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Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application.

Xianbang Sun1, Katia Bulekova2, Jian Yang1

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.

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We developed a new statistical framework for testing heteroplasmy associations. This method effectively identifies links between mitochondrial DNA variants and aging or diabetes in large human populations.

Keywords:
Association analysisGene-based testHeteroplasmyMitochondrial DNA sequencing

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Area of Science:

  • Genetics
  • Mitochondrial Biology
  • Statistical Genetics

Background:

  • Heteroplasmy, the presence of multiple mitochondrial DNA (mtDNA) variants within a cell, is implicated in aging and disease.
  • Existing association testing frameworks may not be optimal for heteroplasmy due to its unique characteristics.

Purpose of the Study:

  • To develop and validate a comprehensive statistical framework for association testing of heteroplasmy.
  • To identify associations between heteroplasmy and aging or diabetes in large human cohorts.

Main Methods:

  • A framework combining variant allele fraction (VAF) thresholds and gene-based tests was developed.
  • Simulations assessed type I error rates and power of different burden tests, including adaptive burden, variable threshold, and z-score weighting tests, compared to SKAT and original burden tests.
  • Association analyses were performed on whole-genome sequencing data from 17,507 individuals of African and European ancestries, including cohort- and ancestry-specific analyses and meta-analyses.

Main Results:

  • Gene-based tests maintained appropriate type I error rates.
  • Burden-extension tests outperformed SKAT and original burden tests 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 (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 populations.
  • mtDNA-encoded genes/regions show varying rates in somatic aging.
  • Further research is needed to validate the identified associations.