Association analysis of mitochondrial DNA heteroplasmic variants: methods and application

Area of Science:

• Genetics and Genomics
• Bioinformatics and Statistical Genetics
• Human Population Studies

Background:

• Mitochondrial DNA (mtDNA) heteroplasmy, the presence of multiple mtDNA variants within a cell, is implicated in aging and disease.
• Robust statistical methods are needed to identify associations between heteroplasmy and complex traits in large human populations.
• Existing methods may not fully capture the burden of heteroplasmy across multiple variants within a gene or region.

Approach:

• Developed and validated a comprehensive association testing framework for heteroplasmy using simulated and real-world data.
• Integrated a variant allele fraction (VAF) threshold with multiple gene-based tests, including burden-extension tests (adaptive, variable threshold, z-score weighting).
• Applied the framework to whole-genome sequencing data from 17,507 individuals of African and European ancestries, performing cohort- and ancestry-specific analyses and meta-analyses.

Key Points:

• Gene-based tests maintained appropriate type I error rates (α=0.001) in simulations.
• Burden-extension tests outperformed traditional methods (SKAT, original burden test) when 5% or more heteroplasmic variants were linked to an outcome.
• Significant associations were found between heteroplasmy in RNR1 and RNR2 genes and advanced aging (Original Burden test, p<0.001).
• SKAT identified significant associations between diabetes and aggregated heteroplasmy in protein-coding genes (p<0.001).

Conclusions:

• The proposed statistical framework is a valuable tool for association testing of heteroplasmy with disease traits in large human populations.
• mtDNA-encoded genes/regions likely exhibit varying rates in somatic aging.
• Further research is warranted to validate the identified associations between heteroplasmy, aging, and diabetes.