{"doi":"10.1101/2024.01.12.24301233","title":"Association analysis of mitochondrial DNA heteroplasmic variants: methods and application","abstract":"ABSTRACT 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 RNR1 and RNR2 genes ( p <0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations ( p <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.","journal":"medRxiv","year":2024,"id":493271,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9444,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":377731,"name":"Katia Bulekova","orcid":null,"position":1,"is_corresponding":false},{"id":5317,"name":"Jian Yang","orcid":"0000-0003-2001-2474","position":2,"is_corresponding":false},{"id":1033600,"name":"M M Lai","orcid":"0000-0003-3666-838X","position":3,"is_corresponding":false},{"id":571541,"name":"Achilleas Pitsillides","orcid":"0000-0002-2658-1566","position":4,"is_corresponding":false},{"id":449507,"name":"Xue Liu","orcid":"0000-0002-4196-3597","position":5,"is_corresponding":false},{"id":754722,"name":"Yuankai Zhang","orcid":"0000-0002-2318-7539","position":6,"is_corresponding":false},{"id":24754,"name":"Xiuqing Guo","orcid":"0000-0002-5264-5068","position":7,"is_corresponding":false},{"id":1183394,"name":"Yong Qian","orcid":"0000-0002-5186-0938","position":8,"is_corresponding":false},{"id":11262,"name":"Laura M. 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Fitzpatrick","orcid":"0000-0003-1941-6586","position":18,"is_corresponding":false},{"id":245362,"name":"Claudia L. Satizábal","orcid":"0000-0002-1115-4430","position":19,"is_corresponding":false},{"id":24939,"name":"Dan E. 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