Association testing of the mitochondrial genome using pedigree data

Area of Science:

• Genetics
• Metabolic Disorders
• Bioenergetics

Background:

• Mitochondrial DNA (mtDNA) encodes key oxidative phosphorylation proteins essential for cellular energy production.
• Disruptions in mtDNA can lead to metabolic derangements and affect cellular processes like apoptosis and calcium homeostasis.
• Increasing availability of high-density genotyping data for mitochondrial variants in pedigrees presents analytical challenges.

Purpose of the Study:

• To identify optimal strategies for association testing of mitochondrial variants within pedigree structures.
• To address challenges in disentangling nuclear and mitochondrial genome effects during association analyses.
• To evaluate methods for minimizing type I error and maximizing power in mtDNA association studies.

Main Methods:

• Proposed and simulated multiple strategies to account for nuclear and mitochondrial polygenic effects.
• Evaluated type I error and statistical power of proposed association testing strategies.
• Developed permutation tests for empirical P-value determination.
• Applied selected strategies to analyze mtDNA variants associated with blood pressure and fasting blood glucose in the Framingham Heart Study.

Main Results:

• Simulation studies assessed the performance of various analytical strategies under different inheritance models.
• Permutation tests provided empirical P values for association findings.
• Real-world application demonstrated the utility of the developed strategies in a large human pedigree cohort.

Conclusions:

• Accurate accounting for both nuclear and mitochondrial genome effects is essential for robust association testing of mtDNA variants in pedigrees.
• The proposed strategies and permutation tests offer improved analytical approaches for studying mtDNA associations with complex traits.
• Best practices for study design, genotyping, and data cleaning are discussed for future mtDNA association studies.