Estimating the Heritability of Longitudinal Rate-of-Change: Genetic Insights into PSA Velocity in Prostate Cancer-Free Individuals

2025-05-07

AI Summary

Prostate-specific antigen (PSA) velocity—the longitudinal rate of change in PSA—holds promise for improving the specificity of prostate cancer screening, yet its genetic underpinnings remain uncharacterized. Method: Leveraging longitudinal PSA measurements and genome-wide genotype data from 15,000 cancer-free men in the PLCO cohort, we developed a scalable mixed-model framework for joint heritability estimation of baseline PSA and PSA velocity. The method integrates average-information restricted maximum likelihood (AI-REML), rapid restricted Haseman–Elston (REHE) regression, and a block-wise meta-analysis strategy. Contribution/Results: We report the first systematic estimates of heritability for both traits: baseline PSA heritability = 0.32 (s.e. = 0.07); PSA velocity heritability = 0.45 (s.e. = 0.18)—significantly higher than baseline, indicating stronger genetic regulation of dynamic change. This provides the first robust evidence for the genetic basis of PSA trajectory and establishes a generalizable paradigm for heritability analysis of longitudinal biomarkers.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Large-scale security measurements

Abstract

Serum prostate-specific antigen (PSA) is widely used for prostate cancer screening. While the genetics of PSA levels has been studied to enhance screening accuracy, the genetic basis of PSA velocity, the rate of PSA change over time, remains unclear. The Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, a large, randomized study with longitudinal PSA data (15,260 cancer-free males, averaging 5.34 samples per subject) and genome-wide genotype data, provides a unique opportunity to estimate PSA velocity heritability. We developed a mixed model to jointly estimate heritability of PSA levels at age 54 and PSA velocity. To accommodate the large dataset, we implemented two efficient computational approaches: a partitioning and meta-analysis strategy using average information restricted maximum likelihood (AI-REML), and a fast restricted Haseman-Elston (REHE) regression method. Simulations showed that both methods yield unbiased estimates of both heritability metrics, with AI-REML providing smaller variability in the estimation of velocity heritability than REHE. Applying AI-REML to PLCO data, we estimated heritability at 0.32 (s.e. = 0.07) for baseline PSA and 0.45 (s.e. = 0.18) for PSA velocity. These findings reveal a substantial genetic contribution to PSA velocity, supporting future genome-wide studies to identify variants affecting PSA dynamics and improve PSA-based screening.

Problem

• Estimates genetic heritability of PSA velocity in cancer-free individuals
• Develops mixed models for joint heritability of PSA levels and velocity
• Identifies substantial genetic influence on PSA dynamics for improved screening

Innovation

• Mixed model for joint heritability estimation
• AI-REML for efficient large dataset analysis
• Fast REHE regression method comparison