Quantifying deleterious effects of regulatory variants.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Genome-wide association studies (GWAS) identify numerous risk variants in non-coding DNA.
• Understanding how these variants alter gene expression and cellular variability is crucial for linking genotype to phenotype.
• Non-coding variants often impact gene regulation by affecting enhancer activity.

Purpose of the Study:

• To develop a computational method (CAPE) for predicting the impact of genetic variants on enhancer function.
• To assess the ability of CAPE to identify causative cis-regulatory variations.
• To provide a tool for prioritizing disease-associated variants based on their predicted regulatory effects.

Main Methods:

• CAPE learns sequence signatures of enhancers from large-scale sequencing data (ChIP-seq, DNase-seq).
• It models the effect of single nucleotide substitutions on these enhancer signatures.
• The method is trained on tissue-specific enhancer datasets and known functional variants.

Main Results:

• CAPE accurately identifies genetic variants that disrupt transcription factor binding and alter enhancer activity.
• The method precisely identifies expression quantitative trait loci (eQTLs) and DNase I sensitivity quantitative trait loci (dsQTLs) in a tissue-specific manner.
• CAPE demonstrates superior precision compared to existing methods for detecting functional cis-regulatory variation.

Conclusions:

• CAPE is an effective computational tool for predicting the functional impact of genetic variants on enhancers.
• The method facilitates the prioritization of disease-associated variants by assessing their regulatory potential in specific tissues.
• CAPE enhances the understanding of genotype-phenotype relationships driven by non-coding genetic variation.