Mutational constraint analysis workflow for overlapping short open reading frames and genomic neighbors.

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Human Genetics

Background:

• The human genome contains a 'dark genome' with unexplored elements like short open reading frames (sORFs).
• sORFs have the potential to be translated into microproteins, but their functions and clinical relevance are largely unknown.
• A lack of population-level constraint metrics for genetic variants in sORFs hinders disease relevance prediction.

Purpose of the Study:

• To analyze population-level constraint metrics for genetic variants within a consensus set of sORFs.
• To assess the genomic context and constraint of sORFs using the gnomAD 4.0 dataset.
• To identify potentially functionally important sORFs comparable to canonical genes.

Main Methods:

• Utilized the gnomAD 4.0 dataset to analyze genetic variant constraint.
• Focused analysis on a consensus set of short open reading frames (sORFs) and their neighboring genomic regions.
• Compared constraint metrics of sORFs against canonical genes and genomic contexts.

Main Results:

• sORFs are generally found within moderately constrained genomic regions.
• A subset of sORFs, identified within the GENCODE dataset, exhibits high constraint levels.
• These highly constrained sORFs show constraint comparable to well-established canonical genes.

Conclusions:

• The study provides the first population-level constraint metrics for genetic variants in sORFs.
• Identified highly constrained sORFs suggest significant biological functions and potential roles in human disease.
• These findings pave the way for deeper investigation into the functional landscape of the dark genome.