Nuclear-embedded mitochondrial DNA sequences in 66,083 human genomes

Wei, Wei; Schon, Katherine R.; Elgar, Greg; Orioli, Andrea; Tanguy, Melanie; Giess, Adam; Tischkowitz, Marc; Caulfield, Mark J.; Chinnery, Patrick F.

doi:10.1038/s41586-022-05288-7

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Published: 05 October 2022

Nuclear-embedded mitochondrial DNA sequences in 66,083 human genomes

Nature volume 611, pages 105–114 (2022)Cite this article

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Abstract

DNA transfer from cytoplasmic organelles to the cell nucleus is a legacy of the endosymbiotic event—the majority of nuclear-mitochondrial segments (NUMTs) are thought to be ancient, preceding human speciation^1,2,3. Here we analyse whole-genome sequences from 66,083 people—including 12,509 people with cancer—and demonstrate the ongoing transfer of mitochondrial DNA into the nucleus, contributing to a complex NUMT landscape. More than 99% of individuals had at least one of 1,637 different NUMTs, with 1 in 8 individuals having an ultra-rare NUMT that is present in less than 0.1% of the population. More than 90% of the extant NUMTs that we evaluated inserted into the nuclear genome after humans diverged from apes. Once embedded, the sequences were no longer under the evolutionary constraint seen within the mitochondrion, and NUMT-specific mutations had a different mutational signature to mitochondrial DNA. De novo NUMTs were observed in the germline once in every 10⁴ births and once in every 10³ cancers. NUMTs preferentially involved non-coding mitochondrial DNA, linking transcription and replication to their origin, with nuclear insertion involving multiple mechanisms including double-strand break repair associated with PR domain zinc-finger protein 9 (PRDM9) binding. The frequency of tumour-specific NUMTs differed between cancers, including a probably causal insertion in a myxoid liposarcoma. We found evidence of selection against NUMTs on the basis of size and genomic location, shaping a highly heterogenous and dynamic human NUMT landscape.

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Main

The transfer of genes from cytoplasmic organelles to the cell nucleus underpins the endosymbiotic theory of the origin of mitochondria³. Higher-order organisms have progressively smaller mitochondrial genomes, reflecting the translocation of mitochondrial genes into the nuclear genome over evolutionary time, facilitating the co-ordinated synthesis of organellar proteins by the cytosolic translational machinery². This process has left fragments of non-expressed mitochondrial DNA (mtDNA) throughout the non-coding space, with many NUMTs being shared across species, reflecting their ancient origin⁴. Recently, whole-genome sequencing (WGS) has identified ultra-rare NUMTs in humans⁵, implying that mtDNA–nuclear transfer is an ongoing process, but the rate of germline NUMT formation remains unknown. Novel mtDNA-nuclear incursions have important implications, as they can potentially disrupt protein-coding genes, causing disease^6,7,8,9, and create artefacts resembling mixed populations of mtDNA^10,11 (pseudo-heteroplasmy). Inadvertently interpreting the NUMT sequence as a mtDNA variant could confound the diagnosis of mitochondrial diseases¹² and raise questions about the possible paternal inheritance of mtDNA¹³.

Large-scale WGS projects present an opportunity to characterize human NUMTs in greater depth than in other species. Here we describe the landscape of human NUMTs in 66,083 individuals, including 8,201 mother–father–child trios and 12,509 tumour–normal tissue pairs within the 100,000 Genomes Project in England. This provides a resource for the interpretation of mtDNA variants across diverse populations and for our understanding nuclear genome evolution. The results are available in a searchable online database as https://wwei.shinyapps.io/numts/.

Atlas of human germline NUMTs

We initially studied 68,348 genomes from 67,875 participants in the Genomics England Rare Disease Project¹⁴. After all quality control (QC) steps (Methods), we studied 25,436 males and 28,138 females from 0 to 99 years of age (Extended Data Fig. 1a,b), including 8,201 trios whose reported relatedness was consistent with genomic predictions (Methods). Using a validated short-read NUMT detection pipeline^5,15 (Fig. 1a), we identified 335,891 NUMTs that are not present in the reference sequence based on at least two discordant read pairs detected in 53,535 individuals (>99.9%), including 3,829 different NUMTs (Extended Data Fig. 1d,e). Increasing the stringency for NUMT detection to at least 5 discordant read pairs refined the yield to 254,195 NUMTs (1,637 distinct NUMTs in 53,507 (99.87%) individuals) that are not present in the reference sequence (Fig. 1b,c and Supplementary Table 1). This higher-stringency dataset forms the basis of the results reported here, in which we refer to NUMTs as common (frequency (F) ≥ 1%), rare (0.1% ≤ F < 1%), ultra-rare (F < 0.1%) or private (detected in only one family). Long-read sequencing validated our NUMT calling pipeline in 99% of cases (182 out of 184 NUMTs from 39 individuals; Fig. 1a) (Methods).

**Fig. 1: NUMT detection in 53,574 individuals.**

Individuals had an average of 4.7 NUMTs (s.d. = 1.6) that were not present in the reference sequence (Fig. 1d). There was no difference between males and females (P value = 0.834, Wilcoxon rank-sum test; Extended Data Fig. 1f) or with age (P value = 0.95, Pearson’s correlation; Extended Data Fig. 1g). A total of 1,615 distinct NUMTs (98.7%) seen in 26.2% of individuals were not present in the reference sequence and were rare or ultra-rare (F < 1%), 1,567 different NUMTs (96.1%) seen in 14.2% of individuals were ultra-rare (F < 0.1%), and 1,039 (63.7%) NUMTs seen in 3.6% of individuals were private (NUMTs detected in only one family) (Fig. 1e and Extended Data Fig. 1d). As expected, the majority (71.4%) of the common NUMTs (F ≥ 1%) had been reported previously^16,17,18,19 (Supplementary Table 2). Thus, combining the rare and ultra-rare NUMTs with the common NUMT data, we identified 1,564 NUMTs that, to our knowledge, had not been reported previously (Fig. 1e) (Methods). Defining mtDNA breakpoints at both ends (Fig. 1a), NUMTs ranged in size from 24 bp to the whole of the mitochondrial genome (median 156 bp, mean 1,597 bp and s.d 3,651 bp). The majority of NUMTs were short insertions (63.2% of NUMTs were less than 200 bp and 77.8% were less than 500 bp in size) (Fig. 1f), with an inverse relationship between NUMT size and the population frequency (P = 0.021, R² = −0.058, Pearson’s correlation test; Fig. 1g), consistent with ongoing selection against large NUMTs. In keeping with this, we observed major differences in the frequency and distribution of NUMTs between different ethnic groups, with African and East Asian individuals being the most distinct in relation to the NUMT frequencies and chromosomal locations involved (Fig. 2 and Extended Data Fig. 2).

**Fig. 2: NUMTs in the different populations.**

Some NUMTs exhibited complex structures, identified by detecting split reads mapping only to mtDNA followed by stringent QC filtering (Fig. 1a and Extended Data Fig. 3a–d). Analysis of 5,885 mtDNA–mtDNA split reads found in 3,197 trios showed that 544 were inherited from the fathers and 560 were from the mothers. One-hundred and eleven individuals shared the same rare mtDNA–mtDNA split reads within 58 ultra-rare NUMTs (F < 0.1%), as seen for likely concatenated NUMTs⁵ (Extended Data Fig. 3a,b). Oxford nanopore long-read sequencing was performed on five families (Fig. 1a and Extended Data Fig. 3c), validating the concatenated NUMT structure. Oxford nanopore sequencing also enabled us to determine the methylation status²⁰ of NUMTs detected in 39 individuals (Fig. 3a) (Methods). The examined NUMTs showed increased CpG methylation relative to true mtDNA reads, which are not methylated²¹, including paternally-transmitted concatenated ‘mega-NUMTs’ that share the same methylation pattern across two generations (Fig. 3b,c and Extended Data Fig. 3e), suppressing their expression within the nuclear genome²². Concatenated NUMTs can mimic the paternal transmission of mtDNA, generating a mixed haplotype resembling mtDNA heteroplasmy⁵. Here we show they increase the likelihood of detecting mixed alleles resembling heteroplasmy (when compared with the individuals not carrying concatenated NUMTs; P < 6.02 × 10⁻⁸ for allele fractions (AF) > 2%, P < 3.09 × 10⁻¹⁵ for AF > 1%; Wilcoxon rank-sum test) (Extended Data Fig. 3f).

**Fig. 3: Characteristics of NUMTs in humans.**

Analysis of NUMT segregation in 8,201 complete mother–father–child trios revealed three private NUMTs from two families that were not seen in either parent, indicating a de novo germline NUMT mutation rate of 2.44 × 10⁻⁴ per generation (95% confidence interval 2.95 × 10⁻⁵ to 8.81 × 10⁻⁴) (Fig. 3d and Extended Data Fig. 4). In each case, the de novo NUMT sequence did not align with any other site in assemblies of the nuclear genomes of the child, making it unlikely that the NUMTs originated from within the nuclear DNA. None of other NUMTs detected in each child and their parents carried the same NUMT sequence as the de novo NUMT insertions, even after increasing the mapping sensitivity by dropping the requirements from at least five discordant reads to two discordant reads. The de novo NUMTs were also not present in the reference genome or in published lists of NUMTs (Supplementary Table 2). The de novo NUMT frequency is likely to be an underestimate because of the difficulty of determining the origin of short NUMTs, although we cannot absolutely exclude the possibility of apparent de novo NUMTs arising from other parts of the nuclear genome and as opposed to a new mtDNA insertion event.

Characteristics of NUMT insertions

Next, we studied the mtDNA and nuclear DNA context of the NUMTs, which were found on all nuclear chromosomes (Fig. 2d) and involved the entire mtDNA (Fig. 1b,c). The 3,184 corresponding mtDNA breakpoints were enriched in the non-coding displacement loop (D-loop) (P = 0.001)—particularly in three hypervariable segment regions (HV1, P = 0.002; HV2, P = 0.001; and HV3, P = 0.006)—and both heavy strand (OHR, P = 0.002) and light strand (OLR P = 0.016) origins, and were less likely to involve MT-ATP6 (P = 0.001), MT-ND2 (P = 0.015) and MT-ND3 (P = 0.034) (Fig. 3e and Extended Data Fig. 5a,b). This was supported by the distribution of the mtDNA fragments (P = 0.03, odds ratio = 1.14, 95% confidence interval 1.01–1.28, Fisher's exact test) (Fig. 3e). There was a weak correlation between the germline NUMT mtDNA breakpoints and the location of known deletion breakpoints in mtDNA, which exhibited marginal significance (P = 0.047, R² = 0.24, Pearson correlation) (Fig. 3f and Extended Data Fig. 5c). Overall, we observed a strong positive correlation between the length of each chromosome and the number of NUMTs detected on each chromosome after accounting for other genomic features (P = 1.42 × 10⁻⁶, linear regression test). However, chromosomes 3, 6 and 21 had a larger number of NUMTs per Mb than the remaining autosomes (chromosome 3, P = 0.03; chromosome 6, P = 0.005; chromosome 21, P = 0.03, two-tailed permutation test), and the X chromosome had a reduced number of NUMTs per Mb (P = 0.001). Two hundred and twenty-eight NUMTs were observed on the X chromosome, with the expected approximately twofold higher number in females than males (151 of the 28,138 females, and 75 of the 25,426 males; Fisher exact test P = 1.713 × 10⁻⁵, odds ratio = 1.824, 95% confidence interval 1.374–2.441). The Y chromosome was not analysed owing to the complex duplicated structure limiting confident alignment.

Previous reports of local sequence characteristics associated with NUMT insertion²³ prompted a comprehensive analysis of the proximity of the unique NUMTs to the centromere, genomic duplications, simple repeats, dbRIP HS-ME (retrotransposon insertion polymorphisms, human-specific mobile elements), regulatory elements, CpG islands, satellites and retrotransposons (including long interspersed elements (LINEs) and short interspersed elements (SINEs)). Common and rare NUMTs (F ≥ 0.1%) were more likely to occur near or within genomic duplications (P = 0.030), and ultra-rare NUMTs were enriched in regulatory elements (P = 0.011), SINEs (P = 0.003), simple repeats (P = 0.006) and introns P = 0.003 (Fig. 3g and Supplementary Figs. 1–3). No common NUMTs were within the 500 bp region flanking transcription start sites (TSS), consistent with selection against NUMTs that disrupt gene function (Fig. 3h and Extended Data Fig. 5d). Consistently, gene tolerance scores²⁴ (pLI) were inversely correlated with the frequency of NUMTs in the population (Fig. 3i).

Atlas of tumour-specific NUMTs

Next, we studied 26,488 cancer WGS from the Genomics England Cancer project. After QC steps (Methods), we analysed 12,509 paired WGS from tumours and healthy tissues representing the germline for 21 cancer types (Extended Data Fig. 6a–d and Supplementary Table 3). Overall, tumours had a higher mean number of NUMTs (6.5 ± 2.2 (mean ± s.d.)) that were not present in the reference sequence than the corresponding normal tissue (4.8 ± 1.6; P < 2.2 × 10⁻¹⁶, Wilcoxon rank-sum test) (Fig. 4a and Supplementary Fig. 4). This difference probably reflects the tumour itself, rather than the normal tissue in each case, because the mean number of NUMTs did not differ between different normal tissue types (the average detected NUMT was 4.7 in saliva cells, 5 in skin fibroblasts and 4.9 in blood samples; saliva versus blood, P = 0.24, estimate = −0.1; fibroblast versus blood P = 0.67, estimate = −0.1, linear regression test) (Extended Data Fig. 6e). The frequency of cancer germline NUMTs was not different from the frequency of germline NUMTs measured in the Rare Disease Project participants (P = 0.924, linear regression test accounting for sequencing depth) (Extended Data Fig. 6f). There were no sex differences in the NUMT distribution (Supplementary Fig. 5). For most tumours, there was no correlation between the age of an individual at diagnosis and the number of NUMTs (Extended Data Fig. 6b and Supplementary Fig. 6a). However, the mean number of NUMTs was lower in haematological malignancies from older individuals, probably reflecting their origin in clonal haematopoiesis²⁵ (P = 3.29 × 10⁻³, estimate = −0.007, linear regression).

Next, we focussed on a subgroup of the tumour-specific NUMTs that were not present in any other non-cancer genome, which provided high confidence that these NUMTs arose either in somatic tissues leading to the cancer, or in the cancer itself. Three hundred and seventy nine of these de novo NUMTs were seen in 251 tumours (2.3%) from 10,713 tumour–normal pairs, giving a rate of 3.56 × 10⁻² per cancer per genome (95% confidence interval 3.38 × 10⁻² to 3.74 × 10⁻²) (Fig. 4b,c and Supplementary Table 4; Methods), which is higher than the germline rate (P = 2.08 × 10⁻⁵⁹, Fisher's exact test) and consistent with previous reports^15,26. Eighty-two tumours carried more than one de novo NUMT, which was more than expected by chance (P < 2.2 × 10⁻¹⁶, Fisher's exact test) (Fig. 4b). The mean number of tumour-specific NUMTs was 0.035 (s.d. = 0.29), with a median length of 396 bp (first quartile 250 bp, third quartile 524 bp, mean = 1,197 bp), which was higher than the number of germline NUMTs (median = 156 bp, first quartile 97 bp, third quartile 382 bp) (P < 2.2 × 10⁻¹⁶, Wilcoxon rank-sum test) (Fig. 4d,e). These findings are consistent with cancer driving NUMT formation. The proportion of tumours with a de novo NUMT depended on the tumour type, with renal and colorectal tumours having fourfold fewer NUMTs than breast cancers (P = 1.93 × 10⁻⁶, Fisher's exact test) and around 7.5-fold fewer than bladder cancers (P = 3.42 × 10⁻⁴, Fisher's exact test) (Fig. 4f), which had more NUMTs than the other tumour types (Fig. 4g,h), as shown previously²⁶. The average number of tumour-specific NUMTs did not correlate with age (Supplementary Fig. 6b), implying that they arose during carcinogenesis, and not in somatic cells throughout life before cancer formation. A comparison with ref. ²⁶ is shown in Supplementary Table 5.

The signature of de novo NUMTs in cancer

The mtDNA segments forming de novo tumour NUMTs differed from those in the germline (Fig. 3e): they were less likely to involve MT-CO3 (P = 7.7 × 10⁻³), MT-ND4 (P = 3.1 × 10⁻³), MT-ND4L (P = 3.4 × 10⁻³) and MT-ND5 (P = 5.3 × 10⁻³), but more than 2.5-fold more likely to involve the D-loop (P = 3.36 × 10⁻³⁶), largely because of an approximately fourfold over-representation of breakpoints in termination-associated sequence 2 (TAS2) (P = 1.03 × 10⁻⁷, Fisher's exact test) (Extended Data Fig. 5a,b), also reflected in the mtDNA fragments (D-loop, P = 5.51 × 10⁻³⁰, odds ratio = 2.00, 95% confidence interval 1.77–2.25, Fisher's exact test) (Figs. 3e and 4c). This could explain the observed correlation between de novo NUMT breakpoints and known mtDNA deletion breakpoints (P = 0.004, R² = 0.44, Pearson correlation) (Fig. 3f and Extended Data Fig. 5c), which also tend to cluster around the D-loop at the 3′ end²⁷. Tumour-specific NUMTs were more common on chromosome 19 (P = 9.08 × 10⁻⁶) and less common on chromosome 6 (P = 1.53 × 10⁻³) (Fig. 4i) and were more likely to involve repetitive elements (P = 4.24 × 10⁻¹⁶), particularly satellite repeats (P = 0.023) and microsatellites repeats (P = 0.007) than the germline NUMTs (Fig. 3g and Supplementary Fig. 1). Finally, a greater proportion of tumour-specific NUMTs were found within 500 bp, 2,000 bp and 5,000 bp of the TSS than for germline NUMTs (Fig. 3h and Extended Data Fig. 5d). Together, these findings suggest that a combination of local sequence characteristics, genome instability and less opportunity for selection to remove specific NUMTs due to relaxed evolutionary constraints explains why the NUMT landscape differs from the germline.

Adverse consequences of NUMT insertion

Nine hundred and forty six (58%) germline NUMTs were observed in gene regions, with the majority (85.8%, n = 812) being enriched in introns versus exons (P = 0.01, permutation test) (Fig. 3g and Supplementary Figs. 1–3). No common or rare NUMTs (F > 0.1%) were found in the coding DNA sequences (CDS) (P = 0.039 permutation test), and none were predicted to cause rare disease (Methods and Supplementary Information, ‘Results’), consistent with NUMTs being under evolutionary constraint. Two hundred and twenty tumour-specific NUMTs were found in gene regions, including 13 in CDS, 3 affecting stop codons, 4 affecting start codons, 16 in 3′ or 5′ untranslated regions (UTRs). Eight tumours harboured tumour-specific NUMTs inserted into genes on the COSMIC Cancer Gene Census list²⁸ (two in FHIT, which is a fragile genomic site²⁹, and one each in CTNNA2, DDIT3, WIF1, BCL11B, KDM5A and AKT2) (Supplementary Table 4). One tumour had a NUMT insertion in an intron of FANCI, which is involved in DNA repair. Complex rearrangements with NUMT insertion at the site of chromosomal translocations were also seen in three out of eight tumour samples (Fig. 4j and Extended Data Fig. 7a). One myxoid liposarcoma tumour carried a FUS–DDIT3 chimeric fusion oncoprotein caused by a complex rearrangement involving a NUMT insertion (Fig. 4j and Extended Data Fig. 7a). FUS–DDIT3 fusions are present in 90% of myxoid liposarcomas³⁰, implicating NUMT in carcinogenesis in the individual in our study. Three private NUMTs in non-tumour tissue were not found in the matched breast tumours, potentially influencing prognosis through the loss of DSG2³¹ and TCAM1P³² (Fig. 4k and Extended Data Fig. 7b). Two normal tissues from individuals with haematological cancer carried extremely high numbers of NUMTs that were not present in the tumour tissues (Extended Data Fig. 7c), probably reflecting clonal proliferation.

NUMT insertion and modification

NUMT breakpoints were more likely to involve nCC/CCn trinucleotides on the mtDNA genome and less likely to involved nTT/TTn on both the nuclear genome and mtDNA (Fig. 5a, Extended Data Fig. 8a and Supplementary Table 6). Extending the analysis to 2 bp, 3 bp and 4 bp beyond the mtDNA breakpoint showed that poly-C tracts were 8, 12 and 18 times more numerous than expected by chance (P = 7.57 × 10⁻¹⁰, P = 2.13 × 10⁻⁵ and P = 6.3 × 10⁻⁵), implicating microhomology in NUMT insertion events through recombination. We also observed overlapping sequence microhomology (≥1 bp) in 51.9% of the NUMT breakpoints (P = 2.05 × 10⁻⁴⁵, Fisher's exact test), consistent with microhomology-mediated end joining (MMEJ) during some NUMT formation; blunt-end repair in 27.6% of the NUMT breakpoints and short-nucleotide insertions in 20.5% of the NUMT breakpoints, implicating non-homologous end joining^33,34 (Fig. 5b,c). A greater proportion of tumour-specific NUMTs (64.1%) had overlapping sequence microhomology than germline NUMTs (P = 5.22 × 10⁻¹⁰, Fisher's exact test) (Fig. 5c).

**Fig. 5: Molecular mechanism of NUMT formation.**

We also observed enrichment of the cancer trinucleotide mutation signatures³⁵ S2 (P = 6.93 × 10⁻⁷), S3 (P = 4.68 × 10⁻¹³) and S13 (P = 1.72 × 10⁻¹⁸) in cancers carrying the tumour-specific NUMTs (Fig. 5d). NUMT insertion resembles transposon jumping previously associated with S2 and S13³⁵, and S3 is linked to failed double-strand break (DSB) repair by homologous recombination, in which NUMTs have a role³⁵. Signatures 2 and 13 are also enriched for APOBEC-mediated point mutations, which can also induce DSBs³⁶. Thus there appear to be common molecular mechanisms behind somatic mutation in cancers and NUMT formation.

In keeping with this, germline NUMTs were more likely to be found within 3 kb of a PRDM9-binding site (P = 0.003, permutation test), and tumour-specific NUMTs were more likely to be found within 1 kb of a PRDM9-binding site (P = 0.003, permutation test) (Fig. 5e and Extended Data Fig. 8c). PRDM9 is implicated in DSB repair and determines crossover hotspots during meiosis³⁷, so co-location is consistent with NUMTs having a role in DSB repair^33,34. Thus, several different molecular mechanisms are involved in NUMT formation, all of which are related to nuclear genome instability. In keeping with this, tumour samples carrying missense mutations in DNA repair oncogenes^38,39 were more likely to harbour tumour-specific NUMTs than the remaining tumours (77.7% versus 63.1%, Fisher’s exact test P = 5.05 × 10⁻⁶, 95% confidence interval 1.44–2.68, odds ratio = 1.95) (Fig. 5f and Supplementary Fig. 7).

It has been suggested that NUMTs are mobile after the initial insertion event⁴⁰, hitch-hiking on other transmissible elements. We found several examples supporting this hypothesis (Fig. 5g). We also found NUMTs associated with large deletions, insertions, copy number gain or loss, and particularly at the breakpoints of complex structural variants (Extended Data Fig. 9a,b). Several cancers contained extensive NUMT rearrangements, with evidence of insertion into the nuclear genome before a nuclear–nuclear translocation (Fig. 5h). We also found two examples in which multiple fragments of the mtDNA were embedded throughout the genome across multiple chromosomes (Fig. 5i and Extended Data Fig. 9c,d) resembling the extreme rearrangements seen in chromothripsis⁴¹ (mito-chromothripsis).

Molecular evolution of NUMT sequences

To understand the molecular evolution of the mtDNA sequences after their insertion into the nuclear genome, we determined the complete nucleotide sequence of 931 different NUMTs incorporating 144,805 bp, where complete local assembly of NUMTs was possible from short-read sequencing (Methods). The results of this analysis are reported in Fig. 6 and Supplementary Information, ‘Results’. Finally, we estimated the age of 429 NUMT insertions (Methods). The majority (more than 90%) were less than 0.1 million years old and 41 (9.5%) were more than 0.1 milion years old, with a range of up to 3.75 million years (Fig. 6d and Supplementary Table 1). As expected, the older NUMTs were more common in the population (Fig. 6e), particularly in African genomes (Extended Data Fig. 8d), and were more likely to carry NUMT-specific mutations than the younger NUMTs (total group A: P = 7.2 × 10⁻³, odds ratio = 2.92, 95% confidence interval 1.27–6.39; subgroup B: P = 3.9 × 10⁻⁴, odds ratio = 2.92, 95% confidence interval 1.27–6.39; subgroup C: P = 9.0 × 10⁻⁴, odds ratio = 8.06, 95% confidence interval 2.18–28.27, Fisher's exact test) (Fig. 6f). Together these findings indicate ongoing NUMT insertion and evolution throughout human evolution.

Discussion

NUMTs were considered ancient remnants of previous mtDNA translocation events that were often shared between related species⁴². Here we show that NUMT formation is an ongoing process, with de novo germline events occurring approximately once in every 10⁴ births and somatic insertions occurring once in every 10³ cancers. This leads to high NUMT diversity within the human population, with 14.2% of individuals harbouring an ultra-rare NUMT found in less than 1 in 1,000 people. The wholesale transfer of mtDNA fragments into the nucleus genome would inevitably increase the size of the human genome³. However, the inverse correlation between NUMT size and the frequency of its occurrence in the population points towards a selective process counter-balancing NUMT insertion, maintaining genome size and removing NUMTs that influence gene expression. Co-location of NUMTs with PRDM9-binding sites would facilitate their removal in the germline because PRDM9 determines sites of recombination hotspots during meiosis³⁷. In this way, NUMTs can act as ‘temporary fixes’ resembling a sticking-plaster, repairing DSBs until they are removed during meiosis. The higher burden and distribution of NUMTs in cancers probably reflects a heightened state of genome instability in the absence of selection over short time periods.

Although NUMTs can involve the entire mtDNA molecule, NUMT breakpoints were more common in the non-coding D-loop, including the origins of heavy and light strand replication. This raises the possibility that mtDNA deletions are involved in NUMT formation. However, a more compelling explanation involves mtDNA transcription and associated replication, which originates in the D-loop⁴³. The recent description of mitochondrial herniations and BSX–BAK macropores provide one route⁴⁴, potentially involving RNA intermediates leaking into the cytoplasm following mtDNA DSBs⁴⁵. This could also occur in single cells, contributing to somatic mosaicism.

The translocation of organelle genes into the nucleus has had a key role in establishing the symbiotic relationship between mitochondria and eukaryotic cells. Here we show that the mechanisms of DNA transfer remain active and modify the germline approximately once in every in 4,000 births. It is therefore conceivable that an endosymboisis that began around 1.45 billion years ago is not yet complete.

Methods

Study samples

We studied 68,348 genomes from whole-blood DNA in Genomics England Rare Disease Project and 26,488 cancer genomes from Genomics England Cancer Project. DNA was extracted and processed based on the Genomics England Sample Handling Guidelines (https://legacy.genomicsengland.co.uk/about-genomics-england/the-100000-genomes-project/information-for-gmc-staff/sample-handling-guidance/). DNA samples were received in FluidX tubes (Brooks) and accessioned into Laboratory Management Information System (LIMS) at UK Biocentre. Following automated library preparation, libraries were quantified using automated quantitative PCR, clustered and sequenced. Libraries were prepared using the Illumina TruSeq DNA PCR-Free High Throughput Sample Preparation kit or the Illumina TruSeq Nano High Throughput Sample Preparation kit⁴⁶.

Ethical approval

Ethical approval was provided by the East of England Cambridge South National Research Ethics Committee under reference number 13/EE/0325, with participants providing written informed consent for this approved study. All consenting participants in the Rare Disease arm of the 100,000 Genomes Project were enrolled via 13 centres in the National Health Service (NHS) covering all NHS patients in England.

Quality control checks of rare disease genomes

All the samples were passed an initial QC check based on sequencing quality and coverage from the sequencing provider (Illumina) and Genomics England internal QC checks (https://research-help.genomicsengland.co.uk/display/GERE/Sample+QC). We only included the samples aligned to the Homo sapiens NCBI GRCh38 assembly with decoys (N = 58,335). All the samples were sequenced to produce at least 85 Gb of sequence data with sequencing quality of at least 30. Alignments covered at least 95% of the genome at 15x or above with well-mapped reads (mapping quality > 10) after discarding duplicates. Additionally, all included samples have passed a set of basic QC metrics: (1) sample contamination (VerifyBamID freemix⁴⁷) < 0.03, (2) ratio of single nucleotide variant (SNV) Heterozygous-to-Homozygous (Het-to-Hom) calls < 3, (3) total number of SNVs between 3.2 M–4.7 M, (4) array concordance > 90%, (5) median fragment size > 250 bp, (6) excess of chimeric reads < 5%, (7) percentage of mapper reads > 60%, and (8) percentage of AT dropout < 10%. 57,961 genomes were passed WGS QCs. We further excluded the samples with the average depth of mitochondrial genomes below 500x after re-aligned the mitochondrial reads (see details below). For the rare disease genomes study, we included 53,574 individuals, 25,436 male and 28,138 females, age from 0 to 99 years (Extended Data Fig. 1a,b). The average depth of WGS was 42x (s.d. = 7.7x) and average depth of mtDNA was 1,990x (s.d = 866x) (Extended Data Fig. 1c).

Family QC checks

In the family related analysis, WGS family selection quality checks are processed for rare disease genomes, reporting abnormalities of sex chromosomes and reported versus genetic sex summary checks (computed from family relatedness, mendelian inconsistencies, and sex chromosome checks). For the sex determination, the coverage data for the X and Y chromosomes was compared to the average coverage for the sample autosomes using PLINK v1.90⁴⁸ (www.cog-genomics.org/plink/1.9/). The resulting output is compared with the participant sex provided at sample collection. Relatedness checks were based on verification of the mendelian inconsistencies between members of a trio/family. The individual VCF files were merged into a family VCF with BCFTools (v1.3.1)⁴⁹ and the mendelian inconsistencies again checked with PLINK. The relationships are also checked by calculated genomic identity-by-descent values for all pairwise relationships in a family using PLINK and comparing with expected values for reported relationship (https://research-help.genomicsengland.co.uk/). We further processed an independent relatedness check using our previously published method⁵⁰. In brief, a list of 32,665 autosomal SNPs was selected to estimate relatedness. By filtering the merged VCF and the 1000 Genomes reference set⁵¹ with the selected SNPs, the pc-relate function from the GENESIS package was applied to obtain the pairwise relatedness⁵². The first 20 principal components were used to weight the population structure, and the reference set was used to increase genetic diversity accounted for by the principal component analysis. Finally, we included 8,201 families whose relatedness was consistent between two independent prediction methods and the clinical records.

QC checks of cancer genomes

We initially studied 26,488 cancer genomes from Genomics England Cancer Project. Samples were prepared using an Illumina TruSeq DNA Nano, TruSeq DNA PCR-Free or FFPE library preparation kit and then sequenced on a HiSeq X generating 150 bp paired-end reads. Germline samples were sequenced to produce at least 85 Gb of sequences with sequencing quality of at least 30. For tumour samples at least 212.5 Gb was required. Alignments for the germline sample covered at least 95% of the genome at 15x or above with well-mapped reads (mapping quality > 10) after discarding duplicates (https://research-help.genomicsengland.co.uk/).

For the sample cross-contamination checks, germline samples are processed with VerifyBamID⁴⁷ algorithm and PASS status is assigned to the samples with less than 3% of contamination. Tumour samples were processed with the ConPair algorithm⁵³ with a PASS status indicating contamination is below 1% as described in https://research-help.genomicsengland.co.uk/display/GERE/10.+Further+reading+and+documentation?preview=/38047056/45023724/Cancer%2520Analysis%2520Technical%2520Information%2520Document%2520v1-11%2520main.pdf#id-10.Furtherreadinganddocumentation-TechnicalDocumentation.

After the QC steps described above, 12,509 tumour–normal tissue pairs from 12,509 tumour samples and 11,913 matched normal tissue (germline) samples from 11,909 individuals remained. Samples were prepared using 5 different methods (FF, FFPF, CD128 sorted cells, EDTA and ASPIRATE) and three different library types (PCR, PCR-FFPE and PCR-free). We performed the additional QCs by comparing the average number of NUMTs were detected from the samples prepared by different methods and library types. We observed that the average number of NUMTs was significantly different between different groups (Supplementary Fig. 8a). To avoid possible bias caused by sample preparation and library type, we only included the 10,713 tumour–normal sample pairs prepared using FF and library type PCR-free from 9648 individuals across 21 cancer types (Extended Data Fig. 6a). The average WGS depth of tumour sample was 117x (s.d. 10.1x) and the average WGS depth of germline was 43x (s.d. 9.3x) (Supplementary Fig. 8b). The average mtDNA depth of tumour sample was 27,119x (s.d. 13,642x) and the average mtDNA depth of germline was 3,549x (s.d. 2,452x) (Supplementary Fig. 8c).

Inferencing ancestry from nuclear genome sequencing data

Broad genetic ancestries were estimated using ethnicities from the 1000 genomes project phase 3 (1KGP3)⁵¹ as the truth, by generating PCs for 1KGP3 samples and projecting all participants onto these. We included five broad super-populations: African (AFR), Admixed American (AMR), East Asian (EAS), South Asian (SAS) and European (EUR). The brief steps were as follows: (1) all unrelated samples were selected from the 1KGP3, (2) we selected 188,382 high quality SNPs in our dataset, (3) we further filtered for MAF > 0.05 in 1KGP3 (as well as in our data), (4) we calculated the first 20 principal components using GCTA⁵⁴, (5) we projected the individual data onto the 1KGP3 principal component loadings, (6) we trained a random forest model to predict ancestries based on (i) first 8 1KGP3 principal components, (ii) set Ntrees = 400, (iii) train and predict on 1KGP3 AMR, AFR, EAS, EUR and SAS super-populations. The full details can be found at https://research-help.genomicsengland.co.uk/display/GERE/Ancestry+inference. Genetic ancestry was also predicted and checked using our previously published method⁵⁰. The individuals who were not assigned to any of 5 super-populations were labelled as ‘OTHER’. We predicted 1,280 AFR, 170 AMR, 342 EAS, 5,758 SAS, 42,202 EUR and 3,363 OTHER in this study (Fig. 2a). In the cancer germline genomes, we included 312 AFR, 17 AMR, 71 EAS, 338 SAS, 8,348 EUR and 314 OTHER (Extended Data Fig. 6c,d).

We performed a uniform manifold approximation and projection (UMAP)⁵⁵ based on the NUMTs which were unique to each population in rare disease genomes. UMAP was analysed using the UMAP package with default parameters in R and visualized using the M3C package⁵⁶ in R.

Extracting mitochondrial DNA sequences and detecting variants

The subset of sequencing reads which aligned to the mitochondrial genome were extracted from each WGS BAM file using Samtools⁵⁷. We ran MToolBox (v1.0)⁵⁸ on the resulting smaller BAM files to generate the re-aligned mtDNA BAM files. The re-aligned BAM files were used to call the variants. We also used the second variant caller VarScan2⁵⁹ to call mtDNA variants from the re-aligned BAM files (--strand-filter 1, --min-var-freq 0.001, --min-reads2 1, --min-avg-qual 30). The mpileup files used in VarScan2 were generated by Samtools with options -d 0 -q 30 -Q 30. The allele fractions were extracted from VarScan2. We retained only single nucleotide polymorphisms (SNPs) with more than 2 reads on each strand for the minor allele. Variants falling within low-complexity regions (66–71, 300–316, 513–525, 3106–3107, 12418–12425 and 16182–16194) were excluded.

Mitochondrial DNA haplogroup assignment was performed using HaploGrep2^60,61.

Detecting NUMTs and breakpoints not present in the reference sequence

To detect NUMTs, we used a previously published and validated method^5,15. From the aligned WGS BAM files we extracted the discordant read pairs using samblaster⁶² and included the read pairs where one end aligns to nuclear genome and the other end aligns to the mtDNA reference sequence. The reads with mapping quality equal to zero were discarded. The discordant reads were then clustered together based on sharing the same orientation and whether they were within a distance of 500 bp. We detected the clusters supported by at least two pairs of discordant reads, and filtered out the clusters supported by less than five pairs of discordant reads in our main analysis. The NUMTs within a distance of 1,000 bp on both nuclear DNA and mtDNA were grouped as the same NUMT. We generated two sets of NUMTs based on the NUMTs supported by at least two pairs of discordant reads and at least five pairs of discordant reads (Supplementary Table 1). We observed a weak correlation of the average number of NUMTs and WGS depth (R² = 0.134, P < 2.2 × 10⁻¹⁶) and mitochondrial genome depth (R² = 0.092, P < 2.2 × 10⁻¹⁶) (Supplementary Figs. 9a,b) indicating that, although some NUMTs may be missed due to low depth, they are unlikely to have an impact on our conclusions. There was no detected difference of the number of detecting reads with the frequency of NUMTs, suggesting the detection of NUMTs were not biased by the sequencing quality (Supplementary Fig. 9c).

To identify putative breakpoints spanning nuclear DNA and a mtDNA-derived sequence (nuclear-mtDNA breakpoints), we searched for the split reads within a distance of 1,000 bp of discordant reads which were then re-aligned using BLAT⁶³. We further analysed the re-aligned reads where one end of the read mapped to nuclear DNA and the other end of the same read mapped to mtDNA-derived sequence. We defined the breakpoints by at least three split reads within the same NUMT. Each NUMT should have one nuclear breakpoint and two mitochondrial breakpoints, with the exception of NUMTs occurring with other nuclear genome structure variations. The breakpoints with 200 bp flanking regions on nuclear genome were annotated using gencode v29⁶⁴, gnomAD for pIL scores⁶⁵ and a list of datasets were downloaded from UCSC⁶⁶ and the publications (see details below). When the NUMTs were involved in multiple genes, we kept the genes with the highest pIL score. The breakpoints on the mitochondrial genome were annotated using MitoMap⁶⁷.

Detecting concatenated NUMTs

To detect putative concatenated NUMTs, first we searched for the breakpoints spanning two locations on the mtDNA-derived sequence (mtDNA–mtDNA breakpoints). We extracted the split reads which only aligned to mtDNA sequence. Those split reads were further re-aligned using BLAT. We analysed the reads where the two ends of the same read mapped to two locations on the mtDNA sequence. We then filtered the breakpoints as follows: (1) each breakpoint had at least 3 split reads observed in at least one individual, (2) each breakpoint had at least 2 split reads observed in the same individual, (3) we excluded the split reads mapped to nearby the start and end of mtDNA genome (the beginning and end of D-loop region), (4) we excluded two concatenated positions less than 50 bp away (they may be mtDNA deletions). Note our method had its limitations—we were not able to separate mtDNA–mtDNA breakpoints within NUMTs from true mtDNA if the breakpoints located around the beginning and end of D-loop region. Thus, our analysis likely missed the concatenated NUMTs where mtDNA–mtDNA breakpoints around the beginning and end of D-loop region. However, our aim was to detect confident concatenated NUMTs and show concatenated NUMTs exist in the humans. After applying the stringent filtering (above), we detected 8,686 breakpoints from 151 different mtDNA–mtDNA breakpoints in 8,450 individuals (Extended Data Fig. 3d). 279 out of 8,686 breakpoints (140 different breakpoints) from 148 individuals were ultra-rare (frequency < 0.1%). One breakpoint (12867–14977) was exceptionally common (frequency 38.4%), which was also commonly seen in an independent dataset in our previous study⁵. To confirm mtDNA–mtDNA breakpoints from the nuclear genome, we performed two independent analyses: (1) we compared the mtDNA–mtDNA breakpoints observed in the offspring and their two parents. If the mtDNA–mtDNA breakpoints were present in the offspring and their fathers, but not in their mothers, we defined them as father-transmitted mtDNA–mtDNA breakpoints. If the mtDNA–mtDNA breakpoints were present in the offspring and their mothers, but not in their fathers, we defined them as mother-transmitted mtDNA–mtDNA breakpoints. Note we were not able to identify the transmission patterns if the mtDNA–mtDNA breakpoints were present in all three family members using the short-read sequencing technique. (2) For the rare and ultra-rare mtDNA–mtDNA breakpoints (F < 1%), we checked whether the individuals carrying the same mtDNA–mtDNA breakpoints also carried the same NUMT.

Comparing to known NUMTs

Known NUMTs were downloaded from UCSC and previous publications^16,17,18,19. Bedtools⁴⁹ was used to search for the known NUMTs in our dataset. Using a conservative approach, we defined the NUMTs as known providing the known NUMTs within 1,000 bp NUMT flanks (upstream 500 bp + downstream 500 bp) detected in this study on the nuclear genome, regardless of the fragments of inserted mtDNA sequences.

Enrichment analysis

For the enrichment analysis on both nuclear and mtDNA genomes, we studied 1,637 different confident NUMTs with at least 5 discordant reads using a 2-tailed permutation test. Genomics duplications, simple repeats, dbRIP_HS-ME⁹⁰, regulatory elements, CpG islands, satellites, retrotransposons (including LINEs and SINEs) and TSS were downloaded from UCSC⁶⁶ (https://genome.ucsc.edu/). Using this information to compute the frequency of each dataset in 200 bp NUMT flanks (upstream 100 bp + downstream 100 bp). Empirical P values were calculated by resampling 1,000 sets of random positions matched to observed NUMTs. For the enrichment on each nuclear genome chromosome, we excluded the Y chromosome due to the complex duplicated structure of Y chromosome sequences limiting confident alignment.

To investigate the relationship between different chromosomes and NUMTs, we applied linear regression in R (http://CRAN.R-project.org/)⁶⁸.

$${\rm{lm}}\,({\rm{Nnumt}}\sim {\rm{Lchr}}+{\rm{Pcentro}}+{\rm{Pcpg}}+{\rm{Pline}}+{\rm{Pltr}}+{\rm{Pretroposon}}+{\rm{Psine}}+{\rm{Pmicrosat}}+{\rm{Prmsk}}+{\rm{Prepeats}}+{\rm{Pdups}}+{\rm{Preg}})$$

where Nnumt is number of NUMTs detected in each chromosome, Lchr is the length of chromosome, Pcentro, Pcpg, Pline, Pltr, Pretroposon, Psine, Pmicrosat, Prmsk, Prepeats, Pdups and Preg are log₂-transformed proportions of centromere, CpG islands, LINES, LTRs, retroposon, SINEs, microsatellites, repeats, simple repeats, genomics duplications and regulatory elements on each chromosome.

Comparing NUMTs with mitochondrial DNA deletions

To study the relationship between NUMT insertion and mitochondrial deletion, we compared the frequency of NUMT breakpoint with the frequency of mitochondrial DNA deletion breakpoint. A list of 1,312 mtDNA deletions were downloaded from mitoBreak database⁶⁹. We calculated the frequencies of breakpoints in different mtDNA regions—D-loop, 13 coding genes, 2 RNAs and combined 22 tRNAs, and compared the distribution with the distribution of breakpoints for germline and tumour-specific NUMTs using linear regression.

Searching for de novo NUMTs in rare disease trios and tumour-specific NUMTs in cancer genomes

We used the most conservative methods to define the de novo NUMTs from father–mother–offspring trios. We only included NUMTs with at least five pairs of discordant reads in the offspring and none of discordant read detected in the parents.

We applied for the same approach to define tumour-specific NUMTs in cancer genomes. Tumour-specific NUMTs were defined by at least five pairs of discordant reads in the tumour samples and none of discordant reads in the matched normal samples. Lost NUMTs in cancer genomes were defined by at least five pairs of discordant reads in the normal samples and no more than one pair of discordant reads in the matched tumour samples.

Estimating the rate of de novo NUMTs in trios and tumour-specific NUMTs in cancer genomes

De novo NUMT insertion rate in trios and cancer genomes was estimated as follows:

$$\rho ({\rm{germline}})={\rm{NumtTtrio}}/{\rm{Ntrio}}$$

$$\rho ({\rm{tumour}})={\rm{NumtTumour}}/{\rm{Ngenome}}$$

where ρ(germline) is the rate of de novo NUMT insertion in trios, ρ(tumour) is the rate of tumour-specific NUMT insertion in tumour samples, NumtTtrio is the number of de novo NUMT event in trios, NumtTumour is the number of tumour-specific NUMTs, Ntrio is the number of total trios and Ngenome is the number of total normal–tumour pairs.

Analysing the correlation of tumour-specific NUMTs and cancer types

To understand the relationship between donor age, sex and the average number of NUMTs, we applied linear regression to each dataset using R (http://CRAN.R-project.org/).

Model 1 < − lm(N ∼ Age + Sex + DPmt)

Model 2 < − lm(Nsoma ∼ Age + Sex + DPmt)

Where N and Nsoma are average numbers of NUMTs and tumour-specific NUMTs, Age is donor age, Sex is donor sex and DPmt is average mitochondrial DNA sequencing depth.

Detecting cancer SNVs, indels and structural variants

Read alignment against human reference genome GRCh38-Decoy+EBV was performed with ISAAC (version iSAAC-03.16.02.19)⁷⁰, SNVs and short insertions–deletions (indels) variant calling together with tumour − normal subtraction was performed using Strelka (version 2.4.7)⁷¹. Strelka filters out the following germline variant calls: (1) all calls with a sample depth three times higher than the chromosomal mean, (2) site genotype conflicts with proximal indel call, (3) locus read evidence displays unbalanced phasing patterns, (4) genotype call from variant caller not consistent with chromosome ploidy, (5) the fraction of basecalls filtered out at a site > 0.4, (6) locus quality score < 14 for heterozygous or homozygous SNP, (7) locus quality score < 6 for heterozygous, homozygous or het-alt indels, (8) locus quality score < 30 for other small variant types or quality score is not calculated. Strelka filters out the following somatic variant calls: (1) all calls with a normal sample depth three times higher than the chromosomal mean, (2) all calls where the site in the normal sample is not a homozygous reference, (3) somatic SNV calls with empirically fitted VQSR score < 2.75 (recalibrated quality score expressing the phred scaled probability of the somatic call being a false positive observation), (4) somatic indels where fraction of basecalls filtered out in a window extending 50 bases to either side of the indel’s call position is > 0.3, (5) somatic indels with quality score < 30 (joint probability of the somatic variant and a homo ref normal genotype), (6) all calls that overlap LINE repeat region.

Structural variants (SVs) and long indel (>50 bp) calling was performed with Manta (version 0.28.0)⁷² which combines paired and split-read evidence for SV discovery and scoring. Copy number variants (CNVs) were called with Canvas (version 1.3.1)⁷³ that employs coverage and minor allele frequencies to assign copy number. These tools filter out the following variant calls: (1) Manta-called SVs with a normal sample depth near one or both variant break-ends three times higher than the chromosomal mean, (2) Manta-called SVs with somatic quality score < 30, (3) Manta-called somatic deletions and duplications with length > 10kb, (4) Manta-called somatic small variant (<1kb) where fraction of reads with MAPQ0 around either break-end > 0.4, (5) Canvas-called somatic CNVs with length < 10kb, (6) Canvas-called somatic CNVs with quality score < 10. The full details of bioinformatics pipeline can be found at https://research-help.genomicsengland.co.uk/pages/viewpage.action?pageId=38046624.

Searching for the evidence of the mechanism of NUMT insertions

PRDM9

PRDM9 determines the locations of meiotic recombination hotspots where meiotic DNA DSBs are formed. To investigate the mechanism of NUMT insertions, we compared the NUMTs with a set of 170,198 published PRDM9-binding peaks cross the genome⁷⁴. We counted the number of NUMTs overlapping PRDM9-binding peaks and performed the permutation analysis (see the details in ‘Enrichment analysis’). Next, we calculated the distance between the breakpoint of each NUMT (from both the germline and tumour-specific NUMTs) with the nearest PRDM9-binding site.

Human DNA repair genes

A list of known human DNA repair genes was downloaded from Human DNA Repair Genes website (https://www.mdanderson.org/documents/Labs/Wood-Laboratory/human-dna-repair-genes.html)^38,39. We extracted the somatic missense mutations in DNA repair genes from all cancer samples, and compared the relationship between samples carrying the mutations and tumour-specific NUMTs.

Somatic mutational signatures

Somatic mutation signatures are the consequence of multiple mutational processes that the human body is subjected to throughout life. Each different process generates a unique combination of mutation types that are called mutation signatures (https://cancer.sanger.ac.uk/signatures/signatures_v2/). Mutational signature was computed using the R package nnls (https://CRAN.R-project.org/package=nnls). The details of how the signatures were computed is described in Alexandrov et al., 2013⁷⁵ and online document https://research-help.genomicsengland.co.uk/pages/viewpage.action?pageId=38046624.

Assessing clinical significance

Rare disease participants with no known genetic diagnosis

The Genomics England PanelApp (https://panelapp.genomicsengland.co.uk/)⁷⁶ list of genes and genomic entities were used to provide a list of potential disease genes (N = 5,883). NUMTs were identified that had a frequency of < 1%, and their breakpoints within 200 bp flanking regions of one of these genes. Consequence annotation was done with gencode v29, including gene, intron, exon, CDS, start codon, stop codon, five prime UTR and three prime UTR regions⁶⁴. NUMTs which were annotated as falling in an exon were analysed in detail. For each gene, we considered the strength of evidence that the gene is associated with a disease, the inheritance pattern of the disorder, the reported types of pathogenic variants and reported mechanism of disease (for example, haploinsufficiency, gain of function or repeat expansion), using information from OMIM (https://omim.org/)⁷⁷ and by searching PubMed (https://pubmed.ncbi.nlm.nih.gov/). For the established disease genes, we considered available clinical information for each proband which included their Human Phenotype Ontology terms⁹¹, family history and age at enrolment. We assumed that the rare NUMT was present on one allele only, unless it was present in both parents or there was documented consanguinity (where parental data was not available). For recessive disorder genes containing a NUMT, we looked whether it was present in one or both parents (if available), whether there was a family history of consanguinity, and at the sequence data to see whether there was a second rare variant. The location of the NUMT insertion was explored in UCSC genome browser⁶⁶.

Rare disease participants with a genetic diagnosis

Participants with a confirmed genetic diagnosis were identified from the Genomic Medicine Centre exit questionnaire (https://research-help.genomicsengland.co.uk/pages/viewpage.action?pageId=38046767). Genomic coordinates of the causative variant were compared with the genomic coordinates of the NUMTs using bedtools⁴⁹.

Rare disease NUMTs in participants with mitochondrial DNA maintenance disorders

Participants with mitochondrial DNA maintenance disorders⁷⁸ were identified from the Genomic Medicine Centre exit questionnaire and from our previous analysis of participants with suspected mitochondrial disorders⁷⁹. We also identified affected family members who had genome sequencing data available. 122 NUMTs were detected from 20 individuals. Only 4 NUMTs (2 different NUMTs) from two families in exons. We compared the average number of NUMTs in these participants to the rest of the rare disease participants.

Cancer genomes

To determine whether a NUMT insertion was a driver mutation in the development of cancers, NUMTs with 200 base pairs flanking region were identified which were located genes of interest. Our genes of interest were defined as those on the COSMIC (Catalogue of Somatic Mutations in Cancer) Cancer Gene Census list (tier 1 and tier 2) which includes genes known to contain mutations causally implicated in cancer²⁸. We also used a list of known human DNA repair genes^38,39. The location of the NUMT insertion in relation to these gene lists was explored in the UCSC genome browser.

Validating the NUMTs using long-read sequencing

To validate NUMT detection in short-read sequencing, we carried out whole-genome sequencing on Oxford Nanopore PromethION in 39 individuals from rare disease genomes. To maximize sequencing yield, 4 μg of germline DNA from 100KGP participants was fragmented to 15–30 Kb with Covaris g-tubes (4,000 rpm, 1 min, 1–3 passes until the desired length was achieved) and then depleted of low molecular weight DNA (<10 Kb) with the Short Read Eliminator kit (Circulomics, SS-100-101-01) as described by the manufacturer. After checking DNA size distribution on an Agilent Femto Pulse system, a sequencing library was generated with the Oxford Nanopore SQK-LSK109 kit, starting from 1.2 µg of high molecular weight-enriched DNA. Samples were quantified with a Qubit fluorometer (Invitrogen, Q33226) and 500 ng loaded onto a PromethION R.9.4.1 flow cell following manufacturer’s instructions. In experiments where throughput was limited by a rapid increase in unavailable pores, the library was re-loaded following a nuclease flush ~20hrs after the initial run. Base-calling was performed with Guppy-3.2.6/3.2.8 in high accuracy mode. Full details of the protocol can be found at https://research-help.genomicsengland.co.uk/display/GERE/Genomic+Data+from+ONT?preview=/38046759/38047942/v1_protocol_ONT_LSK109.pdf. Sequencing reads were aligned to GRCh38 using minimap2⁸⁰ version 2.17. QC statistics and plots were generated using Nanoplot⁸¹ version 1.26.0. The full details of bioinformatics pipeline can be found at https://research-help.genomicsengland.co.uk/display/GERE/Genomic+Data+from+ONT?preview=/38046759/38047944/PromethION%20SV%20calling%20pipeline%20GRCh38.docx. We then extracted the long reads aligned to the same region where a NUMT detected using short-read sequencing from the same individual. The extracted long reads were re-aligned using BLAT. The observed NUMTs were also manually inspected on Integrated Genomics Viewer (IGV)⁸². 182 out of 184 NUMTs (29 out of 31 distinct NUMTs) detected using short-read sequencing were also seen in long-read sequencing data. Two NUMTs from the same individual were missing in long-read sequencing likely due to the low number of aligned reads in long-read sequencing.

Detecting methylation state of NUMTs using long-read sequencing

Whole-genome-wide methylation detection was carried out using call-methylation function from Nanopolish v0.13.3⁸³ in 39 individuals. The methylation detection output includes the position of the CG dinucleotide on the reference genome, the ID of the read that was used to make the call, and the log-likelihood ratio. We extracted the long reads mapped to mtDNA genome, and further grouped them into two groups: (1) long reads also mapped to nuclear genome, (2) long reads only mapped to mtDNA genome. Next, we calculated methylation frequency of each site using the calculate_methylation_frequency.py script from the package in each read group. The methylation calls detected by the 1st group were from NUMTs, and the calls detected by the 2nd group were from true mtDNA. We used the methylation profile of true mtDNA as reference, and NUMTs methylation was estimated as the log₂ ratio of methylation frequency of each site between NUMTs and true mtDNA from the same individual. Note, if the individuals carried concatenated NUMTs, the calls detected by 2nd group were from mixed true mtDNA and concatenated NUMTs. We were not able to separate the long reads mapped to the middle of concatenated NUMTs where the reads also only mapped to mtDNA genome and true mtDNA genome.

In this analysis, we focused on the concatenated NUMTs and the large NUMTs where long reads were confidently aligned to NUMTs. We only included the calls with at least 3 reads mapped to NUMTs and at least 10 reads mapped to true mtDNA sequences. We also used 4 reads, 5 reads, 6 reads, 7 reads, 8 reads 9 reads and 10 reads as the cut-offs to detect NUMTs methylation. We observed the same distribution of methylation frequency across different cut-offs (Fig. 3a), indicating read-thresholds did not affect our results.

Detecting mutations within the NUMT insertions

We performed a de novo assembly of all 335,891 NUMTs detected in this study. The steps of processes were: (1) we clustered the discordant reads detected from each NUMT in the same individual. (2) The consensus sequence of NUMT contig was generated using CAP3⁸⁴. (3) The contigs were then aligned against mitochondrial reference genome⁸⁵ using Blat⁶³ and Clustal Omega⁸⁶. (4) The aligned sequences from Clustal Omega were used to detect the nucleotide changes between NUMT sequences and mitochondrial reference genome sequences using BioPython⁸⁷. To ensure the confident calls, we applied the additional filtering as follows: (1) we only included NUMTs shorter than 1,000 bp; (2) we excluded the variants within 5 bp of NUMT breakpoints; (3) we removed the variants where the aligned reference allele were different from mtDNA reference genome at the same position; (4) we only included single nuclear variations; (5) we excluded the individuals carrying many more variants than the overall population (> mean number of variants + 3 × s.d.).

To define NUMT-specific variants, we applied the additional filtering: (1) we excluded variants present more than 50% individuals carrying the same common or rare NUMTs and 75% individuals carrying the same ultra-rare NUMTs. This stringent filtering strategy was designed to provide maximum confidence that any NUMT-specific variants were highly likely to have occurred after NUMT sequences have inserted into nuclear genome, compromising the sensitivity of the analysis. (2) We excluded variants only detected in 1 individual to minimize the likelihood of sequencing errors; (3) to obtain the most confident NUMT-specific mutations, we only included the variants detected in at least two individuals from the same family. In the main text, we reported 3 groups of NUMT-specific variants. Total group A, after applying step (1); subgroup B, after step (2); and subgroup C, after step (3).

Estimating the ages of NUMTs

The age of NUMTs was estimated using the method described previously¹⁹. We aligned the mitochondrial sequences from human, chimpanzee and the consensus sequence from each NUMT contig using Clustal Omega. The ancestral mitochondrial sequences from chimpanzee was downloaded from ENSEMBL(Pan_tro_3.0). The aligned sequences were used to generate the nucleotide changes using BioPython. We calculated the ratio of the number of sites that matched human allele to the total number of sites where the human and ancestral mitochondrial sequences differ within each NUMT region. The ratio was used to derive an approximate age for each NUMT, relative to an estimated human-chimpanzee divergence time of 6 million years. To ensure the confident results, we applied the filtering as follows: (1) we only included NUMTs with length between 50 and 1,000 bp; (2) we excluded NUMTs without different allele between human and chimpanzee; (3) the age was estimated from more than 50% of individuals carrying the same NUMT and at least in 2 individuals. After applying this filtering, we excluded all the private NUMTs which were only seen in one individual. (4) We excluded concatenated NUMTs.

Statistical analysis and plotting

All statistical analyses in this study were suggested in the text and performed using R⁶⁸ (http://CRAN.R-project.org/) and Python (http://www.python.org). Figures were generated using R and Matplotlib (https://matplotlib.org) in Python. Circos plots were made using Circos (http://circos.ca/)⁸⁸. Chromosome maps were made using chromoMap⁸⁹.

A web interface to deposit NUMTs detected in this study was developed using Shiny v1.7.1 (https://CRAN.R-project.org/package=shiny)(https://cran.r-project.org/web/packages/shiny/index.html)⁹².

Web resources

NUMTs detected in this study are publicly available through a web interface at https://wwei.shinyapps.io/numts/.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

WGS data from the participants enrolled in 100,000 Genomes Project can be accessed via Genomics England Limited following the procedure outlined at: https://www.genomicsengland.co.uk/about-gecip/joining-research-community/. In brief, applicants from registered institutions can apply to join one of the Genomics England Clinical Interpretation Partnerships, and then register a project enabling, access to the Genomics England Research Environment 2 h after completing online training. H. sapiens NCBI GRCh38 assembly can be found at https://www.ncbi.nlm.nih.gov/assembly/. Gencode v29 can be found at https://www.gencodegenes.org/human/release_29.html. Human genome annotation files can be found at https://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/. The ancestral mitochondrial sequences from Chimpanzee can be found at https://www.ensembl.org/Pan_troglodytes/Info/Index.

Code availability

Code used in the study is available at https://github.com/WeiWei060512/NUMTs-detection.git and https://doi.org/10.5281/zenodo.6966017.

References

Roger, A. J., Munoz-Gomez, S. A. & Kamikawa, R. The origin and diversification of mitochondria. Curr. Biol. 27, R1177–R1192 (2017).
Article CAS PubMed Google Scholar
Gray, M. W., Burger, G. & Lang, B. F. Mitochondrial evolution. Science 283, 1476–1481 (1999).
Article ADS CAS PubMed Google Scholar
Hazkani-Covo, E., Zeller, R. M. & Martin, W. Molecular poltergeists: mitochondrial DNA copies (numts) in sequenced nuclear genomes. PLoS Genet. 6, e1000834 (2010).
Article PubMed PubMed Central Google Scholar
Lopez, J. V., Yuhki, N., Masuda, R., Modi, W. & O'Brien, S. J. Numt, a recent transfer and tandem amplification of mitochondrial DNA to the nuclear genome of the domestic cat. J. Mol. Evol. 39, 174–190 (1994).
Article ADS CAS PubMed Google Scholar
Wei, W. et al. Nuclear-mitochondrial DNA segments resemble paternally inherited mitochondrial DNA in humans. Nat. Commun. 11, 1740 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Willett-Brozick, J. E., Savul, S. A., Richey, L. E. & Baysal, B. E. Germ line insertion of mtDNA at the breakpoint junction of a reciprocal constitutional translocation. Hum. Genet. 109, 216–223 (2001).
Article CAS PubMed Google Scholar
Borensztajn, K. et al. Characterization of two novel splice site mutations in human factor VII gene causing severe plasma factor VII deficiency and bleeding diathesis. Br. J. Haematol. 117, 168–171 (2002).
Article CAS PubMed Google Scholar
Turner, C. et al. Human genetic disease caused by de novo mitochondrial-nuclear DNA transfer. Hum. Genet. 112, 303–309 (2003).
Article PubMed Google Scholar
Ahmed, Z. M. et al. Nonsyndromic recessive deafness DFNB18 and Usher syndrome type IC are allelic mutations of USHIC. Hum. Genet. 110, 527–531 (2002).
Article CAS PubMed Google Scholar
Pagnamenta, A. T., Wei, W., Rahman, S. & Chinnery, P. F. Biparental inheritance of mitochondrial DNA revisited. Nat. Rev. Genet. 22, 477–478 (2021).
Article CAS PubMed Google Scholar
Luo, S. et al. Biparental inheritance of mitochondrial DNA in humans. Proc. Natl Acad. Sci. USA 115, 13039–13044 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Maude, H. et al. NUMT confounding biases mitochondrial heteroplasmy calls in favor of the reference allele. Front. Cell. Dev. Biol. 7, 201 (2019).
Article ADS PubMed PubMed Central Google Scholar
Salas, A., Schonherr, S., Bandelt, H. J., Gomez-Carballa, A. & Weissensteiner, H. Extraordinary claims require extraordinary evidence in asserted mtDNA biparental inheritance. Forensic Sci. Int. Genet. 47, 102274 (2020).
Article CAS PubMed Google Scholar
Smedley, D. et al. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care—preliminary report. N. Engl. J. Med. 385, 1868–1880 (2021).
Article CAS PubMed Google Scholar
Ju, Y. S. et al. Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells. Genome Res. 25, 814–824 (2015).
Article CAS PubMed PubMed Central Google Scholar
Simone, D., Calabrese, F. M., Lang, M., Gasparre, G. & Attimonelli, M. The reference human nuclear mitochondrial sequences compilation validated and implemented on the UCSC genome browser. BMC Genomics 12, 517 (2011).
Article PubMed PubMed Central Google Scholar
Calabrese, F. M., Simone, D. & Attimonelli, M. Primates and mouse NumtS in the UCSC Genome Browser. BMC Bioinf. 13, S15 (2012).
Article CAS Google Scholar
Li, M., Schroeder, R., Ko, A. & Stoneking, M. Fidelity of capture-enrichment for mtDNA genome sequencing: influence of NUMTs. Nucleic Acids Res. 40, e137 (2012).
Article CAS PubMed PubMed Central Google Scholar
Dayama, G., Emery, S. B., Kidd, J. M. & Mills, R. E. The genomic landscape of polymorphic human nuclear mitochondrial insertions. Nucleic Acids Res. 42, 12640–12649 (2014).
Article CAS PubMed PubMed Central Google Scholar
Gigante, S. et al. Using long-read sequencing to detect imprinted DNA methylation. Nucleic Acids Res. 47, e46 (2019).
Article CAS PubMed PubMed Central Google Scholar
Bicci, I., Calabrese, C., Golder, Z. J., Gomez-Duran, A. & Chinnery, P. F. Single-molecule mitochondrial DNA sequencing shows no evidence of CpG methylation in human cells and tissues. Nucleic Acids Res. 49, 12757–12768 (2021).
Article CAS PubMed PubMed Central Google Scholar
Huang, C. Y., Grunheit, N., Ahmadinejad, N., Timmis, J. N. & Martin, W. Mutational decay and age of chloroplast and mitochondrial genomes transferred recently to angiosperm nuclear chromosomes. Plant Physiol. 138, 1723–1733 (2005).
Article CAS PubMed PubMed Central Google Scholar
Ju, Y. S. et al. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer. eLife 3, e02935 (2014).
Article PubMed Central Google Scholar
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Article CAS PubMed PubMed Central Google Scholar
Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).
Article PubMed PubMed Central Google Scholar
Yuan, Y. et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat. Genet. 52, 342–352 (2020).
Article CAS PubMed PubMed Central Google Scholar
Samuels, D. C., Schon, E. A. & Chinnery, P. F. Two direct repeats cause most human mtDNA deletions. Trends Genet. 20, 393–398 (2004).
Article CAS PubMed Google Scholar
Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
Article CAS PubMed PubMed Central Google Scholar
Matsuyama, A. et al. Fragile site orthologs FHIT/FRA3B and Fhit/Fra14A2: evolutionarily conserved but highly recombinogenic. Proc. Natl Acad. Sci. USA 100, 14988–14993 (2003).
Article ADS CAS PubMed PubMed Central Google Scholar
Goransson, M. et al. The myxoid liposarcoma FUS–DDIT3 fusion oncoprotein deregulates NF-κB target genes by interaction with NFKBIZ. Oncogene 28, 270–278 (2009).
Article CAS PubMed Google Scholar
Qin, S. et al. DSG2 expression is correlated with poor prognosis and promotes early-stage cervical cancer. Cancer Cell Int. 20, 206 (2020).
Article CAS PubMed PubMed Central Google Scholar
Rao, X. et al. MicroRNA-221/222 confers breast cancer fulvestrant resistance by regulating multiple signaling pathways. Oncogene 30, 1082–1097 (2011).
Article CAS PubMed Google Scholar
Blanchard, J. L. & Schmidt, G. W. Mitochondrial DNA migration events in yeast and humans: integration by a common end-joining mechanism and alternative perspectives on nucleotide substitution patterns. Mol. Biol. Evol. 13, 893 (1996).
Article CAS PubMed Google Scholar
Ricchetti, M., Tekaia, F. & Dujon, B. Continued colonization of the human genome by mitochondrial DNA. PLoS Biol. 2, E273 (2004).
Article PubMed PubMed Central Google Scholar
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Seplyarskiy, V. B. et al. APOBEC-induced mutations in human cancers are strongly enriched on the lagging DNA strand during replication. Genome Res. 26, 174–182 (2016).
Article CAS PubMed PubMed Central Google Scholar
Myers, S. et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination. Science 327, 876–879 (2010).
Article ADS CAS PubMed Google Scholar
Wood, R. D., Mitchell, M. & Lindahl, T. Human DNA repair genes, 2005. Mutat. Res. 577, 275–283 (2005).
Article CAS PubMed Google Scholar
Wood, R. D., Mitchell, M., Sgouros, J. & Lindahl, T. Human DNA repair genes. Science 291, 1284–1289 (2001).
Article ADS CAS PubMed Google Scholar
Hazkani-Covo, E., Sorek, R. & Graur, D. Evolutionary dynamics of large numts in the human genome: rarity of independent insertions and abundance of post-insertion duplications. J. Mol. Evol. 56, 169–174 (2003).
Article ADS CAS PubMed Google Scholar
Li, Y. et al. Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia. Nature 508, 98–102 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Bensasson, D., Feldman, M. W. & Petrov, D. A. Rates of DNA duplication and mitochondrial DNA insertion in the human genome. J. Mol. Evol. 57, 343–354 (2003).
Article ADS CAS PubMed Google Scholar
Gustafsson, C. M., Falkenberg, M. & Larsson, N. G. Maintenance and expression of mammalian mitochondrial DNA. Annu. Rev. Biochem. 85, 133–160 (2016).
Article CAS PubMed Google Scholar
McArthur, K. et al. BAK/BAX macropores facilitate mitochondrial herniation and mtDNA efflux during apoptosis. Science 359, eaao6047 (2018).
Article PubMed Google Scholar
Tigano, M., Vargas, D. C., Tremblay-Belzile, S., Fu, Y. & Sfeir, A. Nuclear sensing of breaks in mitochondrial DNA enhances immune surveillance. Nature 591, 477–481 (2021).
Article ADS CAS PubMed Google Scholar
Caulfield, M. et al. The national genomics research and healthcare knowledgebase. figshare https://doi.org/10.6084/m9.figshare.4530893.v5 (2019).
Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012).
Article CAS PubMed PubMed Central Google Scholar
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Article PubMed PubMed Central Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Article CAS PubMed PubMed Central Google Scholar
Wei, W. et al. Germline selection shapes human mitochondrial DNA diversity. Science 364, eaau6520 (2019).
Article CAS PubMed Google Scholar
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Article Google Scholar
Conomos, M. P., Reiner, A. P., Weir, B. S. & Thornton, T. A. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98, 127–148 (2016).
Article CAS PubMed PubMed Central Google Scholar
Bergmann, E. A., Chen, B. J., Arora, K., Vacic, V. & Zody, M. C. Conpair: concordance and contamination estimator for matched tumor-normal pairs. Bioinformatics 32, 3196–3198 (2016).
Article CAS PubMed PubMed Central Google Scholar
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Article CAS PubMed PubMed Central Google Scholar
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2020).
John, C. R. et al. M3C: Monte Carlo reference-based consensus clustering. Sci. Rep. 10, 1816 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Article PubMed PubMed Central Google Scholar
Calabrese, C. et al. MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing. Bioinformatics 30, 3115–3117 (2014).
Article CAS PubMed PubMed Central Google Scholar
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
Article CAS PubMed PubMed Central Google Scholar
van Oven, M. & Kayser, M. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum. Mutat. 30, E386–E394 (2009).
Article PubMed Google Scholar
Weissensteiner, H. et al. HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res. 44, W58–W63 (2016).
Article CAS PubMed PubMed Central Google Scholar
Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).
Article CAS PubMed PubMed Central Google Scholar
Kent, W. J. BLAT-the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).
CAS PubMed PubMed Central Google Scholar
Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).
Article CAS PubMed Google Scholar
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).
Article CAS PubMed PubMed Central Google Scholar
Kogelnik, A. M., Lott, M. T., Brown, M. D., Navathe, S. B. & Wallace, D. C. MITOMAP: a human mitochondrial genome database. Nucleic Acids Res. 24, 177–179 (1996).
Article CAS PubMed PubMed Central Google Scholar
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Damas, J., Carneiro, J., Amorim, A. & Pereira, F. MitoBreak: the mitochondrial DNA breakpoints database. Nucleic Acids Res. 42, D1261–D1268 (2014).
Article CAS PubMed Google Scholar
Raczy, C. et al. Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms. Bioinformatics 29, 2041–2043 (2013).
Article CAS PubMed Google Scholar
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Article CAS PubMed Google Scholar
Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).
Article CAS PubMed Google Scholar
Roller, E., Ivakhno, S., Lee, S., Royce, T. & Tanner, S. Canvas: versatile and scalable detection of copy number variants. Bioinformatics 32, 2375–2377 (2016).
Article CAS PubMed Google Scholar
Altemose, N. et al. A map of human PRDM9 binding provides evidence for novel behaviors of PRDM9 and other zinc-finger proteins in meiosis. eLife 6, e28383 (2017).
Article PubMed PubMed Central Google Scholar
Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).
Article CAS PubMed PubMed Central Google Scholar
Martin, A. R. et al. PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nat. Genet. 51, 1560–1565 (2019).
Article CAS PubMed Google Scholar
Hamosh, A., Scott, A. F., Amberger, J., Valle, D. & McKusick, V. A. Online Mendelian Inheritance in Man (OMIM). Hum Mutat 15, 57–61 (2000).
Article CAS PubMed Google Scholar
Adam, M. P. et al. in GeneReviews (eds El-Hattab, A. W. et al.) Mitochondrial DNA maintenance defects overview. (University of Washington, 1993).
Schon, K. R. et al. Use of whole genome sequencing to determine genetic basis of suspected mitochondrial disorders: cohort study. Brit. Med. J. 375, e066288 (2021).
Article PubMed PubMed Central Google Scholar
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
Article CAS PubMed PubMed Central Google Scholar
De Coster, W., D'Hert, S., Schultz, D. T., Cruts, M. & Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34, 2666–2669 (2018).
Article PubMed PubMed Central Google Scholar
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Article CAS PubMed PubMed Central Google Scholar
Loman, N. J., Quick, J. & Simpson, J. T. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods 12, 733–735 (2015).
Article CAS PubMed Google Scholar
Huang, X. & Madan, A. CAP3: A DNA sequence assembly program. Genome Res. 9, 868–877 (1999).
Article CAS PubMed PubMed Central Google Scholar
Andrews, R. M. et al. Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA. Nat. Genet. 23, 147 (1999).
Article CAS PubMed Google Scholar
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
Article PubMed PubMed Central Google Scholar
Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).
Article CAS PubMed PubMed Central Google Scholar
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
Article CAS PubMed PubMed Central Google Scholar
Anand, L. & Rodriguez Lopez, C. M. ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes. BMC Bioinformatics 23, 33 (2022).
Article CAS PubMed PubMed Central Google Scholar
Wang, J. et al. dbRIP: A highly integrated database of retrotransposon insertion polymorphisms in humans. Hum. Mutat. 27, 323–329 (2006).
Köhler, S. et al. The human phenotype ontology in 2021. Nucleic Acids Res. 49, D1207–D1217 (2020).
Winston Chang et al. shiny: Web Application Framework for R. R package version 1.7.1. https://CRAN.R-project.org/package=shiny (2021).

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Acknowledgements

This study makes use of data generated by the Genomics England 100,000 Genomes Project. The main source of funding for the BioResource and Genomics England is provided by the National Institute for Health Research of England (NIHR, http://www.nihr.ac.uk). This work was also made possible by funding from the UK Medical Research Council (MRC) to create the UK Clinical Genomics Datacentre. P.F.C. is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z), and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), the Medical Research Council (MRC) International Centre for Genomic Medicine in Neuromuscular Disease (MR/S005021/1), the Leverhulme Trust (RPG-2018-408), an MRC research grant (MR/S035699/1), an Alzheimer's Society Project Grant (AS-PG-18b-022) and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. K.R.S. was supported by an MRC strategic award to establish an International Centre for Genomic Medicine in Neuromuscular Diseases (ICGNMD) MR/S005021/1. M.J.C. is an NIHR Senior Investigator and this work forms part of the portfolio of research of the NIHR Barts Biomedical Research Centre. Genomics England and the 100,000 Genomes Project was funded by the National Institute for Health Research, the Wellcome Trust, the Medical Research Council, Cancer Research UK, the Department of Health and Social Care and NHS England. We thank all the participants and healthcare teams who made this study possible. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

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Authors and Affiliations

Department of Clinical Neuroscience, School of Clinical Medicine, University of Cambridge, Cambridge, UK
Wei Wei, Katherine R. Schon & Patrick F. Chinnery
Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK
Wei Wei, Katherine R. Schon & Patrick F. Chinnery
Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
Katherine R. Schon & Marc Tischkowitz
Genomics England, London, UK
Greg Elgar, Andrea Orioli, Melanie Tanguy & Adam Giess
William Harvey Research Institute, Queen Mary University of London, London, UK
Mark J. Caulfield

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Wei Wei
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Katherine R. Schon
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Greg Elgar
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Andrea Orioli
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Melanie Tanguy
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Adam Giess
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Marc Tischkowitz
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Patrick F. Chinnery
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Contributions

P.F.C. and W.W. conceived of the overall study. W.W. performed the bioinformatic and statistical analysis with additional contributions from K.R.S. and M. Tischkowitz for the cancer gene analysis, K.R.S. for the rare disease gene analysis, and G.E., A.O., M. Tanguy and A.G. for the long-read sequencing. M.J.C. had a key role in advising and overseeing access to the Genomics England dataset. P.F.C. and W.W. wrote the first draft of the manuscript, which all authors critically edited. P.F.C. supervised the study and sought the funding.

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Correspondence to Patrick F. Chinnery.

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Nature thanks Young Seok Ju, Brett Kaufman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Whole genome sequencing in 53,574 individuals from the Genomics England Rare Disease Project and detected NUMT insertions.

a. Histogram of individuals’ age. b. Pie chart of individuals’ sex determined from the rare disease genomes. c. Letter-value plots of sequencing depth of whole genome sequencing (left) and mitochondrial genome sequencing (right) from the rare disease genomes. The middle line represents the median (50th percentile). Each successive level outward contains half of the remaining data. The first two sections out from the centre line contain 50% of the data. The next two sections contain 25% of the data. This continues until at the outlier level. The outliers are plotted as diamonds. d. Overview of the frequencies of the NUMTs detected by at least 2 pairs of discordant reads. Common = population frequency (F) > = 1%; rare = F < 1% but > = 0.1%; ultra-rare F < 0.1% in the population. e. Histogram of the average number of NUMTs per individual that were not present in the reference sequence and were detected by at least 2 pairs of discordant reads. f. Letter-value plots of the average number of NUMTs detected by at least 5 pairs of discordant reads from each individual, male and female shown separately. The middle line represents the median (50th percentile). Each successive level outward contains half of the remaining data. The first two sections out from the centre line contain 50% of the data. The next two sections contain 25% of the data. This continues until at the outlier level. The outliers are plotted as diamonds. g. Correlation of individual age and the average number of NUMTs detected. Regression line shown in red.

Extended Data Fig. 2 NUMTs detected in the different populations.

a. Chromosome map of NUMTs detected in African, American, East Asian, South Asian and European genomes. Chromosomal locations of different NUMT insertions coloured by the frequency (F) of NUMTs. Dots show the locations of the NUMTs. b. A uniform manifold approximation and projection (UMAP) of germline NUMTs in all populations and 4 sub-populations. c. Chromosomal locations of NUMTs were significantly greater / less detected in the different populations.

Extended Data Fig. 3 Concatenated NUMTs and long-read sequencing validation.

a. Circos plots show 4 individuals from 2 families shared 5 mtDNA-mtDNA breakpoints which were exclusively present in 4 individuals, and also shared an ultra-rare NUMT insertion which was only seen in the same 4 individuals. b. Circos plots show 8 individuals shared 1 mtDNA-mtDNA breakpoint which was exclusively present in these 8 individuals, and also shared a NUMT insertion which was only seen in the same 8 individuals. Blue arrows point to the shared NUMTs. Red arrows point to the shared mtDNA-mtDNA breakpoints. c. Model showing the formation of concatenated NUMTs and our strategy for their detection using both long-read sequencing and short-read sequencing. mtDNA and nuclear genome sequences are shown in orange and blue. Reads mapped to both mtDNA and nuclear genome sequences are shown in grey, mapped to only mtDNA sequences are in orange and mapped to only nuclear genome sequences in blue. d. Circos plot of mtDNA-mtDNA breakpoints detected in the rare disease genomes. mtDNA-mtDNA breakpoints were detected by split reads mapping only to mtDNA. Complex concatenated NUMTs contain multiple mtDNA fragments. Detection of mtDNA-mtDNA breakpoints support the putative concatenated NUMTs. Common and rare mtDNA-mtDNA breakpoints (frequency > = 0.1%) shown in red links. Ultra-rare mtDNA-mtDNA breakpoints (frequency < 0.1%) shown in blue links. e. Circos plot shows the methylation frequency of a rare NUMT (insertion mt.12314 – 9526 bp, frequency = 0.26%) detected in 4 members from the same family (father, mother, sibling and proband). Circles from the outside to the inside indicate the following: (1) methylation frequency of NUMTs detected by split long-reads in father, mother, sibling and proband, (2) ratio of methylation frequency between NUMTs and “true” mtDNA sequences in all 4 family members. Green dots were the sites methylated in NUMTs. Colour key corresponds to the methylation frequencies. f. Letter-value plots of the average number of observed mtDNA variants (left – variant frequency > 1%, right - variant frequency > 2%), individuals carrying putative concatenated NUMTs and without putative concatenated NUMTs shown, separately. Variants observed in the individuals carrying putative concatenated NUMTs are mixed variants from both mtDNA sequence and NUMTs. The middle line represents the median (50th percentile). Each successive level outward contains half of the remaining data. The first two sections out from the centre line contain 50% of the data. The next two sections contain 25% of the data. This continues until at the outlier level. The outliers are plotted as diamonds.

Extended Data Fig. 4 IGV alignment of de novo NUMTs in the rare disease genomes.

Integrative Genomics Viewer (IGV) screenshots show the aligned reads corresponding to three de novo NUMTs observed in two families. Teal bars indicate the aligned reads which mapped to the nuclear DNA where their mates mapped to the mtDNA. In family 1, offspring carried two NUMTs within the same gene, but not seen in either of the parents. In family 2, offspring carried a NUMT which was not seen in either of the parents.

Extended Data Fig. 5 Frequency of NUMT breakpoints on mtDNA genome and the distance of NUMT location to nuclear transcription start sites (TSS).

a. Normalized frequency of NUMT breakpoints in each mtDNA region. Black lines are expected frequency. Top blue area plot shows the frequency of breakpoints from germline NUMTs. Bottom red area plot shows the frequency of breakpoints from tumour-specific NUMTs. Mitochondrial regions are shown in the different colours at the bottom of each plot. Red boxes highlight the regions where the frequencies were significantly greater than the expected by chance. Blue boxes highlighted the regions where the frequencies significantly less than the expected by chance. b. Normalized number of NUMTs within each Dloop region. Stars represent the NUMTs were significantly enriched in each region (permutation test). Circles labelled P values were from the comparison of germline and tumour-specific NUMTs (two-sided Fisher’s exact test). c. Correlation of frequencies of deletion breakpoints and NUMT breakpoints in each mtDNA region from germline and tumour-specific NUMTs. d. Histogram of distance of NUMTs location to transcription start sites (TSS). Germline, germline common & rare, ultra-rare and tumour-specific NUMTs are shown, separately.

Extended Data Fig. 6 Whole genome sequencing in 12,509 normal-tumour pairs from the Genomics England Cancer Project and detected NUMT insertions.

a. Pie chart of proportion of sample size from each cancer type included in this study. b. Histogram of tumour donor age from all cancer types (bottom right) and each cancer type. c. Projection of the nuclear genotypes at common SNPs onto the two leading principal components (PC1 and PC2) computed with the 1000 Genomes dataset from the cancer genomes, with individuals coloured by their assigned nuclear ancestry. d. Proportion of sample size from each population in the cancer genomes. e. Number of NUMTs detected in the different tissue types from the matched normal tissue samples taken from cancer participants. The middle line represents the median (50th percentile). Each successive level outward contains half of the remaining data. The first two sections out from the centre line contain 50% of the data. The next two sections contain 25% of the data. This continues until at the outlier level. The outliers are plotted as diamonds. f. Number of NUMTs detected in the rare disease blood samples and the matched normal tissue samples taken from cancer participants. The middle line represents the median (50th percentile). Each successive level outward contains half of the remaining data. The first two sections out from the centre line contain 50% of the data. The next two sections contain 25% of the data. This continues until at the outlier level. The outliers are plotted as diamonds.

Extended Data Fig. 7 Examples of IGV alignment of NUMTs.

a. Examples of IGV alignment of tumour-specific NUMTs coupled with other translocation variations in the nuclear genome. Teal bars indicate the aligned reads which mapped to the nuclear DNA where their mates mapped to the mtDNA. Other non-grey colour bars indicate the aligned reads which mapped to one nuclear chromosome where their mates mapped to a different nuclear chromosome. For example, Cancer sample 1 had one NUMT (teal bars) on chromosome 5 and another translocation variation between chromosome 5 and chromosome 13 (orange bars) in the same region (left). The same translocation variation was also seen on chromosome 13 (right). The aligned reads mapped to chromosome 13 where their mates mapped to chromosome 5 (steel blue bars). b. An example of IGV alignment of tumour lost NUMTs. IGV screenshots show the aligned reads corresponding to the lost NUMTs in one breast tumour sample. Teal bars indicate the aligned reads which mapped to the nuclear DNA where their mates mapped to the mtDNA. NUMTs only present in the matched normal sample but not in the tumour sample, with the average sequencing depth of tumour sample (128x) was more than three-times deeper than the matched normal sample (40x). c. Cirocs plot illustrates an example of lost NUMT in a haematological tumour sample. The links represent all NUMTs detected in either normal sample or tumour sample. The tumour sample lost many NUMTs across the whole genome, with the average sequencing depth of tumour sample (116x) was more than twice deeper than the matched normal sample (40x).

Extended Data Fig. 8 NUMT nuclear breakpoints, relation to PRDM9 binding sites, and NUMT age.

a. Frequencies of trinucleotides around germline NUMTs breakpoints. The breakpoints of nuclear genome are shown at the top and mtDNA genomes at the bottom, common&rare, ultra-rare NUMTs and the expected frequencies shown in the different colours. Trinucleotides of breakpoint flanks more likely occurred in nCC/CCn on mtDNA genome and less likely in nTT/TT on both nuclear and mtDNA genomes, particularly for ultra-rare NUMTs. The same trend was not seen in the tumour-specific NUMTs (b), indicating the signal is driven by biology, but not the sequencing artefacts. b. Frequencies of trinucleotides around tumour-specific NUMTs breakpoints in the nuclear genome (top) and mtDNA genomes (bottom), tumour-specific NUMTs and the expected frequencies shown in the different colours. P values # < 0.1, * < 0.05, < 0.01 **, < 0.001 ***, < 0.0001 **** (two-sided Fisher’s exact test) (Supplementary Table 6). c. Distribution of the distance between PRDM9 binding sites and tumour-specific NUMTs within each tumour type. d. Age of NUMTs estimated in this study. Y axis shows the frequencies of NUMTs in African and non-African populations. The frequencies of NUMTs were different between African and non-African, particularly for the older NUMTs which were more common seen in African population.

Extended Data Fig. 9 IGV alignments of NUMTs and nuclear chromosomal structure variations.

a. An example of mtDNA fragment inserted into two edges of a CNV duplication. b. An example of mtDNA fragment inserted into two edges of a large deletion. Teal bars indicate the aligned reads which mapped to the nuclear DNA where their mates mapped to the mtDNA, and were highlighted in the teal. c. d. Two examples of cancer genomes carrying mito-chromothripsis observed in this study. c. The sequencing depth of nuclear genome is shown at the top panel. Examples of the read alignment of NUMTs from IGV are shown at the bottom. Reads are coloured by the pair orientation and the chromosome on which their mates can be found. d. The sequencing depth of nuclear genome is shown at the top panel. Teal dots are the locations of NUMT insertions. Examples of the read alignment of NUMTs from IGV are shown at the bottom. Reads are coloured by the pair orientation and the chromosome on which their mates can be found.

Supplementary information

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This file contains supplementary results, references for the supplementary results and Supplementary Figs. 1–9.

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Wei, W., Schon, K.R., Elgar, G. et al. Nuclear-embedded mitochondrial DNA sequences in 66,083 human genomes. Nature 611, 105–114 (2022). https://doi.org/10.1038/s41586-022-05288-7

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Received: 09 January 2022
Accepted: 29 August 2022
Published: 05 October 2022
Issue Date: 03 November 2022
DOI: https://doi.org/10.1038/s41586-022-05288-7

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