Quantifying constraint in the human mitochondrial genome.

Area of Science:

• Genetics
• Genomics
• Molecular Biology

Background:

• Mitochondrial DNA (mtDNA) plays a crucial role in health and disease.
• Constraint models are vital for identifying genetic variations linked to human phenotypes.
• Existing nuclear constraint models are unsuitable for mtDNA due to its unique characteristics.

Purpose of the Study:

• To develop and apply a novel mitochondrial genome constraint model.
• To analyze mtDNA variation within the large-scale Genome Aggregation Database (gnomAD).
• To identify deleterious mtDNA variants underlying human health and disease.

Main Methods:

• Developed a mitochondrial genome constraint model.
• Compared observed mtDNA variation in gnomAD with expected variation under neutrality.
• Utilized a mtDNA mutational model and maximum heteroplasmy data for calculations.
• Computed constraint metrics for mitochondrial genes, regions, and sites.

Main Results:

• Observed significant depletion of expected mtDNA variation, indicating undetected deleterious variants.
• Identified varying intolerance to variation across mitochondrial protein, tRNA, and rRNA genes.
• Characterized regional and local constraint, revealing enrichment of pathogenic variation.
• Discovered constraint in often overlooked sites, including rRNA and noncoding regions.

Conclusions:

• The developed mitochondrial constraint model effectively identifies deleterious mtDNA variation.
• Constraint metrics enhance the discovery of genetic variants associated with rare and common phenotypes.
• The model provides insights into functionally important mtDNA domains and their disease relevance.