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. Author manuscript; available in PMC: 2014 Aug 29.
Published in final edited form as: J Genet Genomics. 2013 Dec 8;40(12):607–615. doi: 10.1016/j.jgg.2013.10.003

Very Low-Level Heteroplasmy mtDNA Variations Are Inherited in Humans

Yan Guo 1,*, Chung-I Li 2, Quanhu Sheng 1, Jeanette F Winther 3, Qiuyin Cai 4, John D Boice Jr 5, Yu Shyr 1,*
PMCID: PMC4149221  NIHMSID: NIHMS608621  PMID: 24377867

Abstract

Little is known about the inheritance of very low heteroplasmy mitochondria DNA variations. Even with the development of new next-generation sequencing methods, the practical lower limit of measured heteroplasmy is still about 1% due to the inherent noise level of the sequencing. In this study, we sequenced the mitochondrial genome of 44 individuals using Illumina high-throughput sequencing technology and obtained high-coverage mitochondria sequencing data. Our study population contains many mother-offspring pairs. This unique study design allows us to bypass the usual heteroplasmy limitation by analyzing the correlation of mutation levels at each position in the mtDNA sequence between maternally related pairs and non-related pairs. The study showed that very low heteroplasmy variants, down to almost 0.1%, are inherited maternally and that this inheritance begins to decrease at about 0.5%, corresponding to a bottleneck of about 200 mtDNA.

Keywords: Maternal inheritance, Next-generation sequencing, High-depth sequencing, Heteroplasmy, mtDNA mutations, Bottleneck

INTRODUCTION

Typically, there are over 1,000 mitochondria in each mammalian cell, and each mitochondrion harbors 2–10 copies of mitochondria DNA (Robin and Wong, 1988). Thus, mtDNA mutations are often heteroplasmic, with a mixture of mutant and wild-type mtDNA copies within a cell (Durbin et al., 2010; Ng et al., 2010). Researchers have found that heteroplasmy could promote tumor growth (Lewis et al., 2000; Park et al., 2009) and is closely associated with aging (Kann et al., 1998; Smigrodzki and Khan, 2005; Sondheimer et al., 2011) although both points are somewhat controversial. Due to limitations in the available sequencing technology, earlier studies of mitochondrial mutations (Holt et al., 1990; Lombes et al., 1992; Simon et al., 2003; Wong et al., 2002) had limited ability to detect low-level heteroplasmy. Low-level mtDNA heteroplasmies (less than 5% mutant) are not readily GeneChip Human Mitochondrial Resequencing Array 2.0. Real-time polymerase chain reaction (RT-PCR) techniques may detect heteroplasmies down to near the 1% mutation level, but each measurement must be designed for just one specific heteroplasmic site, making this method unsuitable for the discovery of heteroplasmic mutations across the entire mitochondrial genome. In recent years, high-throughput sequencing technologies have matured in step with reduced costs. The high-coverage generated by the new high-throughput sequencing methods provides a powerful tool for the study of mtDNA heteroplasmy (Guo et al., 2012a; Tang and Huang, 2010).

Previous studies on heteroplasmy using high-depth sequencing usually chose a heteroplasmy threshold from 1% to 2% (Goto et al., 2011; Guo et al., 2012a; Ng et al., 2010) or even as high as 10% (Durbin et al., 2010). At very low heteroplasmy levels, it becomes impossible to distinguish between sequencing errors and the true mtDNA sequence due to the baseline error rates of high-throughput sequencing. For this reason, low-level heteroplasmy has not yet been studied widely. However, low-level mtDNA heteroplasmy is of fundamental importance since every de novo mtDNA mutation must start out at very low levels (Elson et al., 2001). The question of whether low-level mtDNA mutations survive the mitochondrial bottleneck between mother and child is an important key to understanding how mtDNA variations, including pathogenic variations, become established within families. Typically, studies of families with known pathogenic mtDNA mutations showed that the mutation levels can have large shifts between mother and offspring (Chinnery et al., 2000). However, these studies have been limited to mutations at levels > 1%, and the theory of random drift inheritance (Wonnapinij et al., 2008) predicts that the variance in the offspring mutation levels will decrease rapidly as the maternal mutation level decreases. This decrease in variance for very low-level heteroplasmic mutations may make them more stable across generations. For all of these reasons, we tested for the inheritance of very low-level mtDNA mutations. In the current study, using high-throughput sequencing technology, we showed that heteroplasmic variations at mutation levels as low as 0.15% are inherited in humans.

RESULTS

Heteroplasmy in Maternal Pair

High-quality sequencing data with a median depth for all samples of 3981 × were generated. Homoplasmic SNPs were inherited without exception within each family, and all maternally related individuals within the same family shared the same detailed sub-haplogroup (Table 1), validating both the sequencing and the maternal relatedness of the subjects. In addition to the homoplasmic SNPs, we also found 12 different mutations that were heteroplasmic at mutation level of >1% in both mother and offspring (Table 2). In two families with multiple offspring, the heteroplasmic mutation was found in all offspring (Table 2). As has been observed in families transmitting pathogenic mtDNA mutations (Chinnery et al., 2000), the heteroplasmic levels in the mother and offspring were different, with a minimum change of <1% and a maximum change of over 31%. Of the 15 heteroplasmic mutation transmissions observed, eight had lower heteroplasmy levels in the offspring while seven had higher heteroplasmy levels, consistent with neutral drift.

Table 1.

Haplogroup information of all families

Family ID Haplogroup Number of children
Fin I4 1
T07 K1a1b1 1
T10 H1c1 1
T13 H11a2 1
T16 H1a 1
T20 H36a 2
T21 U5a1a1 1
T25 H 1
T26 J1c5a 2
T27 U5a2a 1
T28 H1c3 1
T37 V 2
T38 T1a1 2
T39 J1c2 1
T41 HV9a 2
T45 H2a2a 1
T46 U5a1b 2
T54 K1a1b1 3
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Table 2.

Heteroplasmic sites with MAF > 1%

mtDNA
variant		 Family
ID		 Mother’s
mutation
frequency		 Child’s
mutation
frequency		 Reference
allele		 Alternative
allele		 Adjacent
sequence
T146C T45 19.20% 3.10% T C GCCT/CCAT
T195C T07 37.20% 35.60% T C TACT/CTAC
T310C T37 35.00% 35.90% T C CCCT/CCCC
G316C T10 2.80% 2.20% G C CCCG/CCTT
T6292C T45 30.30% 10.80% T C CCCT/CCCC
C12091T T39 12.50% 28.80% C T ATTC/TTCC
C12972T T26 48.40% 40.10% C T CCAC/TTAC
G13927A T13 3.00% 1.80% G A CTAG/ACAT
G15354A T07 10.10% 28.00% G A AACG/AGGA
G15733A T16 8.10% 6.30% G A GCCG/ACAG
A16398G T46 11.50% 27.30% A G TTGA/GCCA
A16398G T46 11.50% 43.00% A G TTGA/GCCA
A4226G T54 17.30% 29.10% A G CATA/GCCC
A4226G T54 17.30% 15.80% A G CATA/GCCC
A4226G T54 17.30% 19.40% A G CATA/GCCC
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Heteroplasmy Inheritance Analysis between Related and Unrelated Pairs

We now turn to an analysis of the inheritance of low heteroplasmy-level mtDNA variants. The observation of inheritance of specific mutations is limited by the noise level of the sequencing method, which can be taken as approximately 1% for base-calling Phred scores of 20 or better. For each pair of samples, we calculated a correlation of the mutation levels at each site. Instead of analyzing the inheritance of heteroplasmy at specific sites, which may be compromised by sequencing errors, we analyzed the distribution of these correlation coefficients. We placed a series of upperlimit filters on the heteroplasmy levels used in the correlation (see Materials and Methods for details) in order to specifically test for the inheritance of low-level heteroplasmic sites. The overall depth of coverage and heteroplasmy information for all 44 samples can be found in Table S1. Table S1 contains depth and heteroplasmy information for all samples and all positions in rCRS. After filtering for quality, we observed, on average, 6000 instances of low-level heteroplasmy (<1% and >0.1%) per sample. At such low levels, it is difficult to distinguish between true signal and heteroplasmy.

Our set of 35 maternally related pairs consisted of 26 mother-child pairs and 9 sibling pairs. We first tested to see if there was a difference in correlation between mother-offspring pairs and sibling pairs. The correlations for mother-child pairs were not significantly different from those for sibling pairs (Fig. 1) at all heteroplasmy levels except arguably at the ≤ 100% level which only had a marginally significant P-value of 0.09 (Table S2). Since all lower thresholds on the mtDNA heteroplasmy level consistently showed no significant difference between mother-offspring and sibling pairs, we combined the mother-child pairs and sibling pairs as maternally-related pairs for the remainder of the analysis.

Fig. 1. Different heteroplasmy levels between sibling pairs and mother-child pairs.

Fig. 1

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Box plots represent the probability distributions of the correlation coefficient for the mutation levels between sibling pairs and mother-child pairs. Progressively decreasing (from left to right) thresholds on the mtDNA mutation level indicate how the correlations decrease as the mutation level drops. See Material and Methods for details on the correlation calculation.

The heteroplasmy levels at each site in the mitochondrial genome were compared for each pair of individuals in the study (see Materials and Methods for details). We calculated correlation coefficients for the heteroplasmy levels at all sites that passed quality control criteria in both individuals of each pair. As expected, the Pearson's correlation coefficients of the related pairs were significantly higher than those of the unrelated pairs (Fig. 2). To our surprise, even the non-maternally related pairs had a median correlation coefficient greater than zero. This may be due to non-random sequencing errors that escaped our quality control criteria, or it could be due to low levels of true mtDNA mutations at specific mutation hotspots (Stoneking, 2000) that are common across even unrelated individuals, though the latter possibility is speculative. Since even the unrelated pairs had a positive median correlation coefficient, we used comparisons between maternally related pairs and unrelated pairs as a test for inheritance. When examining all positions without restricting the heteroplasmy level, the median Pearson's correlation coefficient in all related pairs was 0.716, while the median Pearson's correlation coefficient of all unrelated pairs was significantly lower at 0.129 (P-value = 8 × 10−18, Fig. 2, left side).

Fig. 2. Different heteroplasmy levels between related pairs and unrelated pairs.

Fig. 2

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Distributions of the correlation coefficients for the mtDNA mutation level are plotted as in Fig. 1.

In order to determine whether we could detect the inheritance of low-level heteroplasmy, we applied a series of decreasing upper thresholds on the heteroplasmy level (see Materials and Methods for details). As we lowered the threshold of heteroplasmy level from < 100% to < 0.15%, the median Pearson's correlation in unrelated pairs stayed at approximately the same level near zero, while the median correlation for related pairs decreased gradually, eventually reaching zero at a heteroplasmy level of < 0.1%. Even at the heteroplasmy level < 0.15%, we were still able to observe a positive mean correlation in heteroplasmy in the related pairs (median r = 0.154), and the median correlation in the related pairs was still stronger at a highly significant level (P = 7 × 10−5) than in unrelated pairs (r = 0.053). We also counted the number of heteroplasmic sites between related and unrelated pairs (Fig. S1). Similar numbers of shared heteroplasmic sites between related and unrelated pairs of all heteroplasmy level cutoffs were observed. This suggests that many heteroplasmic sites could result from low-level noise and sequencing error. It is very difficult to distinguish between true heteroplasmy signal and error. Only by conducting correlation analysis were we able to draw the conclusion that the shared heteroplasmic sites between related pairs are closer than the sites of unrelated pairs, which can be explained as inheritance.

Bootstrap tests, performed 10,000 times, showed that related pairs were significantly (P < 0.05) more correlated than unrelated pairs at all heteroplasmy level thresholds (Table 3). For detail of bootstrap tests please see Materials and Methods section. Wilcoxon rank sum tests were concordant with the results of the bootstrap tests except at heteroplasmy level < 0.1% where the Pearson's correlation coefficient of related pairs was only marginally significant (P = 0.09 by Wilcoxon test).

Table 3.

Test results of unrelated pairs vs. related pairs at different heteroplasmy levels

Heteroplasmy level
threshold		 P-value
(Resampling Test*)		 P-value
(Wilcoxon Rank Sum
Test)
≤100% <0.0001 7.8 × 10−18
≤5% <0.0001 1.0 × 10−12
≤1% <0.0001 5.8 × 10−12
≤0.5% <0.0001 2.1 × 10−11
≤0.25% <0.0001 6.9 × 10−9
≤0.2% 0.0001 1.7 × 10−7
≤0.15% 0.0012 7.1 × 10−5
≤0.1% 0.0119 0.09
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*

The resampling test was performed for 10,000 iterations

Heteroplasmy Level in D-loop

Finally, we split the analysis into two regions: sites within the D-loop and sites outside the D-Loop. We observed that maternally related pairs have significantly stronger Pearson's correlation coefficients than unrelated pairs in both the D-loop and non-D-loop regions (Fig. 3). Within the maternally related pairs, the D-loop sites had a higher median correlation coefficient than non-D-loop sites (P-values = 0.037 to 0.0001) (Table S3). For unrelated pairs, the D-loop sites had an even higher median correlation coefficient than non-D-loop sites (P -values < 0.0001) (Table S4). The inheritance of low-level heteroplasmy for both related and un-related pairs, down to 0.1% heteroplasmy, was observed in both the D-loop and non-D-loop sections of the genome. The slightly higher correlation in the D-loop may be explained by the higher mutation frequency in the D-Loop compared to non-D-Loop regions (Pereira et al., 2009).

Fig. 3. Variants in the D-loop and outside of the D-loop.

Fig. 3

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Distributions of the correlation coefficients for the mtDNA mutation level are plotted as in Fig. 1. A: sites outside of the D-loop. B: sites within the D-Loop.

DISCUSSION

It is well established that mtDNA heteroplasmic variations at moderate to high mutation levels in humans are inherited from mother to children. However, previously there has been limited evidence for low-level heteroplasmy inheritance in humans. For example, Payne et al. (Payne et al., 2013) have recently shown that some low-level heteroplasmy can be inherited using 454 pyrosequencing technology on two short amplicons of a few hundred base pairs. Yao et al. (Yao et al., 2013) have also demonstrated maternally inherited heteroplasmy at the level of 10% or greater.

In principle, the bottleneck in the inheritance of mtDNA from mother to offspring could act to filter out low-level mutations efficiently. Furthermore, sequencing errors confound the direct measurement of low mutation level variants, even with the new sequencing technology. In this study, it has been shown that significant correlations in mutation level can be observed even at heteroplasmy level < 0.15% and that these correlations are significantly stronger in maternally related pairs than those in arbitrary non-related pairs. The results suggest that not all heteroplasmy signals below 1% are sequencing errors. On the contrary, a significant portion of these heteroplasmy signals are inherited from the mother. However, the heteroplasmy signals become increasingly difficult to distinguish from sequencing errors as the heteroplasmy level decreases to approximately 0.1%. In addition to Pearson’s correlation, we also conducted analysis using Spearman’s correlation which resulted in weaker inheritance signals overall. However, related pairs still have stronger correlation than non-related pairs consistently across all heteroplasmy thresholds.

During the formation of the female germ line in early embryogenesis, the mtDNA copy number per cell drops to a relatively low value, forming a bottleneck in the inheritance of mtDNA. This bottleneck should act as a barrier limiting the inheritance of mtDNA mutations with very low mutation levels, as the probability of these low-level mutations passing through the bottleneck to an offspring should be low. Independent measurements (Cree et al., 2008; Wai et al., 2008) of this minimum mtDNA copy number in mice show a value of about 200 mtDNA molecules per cell at the initial formation of the primordial germ cells, shortly after implantation of the embryo. The very limited data in humans show that the variance in the mutation levels in both oocytes and offspring is approximately three times larger in humans than in mice (Wonnapinij et al., 2010), indicating an even deeper bottleneck in humans than in mice. A possible confounding factor here is that almost all of our current knowledge of mtDNA inheritance in humans comes from the study of pathogenic mutations in patient families. The new deep-sequencing methods and our new analysis method presented here allow us to investigate the inheritance of all heteroplasmic mtDNA mutations in the general population.

Our results show that for both related and unrelated pairs, the D-loop sites had a higher median correlation coefficient than non-D-loop sites. This could be explained by the high mutation rate and the small region (1124 base pairs) of the D-loop, which might result in highly recurrent mutation at the same position across multiple samples. Thus, pairs in D-loop regions produce higher pairwise correlation coefficients than do pairs in non-D-loop regions.

Given the small number of mtDNA molecules that pass from mother to offspring, the capacity for inheriting very low-level mtDNA mutations does appear to be quite limited. The evidence presented here shows that maternal inheritance of very low-level mtDNA mutations, down to the 0.15% mutation level (corresponding to only one in 670 molecules), clearly does occur in the general population. It is possible that low-level mtDNA mutations are so numerous that even with a deep bottleneck, some of these rare variants are still inherited by the offspring, forming the correlations shown here. Indeed, our results in Fig. 2 show that the correlations in low-level mutations are reasonably steady down to a level of 0.5% before beginning to decrease. This is consistent with a bottleneck size of approximately 200 mtDNA (the inverse of 0.5%, where the correlations in Fig. 2 begin to decrease).

MATERIALS & METHODS

Data Collection

Permissions for the study were granted by the Danish Data Protection Agency (2001-41-1113) and the Danish Scientific Ethical Committee ([KF] 01-150/01 and [KF] 11-129/02). Informed consent of the Finnish family was obtained, as was local institutional review board approval. Approval was also obtained from the Ethical Committee of the Hospital District of Varsinais-Suomi in Finland. Blood samples were taken from 44 subjects from 17 Danish families and one Finnish family. The same samples have been used in a variety of previous studies (Curwen et al., 2010; Curwen et al., 2005; Tawn et al., 2011; Tawn et al., 2005) that describe in detail the sample collection and DNA extraction protocols. Because mitochondria are solely maternally inherited, and paternal samples were not available, only mothers and their offspring were included in this study. In the 44 samples, there are a total of 35 maternally related pairs (26 mother-child pairs and 9 sibling pairs), and there are 911 unrelated pairs. Details for each family and their haplogroup information are described in Table 1. These families were ascertained as a study of the potential effect of cancer radiation therapy on future offspring, and thus all of the mothers in this study were survivors of childhood cancer who underwent radiation therapy including radiation of the uterus. In that study, no significant associations were found between the radiation dosage and the amount of heteroplasmic mtDNA mutations in either the mothers or the offspring (Guo et al., 2012a).

Mitochondria Enrichment and Sequencing

mtDNA enrichment was done using the amplification kit from Affymetrix's Genechip Human Mitochondria Resequencing Array 2.0 (Affymetrix, USA). Genomic DNA was amplified using PCR with two primer sets, mito3 and mito1–2. The forward primer of mito3 is 5′-TCATTTTTATTGCCACAACTAACCTCCTCGGACTC-3′, and the reverse primer is 5′-CGTGATGTCTTATTTAAGGGGAACGTGTGGGCTAT-3′; the forward primer of mito1–2 is 5′-ACATAGCACATTACAGTCAAATCCCTTCTCGTCCC-3′, and the reverse primer is 5′-ATTGCTAGGGTGGCGCTTCCAATTAGGTGC-3′. The two primer sets generate fragments of 7814bp and 9307bp, respectively. This protocol specifically amplifies the entire mitochondrial genome from genomic DNA using overlapping primers to eliminate the bias that may be introduced from the PCR method. The enriched mtDNA were barcoded and sequenced using the Illumina GAII high-throughput sequencing platform (Illumina, USA).

Sequencing Data Processing

To deal with mtDNA sequence homologs (NUMT), we first aligned all reads against the human reference genome hg19 without mitochondria reference BWA (Li and Durbin, 2009). Then, we aligned the unmapped reads against the revised Cambridge mitochondrial reference sequence (GenBank: NC_012920) (Andrews et al., 1999). There is an artificially inserted “N” in the rCRS at position 3106 (Andrews et al., 1999). When performing alignment using rCRS, false heteroplasmies, SNPs, and small indels can be introduced around the position 3106 (Guo et al., 2012a). To avoid this, we deleted the “N” from rCRS before conducting alignment. Later, all positions after 3106 were shifted by one base to keep consistency with rCRS.

The aligned BAM (Li et al., 2009) files were locally realigned using the Genome Analysis Toolkit (GATK) (McKenna et al., 2010) developed by the Broad Institute. To further increase the local realignment accuracy, we built SNP and indel references in Variant Call Format (VCF) (Danecek et al., 2011) using the existing SNP and indel lists reported on mitomap.org and incorporated the information into the local realignment process. After local realignment, we performed base quality score recalibration on the realigned BAM files using GATK's recalibration tool. The recalibrated BAM files were filtered by removing all reads with mapping quality (MQ) Phred score (Cock et al., 2010) less than 20 and all bases with base quality (BQ) Phred score less than 20 (corresponding to a probability of base calling error of less than 1%). After applying these filters, a significant amount of heteroplasmy rate was adjusted. For example, for sample #22, 12,288 sites of the mitochondrial genome had heteroplasmy level changes. After applying MQ and BQ filters, a portion of the low quality bases were removed, which resulted in a decrease in the heteroplasmy rate of 9181 sites and an increase in the heteroplasmy rate of 3107 sites. Allele call information of each allele on both the forward and reverse strands separately was extracted for all 16,569 positions of the mtDNA reference sequence. Multiple thresholds for the filter were tested, and the conclusion of the study remained the same. The overall workflow can be seen in Fig. S2.

SNP Identification

SNP variant calling on the filtered BAM files was done using GATK's Unified Genotyper and GLFMultiples, developed by the Abecasis Lab at the University of Michigan (http://genome.sph.umich.edu/wiki/GlfMultiples). Both variance callers have been widely utilized in various studies, including the 1000 Genomes Project (Durbin et al., 2010). Final SNP lists were generated by forming a consensus from the results of the two base callers. Indels were also called using GATK’s Indel Genotyper 2.0 and Samtools’ Indel caller. The results of SNP and indel calling for this data have been previously reported (Guo et al., 2012a). For quality control purposes, SNPs and indels were individually examined through allele counting. Haplogroup information for each sample was accomplished by checking the SNP results against phylotree.org's mtDNA phylogeny tree (van Oven and Kayser, 2009). For quality control, we only analyzed sites with sequencing depth ≥ 400 and strand bias score ≤ 1. Strand bias is a novel way of filtering out suspicious positions from sequencing data. The definition of strand bias score can be found in (Guo et al., 2012b), and it has been implemented in many existing tools such as SAMtools (Li et al., 2009), GATK (McKenna et al., 2010), Varscan (Koboldt et al., 2009), and MitoSeek (Guo et al., 2013). The requirement that the strand bias score should be ≤ 1 removes the obvious sequencing errors due to short polynucleotide tracts.

Heteroplasmy Inheritance Analysis

For each site that passed quality control, we identified the major allele in each subject. The heteroplasmy level for a position of a subject was defined as 1 minus the frequency of the major allele. Subjects from the study were paired, and we compared maternally related pairs to unrelated pairs. We examined the inheritance of mtDNA variants grouped by different heteroplasmy levels (<100%, <5%, <1%, <0.5%, <0.25%, <0.20%, <0.15%, <0.1%). For each heteroplasmy-level threshold, a site was included in the analysis if one member of the pair had a measured heteroplasmy level below the threshold. For example, a site with a heteroplasmy level of 7% in one person and 0.23% in the other person of the pair would be included in all analyses from 100% down to the 0.25% threshold. To meet the criteria for analysis, a site must have passed quality control in both paired subjects, and the heteroplasmy level of the site must not have exceeded the heteroplasmy-level threshold in at least one member of the pair. Pearson’s correlation coefficient was calculated over all sites meeting these criteria across the entire mitochondrial genome. As a secondary analysis, the correlation coefficient calculation was split into the D-loop (positions 16,024–16,569 and 1–576) and the coding region (577–16,023). Furthermore, heteroplasmy sites were verified using alternative analysis methods (Li and Stoneking, 2012) and (Li et al., 2010).

There were 35 maternally related pairs and 911 unrelated pairs in this study. Because the total number of possible unrelated pairs was significantly more than that of related pairs, we performed both a bootstrap test and a Wilcoxon rank sum test to assess the significance of the comparisons between related and unrelated pairs. The Wilcoxon rank sum test was performed between all related pairs and unrelated pairs. For the bootstrap test, we randomly drew 35 unrelated pairs from the unrelated pair pool with replacement and compared the median Pearson correlation coefficient of the unrelated pairs with that of the 35 related pairs. We repeated this process 10,000 times for each heteroplasmy-level threshold. The P-value was computed as the number of times that the median Pearson correlation coefficient of the unrelated pairs was higher than that of the related pairs.

Supplementary Material

Supplementary Figure 1. Fig. S1. Number of heteroplasmy sites in related and unrelated pairs.

The number of heteroplasmy sites was computed using the criteria described in MATERIALS and METHODS – Heteroplasmy inheritance analysis section. Using different heteroplasmy threshold, no difference of number of heteroplasmy sites between related and unrelated pairs were observed.

Supplementary Figure 2. Fig. S2. Workflow of sequencing data processing.

Fig. S2. describes our mitochondria sequencing data processing workflow. By aligning to nuclear genome first, we effectively reduced the number of false alignments can be introduced from NUMT

ACKNOWLEDGEMENTS

We thank the families for participating in this study, and Brian Møllgren, Rigshospitalet, for collection of blood samples. The study was supported by the grant from the National Cancer Institute (RO1 CA104666). DNA sample preparation was conducted at Survey and Biospecimen Shared Resource, which is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA68485). We would also like to thank Peggy Schuyler and Margot Bjoring for their editing support.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure 1. Fig. S1. Number of heteroplasmy sites in related and unrelated pairs.

The number of heteroplasmy sites was computed using the criteria described in MATERIALS and METHODS – Heteroplasmy inheritance analysis section. Using different heteroplasmy threshold, no difference of number of heteroplasmy sites between related and unrelated pairs were observed.

NIHMS608621-supplement-Supplementary_Figure_1.jpg (644.9KB, jpg)
Supplementary Figure 2. Fig. S2. Workflow of sequencing data processing.

Fig. S2. describes our mitochondria sequencing data processing workflow. By aligning to nuclear genome first, we effectively reduced the number of false alignments can be introduced from NUMT

NIHMS608621-supplement-Supplementary_Figure_2.jpg (346.8KB, jpg)

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