Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction

2026-05-29

AI Summary

This study addresses the limited modeling of local regulatory architecture in existing genotype-based cis-expression prediction methods. To overcome this, the authors propose a block-sparse Bayesian sparse linear mixed model (bsBSLMM), which for the first time integrates the sparse structure of linkage disequilibrium (LD) blocks with transcription start site (TSS) annotation priors within a unified Bayesian framework. By incorporating an LD-block spike-and-slab prior and a TSS-informed SNP inclusion prior, bsBSLMM enhances both biological interpretability and predictive performance. Evaluated on the GEUVADIS dataset and an independent osteoporosis cohort, bsBSLMM substantially increases the number of predictable genes and prediction accuracy, and identifies more disease-associated genes and pathways in transcriptome-wide association studies (TWAS).

Abstract

Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture. We present block-sparse Bayesian sparse linear mixed model (bsBSLMM), an extension of Bayesian sparse linear mixed model (BSLMM) that incorporates linkage disequilibrium (LD)-block spike-and-slab sparsity and a transcription start site (TSS)-informed SNP inclusion prior. Across 23,098 genes from GEUVADIS European-ancestry lymphoblastoid cell lines, bsBSLMM retained more predictable genes than BSLMM, LASSO, BLUP, TIGAR elastic net, and TIGAR Dirichlet-process regression under matched evaluation criteria. Compared with BSLMM, bsBSLMM improved held-out prediction performance for most shared genes, with gains driven primarily by LD-block sparsity and further enhanced by the TSS-informed prior. Variants selected by bsBSLMM showed stronger enrichment in GM12878 DNase and H3K27ac regulatory regions than variants selected by BSLMM. In transcriptome-wide association study (TWAS) analysis, bsBSLMM recovered established inflammatory bowel disease signals, including IL23R, and identified additional genome-wide significant genes not detected by BSLMM. Independent validation in the Louisiana Osteoporosis Study reproduced the increased prediction yield across ancestries and recovered biologically relevant bone mineral density pathways in downstream TWAS and gene set enrichment analyses. These results demonstrate that incorporating LD-block structure and biologically informed SNP priors improves cis-expression prediction and enhances downstream TWAS discovery.

Problem

• cis-expression prediction
• linkage disequilibrium
• transcription start site
• TWAS
• regulatory architecture

Innovation

• block-sparse Bayesian modeling
• LD-block sparsity
• TSS-informed prior
• cis-expression prediction
• transcriptome-wide association study