Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression

Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study

BACKGROUND

Ventilator-associated pneumonia (VAP) is one of the most important hospital acquired infections in patients requiring mechanical ventilation (MV) in the intensive care unit, but the effective and robust predictable tools for VAP prevention were relatively lacked.

METHODS

This study aimed to establish a weighted risk scoring system to examine VAP risk among a two-stage VAP case-control study, and to evaluate the diagnostic performance of risk factor score (RFS) for VAP. We constructed a prediction model by least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost) models in 363 patients and 363 controls, and weighted RFS was calculated based on significant predictors. Finally, the diagnostic performance of the RFS was testified and further validated in another 177 pairs of VAP case-control study.

RESULTS

LASSO, RF and XGBoost consistently revealed significant associations of length of stay before MV, MV time, surgery, tracheotomy, multiple drug resistant organism infection, C-reactive protein, PaO2, and APACHE II score with VAP. RFS was significantly linearly associated with VAP risk [odds ratio and 95 % confidence interval = 2.699 (2.347, 3.135)], and showed good discriminations for VAP both in discovery stage [area under the curve (AUC) = 0.857] and validation stage (AUC = 0.879).

CONCLUSIONS

Results of this study revealed co-occurrence of multiple predictors for VAP risk. The risk factor scoring system proposed is a potentially useful predictive tool for clinical targets for VAP prevention.

Genetic Causes of Sudden Cardiac Arrest in the Community

Background

Annually 300,000 Americans experience sudden cardiac arrest (SCA). Studies in referral SCA cohorts have observed rare variants in genes associated with arrhythmia and cardiomyopathy. We sought to: (1) establish the population prevalence of rare disease-causing variants in a set of candidate genes and (2) confirm the association of disease-causing variants in these genes with SCA in two prospective population-based studies.

Methods

SCA patients (n=3264) were accrued from the Oregon Sudden Unexpected Death Study and the PREdiction of Sudden death in mulTi-ethnic cOmmunities (PRESTO) study and compared to control patients (n=13713) from the Atherosclerosis Risk in Communities (ARIC) study. Whole genome sequencing was performed. Disease-causing (likely pathogenic or pathogenic) variants in candidate genes associated with arrhythmia/cardiomyopathy were identified using updated American College of Medical Genetics and Genomics criteria. Gene- collapsing case-control analysis was performed using the conditional logistic regression-sequence kernel association test.

Results

We identified 300 disease-causing variants, the majority of which were in cardiomyopathy genes (71%). There were 136 patients (4.2%) in the SCA group and 351 patients (2.6%) in the control group with one or more disease-causing variants (OR 1.66, 95% confidence interval 1.33-2.07, p<0.001). We identified 13 genes associated with an increased risk of SCA, nine associated with cardiomyopathy ( BAG3, DSC2, DSG2, FLNC, LMNA, MYBPC3, TNNI3, TNNT2, TTN ) and four with arrhythmia ( CACNA1C, CASQ2, KCNH2, KCNQ1 ).

Conclusions

Disease-causing variants in cardiomyopathy genes were the predominant genetic cause of SCA. These findings inform which genes to include in genetic screening for SCA.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.