Linking mitochondrial DNA (mtDNA) mutations to patient outcomes has been a serious challenge. 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 can hamper clinicians as they try to inform patients regarding the etiology of their metabolic disease. Multiple sequence alignments using samples ranging across multiple organisms and taxa are often deployed to assess the overall conservation of any site within a mtDNA-encoded macromolecule and to determine the acceptability of any given variant at a particular position. However, the utility of multiple sequence alignments in pathogenicity prediction can be restricted by factors including sample set bias, alignment errors, and sequencing errors. Here, we describe a novel and empirical approach for assessing site-specific conservation and variant acceptability that depends upon phylogenetic analysis and ancestral prediction and minimizes current alignment limitations. Next, we use machine learning to predict the pathogenicity of thousands of so-far-uncharacterized human alleles catalogued in the clinic. Our work demonstrates that a substantial portion of encountered mtDNA alleles not yet characterized as harmful are, in fact, likely to be deleterious. Beyond general applications of our methodology that lie outside of mitochondrial studies, our findings are likely to be of direct relevance to those at risk of mitochondria-associated illness.
|DOI - pysyväislinkit|
|Tila||Julkaistu - 11 tammikuuta 2020|
|OKM-julkaisutyyppi||D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys|