A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

Abstract

Linking mitochondrial DNA (mtDNA) variation to clinical outcomes remains a formidable challenge. Diagnosis of mitochondrial disease is hampered by the multicopy nature and potential heteroplasmy of the mitochondrial genome, differential distribution of mutant mtDNAs among various tissues, genetic interactions among alleles, and environmental effects. Here, we describe a new approach to the assessment of which mtDNA variants may be pathogenic. Our method takes advantage of site-specific conservation and variant acceptability metrics that minimize previous classification limitations. Using our novel features, we deploy machine learning to predict the pathogenicity of thousands of human mtDNA variants. Our work demonstrates that a substantial fraction of mtDNA changes not yet characterized as harmful are, in fact, likely to be deleterious. Our findings will be of direct relevance to those at risk of mitochondria-associated metabolic disease.