Conference Agenda

Polygenic scores (PGS) quantify genetic predisposition to complex traits and clinical outcomes based on genotype data. This talk addresses recent computational and biostatistical challenges in PGS modelling, including their integration with environmental risk factors. First, training PGS models on high-dimensional and large-scale genotype data with hundreds of thousands of genetic variants and individuals requires scalable yet interpretable statistical learning methods. Second, the transferability of PGS models to diverse populations with different ancestries remains limited, as models are typically trained on cohorts predominantly of European ancestry. Third, the evaluation of predictive performance is complicated by different and sometimes conflicting definitions of the commonly used R-squared measure on test data. To address these challenges, scalable statistical learning approaches for PGS modelling based on individual-level genotype data are presented, including boosting and anchor regression. Finally, open problems and directions for future research are highlighted, with the aim of improving robustness, interpretability and gene–environment integration in personalized medicine.

Time-to-event analysis is fundamental for studying patient survival in modern biomedical research, particularly in the presence of high-dimensional covariate information. When survival data are collected over long time horizons, population heterogeneity naturally arises due to evolving clinical practices and patient characteristics. Mixtures of Cox proportional hazards models offer an effective way to account for such heterogeneity by modeling latent subpopulations with distinct risk profiles.

In high-dimensional settings, feature selection is crucial for improving model interpretability and predictive performance. This talk presents a robust feature selection approach for mixtures of Cox models based on a combined ℓ1–ℓ2 penalty, which encourages sparsity while stabilizing estimation across mixture components. The resulting optimization problem is non-smooth and challenging to solve within mixture models. We address this challenge by developing an efficient Expectation–Maximization (EM) algorithm that effectively handles the non-smooth penalty structure.

Empirical results demonstrate that the proposed method improves patient-specific survival time prediction across heterogeneous populations while achieving stable and interpretable feature selection.

The Cox proportional hazards model is a widely used method for analyzing clinical time-to-event data. In its standard form, the Cox model assumes the covariate effects on the hazard function to be constant over time. However, in many clinical settings, covariate effects may vary with time, and covariate interactions may significantly influence survival. Selecting interactions and time-varying effects within the Cox model framework may be challenging and often requires manual pre-screening followed by model selection steps. These selection steps are often carried out through automated stepwise procedures, which, however, can be unstable or even infeasible—particularly if a large number of potential effects is considered.

We introduce a linked-shrinkage adaptive elastic net procedure for selecting two-way interactions and time-varying effects in Cox regression models. The proposed approach integrates an adaptive elastic net with penalty weights derived from an initial ridge regression that includes main effects only. Time-varying effects are modeled as piecewise constant functions. Penalty weights for interactions and time-varying terms are specified using a linked-shrinkage strategy based on the pre-estimated main effects, such that these effects are penalized more strongly than the main effects. We assessed the proposed modeling approach through a simulation study based on Weibull-distributed survival times, incorporating various structures of time-varying covariate effects. Using a simulation study, we compared the proposed method with several established approaches, including the classical elastic net extended to the Cox regression model. Model performance was assessed in terms of the mean squared error (MSE) of the estimated survival probabilities and the accuracy of variable selection.

The proposed method reliably identified true time-varying and two-way interaction effects. The true positive rates ranged between 80%-90% depending on the scenario. Compared to standard regularized Cox regression models, the proposed method yielded better performance in terms of MSE and the ability to select informative main/interaction/timevarying effects in a more precise way. Furthermore, we illustrate the proposed approach by analyzing real-world data from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program.

By addressing the limitations of manual covariate selection and stepwise procedures, the proposed method extends penalized estimation techniques to Cox regression with time-varying coefficients. Further, it facilitates the simultaneous selection of relevant interaction terms and time-varying covariate effects.

The high cost of single-cell sequencing often compels large cohort studies to rely on bulk RNA-seq, which presents challenges in resolving tissue heterogeneity and understanding the roles of individual cell types. In bulk RNA-seq analysis, deconvolution is essential for extracting cell-type-specific information. Most tools focus on estimating cell type proportions, but only a few aim to infer cell-type-specific gene expression profiles (ctsGEPs). Among these, very few estimate ctsGEPs at the individual sample level. The technical challenges of this task highlight the need for more advanced approaches capable of generating accurate individual-level ctsGEP estimates. Such estimates are critical for downstream analyses, including cell-type-specific differential expression and expression quantitative trait locus studies.

To address this, we developed a novel deconvolution method to estimate individual-level ctsGEPs and cell type proportions simultaneously from bulk RNA-seq data. Using a hierarchical Bayesian framework, our method captures the stochastic variation of ctsGEPs across individuals. Parameters are estimated via Markov Chain Monte Carlo (MCMC), with hyperparameters optimized for robust inference.

We benchmarked our method using 48 in silico mixtures generated from single-cell RNA-seq data of human brain donors. The results demonstrated strong performance, with correlations of ~0.9 for ctsGEP estimates and >0.6 for gene expression variation across samples for ~80% of genes. Our method outperformed existing tools, reducing Root-Mean-Square Errors by ~16%. Additionally, we showcased its application in cell-type-specific differential expression analysis.

Our method provides a powerful tool to computationally unravel cell-type-specific expression profiles in bulk RNA-seq data, enabling advances in understanding cellular heterogeneity in biological and pathological contexts.