Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction
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...
| Authors | Lei Huang, Hui Shen, Kuan-Jui Su et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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