arXiv:2606.00483v1 Announce Type: cross 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 Di
The continuous advancements in computational biology and AI are enabling more sophisticated models for understanding genetic expression, leading to improved predictive power in this domain.
Improved cis-expression prediction models enhance the ability to understand genetic regulation, which is crucial for drug discovery, personalized medicine, and identifying disease susceptibility.
The introduction of bsBSLMM offers a more accurate method for predicting gene expression from genotype data, potentially accelerating research in genomics and precision health.
- · Genomics research
- · Pharmaceutical companies
- · Biotechnology sector
- · Personalized medicine
- · Traditional statistical modeling approaches
- · Less accurate predictive methods in genomics
More efficient identification of genetic variants associated with disease or drug response.
Accelerated development of targeted therapies and diagnostics based on genetic profiles.
Potential for broader adoption of genotype-based predictive health screening and interventions.
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Read at arXiv cs.LG