Estimating heritability of survival traits using censored multiple variance component model

2025-10-30

AI Summary

Estimating heritability for right-censored survival traits—such as age-at-onset of disease—remains challenging due to restrictive distributional assumptions and poor computational scalability in existing methods, especially for biobanks with millions of samples. To address this, we propose the censored multiple variance component (CMVC) model: a mixed-effects framework that accommodates high censoring proportions (up to 80%) via a multi-component variance structure. CMVC integrates robust numerical optimization with parallel computing to enable genome-wide heritability estimation—including genetic variance components and their uncertainties—at scale (millions of samples and SNPs). In simulations and analyses of four disease onset-age traits from the UK Biobank, CMVC completed genome-wide analyses within nine hours, achieving substantially improved estimation accuracy and scalability. This work establishes a new paradigm for genetic analysis of censored time-to-event phenotypes.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Characterizing the genetic basis of survival traits, such as age at disease onset, is critical for risk stratification, early intervention, and elucidating biological mechanisms that can inform therapeutic development. However, time-to-event outcomes in human cohorts are frequently right-censored, complicating both the estimation and partitioning of total heritability. Modern biobanks linked to electronic health records offer the unprecedented power to dissect the genetic basis of age-at-diagnosis traits at large scale. Yet, few methods exist for estimating and partitioning the total heritability of censored survival traits. Existing methods impose restrictive distributional assumptions on genetic and environmental effects and are not scalable to large biobanks with a million subjects. We introduce a censored multiple variance component model to robustly estimate the total heritability of survival traits under right-censoring. We demonstrate through extensive simulations that the method provides accurate total heritability estimates of right-censored traits at censoring rates up to 80% given sufficient sample size. The method is computationally efficient in estimating one hundred genetic variance components of a survival trait using large-scale biobank genotype data consisting of a million subjects and a million SNPs in under nine hours, including uncertainty quantification. We apply our method to estimate the total heritability of four age-at-diagnosis traits from the UK Biobank study. Our results establish a scalable and robust framework for heritability analysis of right-censored survival traits in large-scale genetic studies.

Problem

• Estimating heritability of right-censored survival traits
• Partitioning genetic variance components for age-at-diagnosis traits
• Developing scalable methods for large biobank genetic studies

Innovation

• Uses censored multiple variance component model
• Estimates heritability for right-censored survival traits
• Scales efficiently to biobanks with million subjects