For homoplasmic as well as heteroplasmic variant detection, HadoopBAM (22 (link)) is applied to split input BAM files. For each chunk, mtDNA-Server filters reads with a mapping quality score <20 (Phred score) and a read length <25 (9 (link)). BAM reads marked as duplicates are filtered within this step. Additionally, mtDNA-Server excludes all reads with an alignment Phred score ≤30 and applies per-BAQ to all reads by default. For this purpose, the GATK (23 (link)) BAQ implementation has been adapted to work with the circular nature of mitochondrial genomes. For each passed read, all bases with a quality Phred score <20 are filtered. Since mtDNA-Server detects point heteroplasmies, recalibration and re-alignment did not affect the result quality and has been excluded. Finally, all passed bases for each site are counted per strand (A, C, G, T, N (unknown base) or d (deletion)). For heteroplasmy detection, several filters and methods are applied: first, mitochondrial hotspots around 309 and 315 as well as 3107 according to the rCRS are excluded. Sites showing coverage <10 bases per strand are filtered. For all remaining sites showing (i) a VAF of ≥ 1% (strand independent) and (ii) an allele coverage of three bases per strand, an ML model is applied (14 ). The ML model takes sequencing errors per base into account and is applied to each strand. All sites with a log likelihood ratio (LLR) of ≥5 are tagged as heteroplasmic sites. Since strands are analyzed independently, mtDNA-Server can filter all heteroplasmic sites with a strand bias score <1 (24 (link),25 (link)). Furthermore, the Wilson and the Agresti-Coull confidence interval is calculated for heteroplasmic variants (7 (link)). The assigned heteroplasmy level is the weighted mean of heteroplasmy of the forward and reverse strand. We further tag positions in low complexity regions (LCR) (9 (link)) and known polymorphic nuclear mitochondrial insertions (NumtS) (26 (link)). LCR tagging is particular necessary for IonTorrent samples, which show a per-base error increase in homopolymeric stretch >4 bases. Finally, the unfiltered pileup format file, annotated files for variants and the HaploGrep files are generated.
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Heteroplasmy
Heteroplasmy
Heteroplasmy refers to the presence of multiple, potentially different mitochondrial DNA (mtDNA) genotypes within a single cell or organism.
This phenomenon can occur due to the co-existence of wild-type and mutant mtDNA, or the presence of multiple mtDNA haplotypes.
Heteroplasmy can have important implications for human health, as certain mtDNA mutations associated with disease can exist in a heteroplasmic state.
Understanding the dynamics and biological significance of heteroplasmy is an active area of research in fields such as mitochondrial genetics, cellular biology, and disease pathogenesis.
The PubCompare.ai platform can help researchers optimize their studies on heteroplasmy by providing intelligent comparisons of protocols from the literature, pre-prints, and patents, thus empowering reproducibility and streamlining the research process.
This phenomenon can occur due to the co-existence of wild-type and mutant mtDNA, or the presence of multiple mtDNA haplotypes.
Heteroplasmy can have important implications for human health, as certain mtDNA mutations associated with disease can exist in a heteroplasmic state.
Understanding the dynamics and biological significance of heteroplasmy is an active area of research in fields such as mitochondrial genetics, cellular biology, and disease pathogenesis.
The PubCompare.ai platform can help researchers optimize their studies on heteroplasmy by providing intelligent comparisons of protocols from the literature, pre-prints, and patents, thus empowering reproducibility and streamlining the research process.
Most cited protocols related to «Heteroplasmy»
Alleles
Deletion Mutation
DNA, Mitochondrial
Genome, Mitochondrial
Heteroplasmy
Homoplasmy
Insertion Mutation
Mitochondria
Neutrophil
The increased coverage of mtDNA NGS studies allows analyzing heteroplasmy levels down to 1% VAF. This poses the risk of interpreting low-level contamination or sample mix-up as real heteroplasmy (27 (link),28 (link)). Contamination is thereby not limited to issues in the laboratory (cross-contamination or sample mix-up), but also carry-over contamination between NGS runs, or even issues in post-processing step for NGS (adapter removal, file merging). Therefore, mtDNA-Server performs a contamination check based on the current phylogeny in order to avoid misinterpretations and erroneous conclusions. As previously outlined (5 (link),29 (link)), positions showing VAF on sample-level can be checked for haplogroup concordance and thereby hinting to intra-sample contamination: mtDNA-Server therefore generates two profiles based on the VAF; a minor (<50%) and major (>50%) profile which is used to perform haplogroup checks with HaploGrep. In case of contamination caused by different mtDNA sequences, the profiles lead to different valid haplogroups. We further take the homoplasmic variants for both profiles into consideration, rendering the classification more reliable, and helping tracing the origin of the sample mix-up. Thereby homoplasmic variants are shared haplogroup defining polymorphisms on the main branch from the rCRS to the mitochondrial Eve (mt-MRCA). Subsequently, heteroplasmic variants are divided into major and minor profiles, which are segregated into different branches in case of contamination (see Supplementary Figure S1).
DNA, Mitochondrial
Genetic Polymorphism
Heteroplasmy
Homoplasmy
Mitochondrial Inheritance
The GS20 emPCR process incorporates the use of the high-fidelity polymerase, Platinum Taq Hifidelity (Invitrogen), an enzyme mixture composed of recombinant Taq DNA polymerase, Pyrococcus spp. GB-D thermostable polymerase and Platinum Taq antibody. This enzyme is marketed partly on its very low misincorporation rate, 2 × 10−6 (Invitrogen). In this study we find the actual rate of misincorporation to be higher (≈7 × 10−4), similar to results from a previous aDNA study that has also specifically examined these properties of this enzyme (8 (link)). To discriminate between true aDNA damage and enzyme error or potential damage that may have arisen during the DNA extraction or that may have been present in the DNA before extraction, we analysed a further dataset of GS20 sequences, generated from a modern DNA extract, comprising 390 965 bp of L.tulipfera cpDNA. These data are part of the first chloroplast genome sequenced using the GS20 (J.E. Carlson, J.H. Leebens-Mack and D.G. Peterson, manuscript in preparation) and constitutes all the sequence reads between np 45 000 and 90 000 of the genome (J.E. Carlson, J.H. Leebens-Mack and S. Schuster, unpublished data). Although we are aware that in theory some complications may be envisioned when comparing cpDNA with mtDNA, at the current time there is a paucity of available datasets that contain sufficiently large amounts of sequence data to enable meaningful statistical comparisons. Thus this dataset provides the most suitable information at this time. The data analysed here have maximal coverage of 36 times, with a mean and modal coverage of 8.7 and 8 times, respectively. The L.tulipfera cpDNA sequences are available at the NCBI Trace Archives (Trace Identifiers 1367656065–1367659980). Analysis of the genomic data produced indicates that levels of heteroplasmy in the sample are negligible, thus unlikely to effect the analyses (J.E. Carlson, J.H. Leebens-Mack and S. Schuster, unpublished data). Furthermore, as DNA from this sample was freshly extracted from modern tissue, miscoding lesions observed in the data are unlikely to be due to anything other than PCR or other sequencing error that arises during the GS20 data production process. The miscoding lesion spectrum was extracted from the data in the same manner as applied to the mtDNA data. For data summary see Table 1 .
A χ2-test of independence was used to investigate whether the distribution of miscoding lesions was the same in the mammoth and chloroplast sequence data. The data were first summarized into six complementary damage pairs (Table 1 ). Subsequently, because nucleotide usage is different between the mammoth and chloroplast data, tests were performed separately on those miscoding lesions that originated from an A or T (A+T), and those that originated from a G or C (G+C).
A χ2-test of independence was used to investigate whether the distribution of miscoding lesions was the same in the mammoth and chloroplast sequence data. The data were first summarized into six complementary damage pairs (
Chloroplast DNA
Chloroplasts
DNA, Ancient
DNA, Mitochondrial
Enzymes
GB-D polymerase
Genome
Genome, Chloroplast
Heteroplasmy
Immunoglobulins
Mammuthus
Nucleotides
Platinum
Pyrococcus
Taq Polymerase
Tissues
Chorion
Diptera
DNA, Mitochondrial
Donors
Embryo
Females
Genome
Genotype
Heptane
Heteroplasmy
Homoplasmy
Households
Larva
Males
Microinjections
Mitochondria
Needles
Pastes
Yeast, Dried
qPCR was carried out on an Applied Biosystems 7300 Real Time PCR machine using iTaq SYBR Green Supermix with ROX (Bio-Rad). 1 uL of genomic DNA (diluted 1:5 in water) was used for each qPCR analysis. To estimate the total amount of mtDNA in a sample (both intact and deletion-bearing), control primers were designed to amplify a 102 bp region of the small ribosomal RNA subunit that displayed no evidence of heteroplasmy. A second set of primers was designed to amplify 101 bp in the 5' end of the ND5 gene, within the deleted region. This product is not expected to amplify only in intact genomes. qPCR data were analyzed to estimate the abundances of the two genome types using the linear regression approach offered by the LinRegPCR software [40 (link)]. ND5 locus products were found to amplify more efficiently than ribosomal RNA products; thus, all ND5 qPCR values were normalized to account for this disparity. Individual deletion genotype proportions were calculated by dividing the estimated abundance of intact genomes by that of the total mitochondrial genomes, and then subtracting that number from one. Four individual L1-stage nematodes were analyzed per natural isolate – the same nematode genomic DNA samples used for conventional PCR assays.
Biological Assay
Deletion Mutation
DNA, Mitochondrial
Genes
Genome
Genome, Mitochondrial
Genotype
Heteroplasmy
Nematoda
Oligonucleotide Primers
Real-Time Polymerase Chain Reaction
Ribosomal RNA
Ribosome Subunits, Small
SYBR Green I
Most recents protocols related to «Heteroplasmy»
In a subset of 23 individuals, skin scales from the sun-exposed preauricular area and from non-UV-exposed buttocks were prepared for the evaluation of UVR-induced mitochondrial DNA (mtDNA) point heteroplasmy (PHP). Only superficial layers adjacent to the stratum corneum and lacking vasculature were used, as it was essential to avoid contamination with blood for the analysis of PHP in keratinocytes only. Details of the mtDNA isolation and analysis of PHP can be found in the Supplementary Materials .
BLOOD
Buttocks
DNA, Mitochondrial
Heteroplasmy
isolation
Keratinocyte
Skin
Whole mt genome of 30 newly recruited pairs was sequenced at a commercial NGS service facility (Genotypic Technology, Bangalore, India). Mitochondrial amplicons were initially generated through PCR to cover the complete human mitochondrial genome. The amplicons were then pooled, cleaned up and subjected to Illumina compatible library preparation by fragmentation and adaptor ligation. The fragmented DNA was further subjected to indexing and enrichment through another round of PCR, followed by purification, quality check and Illumina sequencing (Hiseq/ Nextseq 500 systems). The raw paired-end sequencing data received in fastq format from the service provider were then analyzed in house.
The quality of the reads was assessed with Fastqc (version 0.11.8) [25 ]. Adapters, short reads and reads below accepted quality were removed using cutadapt tool (version 1.18) [26 (link)]. Reads were aligned against indexed revised Cambridge Reference Sequence (rCRS; GenBank accession number NC_012920.01) [27 (link)] with BWA-MEM aligner (version 0.7.12-r1039) [28 ]. Using SAMtools (version 1.9), the resulting sequence alignment mapping (SAM) format file was then converted to a binary alignment map (BAM) [29 (link), 30 (link)]. Same tool was used to sort and index the BAM file and duplicates were removed. Base quality recalibration was done with the tools base quality score recalibration (BQSR), BaseRecalibrator and ApplyBQSR by Genome Analysis Tool Kit (GATK) by Broad Institute of Harvard and MIT (version 4.2.0.0) [31 (link)] upon preprocessing the input files with Picard (version 2.25.1). GATK AnalyzeCovariates tool was additionally used to evaluate the effects of recalibration. The variants were called and filtered with the tools of GATK (version 4.2.0.0), Mutect2—mitochondria mode [32 (link)] and FilterMutectCalls respectively. The variants that passed the applied filters were selected with SelectVariants tool of the same tool kit to produce the final VCF output. Mutant allele fraction >0.90 was considered homoplasmic and 0.1 to 0.90 as heteroplasmic. The variants were visually examined and confirmed with Integrative Genomics Viewer (IGV version 2.9.2) [33 (link)] followed by annotation with Variant Effect Predictor (VEP) [34 (link)] and further reviewed in MITOMAP data base [35 (link)]. Locally installed Haplogrep 2 (version 2.1.25) [36 (link)] and PhyloTree build 17 [37 (link)] were used to assign haplogroups of the final output files.
The quality of the reads was assessed with Fastqc (version 0.11.8) [25 ]. Adapters, short reads and reads below accepted quality were removed using cutadapt tool (version 1.18) [26 (link)]. Reads were aligned against indexed revised Cambridge Reference Sequence (rCRS; GenBank accession number NC_012920.01) [27 (link)] with BWA-MEM aligner (version 0.7.12-r1039) [28 ]. Using SAMtools (version 1.9), the resulting sequence alignment mapping (SAM) format file was then converted to a binary alignment map (BAM) [29 (link), 30 (link)]. Same tool was used to sort and index the BAM file and duplicates were removed. Base quality recalibration was done with the tools base quality score recalibration (BQSR), BaseRecalibrator and ApplyBQSR by Genome Analysis Tool Kit (GATK) by Broad Institute of Harvard and MIT (version 4.2.0.0) [31 (link)] upon preprocessing the input files with Picard (version 2.25.1). GATK AnalyzeCovariates tool was additionally used to evaluate the effects of recalibration. The variants were called and filtered with the tools of GATK (version 4.2.0.0), Mutect2—mitochondria mode [32 (link)] and FilterMutectCalls respectively. The variants that passed the applied filters were selected with SelectVariants tool of the same tool kit to produce the final VCF output. Mutant allele fraction >0.90 was considered homoplasmic and 0.1 to 0.90 as heteroplasmic. The variants were visually examined and confirmed with Integrative Genomics Viewer (IGV version 2.9.2) [33 (link)] followed by annotation with Variant Effect Predictor (VEP) [34 (link)] and further reviewed in MITOMAP data base [35 (link)]. Locally installed Haplogrep 2 (version 2.1.25) [36 (link)] and PhyloTree build 17 [37 (link)] were used to assign haplogroups of the final output files.
Alleles
DNA Library
Genome
Genome, Human
Genome, Mitochondrial
Genotype
Heteroplasmy
Homoplasmy
Homo sapiens
Ligation
Mitochondria
Mitochondrial Inheritance
Sequence Alignment
To nominate genes using GWAS results, we used the following approach to balance clarity with confidence in the gene assignment.
If a variant is inside a gene body (but is non-coding), we consider that gene to be nearest. For case-only heteroplasmy GWAS, when the same locus was significant across multiple heteroplasmy phenotypes, we performed manual integration to arrive at a set of genes supported by the most compelling genetic evidence across variants for each locus. The SSBP1 locus was particularly complex, so we assign SSBP1 (which harbors the max PIP variant) and provide visualization of the full locus (Supplementary figure 9K ). We do not use fine-mapping evidence from variants with PIP > 0.1 that are not assigned to a credible set. All assignments were manually reviewed. In all GWAS visualizations, we label the strength of evidence supporting the gene assignment (e.g., if supported by moderate or high-PIP fine-mapped variants, coding variants).
If a variant is inside a gene body (but is non-coding), we consider that gene to be nearest. For case-only heteroplasmy GWAS, when the same locus was significant across multiple heteroplasmy phenotypes, we performed manual integration to arrive at a set of genes supported by the most compelling genetic evidence across variants for each locus. The SSBP1 locus was particularly complex, so we assign SSBP1 (which harbors the max PIP variant) and provide visualization of the full locus (
Genes
Genome-Wide Association Study
Heteroplasmy
Human Body
Phenotype
SSBP1 protein, human
We used the BedTools (Quinlan & Hall, 2010 (link)) intersect tool (v2.29.2) to identify read alignments completely spanning the chrM:300–318 locus in the mtscATAC-seq data from Walker et al., 2020 (link), obtained with Massachusetts General Hospital IRB approval under protocol #2016P001517. We then iterated over these reads and classified their chrM:302 length variant by extracting the poly-C/G tracts using a regular expression, ‘AA(CCC+[CT]CC+)GC’, anchored on the two constant bp on either side of the variant region to detect the canonical variant structure of two poly-C/G tracts with or without a single intervening A/T. Alleles in matching reads were classified based on the length of their poly-C/G tracts, while alleles in the reads that did not match the regular expression were classified as NA. Next, we filtered out any reads with cell barcodes that were not in the published list of cell calls, and further restricted our analysis to only the cells with at least 20 reads at the chrM:300–318 locus. For each of these high coverage cells, we calculated the fraction of reads showing each of the top three most common length variants (G6AG8, G6AG9, and G6AG10) and aggregated any other detected alleles into the remainder (other) for display as a stacked bar plot. We also estimated bulk heteroplasmy by summing the allele counts from the high coverage cells and re-calculating the fractions for the top three length variants, again with all other alleles being aggregated into the remainder “other” category.
Alleles
Cells
Dietary Fiber
Heteroplasmy
Poly G
poly G-poly C
Walkers
To integrate our variant calls and perform sample and variant QC, we extended a previously developed pipeline (Laricchia et al., 2022 ). Single-sample VCFs emitted from mtSwirl were merged into a single Hail MatrixTable (v0.2.98; (Hail Team, n.d.)) upon which all downstream steps were conducted.
For sample QC, any samples showing homoplasmic variant overlap (seeSupplementary note 1 ) were removed. We observed a significant elevation in heteroplasmic SNV calls among samples with mtCN below 50, with a stabilization of heteroplasmic calls above 50 mtDNA copies per cell (Supplementary figure 7C ), highly suggestive of elevated NUMT contamination in the low copy number samples. Thus, to avoid contamination of our results, all samples with mtCN < 50 were removed. Finally, all samples with evidence of contamination > 2% were removed, as estimated by either (1) mtDNA contamination via Haplocheck 0124 (Weissensteiner et al., 2021 (link)) in mtSwirl, (2) nucDNA contamination, or (3) the presence of multiple haplogroup-defining variants at abnormally low allele fraction. Given the small count of samples processed in 2006 and abnormally elevated mtDNA copy number estimates in these samples (Supplementary figure 3E ), we excluded these samples from all UKB analyses.
For variant QC, (1) variants with a very low heteroplasmy (< 0.01) were called as reference with a heteroplasmy of 0, (2) variants with heteroplasmy below 0.05 were flagged and removed as these are at high risk of being enriched for NUMT-derived signals, and (3) all variant calls flagged by Mutect2 were removed. For all sites, a minimum coverage threshold of 100 was used to distinguish between homoplasmic reference calls and sites without variant calls due to low variant-calling confidence as done previously (Laricchia et al., 2022 ). mtDNA variants were annotated using the Variant Effect Predictor (VEP) v101 (McLaren et al., 2016 (link)) and dbSNP v151 (Sherry et al., 1999 (link)). Variants with at least 0.1% of samples passing filters showing a heteroplasmy between 0 and 0.5 were annotated as “common low heteroplasmy”. Variant calls failing QC were coded with a missing heteroplasmy.
For mtCN, we remove the samples identified during variant callset sample QC showing signs of contamination, abnormal overlapping homoplasmy calls, or which were processed in 2006. Since we expect mtDNA-wide coverage measures, such as mtCN, to be robust to NUMTs, we do not enforce hard cutoffs on mtCN measurements.
For sample QC, any samples showing homoplasmic variant overlap (see
For variant QC, (1) variants with a very low heteroplasmy (< 0.01) were called as reference with a heteroplasmy of 0, (2) variants with heteroplasmy below 0.05 were flagged and removed as these are at high risk of being enriched for NUMT-derived signals, and (3) all variant calls flagged by Mutect2 were removed. For all sites, a minimum coverage threshold of 100 was used to distinguish between homoplasmic reference calls and sites without variant calls due to low variant-calling confidence as done previously (Laricchia et al., 2022 ). mtDNA variants were annotated using the Variant Effect Predictor (VEP) v101 (McLaren et al., 2016 (link)) and dbSNP v151 (Sherry et al., 1999 (link)). Variants with at least 0.1% of samples passing filters showing a heteroplasmy between 0 and 0.5 were annotated as “common low heteroplasmy”. Variant calls failing QC were coded with a missing heteroplasmy.
For mtCN, we remove the samples identified during variant callset sample QC showing signs of contamination, abnormal overlapping homoplasmy calls, or which were processed in 2006. Since we expect mtDNA-wide coverage measures, such as mtCN, to be robust to NUMTs, we do not enforce hard cutoffs on mtCN measurements.
Alleles
Cells
DNA, Mitochondrial
Heteroplasmy
Homoplasmy
Syndrome, Shprintzen
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More about "Heteroplasmy"
Heteroplasmy is a fascinating phenomenon in the realm of mitochondrial genetics, where multiple, potentially different mitochondrial DNA (mtDNA) genotypes coexist within a single cell or organism.
This can occur due to the co-existence of wild-type and mutant mtDNA, or the presence of multiple mtDNA haplotypes.
Understanding the dynamics and biological significance of heteroplasmy is crucial, as certain mtDNA mutations associated with disease can exist in a heteroplasmic state.
Researchers studying heteroplasmy can leverage the power of AI-driven tools like PubCompare.ai to optimize their research process.
This platform provides intelligent comparisons of protocols from the literature, pre-prints, and patents, empowering reproducibility and streamlining the research workflow.
By utilizing PubCompare.ai, researchers can easily locate the best protocols for their heteroplasmy studies, drawing from a wealth of scientific knowledge.
Furthermore, researchers can complement their heteroplasmy studies with various analytical tools and platforms.
For instance, the PyroMark Q24 platform and the QIAamp DNA Mini Kit can be employed for sensitive and accurate mtDNA analysis.
The Nextera XT library preparation kit and the Agilent 2100 Bioanalyzer can be utilized for next-generation sequencing (NGS) of mitochondrial genomes, while the HiSeq 2500 platform can provide high-throughput sequencing capabilities.
To further enhance the research process, researchers can leverage the PyroMark Assay Design Software v2.0 for designing custom assays, the Rotor-Gene Multiplex PCR Kit for sensitive and specific mtDNA quantification, and the QIAamp DNA Blood Mini Kit for efficient DNA extraction from blood samples.
Additionally, the ABI 7900HT Fast Real-Time PCR System and the CLC Genomics Workbench can be employed for advanced data analysis and interpretation.
By incorporating these insights and tools, researchers can delve deeper into the intricacies of heteroplasmy, unraveling its implications for human health and advancing the field of mitochondrial genetics and disease pathogenesis.
This can occur due to the co-existence of wild-type and mutant mtDNA, or the presence of multiple mtDNA haplotypes.
Understanding the dynamics and biological significance of heteroplasmy is crucial, as certain mtDNA mutations associated with disease can exist in a heteroplasmic state.
Researchers studying heteroplasmy can leverage the power of AI-driven tools like PubCompare.ai to optimize their research process.
This platform provides intelligent comparisons of protocols from the literature, pre-prints, and patents, empowering reproducibility and streamlining the research workflow.
By utilizing PubCompare.ai, researchers can easily locate the best protocols for their heteroplasmy studies, drawing from a wealth of scientific knowledge.
Furthermore, researchers can complement their heteroplasmy studies with various analytical tools and platforms.
For instance, the PyroMark Q24 platform and the QIAamp DNA Mini Kit can be employed for sensitive and accurate mtDNA analysis.
The Nextera XT library preparation kit and the Agilent 2100 Bioanalyzer can be utilized for next-generation sequencing (NGS) of mitochondrial genomes, while the HiSeq 2500 platform can provide high-throughput sequencing capabilities.
To further enhance the research process, researchers can leverage the PyroMark Assay Design Software v2.0 for designing custom assays, the Rotor-Gene Multiplex PCR Kit for sensitive and specific mtDNA quantification, and the QIAamp DNA Blood Mini Kit for efficient DNA extraction from blood samples.
Additionally, the ABI 7900HT Fast Real-Time PCR System and the CLC Genomics Workbench can be employed for advanced data analysis and interpretation.
By incorporating these insights and tools, researchers can delve deeper into the intricacies of heteroplasmy, unraveling its implications for human health and advancing the field of mitochondrial genetics and disease pathogenesis.