Joint Bayesian Inference of Genetic Effect Sizes and PK Parameters in Nonlinear Mixed-Effects Models

2026-04-15

AI Summary

This study addresses the challenge of effectively selecting high-dimensional genetic covariates in population pharmacokinetic (PopPK) modeling, which is hindered by limited sample sizes and strong linkage disequilibrium among SNPs. The authors propose the first fully Bayesian single-stage framework that jointly models nonlinear mixed-effects PopPK dynamics and SNP effects, enabling simultaneous inference of model parameters, posterior uncertainties, and variable inclusion probabilities within a single fitting procedure. The approach innovatively integrates multiple sparsity-inducing priors—including Spike-and-Slab, hierarchical Lasso, regularized Horseshoe, R2-D2, and ℓ₁-ball—to achieve robust variable selection. Simulations demonstrate high F1 scores (0.8–0.85) and near-zero false discovery rates. Application to data from 129 Cambodian HIV patients confirms good model calibration, robust identification of primary-effect SNPs, and minimal sensitivity of secondary signals to prior specification.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

High-dimensional genetic covariate selection in population pharmacokinetic (PK) models is challenging due to the cohort's restricted size and high correlation among single-nucleotide polymorphisms (SNPs). We propose a fully Bayesian, single-stage framework that jointly infers nonlinear mixed effect model (NLMEM) parameters and SNP effect sizes, providing coherent posterior uncertainty and inclusion summaries within a single model fit. We compare five sparsity-inducing priors -- Spike-and-Slab, Hierarchical Lasso, Regularized Horseshoe, R2--D2, and the $\ell_1$-ball -- calibrated through effect-size and sparsity targets. In simulations, all priors showed low false-discovery rates around $0$--$0.08$ under the null, and recovered the causal signal under the alternative, with peak $F_1$ scores around $0.8$--$0.85$ under reasonable inclusion cutoffs. Spike-and-Slab was especially attractive because it provides analytical posterior inclusion probabilities directly, while among priors requiring tolerance-based proxy inclusion summaries, the $\ell_1$-ball combined similarly strong recovery with the most stable behavior across tolerance values. On genetic and PK data from the ANRS 12154 study in 129 Cambodians living with HIV and receiving nevirapine, posterior predictive checks indicated adequate calibration and PK parameter inference remained stable across priors. While the dominant signal was robust across priors, additional candidate SNPs showed only partial agreement in ranking and more prior-sensitive effect-size estimates. These results support Bayesian variable selection within joint NLMEM as a principled approach for pharmacogenetic analyses when uncertainty quantification and regularization are central.

Problem

• genetic effect sizes
• population pharmacokinetics
• nonlinear mixed-effects models
• SNP selection
• high-dimensional covariates

Innovation

• Bayesian variable selection
• nonlinear mixed-effects models
• pharmacokinetics
• sparsity-inducing priors
• SNP effect sizes