Failure to replicate a genetic association may provide important clues about genetic architecture

Abstract

Replication has become the gold standard for assessing statistical results from genome-wide association studies. Unfortunately this replication requirement may cause real genetic effects to be missed. A real result can fail to replicate for numerous reasons including inadequate sample size or variability in phenotype definitions across independent samples. In genome-wide association studies the allele frequencies of polymorphisms may differ due to sampling error or population differences. We hypothesize that some statistically significant independent genetic effects may fail to replicate in an independent dataset when allele frequencies differ and the functional polymorphism interacts with one or more other functional polymorphisms. To test this hypothesis, we designed a simulation study in which case-control status was determined by two interacting polymorphisms with heritabilities ranging from 0.025 to 0.4 with replication sample sizes ranging from 400 to 1600 individuals. We show that the power to replicate the statistically significant independent main effect of one polymorphism can drop dramatically with a change of allele frequency of less than 0.1 at a second interacting polymorphism. We also show that differences in allele frequency can result in a reversal of allelic effects where a protective allele becomes a risk factor in replication studies. These results suggest that failure to replicate an independent genetic effect may provide important clues about the complexity of the underlying genetic architecture. We recommend that polymorphisms that fail to replicate be checked for interactions with other polymorphisms, particularly when samples are collected from groups with distinct ethnic backgrounds or different geographic regions.

Figure 1. A. This is an example of power results and marginal penetrance tables for an epistatic model with a heritability of 0.1.

Part A shows power results. As the minor allele frequency approaches the epistatic minor allele frequency, the power to detect the main effect in a replication sample is reduced. A change of 0.07 in minor allele frequency () is enough to drop the power to replicate from 80% to 20% for this model. It is apparent in B that as the minor allele frequency, , of in the sampled population moves from 0.3 to 0.5 the marginal penetrances of the alleles for () become equal and the main effect is lost. When the replication sample is performed at an allele frequency of 0.3 the power to detect a main effect is near 100%, at an allele frequency of 0.4 the power to detect a main effect is near 60%, and at an allele frequency of 0.5 the marginal penetrances are equivalent and no main effect remains.

Figure 2. This figure summarizes power for many models and heritabilities.

The effect described in Figure 1 is consistent across very large to very small heritability models (0.4 to 0.025). In most cases a change in allele frequency of less than 0.1 is enough to reduce the power to replicate a main effect from 80% to 20%. Results shown are for a sample including 800 cases and 800 controls. Results with datasets containing 400 and 800 individuals are similar and can be found in supplementary figures S1 and S2.

Figure 3. This figure shows marginal penetrances under a number of possible replication scenarios.

In part A the marginal penetrances with the allele frequencies found in the discovery phase of a hypothetical genome-wide association study. In part B The marginal penetrances found in the replication phase of a genome-wide association study under a situation where the result would replicate. In part C the marginal penetrances found in the replication phase of a genome-wide association study under a situation where the result would not replicate. In part D the marginal penetrances found in the replication phase of a genome-wide association study under a situation where the allele first discovered as a risk factor would now appear protective.

Figure 4. This flowchart represents a method by which candidate SNPs can be divided into tiers for later evaluation based on statistical results and biological information.

Tier 1 markers are likely to provide the easiest gene-function studies but provide the least new information. Tier 2 markers have the potential to implicate previously unknown genes in known pathways and are also likely to lead to feasible gene-function studies. Tier 3 markers have the greatest potential to implicate new genes and pathways in the disease process, but gene-function confirmation is likely to be the most difficult for these markers, particularly if clues about role are limited.