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Updated: Jun 20, 2026
Genotyping Single Nucleotide Polymorphisms in the Mitochondrial Genome by Pyrosequencing
Published on: February 10, 2023
MtSNPscore: a combined evidence approach for assessing cumulative impact of mitochondrial variations in disease
Anshu Bhardwaj1, Mitali Mukerji, Shipra Sharma
1Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in
Background:
Human mitochondrial DNA (mtDNA) variations have been implicated in a broad spectrum of diseases. With over 3000 mtDNA variations reported across databases, establishing pathogenicity of variations in mtDNA is a major challenge. We have designed and developed a comprehensive weighted scoring system (MtSNPscore) for identification of mtDNA variations that can impact pathogenicity and would likely be associated with disease. The criteria for pathogenicity include information available in the literature, predictions made by various in silico tools and frequency of variation in normal and patient datasets. The scoring scheme also assigns scores to patients and normal individuals to estimate the cumulative impact of variations. The method has been implemented in an automated pipeline and has been tested on Indian ataxia dataset (92 individuals), sequenced in this study, and other publicly available mtSNP dataset comprising of 576 mitochondrial genomes of Japanese individuals from six different groups, namely, patients with Parkinson's disease, patients with Alzheimer's disease, young obese males, young non-obese males, and type-2 diabetes patients with or without severe vascular involvement. MtSNPscore, for analysis can extract information from variation data or from mitochondrial DNA sequences. It has a web-interface http://bioinformatics.ccmb.res.in/cgi-bin/snpscore/Mtsnpscore.pl that provides flexibility to update/modify the parameters for estimating pathogenicity.
Results:
Analysis of ataxia and mtSNP data suggests that rare variants comprise the largest part of disease associated variations. MtSNPscore predicted possible role of eight and 79 novel variations in ataxia and mtSNP datasets, respectively, in disease etiology. Analysis of cumulative scores of patient and normal data resulted in Matthews Correlation Coefficient (MCC) of ~0.5 and accuracy of ~0.7 suggesting that the method may also predict involvement of mtDNA variation in diseases.
Conclusion:
We have developed a novel and comprehensive method for evaluation of mitochondrial variation and their involvement in disease. Our method has the most comprehensive set of parameters to assess mtDNA variations and overcomes the undesired bias generated as a result of better-studied diseases and genes. These variations can be prioritized for functional assays to confirm their pathogenic status.
Insights
A new scoring system, MtSNPscore, helps identify disease-causing mitochondrial DNA variations. This tool analyzes variations to predict their impact on diseases like ataxia and Parkinson's.
Area of Science:
- Genomics
- Bioinformatics
- Medical Genetics
Background:
- Mitochondrial DNA (mtDNA) variations are linked to numerous diseases.
- Identifying pathogenic mtDNA variations is challenging due to over 3000 known variations.
- Existing methods may exhibit bias towards well-studied diseases and genes.
Purpose of the Study:
- To develop a comprehensive weighted scoring system, MtSNPscore, for identifying pathogenic mtDNA variations.
- To assess the cumulative impact of mtDNA variations in patients and normal individuals.
- To provide a flexible, automated tool for evaluating mitochondrial variation's role in disease.
Main Methods:
- Developed a weighted scoring system (MtSNPscore) incorporating literature data, in silico predictions, and population frequencies.
- Implemented MtSNPscore in an automated pipeline.
- Tested the system on Indian ataxia and Japanese mtSNP datasets (Parkinson's, Alzheimer's, obesity, type-2 diabetes).
Main Results:
- Rare variants were found to be the primary contributors to disease-associated variations.
- MtSNPscore identified 8 novel variations in ataxia and 79 in the mtSNP dataset as potentially disease-causing.
- The analysis achieved a Matthews Correlation Coefficient (MCC) of ~0.5 and accuracy of ~0.7, indicating predictive potential.
Conclusions:
- A novel, comprehensive method (MtSNPscore) for evaluating mitochondrial variation and disease association has been developed.
- The method offers a robust assessment of mtDNA variations, mitigating bias.
- Identified variations can be prioritized for functional studies to confirm pathogenicity.
