The Brugada syndrome (BrS) is a rare heritable cardiac arrhythmia disorder associated with ventricular fibrillation and sudden cardiac death. Mutations in the SCN5A gene have been causally related to BrS in 20-30% of cases. Twenty other genes have been described as involved in BrS, but their overall contribution to disease prevalence is still unclear. This study aims to estimate the burden of rare coding variation in arrhythmia-susceptibility genes among a large group of patients with BrS. We have developed a custom kit to capture and sequence the coding regions of 45 previously reported arrhythmia-susceptibility genes and applied this kit to 167 index cases presenting with a Brugada pattern on the electrocardiogram as well as 167 individuals aged over 65-year old and showing no history of cardiac arrhythmia. By applying burden tests, a significant enrichment in rare coding variation (with a minor allele frequency below 0.1%) was observed only for SCN5A, with rare coding variants carried by 20.4% of cases with BrS versus 2.4% of control individuals (P = 1.4 × 10(-7)). No significant enrichment was observed for any other arrhythmia-susceptibility gene, including SCN10A and CACNA1C. These results indicate that, except for SCN5A, rare coding variation in previously reported arrhythmia-susceptibility genes do not contribute significantly to the occurrence of BrS in a population with European ancestry. Extreme caution should thus be taken when interpreting genetic variation in molecular diagnostic setting, since rare coding variants were observed in a similar extent among cases versus controls, for most previously reported BrS-susceptibility genes.

Population stratification is a well-known confounding factor in both common and rare variant association analyses. Rare variants tend to be more geographically clustered than common variants, because of their more recent origin. However, it is not yet clear if population stratification at a very fine scale (neighboring administrative regions within a country) would lead to statistical bias in rare variant analyses. As the inclusion of convenience controls from external studies is indeed a common procedure, in order to increase the power to detect genetic associations, this problem is important. We studied through simulation the impact of a fine scale population structure on different rare variant association strategies, assessing type I error and power. We showed that principal component analysis (PCA) based methods of adjustment for population stratification adequately corrected type I error inflation at the largest geographical scales, but not at finest scales. We also showed in our simulations that adding controls obviously increased power, but at a considerably lower level when controls were drawn from another population.

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