants because of the small sample size or because they are in linkage equilibrium. We recommend including all such important variants in our subset selection approach. Some of these rare variations may result in protein changes that negatively affect the protein function. Using the SIFT (Sorting Intolerant from Tolerant) program [13], one can predict whether a single-nucleotide polymorphism will affect protein function. There is a lot of potential for improving the methods of rare variant association analysis by using all the available data for these variants from pathways and from the variants that disrupt protein-coding functions.

In summary, our proposed method provides a flexible way to select a subset of variants for rare variant association analysis to improve the power of association, while using the information contained in the linkage disequilibrium between the variants. Last but not least, a better understanding of the linkage disequilibrium patterns in rare variants will help us develop novel and efficient ways to exploit the data.

Supporting Information