The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals

Abstract

The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.

Figure 1. Power across all methods, per scenario, as described in Table 1 , for the average across the seven genes.

Footnote: Note that in some scenarios, different methods overlap. This is the case for scenario 1 and 2, where all methods give similar power.

Figure 2. Proportion of causal variants reaching significance as a function of the average effect and proportion of causal variants on average in a gene, employing a SNP-by-SNP analysis.

Figure 3. Continuous traits: Relationship between effect size, proportion of causal variants, and power.

All causal variants have a deleterious effect. Each box corresponds to a different proportion of causal variants involved in the relationship between rare variants and continuous traits (from left to right, 10, 15, 20 and 30%). On the x-axis, effect sizes are in standard deviations and correspond to the absolute value of the average size effect.

Figure 4. Continuous traits: Relationship between effect size, proportion of causal variants, and power, when causal variants are a mixture of protective and deleterious effects.

Each box corresponds to a different proportion of causal variants involved in the relationship between rare variants and continuous traits (from left to right, 10, 15, 20 and 30%). On the x-axis, effect sizes are in standard deviations and correspond to the absolute value of the average size effect.

Figure 5. Dichotomous traits: Relationship between effect size, proportion of causal variants, and power, when causal variants only have a deleterious effect.

Each box corresponds to a different proportion of causal variants involved in the relationship between rare variants and continuous traits (from left to right, 10, 15, 20 and 30%). On the x-axis, effect sizes are in standard deviations and correspond to the absolute value of the average size effect.

Figure 6. Dichotomous traits: Relationship between effect size, proportion of causal variants, and power, when causal variants are a mixture of protective and deleterious effects.

Each box corresponds to a different proportion of causal variants involved in the relationship between rare variants and continuous traits (from left to right, 10, 15, 20 and 30%). On the x-axis, effect sizes are in standard deviations and correspond to the absolute value of the average size effect.