Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks
Feb 27, 2026
Significance: Rare variants (RV) identified through whole genome sequencing hold great promise for elucidating the genetic basis of disease onset, but existing methods for RV association testing are not well suited for time-to-event phenotypes. Here, we develop GATE-STAAR, a scalable and accurate framework integrating frailty modeling with functional annotations. We propose a rare-variant saddlepoint approximation to handle heavy censoring. Through comprehensive simulations and large-scale analysis of approximately 400 K UK Biobank participants, with replication in approximately 230 K All of Us participants, GATE-STAAR uncovers biologically meaningful RV associations while ensuring rigorous control of type I error. This framework is powerful to dissect the genetic architecture of disease onset and progression and advance precision medicine.
Scalable and accurate rare variant association tests for whole genome sequencing time-to-event analysis in large biobanks
Jan 15, 2026
Participation bias in the estimation of heritability and genetic correlation
Dec 31, 2025
The predictive value of the orientation and offset of angle α and angle κ for visual outcomes after trifocal intraocular lens implantation in an Asian cohort
Dec 30, 2025
The Effects of Disease‐Modifying Therapies on Optic Nerve Degeneration in Multiple Sclerosis
Dec 29, 2025
Participation bias in the estimation of heritability and genetic correlation
Jun 24, 2025
We develop a statistical model that systematically evaluates the impact of participation bias on heritability and genetic correlation estimates.
Cofea: correlation-based feature selection for single-cell chromatin accessibility data
Dec 30, 2024
Blood Glucose Levels Moderate the Associations Between IGF-1 Levels and Choroidal Metrics in Patients With Diabetes With Acromegaly Without Diabetic Retinopathy
Dec 29, 2024
Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations
Jul 9, 2024
We propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects to identify gene-trait associations, improving prediction across multiple tissues and datasets.
A robust penalized-regression-based method for multivariable Mendelian randomization using GWAS summary statistics
Dec 31, 2023