Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Abstract

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

Phenotypic extremes in rare variant study designs

“…13 For all genetic studies, selecting the extremes of the phenotype distribution improves power; a concept in genetics that can be traced back to seminal work by Lander and Botstein. 14 More recently, it has been established that extreme sampling performs better than random population-based sampling for single rare variants, with the apparent effect size increasing with more and more stringent selection thresholds, 15 but limited studies have explored the effect when variants are aggregated within a gene. Studies have shown that extreme sampling can enrich for the presence of causal variants 16,17 and, furthermore, that extreme phenotypic sampling and/or a twostage analysis can lead to gains in power.…”

A Biologically Informed Method for Detecting Associations with Rare Variants

“…The extreme phenotypic sampling allows for variant prioritization, but also suffers from "winner's curse" similar to observations made from GWAS [149,150], where the resulting association signals in an extreme phenotypic sample study are often overestimated. To assess the true effect size, the prioritized variants must be assessed in a randomly sampled population of similar ancestry to the original sample.…”

“…In addition, to accurately state that a variant was replicated or not replicated, the replication dataset must be adequately powered to detect an association [149]. For researchers seeking strict replication of rare variant associations, data sets must be quite large [48,150].…”

Genetic Architecture of Type 2 Diabetes

“…GoT2D sequenced samples-Here we describe how we generated, processed, and carried out quality control (QC) on sequence and genotype data for the 2,891 individuals initially chosen for GoT2D from four studies, and how this resulted in 2,657 individuals (1,326 T2D cases and 1,331 non-diabetic controls) for analysis (Extended Data Figure 1). We preferentially sampled early-onset, lean, and/or familial T2D cases and overweight controls with low fasting glucose levels 50 . Specific details of selected samples are provided in Extended Data Table 2 and Supplementary 1.…”