BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

Association of Antidepressant Medication Type With the Incidence of Cardiovascular Disease in the ARIC Study

Background

The association of antidepressant medication type with the risk of cardiovascular disease (CVD) is unclear. We hypothesized that selective serotonin reuptake inhibitors (SSRIs) are associated with lower risks of CVD events relative to tricyclics and other non‐SSRI antidepressants.

Methods and Results

We studied 2027 participants from the ARIC (Atherosclerosis Risk in Communities) study (mean age 63±10 years; 29% men; 78% white) treated with antidepressants at some time between 1987 and 2013. Antidepressant usage was confirmed by participants bringing pill bottles to study visits. CVD events in the study sample were identified, including atrial fibrillation, heart failure, myocardial infarction, and ischemic stroke. Hazard ratios were used to compare CVD events adjusted for sociodemographic and clinical risk factors in SSRIs users (47%) versus non‐SSRI users. Participants were followed from antidepressant initiation up to 2016 for a median of 13.5 years. We identified 332 atrial fibrillation, 365 heart failure, 174 myocardial infarction and 119 ischemic stroke events. CVD risk was similar for SSRIs and non‐SSRI antidepressant users (hazard ratio, 1.10; 95% CI, 0.86–1.41 for atrial fibrillation; hazard ratio, 0.98; 95% CI, 0.77–1.25 for heart failure; hazard ratio, 0.91; 95% CI, 0.64–1.29 for myocardial infarction; and hazard ratio, 1.07; 95% CI, 0.70–1.63 for ischemic stroke).

Conclusions

SSRI use was not associated with reduced risk of incident CVD compared with non‐SSRI antidepressant use. These results do not provide evidence supporting the use of SSRIs compared with tricyclics and other non‐SSRI antidepressants in relation to CVD risk.