Dataset Information

Pathogenic Variant Filtering for Mitochondrial Genome Haplotype Reporting.

ABSTRACT: Given the enhanced discriminatory power of the mitochondrial DNA (mtDNA) genome (mitogenome) over the commonly sequenced control region (CR) portion, the scientific merit of mitogenome sequencing is generally accepted. However, many laboratories remain beholden to CR sequencing due to privacy policies and legal requirements restricting the use of disease information or coding region (codR) information. In this report, we present an approach to obviate the reporting of sensitive codR data in forensic haplotypes. We consulted the MitoMap database to identify 92 mtDNA codR variants with confirmed pathogenicity. We determined the frequencies of these pathogenic variants in literature-quality and forensic-quality databases to be very low, at 1.2% and 0.36%, respectively. The observed effect of pathogenic variant filtering on random match statistics in 2488 forensic-quality mitogenome haplotypes from four populations was nil. We propose that pathogenic variant filtering should be incorporated into variant calling algorithms for mitogenome haplotype reporting to maximize the discriminatory power of the locus while minimizing the reveal of sensitive genetic information.

SUBMITTER: Marshall C

PROVIDER: S-EPMC7599696 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Pathogenic Variant Filtering for Mitochondrial Genome Haplotype Reporting.

Marshall Charla C Sturk-Andreaggi Kimberly K Ring Joseph D JD Dür Arne A Parson Walther W

Genes 20200928 10

Given the enhanced discriminatory power of the mitochondrial DNA (mtDNA) genome (mitogenome) over the commonly sequenced control region (CR) portion, the scientific merit of mitogenome sequencing is generally accepted. However, many laboratories remain beholden to CR sequencing due to privacy policies and legal requirements restricting the use of disease information or coding region (codR) information. In this report, we present an approach to obviate the reporting of sensitive codR data in fore ...[more]

PMID: 32998193

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Project description:The Cryptococcus complex consists of at least seven evolutionary divergent lineages and causes ?200,000 fatal human infections each year worldwide. The dominant lineage is Cryptococcus neoformans which consists of three haploid clades VNI, VNII, and VNB, their haploid hybrids, and various diploids derived from intra- and inter-clade mating events. In this study, we analyzed the mitogenomes of 184 strains of C. neoformans. Our analyses revealed that all 184 mitogenomes contained the same 15 protein-coding genes in the same gene order. However, their mitogenome sizes varied between 24,740 and 31,327 bp, primarily due to differences in the number and size of mitochondrial introns. Twelve nucleotide sites within five mitochondrial genes were found to contain introns in at least one of the 184 strains, ranging from 2 to 7 introns within each mitogenome. The concatenated mitochondrial exon sequences of the 15 protein-coding genes and two rRNA genes showed that VNI, VNII, and VNB strains were separated into distinct clades or sub-clades, largely consistent with results based on nuclear genome SNPs. However, several novel findings were observed. First, one strain of the VNB clade contained mitogenome exon sequences identical to the main VNI mitogenome type but was distant to other VNB mitogenomes. Second, hybrids among clades VNI, VNII, and VNB identified based on their nuclear genome SNPs contained mitogenomes from different clades, with evidence of their mitogenomes inherited from either the MAT a or the MAT ? parents. Third, the eight diploid VNB (C. neoformans) × VNIV (C. deneoformans) hybrids contained recombinant mitogenomes. Fourth, analyses of intron distribution and the paired exon-intron phylogenies for each of the 12 exon-intron pairs suggested frequent gains and losses of mitochondrial introns during the evolution of C. neoformans. The combined mitogenome exon-based phylogeny and intron distributions suggested that clades VNI, VNII and VNB could be further divided into sub-clades. Together, our results revealed a dynamic evolution of mitochondrial genomes in this important human fungal pathogen.

Project description:Purpose:Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods:We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results:For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion:Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

Dataset Information

Pathogenic Variant Filtering for Mitochondrial Genome Haplotype Reporting.

Publications

Pathogenic Variant Filtering for Mitochondrial Genome Haplotype Reporting.

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