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

TitleAssociation analysis of mitochondrial DNA heteroplasmic variants: methods and application.
Publication TypeJournal Article
Year of Publication2024
AuthorsSun, X, Bulekova, K, Yang, J, Lai, M, Pitsillides, AN, Liu, X, Zhang, Y, Guo, X, Yong, Q, Raffield, LM, Rotter, JI, Rich, SS, Abecasis, G, Carson, AP, Vasan, RS, Bis, JC, Psaty, BM, Boerwinkle, E, Fitzpatrick, AL, Satizabal, CL, Arking, DE, Ding, J, Levy, D, Liu, C
Corporate/Institutional AuthorsTOPMed mtDNA working group
Date Published2024 Jan 13
Abstract<p>We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α=0.001. Notably, when 5% or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31% of African Ancestry, mean age of 62, with 58% women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on bothpooled samples and within each ancestry group. Our results suggest that mtDNA-Enco ded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the and genes ( <0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations ( <0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.</p>
Alternate JournalmedRxiv
PubMed ID38260412
PubMed Central IDPMC10802757
ePub date: