A method for scoring the cell type-specific impacts of noncoding variants in personal genomes

Area of Science:

• Human genetics
• Genomic medicine
• Computational biology

Background:

• Millions of genomic variants exist between individuals and the reference genome.
• Interpreting variant impact on phenotype is crucial for human genetics and medicine.
• Noncoding variants are challenging to interpret but critical for gene regulation.

Purpose of the Study:

• To develop a tool, OpenCausal, for prioritizing noncoding variants based on their predicted impact on chromatin accessibility.
• To integrate personal genome data with tissue-specific transcription factor expression profiles.
• To provide a method for fine-mapping genetic risk loci using predicted variant impact and GWAS data.

Main Methods:

• Developed OpenCausal, a prioritization tool using personal genomes and context-specific TF expression profiles.
• Applied OpenCausal to 6,430 GTEx samples across 18 tissues.
• Integrated OpenCausal's predicted 'open scores' with GWAS data for fine-mapping analysis.

Main Results:

• OpenCausal prioritized variants were significantly enriched for expression quantitative trait loci (eQTLs) and chromatin accessibility quantitative trait loci (caQTLs).
• Application to human height GWAS data identified prioritized variants and regulatory elements correlated with phenotypic variation.
• Demonstrated the utility of OpenCausal in prioritizing functionally relevant variants.

Conclusions:

• OpenCausal effectively prioritizes noncoding variants based on their predicted impact on chromatin accessibility.
• The integration strategy enhances the identification of putative causal variants and regulatory elements in GWAS.
• This approach advances the interpretation of personal genomes and the understanding of genetic contributions to human traits and diseases.