GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia

  1. Michael Chong
  2. Pedrum Mohammadi-Shemirani
  3. Nicolas Perrot
  4. Walter Nelson
  5. Robert Morton
  6. Sukrit Narula
  7. Ricky Lali
  8. Irfan Khan
  9. Mohammad Khan
  10. Conor Judge
  11. Tafadzwa Machipisa
  12. Nathan Cawte
  13. Martin O'Donnell
  14. Marie Pigeyre
  15. Loubna Akhabir
  16. Guillaume Paré  Is a corresponding author
  1. Department of Biochemistry and Biomedical Sciences, McMaster University, Canada
  2. Department of Pathology and Molecular Medicine, McMaster University, Canada
  3. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Canada
  4. Thrombosis and Atherosclerosis Research Institute, Canada
  5. Centre for Data Science and Digital Health, Hamilton Health Sciences, Canada
  6. Department of Health Research Methods, Evidence, and Impact, McMaster University, Canada
  7. Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Canada
  8. National University of Ireland, Galway, Ireland
  9. Department of Medicine, University of Cape Town & Groote Schuur Hospital, South Africa
  10. Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA) & Cape Heart Institute (CHI), Department of Medicine, University of Cape Town, South Africa

Abstract

Background:

Mitochondrial DNA copy number (mtDNA-CN) is an accessible blood-based measurement believed to capture underlying mitochondrial (MT) function. The specific biological processes underpinning its regulation, and whether those processes are causative for disease, is an area of active investigation.

Methods:

We developed a novel method for array-based mtDNA-CN estimation suitable for biobank-scale studies, called ‘automatic mitochondrial copy (AutoMitoC).’ We applied AutoMitoC to 395,781 UKBiobank study participants and performed genome- and exome-wide association studies, identifying novel common and rare genetic determinants. Finally, we performed two-sample Mendelian randomization to assess whether genetically low mtDNA-CN influenced select MT phenotypes.

Results:

Overall, genetic analyses identified 71 loci for mtDNA-CN, which implicated several genes involved in rare mtDNA depletion disorders, deoxynucleoside triphosphate (dNTP) metabolism, and the MT central dogma. Rare variant analysis identified SAMHD1 mutation carriers as having higher mtDNA-CN (beta = 0.23 SDs; 95% CI, 0.18–0.29; p=2.6 × 10-19), a potential therapeutic target for patients with mtDNA depletion disorders, but at increased risk of breast cancer (OR = 1.91; 95% CI, 1.52–2.40; p=2.7 × 10-8). Finally, Mendelian randomization analyses suggest a causal effect of low mtDNA-CN on dementia risk (OR = 1.94 per 1 SD decrease in mtDNA-CN; 95% CI, 1.55–2.32; p=7.5 × 10-4).

Conclusions:

Altogether, our genetic findings indicate that mtDNA-CN is a complex biomarker reflecting specific MT processes related to mtDNA regulation, and that these processes are causally related to human diseases.

Funding:

No funds supported this specific investigation. Awards and positions supporting authors include: Canadian Institutes of Health Research (CIHR) Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award (MC, PM); CIHR Post-Doctoral Fellowship Award (RM); Wellcome Trust Grant number: 099313/B/12/A; Crasnow Travel Scholarship; Bongani Mayosi UCT-PHRI Scholarship 2019/2020 (TM); Wellcome Trust Health Research Board Irish Clinical Academic Training (ICAT) Programme Grant Number: 203930/B/16/Z (CJ); European Research Council COSIP Grant Number: 640580 (MO); E.J. Moran Campbell Internal Career Research Award (MP); CISCO Professorship in Integrated Health Systems and Canada Research Chair in Genetic and Molecular Epidemiology (GP)

Editor's evaluation

This is an original human GWAS study that treats mitochondrial copy number variation as a trait, and investigates its genetic basis, as well as its association with (and possible causal role in) various human diseases, such as cancer and dementia. The study identifies 71 significant loci, show that these are significantly over-represented in a priori candidates, and argue convincingly that this could help us understand how mitochondrial copy number is regulated at a cellular level.

https://doi.org/10.7554/eLife.70382.sa0

eLife digest

Our cells are powered by small internal compartments known as mitochondria, which host several copies of their own ‘mitochondrial’ genome. Defects in these semi-autonomous structures are associated with a range of severe, and sometimes fatal conditions: easily checking the health of mitochondria through cheap, quick and non-invasive methods can therefore help to improve human health.

Measuring the concentration of mitochondrial DNA molecules in our blood cells can help to estimate the number of mitochondrial genome copies per cell, which in turn act as a proxy for the health of the compartment. In fact, having lower or higher concentration of mitochondrial DNA molecules is associated with diseases such as cancer, stroke, or cardiac conditions. However, current approaches to assess this biomarker are time and resource-intensive; they also do not work well across people with different ancestries, who have slightly different versions of mitochondrial genomes.

In response, Chong et al. developed a new method for estimating mitochondrial DNA concentration in blood samples. Called AutoMitoC, the automated pipeline is fast, easy to use, and can be used across ethnicities. Applying this method to nearly 400,000 individuals highlighted 71 genetic regions for which slight sequence differences were associated with changes in mitochondrial DNA concentration. Further investigation revealed that these regions contained genes that help to build, maintain, and organize mitochondrial DNA. In addition, the analyses yield preliminary evidence showing that lower concentration of mitochondrial DNA may be linked to a higher risk of dementia.

Overall, the work by Chong et al. demonstrates that AutoMitoC can be used to investigate how mitochondria are linked to health and disease in populations across the world, potentially paving the way for new therapeutic approaches.

Introduction

Mitochondria are semiautonomous organelles present in nearly every human cell that execute fundamental cellular processes including oxidative phosphorylation, calcium storage, and apoptotic signaling. Mitochondrial (MT) dysfunction has been implicated as the underlying cause for many human disorders based on mechanistic in vitro and in vivo studies (Burbulla et al., 2017; Desdín-Micó et al., 2020; Sliter et al., 2018). Complementary evidence comes from recent epidemiological studies that measure mitochondrial DNA copy number (mtDNA-CN), an MT-derived marker that can be conveniently measured from peripheral blood. Since mitochondria contain their own unique set of genomes that are distinct from the nuclear genome, the ratio of mtDNA to nuclear DNA molecules (mtDNA-CN) in a sample serves as an accessible marker of MT DNA abundance per cell (Longchamps et al., 2020). Indeed, observational studies suggest that individuals with lower mtDNA-CN are at higher risk of age-related complex diseases, such as coronary artery disease, sudden cardiac death, cardiomegaly, stroke, portal hypertension, and chronic kidney disease (Tin et al., 2016; Ashar et al., 2017; Zhang et al., 2017; Hägg et al., 2021). Conversely, higher mtDNA-CN levels have been associated with increased cancer incidence (Kim et al., 2015; Hu et al., 2016).

While previous studies demonstrate that mtDNA-CN is associated with various diseases, evidence suggests that it may also play a direct and causative role in human health and disease. For example, in cases of mtDNA depletion syndrome, wherein rare defects in nuclear genes responsible for replicating and/or maintaining mtDNA lead to deficient mtDNA-CN (Gorman et al., 2016), patients manifest with severe dysfunction of energy-dependent tissues (heart, brain, liver, and cardiac and skeletal muscles). So far, 19 genes have been reported to cause mtDNA depletion (Oyston, 1998). In addition to these rare monogenic syndromes, the importance of common genetic variation in regulating mtDNA-CN is an active area of research with approximately 50 common loci identified so far (Cai et al., 2015; Guyatt et al., 2019; Longchamps, 2019; Hägg et al., 2021). In contrast to marked drops in mtDNA-CN by 60–80% as seen in those with rare mtDNA depletion syndromes, the relevance of subtler perturbations in mtDNA-CN in disease risk remains to be determined (Basel, 2020).

Granted, the connection between blood mtDNA-CN and aspects of MT biology remains unclear with many studies showing only moderate correlation between mtDNA-CN and markers of MT function or biogenesis (Frahm et al., 2005; Wachsmuth et al., 2016). Further complicating the interpretation of epidemiological associations between blood mtDNA-CN and disease risk is the fact that (i) blood mtDNA-CN is strongly confounded by blood cell composition, particularly, platelets that are devoid of nuclei and that (ii) blood mtDNA-CN does not correlate well with mtDNA-CN measured in other tissues in which MT dysfunction may be more relevant (Picard, 2021). Accordingly, understanding the genetic determinants of blood mtDNA-CN may provide a better understanding of the etiological processes reflected by this poorly understood MT biomarker in the blood.

To interrogate mtDNA-CN as a potential determinant of human diseases and to better understand its biological relevance to mitochondria, we performed extensive genetic investigations in up to 395,781 participants from the UKBiobank study (Sudlow et al., 2015). We first developed and validated a novel method for biobank-scale mtDNA-CN investigations that leverages single nucleotide polymorphism (SNP) array intensities, called ‘automatic mitochondrial copy (AutoMitoC).’ Leveraging AutoMitoC-based mtDNA-CN estimates, we performed large-scale genome-wide association study (GWAS) and exome-wide association study (ExWAS) to identify common and rare genetic variants contributing to population-level variation in mtDNA-CN. Various analyses were then conducted to build on previous publications regarding the specific genes and pathways underlying mtDNA-CN regulation (Cai et al., 2015; Guyatt et al., 2019; Longchamps, 2019; Hägg et al., 2021). Finally, we applied Mendelian randomization analyses to assess potential causal relationships between mtDNA-CN and disease susceptibility.

Results

AutoMitoC: a streamlined method for array-based mtDNA-CN estimation

We built on an existing framework for processing normalized SNP probe intensities (log2ratio [L2R] values) from genetic arrays into mtDNA-CN estimates known as the ‘MitoPipeline’ (Lane, 2014) (Materials and Methods, Figure 1). The MitoPipeline yields mtDNA-CN estimates that correlate with direct qPCR measurements and has been successfully implemented in several epidemiological investigations (Ashar et al., 2017; Zhang et al., 2017; Figure 1—figure supplements 12). We developed a novel method, ‘AutoMitoC,’ which incorporates three amendments to facilitate large-scale investigations of mtDNA-CN (Figure 1). First, AutoMitoC replaces autosomal signal normalization of common variants with globally rare variants which negates the need for linkage disequilibrium pruning (Materials and Methods, Figure 1—figure supplements 34). As a result, this simplifies derivation of mtDNA-CN estimates in ethnically diverse cohorts by allowing for use of a single, universal variant set for normalization. Second, to detect potentially cross-hybridizing probes, we empirically assess the association of corrected probe signal intensities with off-target genome intensities (Materials and Methods, Supplementary file 1 – Tab 1–2), rather than relying on sequence homology of probe sequences, which is not always available. Last, the primary estimate of MT signal is ascertained using principal component analysis ([PCA]; as opposed to using the median signal intensity of MT probes as per the MitoPipeline) which improves concordance of array-based mtDNA-CN estimates with those derived from alternative methods (Materials and Methods). A detailed description of the development of the AutoMitoC pipeline is provided in the Materials and Methods.

Figure 1 with 6 supplements see all
Schematic summary of the automatic mitochondrial copy (AutoMitoC) pipeline.

The AutoMitoC pipeline is comprised of four major steps: (i) preprocessing, (ii) background correction, (iii) detection of probe cross-hybridization, and (iv) final derivation of mitochondrial DNA copy number (mtDNA-CN) estimates. First, preprocessing is simplified by restricting analysis of autosomal variants to those that have low minor allele frequency ( <0.01) and low genotype missingness ( <0.05). For probes passing quality control, MT and autosomal log2ratio (L2R) values undergo an initial correction for guanine cytosine (GC) waves using the method by Diskin et al., 2008. Samples exhibiting high genomic waviness post GC-correction (L2R SD >0.35) are removed. Second, background correction consists of performing principal component analysis of the autosomal probe L2R values and finding the top k principal components (PCs) that correspond to the ‘elbow’ of the scree plot. In our case, ~70% variance in autosomal L2R values was explained by the top k PCs in both UKbiobank and INTERSTROKE datasets. GC-corrected MT and L2R values are then further adjusted for the top autosomal PCs (representing technical background noise) by taking the residuals of the association between the L2R values versus the k autosomal PCs. Third, we derive a ‘clean’ set of autosomal and MT probes without signs of off-target probe cross-hybridization by empirically testing the GC-corrected and background-corrected L2R values for association with either the sample medians of off-target genome L2R values or self-reported gender (to capture off-target hybridization to sex chromosomes). Fourth, using the ‘clean’ probeset, we repeat the autosomal background correction, extract the top MT PC as a crude measure of mtDNA-CN, change the sign of the MT PC according to association of the MT PC with known predictors of mtDNA-CN that are commonly reported (sex or age), and last, standardize the MT PC values as the final AutoMitoC estimate.

To benchmark performance of AutoMitoC, array-based mtDNA-CN estimates were compared to complementary measures of mtDNA-CN in two independent studies. First, array-based mtDNA-CN estimates were derived in a subset of 34,436 UKBiobank participants with available whole exome sequencing (WES) data. Reference mtDNA-CN estimates were derived from the proportion of WES reads aligned to the MT genome relative to the autosome (Longchamps, 2019). AutoMitoC estimates were significantly correlated with WES estimates (r = 0.45; p<2.23 × 10–308). Since WES data involves enrichment for nuclear coding genes and therefore could result in biased reference estimates for mtDNA-CN, we also performed an independent validation in an ethnically diverse study of 5791 participants where mtDNA-CN was measured using qPCR, the current gold standard assay (Fazzini et al., 2018). Indeed, we observed stronger correlation between AutoMitoC and qPCR-based estimates (r = 0.64; p<2.23 × 10–308; Figure 1—figure supplement 5). Furthermore, AutoMitoC demonstrated robust performance (r > 0.53) across all ethnic strata in the secondary validation cohort including Europeans (N = 2431), Latin Americans (N = 1704), Africans (N = 542), South East Asians (N = 471), South Asians (N = 186), and others (N = 360; ; Figure 1—figure supplement 5). Bland Altman plots also illustrate the extent of agreement between methods (Figure 1—figure supplement 6). For every ethnicity, 95% limits of agreement intervals were smaller than expected by chance. Last, while all analyses hitherto followed the MitoPipeline condition of requiring >40,000 autosomal variants for normalization, we observed comparable performance using even 1000 random very rare probes (MAF <0.001; r = 0.60; p<5 × 10–300) for signal normalization which reduced the runtime from several hours to less than 10 min for these 5791 samples running on 10 CPUs.

In the larger UKBiobank dataset of 395,781 with suitable array-based estimates, the distribution of mtDNA-CN was approximately normal (Figure 2A). To further verify that AutoMitoC-based estimates were indeed capturing mtDNA-CN, we performed association testing between mtDNA-CN values and known predictors including age, sex, ethnicity, and blood cell composition. Consistent with previous reports, every decade increase in age was associated with lower levels (beta = −0.08 SDs; 95% CI, –0.07 to –0.08; p<2.23 × 10–308), and females had higher levels than males (beta = 0.12 SDs; 95% CI, 0.11–0.12; p=1.67 × 10–299). MtDNA-CN levels also differed between ethnicities with South Asians (beta = −0.18; 95% CI, –0.16 to –0.21; p=2.93 × 10–47) and Africans (beta = −0.18; 95% CI, –0.16 to –0.21; p=8.39 × 10–48) having significantly lower levels than Europeans. Collectively, age, sex, and ethnicity accounted for 0.83% of the phenotypic variance in mtDNA-CN levels. All types of cell counts were significantly associated with mtDNA-CN including the standard set of covariates commonly adjusted for in mtDNA-CN investigations, namely, white blood cells (beta = −0.25 SDs in mtDNA-CN levels per 1 SD increase in cell counts; 95% CI, –0.25 to –0.24; p<2.23–308) and platelets (beta = 0.07; 95% CI, 0.06–0.07; p<2.23 × 10–308) (Figure 2B). White blood cell and platelet counts each accounted for 6.3% and 0.5% of the variance in mtDNA-CN levels, respectively (Figure 2B; Figure 2—source data 1). Notably, neutrophil count (beta = −0.31; 95% CI, –0.31 to –0.30; p<2.23–308) was the strongest predictor of mtDNA-CN levels and accounted for more variance (9.9%) than white blood cell and platelet counts combined (8.2%). Collectively, total white blood cell, platelet, and neutrophil counts explained 12.3% variance in mtDNA-CN levels.

Distribution of automatic mitochondrial copy (AutoMitoC) estimates and the influence of blood cell counts.

(A) Histogram illustrating AutoMitoC mitochondrial DNA copy number (mtDNA-CN) estimates in 395,781 UKBiobank participants expressed per SD change in mtDNA-CN. Associations between blood cell counts with mtDNA-CN levels as conveyed by forest plots illustrating effect estimates (left) and a bar plot showing the proportion of variance in mtDNA-CN explained (right). Models were adjusted for age, age2, sex array type, 20 genetic principal components, and ethnicity. Both blood cell counts and mtDNA-CN levels were standardized (mean = 0; SD = 1).

Figure 2—source data 1

Table showing the association of blood cell counts and AutoMitoC mtDNA-CN estimates in the UKBiobank.

Effect estimates (betas, 95% CI, p-values) are expressed in terms of change in mtDNA-CN per 1 SD increase in blood counts. The variance explained (r2) by blood cell counts is also provided.

https://cdn.elifesciences.org/articles/70382/elife-70382-fig2-data1-v2.xlsx

GWAS identifies 70 common loci for mtDNA-CN

A GWAS was performed testing the association of 11,453,766 common genetic variants (MAF >0.005) with mtDNA-CN in 383,476 UKBiobank participants of European ancestry (Figure 3). In total, 9602 variants were associated with mtDNA-CN at genome-wide significance (Figure 3A; Figure 3—figure supplement 1), encompassing 82 independent signals in 72 loci (Supplementary file 2 – Tab 1; Figure 3—source data 1). The genomic inflation factor was 1.16 and the LD score intercept was 1.036, indicating that most inflation in test statistics was attributable to polygenicity. Sensitivity analyses revealed that nuclear mitochondrial DNA (NUMT) interference may have played a role in two independent signals (two loci), which were subsequently discarded, leading to a total of 80 independent signals in 70 loci. These 80 independent genetic signals explained 1.48% variance in mtDNA-CN levels.

Figure 3 with 3 supplements see all
Analyses of common genetic loci associated with mitochondrial DNA copy number (mtDNA-CN).

(A) Manhattan plot illustrating common genetic variant associations with mtDNA-CN. (B) Size distribution of 95% credible sets defined for 80 independent genetic signals. (C) GENE-MANIA-mania protein network interaction exploration showing all DEPICT and gene-mania prioritized genes (top) with functions color coded and a zoom-in highlighting interactors of the key mitochondrial (MT) regulator gene, PPRC1 (bottom). (D) ‘MitoPathway’ counts corresponding to 27 prioritized MitoCarta3 genes encoding proteins with known MT localization.

Figure 3—source data 1

Locus zoom plots for 72 loci and 82 conditionally independent genetic signals.

Variants with the highest fine-mapping posterior probability are labeled by their genomic coordinates (GRCh37). Pairwise correlation between the lead variant and proximal variants were color-coded based on the 1000 Genomes Europeans reference panel.

https://cdn.elifesciences.org/articles/70382/elife-70382-fig3-data1-v2.zip

Fine-mapping via the FINEMAP algorithm (Benner et al., 2016) yielded 95% credible sets containing 2363 genome-wide significant variants. Of the 80 independent genetic associations, 17 (22%) mapped to a single candidate causal variant; 32 (39%) mapped to 5 or fewer variants, and 42 (51%) mapped to 10 or fewer variants (Figure 3B; Supplementary file 2 – Tab 2). Credible sets for 11 genetic signals overlapped with genes responsible for rare mtDNA depletion disorders including DGUOK (3), MGME1 (2), TFAM (2), TWNK (2), POLG2 (1), and TYMP (1) (Supplementary file 2 – Tab 1 & 2). Several associations mapped to coding variants with high posterior probability. DGUOK associations mapped to a synonymous variant (rs62641680; posterior probability = 1) and a nonsynonymous variant (rs74874677; p=1). TFAM associations mapped to a 5’ untranslated region (UTR) variant (rs12247015; p=1) falling within an ENCODE candidate cis-regulatory element with a promotor-like signature and an intronic variant (rs4397793; p=1) with a proximal enhancer-like signature. Last, POLG2 associations mapped to a nonsynonymous variant (rs17850455; p=1). Beyond the six aforementioned mtDNA depletion genes identified at genome-wide significance, suggestive associations were found for POLG (rs2307441; p=1.0 × 10–7), OPA1 (rs9872432; p=5.2 × 10–7), SLC25A10 (rs62077224; p=1.2 × 10–7), and RRM2B (rs3907099; p=4.7 × 10–6). Given these observations, we hypothesized that mtDNA depletion genes may be generally enriched for common variant associations. Indeed, 10 (53%) of 19 known mtDNA depletion genes (Oyston, 1998) harbored at least suggestive mtDNA-CN associations (p<5 × 10–6).

We also compared our findings to genome-wide significant results from two recent GWAS of mtDNA-CN by Hägg et al., 2021 and Longchamps et al., 2022 to better contextualize our findings. Of the 66 independent signals identified by Hägg et al., 2021, we found varying levels of evidence to support 43 (65%) genetic associations; 41 (62%) variants were detected at Bonferroni significance; 2 (3%) variants were detected using a suggestive significance threshold (p<5 × 10–6) (Supplementary file 2 – Tab 3). For the remaining 23 (35%) variants not detected at suggestive significance, we hypothesized that differences in covariate adjustment strategies. Specifically, Hagg et al. did not adjust for platelets which is an important confounder of blood mtDNA-CN levels which might explain the majority of these discrepancies. As a sensitivity analysis, we repeated association testing without adjusting for blood cell traits and were able to recover 19/23 (83%) of the remaining variants at suggestive significance threshold, which suggests that approximately 1 in 3 associations detected by Hägg et al., 2021 may not be robust to adjustment for platelets (Supplementary file 2 – Tab 3). While we could not perform the reverse lookup of our top GWAS hits within the Hägg et al., 2021 GWAS due to a lack of full genome-wide summary statistics, 48 (60%) of our 80 genetic associations are likely novel relative to Hägg et al., 2021 as these variants were not reported as genome-wide significant nor in linkage disequilibrium (r2 >0.10) with their top hits (Supplementary file 2 – Tab 1). Among the 133 independent signals identified by Longchamps et al., 2022, 119 achieved Bonferroni significance, of which 116 were available in our GWAS (Supplementary file 2 – Tab 4). The remaining three variants (or their proxies; r2 >0.7) did not meet the MAF threshold for inclusion (MAF >0.005). Of the 116 detectable variants, we found varying levels of evidence to support 112 (97%) genetic associations; 70 (60%) variants were detected at Bonferroni significance; 15 (13%) variants were detected using a suggestive significance threshold; 27 (24%) variants were detected at nominal significance with concordant directionality (Supplementary file 2 – Tab 4). While we could not perform the reverse lookup of our top GWAS hits within the Longchamps et al., 2022 GWAS, 11 (14%) of our 80 genetic associations are likely novel as these variants were not reported as genome-wide significant nor in linkage disequilibrium with their top hits (Supplementary file 2 – Tab 1). Notably, these 11 associations were also not reported in Hägg et al., 2021.

Additionally, transethnic meta-analysis inclusive of non-Europeans (N = 395,781) was performed but given the small increase in sample size, GWAS findings remained highly similar (Figure 3—figure supplement 2). However, European effect estimates were significantly and highly correlated with those derived from South Asian (r = 0.97; p=2.2 × 10–15) and African (r = 0.88; p=9.1 × 10–5) GWAS analyses (Figure 3—figure supplement 3).

mtDNA-CN loci influence MT gene expression and heteroplasmy

We postulated that differential expression of genes encoded by the MT genome may trigger changes in copy number, and thus a subset of identified loci may affect mtDNA-CN through MT genome transcription. Ali et al., 2019 recently conducted a GWAS to identify nuclear genetic variants associated with variation in mtDNA-encoded gene expression (i.e. mitochondrial expression quantitative trait loci [mt-eQTLs]) (Ali et al., 2019). Nonsynonymous variants in LONP1 (rs11085147) and TBRG4 (rs2304693), as well as an intronic variant in MRPS35 (rs1127787), were associated with changes in MT gene expression across various tissues (Supplementary file 2 – Tab 5). Nominally associated mtDNA-CN loci were also observed to influence MT gene expression including intronic variants in both PNPT1 (rs62165226; mtDNA-CN p=5.5 × 10–5) and LRPPRC (rs10205130; mtDNA-CN p=1.1 × 10–4). Although differences in MT gene expression may be a consequence rather than a cause of variable mtDNA-CN, the analysis performed by Ali et al., 2019 was corrected for factors associated with global changes in the MT transcriptome (Ali et al., 2019). Moreover, the direction of effect estimates between mtDNA-CN and mt-eQTLs varied depending on gene and tissue context. Altogether, such findings imply that some mtDNA-CN loci may regulate mtDNA-CN by influencing MT gene expression.

Heteroplasmy refers to the coexistence of multiple mtDNA alleles within an individual for a particular variant, which is a function of the multicopy nature of the MT genome. A recent GWAS by Nandakumar et al., 2021 for mean heteroplasmy levels in saliva specimen provided initial evidence supporting a shared genetic basis for heteroplasmy and copy number. To further explore the overlap in genetic determinants of these traits, we searched for the previously reported heteroplasmy loci within our mtDNA-CN GWAS. Of 19 matching variants between the GWAS, 4 heteroplasmy loci were also associated with mtDNA-CN at genome-wide significance including variants nearby or within TINCR/LONP1 (rs12461806; mtDNA-CN GWAS p=7.5 × 10–88), TWNK/MPRL43 (rs58678340; p=1.3 × 10–39), TFAM (rs1049432; p=1.5 × 10–21), and PRKAB1 (rs11064881; p=2.6 × 10–10) genes. Consistent with the initial finding from Nandakumar et al., 2021 that the heteroplasmy-increasing TFAM allele was also associated with higher mtDNA-CN, we also observed concordant directionality for the other three variants. No additional mtDNA heteroplasmy loci were identified to influence mtDNA-CN when using a more liberal suggestive significance threshold. Full genome-wide summary statistics for the heteroplasmy GWAS were not publicly available so we could not perform the reverse lookup to examine whether the 80 mtDNA-CN variants affected heteroplasmy.

Genes and pathways implicated in the regulation of mtDNA-CN

DEPICT analysis led to the prioritization of 91 out of 18,922 genes (false discovery rate (FDR) p<0.05; Supplementary file 2 – Tab 6). Among them, 87 of the genes intersected with the GeneMANIA database and were uploaded to the GeneMANIA platform to identify additional functionally related genes (Warde-Farley et al., 2010). GeneMANIA analysis discovered an additional 20 related genes (Supplementary file 2 – Tab 7). Among the 107 total genes prioritized by DEPICT or GeneMANIA (Figure 3C), MT functions were significantly enriched in gene ontology (GO) terms including mitochondrion organization (coverage: 12/225 genes; FDR p=7.4 × 10–5), MT nucleoid (6/34; FDR p=2.2 × 10–4), MT genome maintenance (4/10; FDR p=6.8 × 10–4), and MT matrix (11/257; FDR p=6.8 × 10–4). Visual inspection of the links between key genes involved in these functions highlights PPRC1, a member of the Peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PGC-1A) family of MT biogenesis activators (Scarpulla, 2011), as a potential coordinator of mtDNA-related processes (Figure 3C).

MitoCarta3 is a comprehensive and curated inventory of 1136 human proteins (1120 nuclear) known to localize to the mitochondria based on experiments of isolated mitochondria from 14 nonblood tissues (Rath et al., 2021). We leveraged this recently updated database, that was absent from GeneMANIA, to conduct a complementary set of targeted analyses focused on MT annotations (Supplementary file 2 – Tab 7). First, we hypothesized that prioritized genes would be generally enriched for genes encoding the MT proteome. Overall, 27 (25%) of 107 genes had evidence of MT localization corresponding to a 4.2-fold enrichment (null expectation = 5.9%; p=1.0 × 10–10). Next, given that PPRC1, an activator of MT biogenesis, was prioritized by DEPICT analyses and then linked to central mtDNA regulators in GeneMANIA, we postulated that prioritized genes may be enriched for downstream targets of PGC-1A. PGC-1A induction resulted in a higher mean fold change among prioritized genes (beta = 1.48; 95% CI, 0.60–2.37) as compared to any MT gene (beta = 1.19; 95% CI, –0.76–3.13; t-test p=0.04). Finally, we categorized the 27 MitoCarta3 genes into their respective pathways. Most (16; 57%) genes were members of the ‘Mitochondrial central dogma’ pathway, which represents a nearly threefold enrichment as compared to the frequency of this pathway in the whole MitoCarta3 database (null expectation = 20.7%; p=1.3 × 10–5). Other implicated (albeit not significantly enriched) pathways included ‘Metabolism,’ ‘Mitochondrial dynamics and surveillance,’ ‘Oxidative phosphorylation,’ and ‘Protein import, sorting and homeostasis’ (Figure 3D). Four proteins were annotated as part of multiple pathways including TYMP/SCO2, GTPBP3, MIEF1, and OXA1L (Supplementary file 2 – Tab 7).

Exome-wide association testing uncovers rare coding SAMHD1 mutations as a determinant of mtDNA-CN levels and breast cancer risk

We performed an ExWAS in 147,740 UKBiobank participants with WES data to assess the contribution of rare coding variants. Among 18,890 genes tested, SAMHD1 was the only gene reaching exome-wide significance (Figure 4A; Figure 4—source data 1). The carrier prevalence of rare SAMHD1 mutations was 0.75%, and on average, mutation carriers had higher mtDNA-CN than noncarriers (beta = 0.23 SDs; 95% CI, 0.18–0.29; p=2.6 × 10–19; Figure 4—figure supplement 1). Also, while none of the 19 known mtDNA depletion genes reached Bonferroni significance, a suggestive association was found for TFAM (beta = −0.33; 95% CI, –0.47 to –0.19; p=4.2 × 10–6), and this association was independent of the common TFAM variants (rs12247015; rs4397793) previously identified in the GWAS (beta = −0.33; 95% CI, –0.47 to –0.19; p=8 × 10–6). Rare variants in SAMHD1 and TFAM accounted for 0.06% of the variance in mtDNA-CN levels. Collectively, rare and common loci accounted for 1.55%.

Figure 4 with 1 supplement see all
Rare variant gene burden association testing with mitochondrial DNA copy number and disease risk.

(A) QQ plot illustrating expected vs observed -log10 p-values for exome-wide burden of rare (MAF <0.001) and nonsynymous mutations. (B) Manhattan plot showing phenome-wide significant associations between SAMHD1 carrier status and cancer-related phenotypes.

Figure 4—source data 1

Summary statistics for exome-wide association testing of rare protein-altering variants with mtDNA-CN.

https://cdn.elifesciences.org/articles/70382/elife-70382-fig4-data1-v2.xlsx
Figure 4—source data 2

Summary statistics for phenome-wide association testing of SAMHD1 carrier status.

https://cdn.elifesciences.org/articles/70382/elife-70382-fig4-data2-v2.xlsx

To evaluate whether rare SAMHD1 mutations also influenced disease risk, we conducted phenome-wide association testing of 771 diseases within the UKBiobank. At phenome-wide significance, SAMHD1 mutation carrier status was associated with approximately twofold increased risk of breast cancer (OR = 1.91; 95% CI, 1.52–2.40; p=2.7 × 10–8), as well as greater risk of ‘cancer (suspected or other)’ (OR = 1.52; 95% CI, 1.28–1.80; p=1.1 × 10–6; Figure 4B; Figure 4—source data 2). Exclusion of breast cancer cases was attenuated but did not nullify the association with ‘cancer (suspected or other)’ (OR = 1.36; 95% CI, 1.10–1.67; p=0.004) suggesting that SAMHD1 mutations may also increase risk of other cancers, as has been shown for colon cancer (Rentoft, 2019). To understand whether differences in mtDNA-CN levels between SAMHD1 mutation carriers was a consequence of cancer diagnosis, we repeated association testing with mtDNA-CN excluding cancer patients. In this analysis, the association with mtDNA-CN levels was not attenuated (beta = 0.26; 95% CI, 0.19–0.32; p=7.8 × 10–15) suggesting that the effect of rare SAMHD1 variants on mtDNA-CN levels is not driven by its relationship with cancer status. A summary of MT genes and pathways implicated by common and rare loci is provided in Figure 5.

Graphical summary of mitochondrial genes and pathways implicated by genetic analyses.

Color coding indicates through which set(s) of analyses genes were identified. The image was generated using BioRender (https://biorender.com/).

Mendelian randomization analysis implicates low mtDNA-CN as a causal mediator of dementia

Given that common variant loci overlapped with several mtDNA depletion genes, we postulated that polygenically low mtDNA-CN might cause a milder syndrome with phenotypically similar manifestations. To assess whether mtDNA-CN may represent a putative mediator of mtDNA depletion-related phenotypes, we conducted Mendelian randomization analyses between genetically determined mtDNA-CN and MT disease phenotypes using summary statistics derived from the FinnGen v4 GWAS dataset (Supplementary file 2 – Tab 8).

After accounting for multiple testing of 10 phenotypes, an association between mtDNA-CN and all-cause dementia was found (OR = 1.94 per 1 SD decrease in mtDNA-CN; 95% CI, 1.55–2.32; p=7.5 × 10–4; Figure 6; Figure 6—source data 1). Sensitivity analyses indicated no evidence of global (MR-PRESSO p=0.51; Q-statistic p=0.51) or directional (Egger Intercept p=0.47) pleiotropy. The 27 selected variants accounted for 0.70% of the variance in mtDNA-CN and 0.13% of the risk for dementia, consistent with a causal effect of mtDNA-CN on dementia risk and not vice versa (Steiger p=1.9 × 10–62). Findings were robust across several different MR methods including the weighted median (OR = 2.47; 95% CI, 1.93–3.00; p=0.001), MR-EGGER (OR = 2.41; 95% CI, 1.71–3.11; p=0.02), and GSMR-HEIDI (OR = 1.95; 95% CI, 1.34–2.84; p=0.001) methods. Results also remained statistically significant when using a broader set of genetic instruments including all genome-wide significant loci irrespective of whether genetic variants were located proximally to MitoCarta3 genes (GSMR-HEIDI OR = 1.31; 95% CI, 1.02–1.68; p=0.04). Furthermore, we replicated the dementia association using a second UKBiobank-independent GWAS dataset derived from the International Genomics of Alzheimer’s Disease Consortium (2013) including 17,008 Alzheimer’s disease patients (OR = 1.41; 95% CI, 1.0001–1.98; p=0.04993) (Lambert et al., 2013).

Association of genetically lower mitochondrial DNA copy number with mitochondrial (MT) disease phenotypes.

Coefficient plots for Mendelian randomization analyses of MT disease traits. In the absence of heterogeneity (Egger intercept p>0.05; MR-PRESSO global heterogeneity p>0.05), the inverse variance weighted result was reported. In the presence of balanced pleiotropy (MR-PRESSO global heterogeneity p<0.05), the weighted median result was reported. No set of analyses had evidence for directional pleiotropy (Egger intercept p<0.05).

Figure 6—source data 1

Mendelian randomization analyses of mtDNA-CN versus mitochondrial disease phenotypes.

https://cdn.elifesciences.org/articles/70382/elife-70382-fig6-data1-v2.xlsx

Discussion

We developed a novel method to estimate mtDNA-CN from genetic array data, ‘AutoMitoC,’ and applied it to the UKBiobank study. Extensive genetic investigations led to several key insights regarding mtDNA-CN. First, several novel common and rare genetic determinants of mtDNA-CN were identified, totaling 71 loci. Second, these loci were enriched for MT processes related to dNTP metabolism and the replication, packaging, and maintenance of mtDNA. Third, we observed a strong role for common variation within known mtDNA depletion genes in regulating mtDNA-CN in the general population. Fourth, we found that rare variants in SAMHD1 not only affect mtDNA-CN levels but also confer risk to cancer. Finally, we provided the first Mendelian randomization evidence implicating low mtDNA-CN as a causative risk factor for dementia.

While several investigations for mtDNA-CN have been published, the present study represents the most comprehensive genetic assessment published to date (Cai et al., 2015; Longchamps, 2019; Hägg et al., 2021). Notably, Hägg et al., 2021 recently conducted the largest GWAS for mtDNA-CN in 295,150 UKBiobank participants and identified 50 common loci (Hägg et al., 2021). However, the method developed by Hägg et al., 2021 calibrated SNP probe intensities based on association with WES read depths, which may limit the convenience of the method. In contrast, AutoMitoC only necessitates array probe intensities and does not require any secondary genetic measurements (WES or otherwise) for calibration. In addition, AutoMitoC exhibits superior concordance with WES-based estimates (Hagg r = 0.33; AutoMitoC r = 0.45), which were validated in an independent dataset with gold standard qPCR measurements (AutoMitoC r = 0.64). Further, Hägg et al., 2021 restricted genetic analyses to unrelated European individuals, whereas we incorporated ~100,000 additional individuals and demonstrated consistency in genetic effects between Europeans and non-Europeans (r > 0.88). The greater sample size in combination with more accurate mtDNA-CN estimates may explain the additional 48 (59%) independent common genetic associations identified at genome-wide significance. Furthermore, we took additional precaution to ensure that cataloged NUMTs were not influencing our genetic association results and also to empirically evaluate the robustness of each genetic association against cataloged and uncataloged NUMTs. Finally, in the present study we included complementary explorations of the role of rare variants through ExWAS and phenome-wide association study (PheWAS), as well as, Mendelian randomization analyses to assess disease contexts whereby mtDNA-CN may represent a causal mediator and a potential therapeutic target.

MtDNA-CN has proven to be a biomarker of cardiovascular disease in several large epidemiological studies, with studies often assuming that such relationships are attributable to pathological processes including MT dysfunction, oxidative stress, and inflammation (Tin et al., 2016; Wu et al., 2017; Fazzini et al., 2019; Koller et al., 2020). Consistent with previous GWAS findings, our genetic analyses confirm that mtDNA-CN indeed reflects specific MT functions, but perhaps not the ones commonly attributable to mtDNA-CN (Cai et al., 2015; Guyatt et al., 2019; Hägg et al., 2021). Primarily, differences in mtDNA-CN reflect MT processes related to dNTP metabolism and the replication, maintenance, and organization of mtDNA. Secondarily, genes involved in MT biogenesis, metabolism, oxidative phosphorylation, and protein homeostasis were also identified but do not represent the main constituents. The observed enrichment in common variant associations within mtDNA depletion genes further reinforces the notion that differences in mtDNA-CN first and foremost reflect perturbations in mtDNA-related processes.

No therapy for mtDNA depletion disorders currently exists with treatment mainly consisting of supportive care. Intriguingly, we found that rare variants within SAMHD1 were associated with increased levels of mtDNA-CN. SAMHD1 is a multifaceted enzyme with various functions including tumor suppression through DNA repair activity and maintenance of steady-state intracellular dNTP levels, which has been involved in HIV-1 replication (Baldauf et al., 2012; Kretschmer et al., 2015). Rare homozygous and compound heterozygous loss-of-function mutations in SAMHD1 result in an immune encephalopathy known as Aicardi Goutieres’ syndrome (White et al., 2017). Imbalanced intracellular dNTP pools and chronic DNA damage cause persistent elevations in interferon alpha thus mimicking a prolonged response to HIV-1 infection. While Aicardi Goutieres’ syndrome is a severe recessive genetic disorder, case reports of SAMHD1-related disease often describe heterozygous parents and siblings as being unaffected or with milder disease (familial chilblain lupus 2) (Haskell et al., 2018). In the UKBiobank, the vast majority (99.4%) of individuals possessing SAMHD1 mutations were heterozygote carriers, who had a twofold increased risk of breast cancer. Indeed, our finding that SAMHD1 mutations associate with both elevated mtDNA-CN levels and risk of breast cancer belies the prevailing notion that higher mtDNA-CN is always a protective signature of proper MT function and healthy cells. Such findings may have important clinical implications for genetic screening. First, heterozygous SAMHD1 mutations may be an overlooked risk factor for breast cancer considering that ~1 in 130 UKBiobank participants possessed a genetic mutation conferring twofold elevated risk. Notably, while SAMHD1 mutations have been described previously to be associated with various cancers (Kohnken et al., 2015; Rentoft, 2019), this gene is not routinely screened nor part of targeted gene panels outside the context of neurological disorders (https://www.genedx.com/test-catalog/available-tests/comprehensive-common-cancer-panel/). Second, unaffected parents and siblings of Aicardi Goutieres patients might also present with greater risk of cancer. Third, while SAMHD1 is a highly pleiotropic protein, therapeutic strategies to dampen (but not abolish) SAMHD1 activity might be considered to treat mtDNA depletion disorders caused by defects in nucleotide metabolism. Indeed, Franzolin et al., 2015 demonstrated that siRNA knockdown of SAMHD1 in human fibroblasts with DGUOK mtDNA depletion mutations partially recovered mtDNA-CN (Franzolin et al., 2015).

To our knowledge, we provide the first Mendelian randomization evidence that mtDNA-CN may be causally related to risk of dementia. Although dysfunctional mitochondria have long been implicated in the pathogenesis of Alzheimer’s disease, only recently has mtDNA-CN been tested as a biomarker. Silzer et al., 2019 conducted a matched case-control study of 46 participants and showed that individuals with cognitive impairment had significantly lower blood-based mtDNA-CN (Silzer et al., 2019). Andrews and Goate, 2020 studied the relationship between postmortem brain tissue mtDNA-CN and measures of cognitive impairment in 1025 samples (Andrews and Goate, 2020). Consistent with our findings, a 1 SD decrease in brain mtDNA-CN was associated with lower mini mental state exam (beta = –4.02; 95% CI, –5.49 to –2.55; p=1.07 × 10–7) and higher clinical dementia rating (beta = 0.71; 95% CI, 0.51–0.91). Both studies implicate blood- and brain-based mtDNA-CN as a marker of dementia but were retrospective. In contrast, Yang et al., 2021 observed a significant association between mtDNA-CN and incident risk of neurodegenerative disease (Parkinson’s and Alzheimer’s disease)(Yang et al., 2021). Altogether, our results combined with previous findings suggest that mtDNA-CN represents both a marker and mediator of dementia. Considering that our overall findings suggest that mtDNA-CN reflects numerous MT subprocesses, future studies will be required to disentangle which ones, as reflected by diminished mtDNA-CN, truly mediate dementia pathogenesis.

Several limitations should be noted. First, mtDNA-CN approximated by array-based methods remains imperfectly accurate as compared to qPCR or whole genome sequencing measurements, though we found strong correlation between AutoMitoC and qPCR-based estimates in this study (r = 0.64; p<2.23 × 10–308). AutoMitoC was benchmarked using multiple comparative mtDNA-CN estimation modalities, in independent cohorts, and in various ethnic groups, but further work can be done to assess the reproducibility and error of mtDNA-CN estimates, calibration to absolute counts, and the robustness to different genotyping platforms. Although whole genome sequencing will eventually supplant array-based mtDNA-CN GWAS, we hypothesize that the improvements made in the areas of speed, portability to ethnically diverse studies, and ease-of-implementation, should greatly increase accessibility of mtDNA-CN research as a plethora of genotyping array data is presently available to reanalyze. Second, we did not detect strong evidence of confounding from population structure (or substructure) in our genetic analyses, and we undertook several precautions to mitigate such biases including (i) stratified GWAS analyses by ethnicity, (ii) adjustment for a large number of intraethnic principal components (PCs), and (iii) restriction of downstream analyses to use of European GWAS summary statistics to harmonize with external databases; however, it is still plausible that population substructure may have influenced our findings. Third, Mendelian randomization analyses were underpowered to conduct a broad survey of diseases in which MT dysfunction may play a causal role, and even among the 10 phenotypes tested, statistical power varied by disease prevalence as evidenced by relatively large CI for the dementia association. Accordingly, future studies including larger case sample sizes are necessary to provide more precise effect estimates. Equally as important, we were unable to differentiate whether specific MT subpathways mediated risk of disease. As additional loci are uncovered, such analyses may be feasible. Fourth, variants and genes implicated in the regulation of mtDNA-CN may be specific to blood samples though findings suggest that many mtDNA-CN loci act through genes that are widely expressed in mitochondria across multiple tissues. Future studies are required to determine whether associations are ubiquitous across mitochondria-containing cells and to investigate the role of mtDNA-CN in other tissues. Lastly, whole blood mtDNA-CN reflects a heterogenous mixture of nucleated and unnucleated cells, and despite adjustment for major known confounding cell types, interindividual differences in cell subpopulations not captured by a standard blood cell count may represent an important source of confounding.

Conclusion

Although commonly viewed as a simple surrogate marker for the number of mitochondria present within a sample, genetic analyses suggest that mtDNA-CN is a highly complex biomarker under substantial nuclear genetic regulation. mtDNA-CN reflects a mixture of MT processes mostly pertaining to mtDNA regulation. Accordingly, the true relationship between mtDNA-CN measured in blood samples with human disease remains to be completely defined though we find evidence for mtDNA-CN as a putative causal risk factor for dementia. Future studies are necessary to decipher if mtDNA-CN is directly involved in the pathogenesis of dementia and other diseases or whether other specific MT processes are truly causative.

Materials and methods

The AutoMitoC number pipeline

Background and rationale

Request a detailed protocol

While SNP array data is intended for highly multiplexed determination of genotypes, the raw probe signal intensities used for genotypic inference can also be co-opted to derive estimates of mtDNA-CN. Determining genotypes for a sample at a given variant site relies on contrasting hybridization intensities of allele-specific oligonucleotide probes and then assigning membership to the most probable genotype cluster based on intensity properties (Figure 1—figure supplement 1). Variation in intensities within each genotyping cluster can also be co-opted to deduce variations in copy number. A commonly used metric of probe intensity is the ‘L2R,’ which denotes log2(observed intensity/expected intensity), where the expected intensity is defined as the median signal intensity for a probe conditional on each genotype cluster.

The ‘MitoPipeline’ is a framework for estimating mtDNA-CN from array-based L2R (aka L2R) values developed by Lane, 2014 (Lane, 2014; Zhang et al., 2017). An overview of the MitoPipeline is described in Figure 1—figure supplement 2. To briefly summarize: (i) autosomal and MT L2R values are first corrected for GC waves; (ii) a high-quality set of MT and autosomal markers are selected largely based on visual inspection of genotype clusters and basic local alignment search tool (BLAST) alignment for nonhomologous sequences; (iii) PCA of at least 40,000 autosomal markers is conducted to capture batch effects; (iv) finally, mtDNA-CN is estimated based on median MT L2R value for each sample and then corrected for background noise through residualization of top autosomal PCs.

The MitoPipeline has proven to be effective in estimating mtDNA-CN (correlation coefficient R ~0.5 with direct qPCR estimates) as evidenced by multiple epidemiological studies employing this method (Ashar et al., 2017; Zhang et al., 2017; Fazzini et al., 2019). First, visual inspection of MT probe intensity clusters is recommended to remove probes with poorly differentiated genotype clusters. However, this process is time-consuming (especially for biobank studies that are genotyped across thousands of batches); determination of probes with ‘good’ vs ‘bad’ genotype clustering is subjective; and guidance is only provided for adjudication of polymorphic but not rare or monomorphic markers which may still be informative. Second, in consideration of nuclear and MT sequences with significant sequence similarity due to past and recurrent transposition of MT sequence into the nuclear genome, also known as ‘NUMTs’ (Simone et al., 2011), the MitoPipeline recommends exclusion of MT probes with greater than 80% sequence similarity to the nuclear genome. While determining sequence homology of probes to the nuclear genome may have been feasible with older microarrays wherein probe sequences were often publicized, for many contemporary arrays, including the UKBiobank array, such information is not readily available. Third, LD pruning of common autosomal variants is required to ascertain a set of independent genetic variants, but implementation of this approach within ethnically diverse studies becomes more complex since genetic independence is ancestry dependent. Under the MitoPipeline framework, each ethnicity warrants a unique set of common variants, which not only adds to computational burden but also creates an additional source of variability in terms of performance of the method between ethnicities.

Therefore, we developed a new array-based mtDNA-CN estimation method, which we have dubbed the ‘AutoMitoC’ pipeline, which incorporates three key amendments: (i) autosomal signal normalization utilizes globally rare variants in place of common variants which confer advantages in terms of both speed and portability to ethnically diverse studies, (ii) cross-hybridizing probes are identified by assessing evidence for cross-hybridization via association of signal intensities (rather than using genotype association and identification of homologous sequences through BLAST alignment), and (iii) the primary estimate of MT signal is ascertained using PCA as opposed to using the median signal intensity of MT probes. The rationale underlying these amendments are described in detail in the subsequent sections. An overview of the AutoMitoC pipeline is provided in Figure 1.

Development of AutoMitoC in the UKBiobank study

Request a detailed protocol

To develop the AutoMitoC pipeline we used genetic datasets from the UKBiobank. The UKBiobank is a prospective cohort study including approximately 500,000 UK residents (ages 40–69 years) recruited from 2006 to 2010 in whom extensive genetic and phenotypic investigations have been and continue to be done (Sudlow et al., 2015). All UKBiobank data reported in this manuscript were accessed through the UKBiobank data showcase under application #15,525. All analyses involve the use of genetic and/or phenotypic data from consenting UKBiobank participants.

Two main genetic datasets from the UKBiobank were incorporated in the development of AutoMitoC. First, CNV L2R values derived from genetic arrays (i.e. normalized array probe intensities) for 488,264 samples were downloaded using the ukbgene utility, and their corresponding genotype calls (data field: 22418) were downloaded with gfetch. Second, exome alignment maps (EXOME FE CRAM files and indices; data fields 23163 & 23164) from the first tranche of 49,989 samples released in March, 2019 were downloaded with the ‘ukbfetch’ utility. L2R values were used to derive AutoMitoC mtDNA-CN estimates. For WES data, samtools idxstats was used to derive the number of sequence reads aligning to MT and autosomal genomes, from which an estimate of mtDNA-CN was derived according to the procedure by Longchamps, 2019; these complementary WES-based mtDNA-CN estimates served as a comparator to benchmark AutoMitoC performance.

Initial quality control of 488,264 samples and 784,256 directly genotyped variants was executed in PLINK followed that recommended by the MitoPipeline (i.e. sample call rate >0.96; variant call rate >0.98; Hardy Weinberg equilibrium [HWE] p>1 × 10–5; PLINK mishap p>1 × 10–4; genotype association with sex p>0.00001; LD pruning r2 <0.30; MAF >0.01) (Purcell et al., 2007). Variants within 1 Mb of immunoglobulin, T-cell receptor genes, and centromeric regions were removed. After this quality control procedure, 466,093 samples and 86,677 common variants remained. Next, genomic waves were corrected according to Diskin et al., 2008 using the PennCNV ‘genomic_wave.pl’ script (https://github.com/WGLab/PennCNV/blob/master/genomic_wave.pl) (Wang et al., 2007; Diskin et al., 2008). Samples with high genomic waviness (L2R SD >0.35) before and after GC correction were removed resulting in 431,501 samples with array L2R values corresponding to 86,677 common autosomal variants. Last, we excluded samples representing blood cell count outliers ( >3 SDs) as per Longchamps, 2019 which led to 395,781 participants (Longchamps, 2019). Finally, we took the intersection of European samples with both suitable array and WES data resulting in a final testing dataset of 34,436 European participants. To evaluate the possibility of replacing common autosomal variant signal normalization with rare variants, we also analyzed a set of 79,611 rare variants with an MAF <0.01.

Background correlation between autosomal and MT L2R values

Request a detailed protocol

Inference of relative mtDNA-CN from array data consists of determining the ratio of MT to autosomal probe signal intensities (or normalized L2R values) within each sample. Technical (e.g. batch and plate effects) and latent sample factors confound raw signal intensities for reasons unrelated to DNA quantity. Such confounders induce strong cross-genome correlation, and therefore, a necessary first step is to remove this background noise from autosomal and MT probe intensities.

Indeed, even after correcting autosomal L2R values for genomic waves, we observed significant correlation between individual autosomal probe intensities and the median sample intensity across the 265 MT variants (Figure 1—figure supplement 3A). The extent of cross-genome intensity correlation varied based on MAF, with rare autosomal variants (MAF <0.01; M = 79,611) showing the strongest correlation. We postulate that intensity properties for rare variants, which have a higher prevalence of homozygous genotypes, more strongly resemble those for MT genotypes, which are predominantly homoplasmic. On this basis, we only use rare autosomal variants to represent autosomal signal. While this approach contrasts with the MitoPipeline, which utilizes common genetic variants to represent autosomal signal, restricting autosomal signal normalization to rare variants confers three major advantages while maintaining the same level of concordance with WES estimates (rcommon = 0.50; rrare = 0.49). First, this allows for further streamlining of the pipeline as this precludes the necessity for common variant filters, such as LD pruning. Second, we show that fewer PCs are necessary to capture the same proportion of total variance in signal intensities with rare as opposed to common variants. Approximately 70% of the total variance in rare autosomal intensities was explained by 120 PCs, whereas the same proportion of variance in common autosomal intensities would necessitate more than 1000 PCs (Figure 1—figure supplement 3B). In the UKBiobank, PCs were derived via the eigendecomposition of the empirical covariance matrix conducted in Python 3.6, using NumPy and SciPy (Harris et al., 2020; Virtanen et al., 2020). Third, the set of autosomal markers used in deriving mtDNA-CN remains independent from the set of common autosomal variants analyzed in subsequent GWAS for mtDNA-CN. Effectively, this ensures that common autosomal variants evaluated for association with mtDNA-CN in downstream GWAS analyses are not directly incorporated into autosomal signal normalization, which could otherwise attenuate GWAS signals.

Empirical detection of off-target probes

Request a detailed protocol

After adjustment for 120 autosomal PCs (approximating the elbow of the variance explained curve), there persisted a smaller subset of autosomal probes that were significantly correlated with median MT intensity (Figure 1—figure supplement 4). We hypothesized that such probes either (i) cross-hybridize with the MT genome (i.e. lie within NUMT regions) or (ii) corresponded to genetic loci involved in regulation of mtDNA-CN. As an illustration, Supplementary file 1 – Tab 1 conveys characteristics of the 10 most strongly correlated variants. Four of the top 10 probes were located within 1 Mb of a NUMT region, and in all 4 cases, the sign of the correlation coefficient was negative which might reflect interference of autosomal signal with increasing mtDNA-CN. An additional three probes corresponded to variants within genes that were implicated in MT disorders or regulation of MT processes (Supplementary file 1 – Tab 1).

We further explored whether there was evidence for cross-hybridization between autosomal SNPs and sex chromosomes by regressing adjusted autosomal probe intensities with reported male status. Generally, correlations between autosomal intensities and sex were stronger than those with MT intensities, suggesting that cross-hybridization of autosomal probes to sex chromosomes is more pronounced. For the top 10 sex-associated probes, we performed BLASTn alignment against the human reference genome (GRCh38) using 30 bases surrounding each probe (Supplementary file 1 – Tab 2) (Altschul et al., 1990). All probes had at least one flanking sequence with near-perfect ( >97%) sequence identity to a sex chromosome. For these 10 probes, the sign of the correlation between autosomal intensity and male status was perfectly consistent with homology to X or Y chromosomes, thus supporting the hypothesis that such probes cross-hybridized to sex chromosomes.

Inclusion of autosomal probes with evidence of off-target hybridization to the MT genome or sex chromosomes is problematic. In the former scenario, misattribution of autosomal as MT signal may reduce the effectiveness of normalization. In the latter scenario, inadvertent adjustment for sex through retention of cross-hybridizing autosomal probes may occur if such probes explain substantial variance in autosomal probe intensities and this is particularly problematic given that mtDNA-CN has been robustly shown to differ between genders in epidemiological studies. In preliminary investigations where sex-associated probes were retained, we noticed that several top PCs that perfectly tagged gender. Hypothetically, had these PCs been retained and used for correction of MT signal, then the final mtDNA-CN estimate would have been inadvertently corrected for gender. Therefore, we removed autosomal probes exhibiting at least modest correlation (|r| > 0.05) with reported gender (907; 1.14%) or median MT intensity (193; 0.24%) and then recalculated top autosomal PCs.

PCA-based approach improves concordance with complementary estimates

Request a detailed protocol

After correcting MT probes using the updated set of 120 autosomal L2R PCs, we adopted the MitoPipeline’s approach for estimating mtDNA-CN and calculated the median of corrected MT L2R values to denote an individual’s final mtDNA-CN estimate. Using this median-based approach, array mtDNA-CN estimates demonstrated significant correlation with WES mtDNA-CN estimates (r = 0.33; p<2.23 × 10–308). However, we found that performing PCA across all corrected MT L2R values and then extracting the top MT PC for each sample as the final mtDNA-CN estimate resulted in stronger correlation with WES (r = 0.49; p<2.23 × 10–308).

Independent validation of AutoMitoC in the INTERSTROKE study

Request a detailed protocol

We additionally validated the AutoMitoC pipeline by deriving array-based mtDNA-CN estimates in the INTERSTROKE study and comparing these with parallel qPCR-based measurements, the current gold standard for measuring mtDNA-CN. INTERSTROKE is an international case-control study of stroke including 26,526 participants from 32 countries and 142 centers (O’Donnell et al., 2010). Blood samples have been collected for a subset of approximately 12,000 individuals, of which 9311 have been successfully genotyped using the Axiom Precision Medicine Research Array (PMRA r3). MtDNA-CN estimates for INTERSTROKE were generated according to the AutoMitoC pipeline as illustrated in Figure 1. A further subset of 5791 samples with both suitable array genotypes have undergone qPCR measurement of mtDNA-CN using the plasmid-normalized protocol from Fazzini et al., 2019. Please see Chong et al., 2021 for further details Chong et al., 2021.

GWAS in the UKBiobank

Data acquisition and quality control

Request a detailed protocol

UKBiobank samples were genotyped on either the UK Biobank Array (~450,000) or the UK BiLEVE array (~50,000) for approximately 800,000 variants (Bycroft et al., 2018). Further imputation was conducted by the UKBiobank study team using a combined reference panel of the UK10K and Haplotype Reference Consortium datasets. Imputed genotypes (version 3) for 488,264 UKBiobank participants were downloaded through the European Genome Archive (Category 100319). Samples were removed if they were flagged for any of the UKBiobank-provided quality control annotations (Resource 531; ‘ukb_sqc_v2.txt’) for high ancestry-specific heterozygosity, high missingness, mismatching genetic ancestry, or sex chromosome aneuploidy (‘het.missing.outliers,’ ‘in.white.British.ancestry.subset,’ ‘putative.sex.chromosome.aneuploidy’). Samples were also removed if their submitted gender did not match their genetic sex or if they had withdrawn consent at the time of analysis. Variant quality control consisted of removing variants that had low imputation quality (INFO score <0.30), were rare (MAF <0.005), or were in violation of Hardy Weinberg Equilibrium (HWE p<1 × 10–10). The HWE test was conducted within a subset of unrelated individuals for each ethnic strata, though all related individuals were retained for subsequent GWAS analysis. Lastly, in special consideration of mtDNA-CN as the GWAS phenotype, we also removed variants within ‘NUMTs,’ which refer to regions of the nuclear genome that exhibit homology to the MT genome due to past transposition of MT sequences. Accordingly, NUMTs represent a specific confounder of mtDNA-CN GWAS analyses which may lead to false positive associations. NUMT boundaries were obtained from the UCSC NumtS Sequence (numtSeq) track, which is based on the Reference Human NumtS curated by Simone et al., 2011. All sample and variant quality control of imputed genotypes were executed using qctools and resultant bgen files were indexed with bgenix. Smaller ethnic groups with similar genetic ancestry were consolidated; individuals self-reporting as ‘African’ or ‘Caribbean’ were combined into a larger ‘African’ stratum and individuals self-reporting as ‘Indian’ or ‘Pakistani’ were combined into a ‘South Asian’ stratum.

Association testing

Request a detailed protocol

GWAS were initially conducted in an ethnicity-stratified manner for common variants (MAF >0.005). The number of variants tested for association with mtDNA-CN varied for British (M = 10,728,525), Irish (M = 10,707,537), other White (M = 10,894,497), South Asian (M = 11,350,981), and African (M = 18,981,896) study participants, respectively. To allow for genetic relatedness between participants, GWAS were conducted using the REGENIE framework, which consists of two steps (Mbatchou, 2020). In step 1, mtDNA-CN was predicted using a ridge regression model fit on a set of high-quality genotyped SNPs (MAF >0.01, minor allele count (MAC) >100, genotype and sample missingness above 10%, and passing HWE [p>10−15]) across the whole genome in blocks of 1000 SNPs. In step 2, the linear regression model was used to test the association of all SNPs adjusting for age, age2, sex, chip type, 20 genetic PCs, and blood cell counts (white blood cell, platelet, and neutrophil counts), and conditional on the model from step 1.

Blood cell counts were determined for blood specimen collected at the initial assessment visit using Beckman Coulter LH750 analyzers (https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/haematology.pdf). Information on blood cell counts was retrieved from the UKBiobank data showcase. Individuals with missing values for any blood cell counts (~2.5%) were removed from any subsequent analysis involving blood cell counts. Quality control of blood counts was done following the same procedure as Longchamps, 2019 (Longchamps, 2019). Except for platelet counts, all blood cell counts were log transformed and samples exhibiting outlying values were removed (~4% samples). Lastly, values were standardized to have a mean of 0 and SD of 1.

After ethnicity-specific GWAS were performed, results were combined through meta-analysis using METAL (Willer et al., 2010). European (N = 383,476) and transethnic (N = 395,718) GWAS meta-analyses were performed. Due to the high proportion of Europeans (97%), results from the transethnic meta-analyses strongly resembled that of the European meta-analysis. Accordingly, we report results from the European meta-analysis as the primary GWAS but also provide a comparison of regression coefficients to non-European ethnic groups. To summarize statistical associations, Manhattan plots and quantile-quantile plots were generated by uploading summary statistics into the locus zoom web platform (https://my.locuszoom.org/) (Pruim et al., 2010). LD score regression was performed to calculate the LD score intercept by uploading GWAS results to the LDhub test center (http://ldsc.broadinstitute.org/) (Bulik-Sullivan et al., 2015). As per the instructions, all variants within the major histocompatibility (MHC) region on chromosome 6 were removed prior to uploading. Annovar (version date June 07, 2020) was used to functionally annotate genome-wide significant loci based on their proximity (±250 kb) to genes (RefSeq), predicted effect on amino acid sequence, allele frequency in external datasets (1000 Genomes), clinical pathogenicity (Clinvar), and in silico deleteriousness (CADD), and eQTL information (GTEx v8) (1000 Genomes Project Consortium et al., 2012; GTEX, 2014; Landrum et al., 2016; Rentzsch et al., 2019).

NUMT sensitivity analyses

Request a detailed protocol

To assess whether genome-wide significant associations could be explained by the interference of cryptic MT pseudogenes (NUMTs), we performed sensitivity analyses as per Nandakumar et al., 2021. The MT genome was divided into thirds, and AutoMitoC estimates were rederived for each region (MT:1–6425; MT:6526–11947; MT:11948–16569) using the corresponding MT variants belonging to these three consecutive regions. Association testing was repeated for each region using REGENIE. For a given variant, if at least one region-based analysis yielded a nonsignificant association (p<0.05), we considered this as evidence of NUMT interference.

Fine-mapping of GWAS signals

Request a detailed protocol

We followed a similar protocol to Vuckovic et al., 2020 for fine-mapping mtDNA-CN loci (Vuckovic et al., 2020). Genome-wide significant variants were consolidated into genomic blocks by grouping variants within 250 kb of each other. LDstore was used to compute a pairwise LD correlation matrix for all variants within each block and across all samples included in the European GWAS meta-analysis (Benner et al., 2017). For each genomic block, FINEMAP was used to perform stepwise conditional regression (Benner et al., 2016). The number of conditionally independent genetic signals per genomic block was used to inform the subsequent fine-mapping search parameters. Finally, the FINEMAP random stochastic search algorithm was applied to derive 95% credible sets constituting candidate causal variants that jointly contributed to 95% (or higher) of the posterior inclusion probabilities (Benner et al., 2016).

Mt-eQTL and heteroplasmy look-ups

Request a detailed protocol

Among GWAS hits, we searched for mt-eQTLs using information from Ali et al., 2019, ‘Nuclear genetic regulation of the human mitochondrial transcriptome’ (Ali et al., 2019). When mt-eQTLs also had reported effect estimates, the consistency in direction-of-effects between mt-eQTL and mtDNA-CN associations was reported. Genome-wide significant variants from a heteroplasmy GWAS by Nandakumar et al., 2021 were interrogated in the same manner (Nandakumar et al., 2021).

Gene prioritization and pathway analyses

Request a detailed protocol

The Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT) v.1.1 tool was used to map mtDNA-CN loci to genes based on shared coregulation of gene expression using default settings (Pers et al., 2015). Genome-wide significant variants from the European GWAS meta-analysis were ‘clumped’ into independent loci using PLINK ‘--clump-p1 5e-8 --clump-kb 500 --clump-r2 0.05’ with LD correlation matrix derived from 1000 Genomes Europeans (Purcell et al., 2007). DEPICT was subsequently run on independent SNPs using default settings. DEPICT identified 91 genes in total at an FDR of 0.05. Of the 91 genes, 4 noncoding genes were excluded from subsequent analyses for lack of a match in the GeneMANIA database (Warde-Farley et al., 2010). The excluded genes include a pseudogene (PTMAP3), an intronic transcript (ALMS1-IT1), and two long noncoding RNAs (SNHG15, RP11-125K10.4). The remaining 87 DEPICT-prioritized genes were uploaded to the GeneMANIA web platform (https://genemania.org/), which mines publicly available biological datasets to identify additional related genes based on functional associations (genetic interactions, pathways, coexpression, colocalization, and protein domain homology). Based on the combined list of DEPICT and GeneMANIA identified genes, a network was formed in GeneMANIA maximizing the connectivity between all input genes, using the default setting, ‘Assigned based on query gene.’ Functional enrichment analysis was then performed to identify overrepresented GO terms among all network genes (Ashburner et al., 2000). All network genes with at least one GO annotation were compared to a background comprising all GeneMANIA genes with GO annotations.

MT annotation-based analyses

Request a detailed protocol

To complement the previous pathway analyses, we labeled prioritized genes with MitoCarta3 annotations and performed subsequent statistical enrichment analyses (Rath et al., 2021). MitoCarta3 is an exquisite database of MT protein annotations, which draws from mass spectrophotometry and green fluorescent protein colocalization experiments of isolated mitochondria from 14 different tissues to assign all human genes statuses indicating whether the corresponding proteins are expressed in the mitochondria or not. We tested whether prioritized genes were enriched for the MT proteome by using a binomial test in R. The number of ‘trials’ was set to the total number of DEPICT and GeneMANIA-prioritized genes (107); the number of ‘successes’ was set to the aforementioned gene subset that was labeled as MT proteins by MitoCarta3 (27); finally, the expected probability was set to the number of nuclear-encoded MitoCarta3 genes divided by the total number of genes (1120/18,922). Furthermore, a t-test was used to compare mean PGC-1A induced fold change for the subset of GWAS-prioritized genes expressed in the MT proteome (27) as compared to the mean PGC-1A induced fold change for all 1120 nuclear MitoCarta3-annotated genes. Also, genes were categorized based on MitoCarta3 ‘MitoPathway’ annotations. MitoCarta3 genes with missing values were excluded from this analysis. Lastly, the 27 genes were labeled based on MitoCarta3 ‘MitoPathways.’ Only the top-level pathway (i.e. parent node) was ascribed to each gene.

Genetic analysis of rare variants

Data Acquisition and Quality Control

Request a detailed protocol

Population-level WES variant genotypes (UKB data field: 23,155) for 200,643 UKBiobank participants corresponding to 17,975,236 variants were downloaded using the gfetch utility. These data represent the second tranche of WES data released by the UKBiobank and differ from the first tranche (~50 K samples) which was used for the development of the AutoMitoC pipeline. Quality control of WES data was conducted as follows. First, 11 samples which withdrew consent by the time of analysis were removed. Second, 83,700 monomorphic variants were removed. Third, 369,215 variants with nonmissing genotypes present in less than 90% of samples were removed. Fourth, two samples with call rates less than 99% were removed. Fifth, 18 samples exhibiting discordance between genetic and reported sex were removed. Sixth, through visual inspection of scatterplots of the first two genetic PCs, three outlying samples whose locations strongly departed from their putative ethnicity cluster were removed. Seventh, 35,317 variants deviating from HWE were removed. Eighth, 12,765 samples belonging to smaller ethnic groups with less than 5000 samples (South Asian = 3395; African = 3168; other = 6202) were removed. Ninth, we selected for a maximal number of unrelated samples and excluded 14,156 samples exhibiting third degree or closer relatedness. Finally, 12,394,404 noncoding variants were removed, and 5,176,300 protein-altering variants (stopgain, stoploss, startloss, splicing, missense, frameshift, and in-frame indels) were retained in 173,688 samples.

ExWAS to identify rare mtDNA-CN loci

Request a detailed protocol

Of the 173,688 individuals, suitable AutoMitoC mtDNA-CN estimates were available for 147,740 samples. Rare variant inclusion criteria consisted of variants which were infrequent (MAF <0.001), nonsynonymous, and predicted to be clinically deleterious by Mendelian clinically applicable pathogenicity v.1.4 scores (or were highly disruptive variant types including frameshift indel, stopgain, stoploss, or splicing) (Jagadeesh et al., 2016). Herein, such variants are referred to as ‘rare variants’ for simplicity. For each gene, rare allele counts were added per sample. A minor allele count threshold of at least 10 rare alleles was applied leading to a total of 18,890 genes analyzed (exome-wide significance p<0.05/18,890 = 2.65 × 10–6). Linear regression was conducted using mtDNA-CN as the dependent variable and the rare alleles counts per gene as the independent variable. The same set of covariates used in the primary GWAS analysis was used in the ExWAS analysis.

Phenome-wide association testing for rare SAMHD1 mutation carrier status

Request a detailed protocol

To identify disease phenotypes associated with carrying a rare SAMHD1 mutation, we maximized sample size for phenome-wide association testing by analyzing the larger set of 173,688 WES samples (with or without suitable mtDNA-CN estimates). Disease outcomes were defined using the previously published ‘PheCode’ classification scheme to aggregate ICD-10 codes from hospital episodes (field ID 41270), death registry (field ID 40001 and 40002), and cancer registry (field ID 40006) records (Denny et al., 2013; Wu et al., 2019). Logistic regression was applied to test the association of SAMHD1 mutation carrier status versus 771 PheCodes (phenome-wide significance p<0.05/771 = 6.49 × 10–5) with a minimal case sample size of 300 (Wei et al., 2017). The same set of covariates used in the primary GWAS were also employed in this analysis.

Mendelian randomization analysis

Disease outcomes

Request a detailed protocol

To assess evidence for a causal role of mtDNA-CN on MT disorder-related traits, we first defined a list of testable disease outcomes related to MT disorders. We cross-referenced a list of 36 clinical manifestations of MT disease to FinnGen consortium GWAS (release 4; November 30, 2020) traits (Gorman et al., 2016; Feng, 2020). Among 2444 FinnGen traits, 10 overlapped with MT disease and had a case prevalence greater than 1% and were chosen for two-sample Mendelian randomization analyses. These 10 traits included type 2 diabetes (N = 23,364), mood disorder (N = 20,288), sensorineural hearing loss (N = 12,550), cerebrovascular disease (N = 10,367), migraine (N = 6687), dementia (5675), epilepsy (N = 4558), paralytic ileus and intestinal obstruction (N = 2999), and cardiomyopathy (N = 2342). FinnGen effect estimates and standard errors were used in subsequent Mendelian randomization analyses to define the effect of selected genetic instruments on disease risk.

Genetic instrument selection

Request a detailed protocol

First, genome-wide significant variants from the present European GWAS meta-analysis of mtDNA-CN were chosen (N = 383,476). Second, we matched these variants to the FinnGen v4 GWAS datasets (Feng, 2020). Third, to enrich for variants that directly act through MT processes, we only retained those within 100 kb of genes encoding for proteins that are expressed in mitochondria based on MitoCarta3 annotations (Rath et al., 2021). Fourth, we performed LD pruning in PLINK with 1000 Genomes Europeans as the reference panel to ascertain an independent set of genetic variants (LD r2 <0.01) (Purcell et al., 2007; 1000 Genomes Project Consortium et al., 2012). Lastly, to mitigate potential for horizontal pleiotropy, we further removed variants with strong evidence of acting through alternative pathways by performing a phenome-wide search across published GWAS with Phenoscanner V2 (Kamat et al., 2019). Variants strongly associated with other phenotypes (p<5 × 10–20) were removed unless the variant was a coding mutation located within gene encoding for the MT proteome (MitoCarta3) or had an established MT role based on manual literature review (Rath et al., 2021). Seven genetic variants were removed based on these criteria including rs8067252 (ADAP2), rs56069439 (ANKLE1), rs2844509 (ATP6V1G2-DDX39B), rs73004962 (PBX4), rs7412 (APOE), rs385893 (AK3, RCL1), and rs1613662 (GP6). A total of 27 genetic variants were used to approximate genetically determined mtDNA-CN levels. As sensitivity analyses, a broader set of genetic instruments including variants irrespective of their proximity to MT protein-encoding genes was additionally tested.

Mendelian randomization and sensitivity analyses

Request a detailed protocol

Two-sample Mendelian randomization analyses were performed using the ‘TwoSampleMR’ and ‘MR-PRESSO’ R packages (Hemani et al., 2018; Verbanck et al., 2018). Effect estimates and standard errors corresponding to the 27 genetic variants on mtDNA-CN (exposure) and MT disease phenotypes (outcome) were derived from the European GWAS meta-analysis and FinnGen v4 GWAS summary statistics, respectively. Three MR methodologies were employed including inverse variance weighted (primary method), weighted median, and MR-EGGER methods. MR-PRESSO was used to detect global heterogeneity and p-values were derived based on 1000 simulations. If significant global heterogeneity was detected (p<0.05), a local outlier test was conducted to detect outlying SNPs. After removal of outlying SNPs, MR analyses were repeated. In the absence of heterogeneity (Egger-intercept p>0.05; MR-PRESSO global heterogeneity p>0.05), we reported the inverse-variance weighted result. In the presence of balanced pleiotropy (MR-PRESSO global heterogeneity p<0.05) and absence of directional pleiotropy (Egger-intercept p>0.05), we reported the weighted median result. In the presence of directional pleiotropy (Egger-intercept p<0.05), we reported the MR-EGGER result. Also, the Steiger directionality test was employed to ensure that a greater proportion of variance in mtDNA-CN was explained than risk of the outcome. To further examine the robustness of these findings, MR analyses were also conducted using the generalized summary data-based MR (GSMR) method which incorporates HEIDI outlier removal and accounts for the uncertainty in SNP-outcome effect estimates. Finally, to replicate the two-sample MR finding using an independent outcome dataset without UKBiobank participants, we repeated two-sample MR analyses using the International Genomics of Alzheimer’s Disease Consortium (2013) GWAS meta-analysis including 17,008 cases and 37,154 controls (Lambert et al., 2013).

Data availability

Individual-level UKBiobank genotypes and phenotypes can be acquired upon successful application (https://bbams.ndph.ox.ac.uk/ams/). All individual-level UKBiobank data was accessed as part of application # 15255. FinnGen summary statistics are freely available to download (https://www.finngen.fi/en/access_results). All data products generated as part of this study will be made publicly accessible. Specifically, the AutoMitoC array-based mtDNA-CN estimation pipeline is available on GitHub (https://github.com/GMELab/AutoMitoC, copy archived at swh:1:rev:5669ae00011aece6ed494a3fde241ab3027c59e2). The mtDNA-CN estimates derived in UKBiobank participants have been returned to the UKBiobank and made accessible to researchers through the data showcase (https://biobank.ndph.ox.ac.uk/showcase/). Summary-level association statistics from GWAS have been made publicly available for download from the GWAS catalogue (https://www.ebi.ac.uk/gwas/publications/35023831). All remaining data are available in the main text or supplementary materials.

The following data sets were generated

References

    1. Lambert JC
    2. Ibrahim-Verbaas CA
    3. Harold D
    4. Naj AC
    5. Sims R
    6. Bellenguez C
    7. DeStafano AL
    8. Bis JC
    9. Beecham GW
    10. Grenier-Boley B
    11. Russo G
    12. Thorton-Wells TA
    13. Jones N
    14. Smith AV
    15. Chouraki V
    16. Thomas C
    17. Ikram MA
    18. Zelenika D
    19. Vardarajan BN
    20. Kamatani Y
    21. Lin CF
    22. Gerrish A
    23. Schmidt H
    24. Kunkle B
    25. Dunstan ML
    26. Ruiz A
    27. Bihoreau MT
    28. Choi SH
    29. Reitz C
    30. Pasquier F
    31. Cruchaga C
    32. Craig D
    33. Amin N
    34. Berr C
    35. Lopez OL
    36. De Jager PL
    37. Deramecourt V
    38. Johnston JA
    39. Evans D
    40. Lovestone S
    41. Letenneur L
    42. Morón FJ
    43. Rubinsztein DC
    44. Eiriksdottir G
    45. Sleegers K
    46. Goate AM
    47. Fiévet N
    48. Huentelman MW
    49. Gill M
    50. Brown K
    51. Kamboh MI
    52. Keller L
    53. Barberger-Gateau P
    54. McGuiness B
    55. Larson EB
    56. Green R
    57. Myers AJ
    58. Dufouil C
    59. Todd S
    60. Wallon D
    61. Love S
    62. Rogaeva E
    63. Gallacher J
    64. St George-Hyslop P
    65. Clarimon J
    66. Lleo A
    67. Bayer A
    68. Tsuang DW
    69. Yu L
    70. Tsolaki M
    71. Bossù P
    72. Spalletta G
    73. Proitsi P
    74. Collinge J
    75. Sorbi S
    76. Sanchez-Garcia F
    77. Fox NC
    78. Hardy J
    79. Deniz Naranjo MC
    80. Bosco P
    81. Clarke R
    82. Brayne C
    83. Galimberti D
    84. Mancuso M
    85. Matthews F
    86. Moebus S
    87. Mecocci P
    88. Del Zompo M
    89. Maier W
    90. Hampel H
    91. Pilotto A
    92. Bullido M
    93. Panza F
    94. Caffarra P
    95. Nacmias B
    96. Gilbert JR
    97. Mayhaus M
    98. Lannefelt L
    99. Hakonarson H
    100. Pichler S
    101. Carrasquillo MM
    102. Ingelsson M
    103. Beekly D
    104. Alvarez V
    105. Zou F
    106. Valladares O
    107. Younkin SG
    108. Coto E
    109. Hamilton-Nelson KL
    110. Gu W
    111. Razquin C
    112. Pastor P
    113. Mateo I
    114. Owen MJ
    115. Faber KM
    116. Jonsson PV
    117. Combarros O
    118. O’Donovan MC
    119. Cantwell LB
    120. Soininen H
    121. Blacker D
    122. Mead S
    123. Mosley TH
    124. Bennett DA
    125. Harris TB
    126. Fratiglioni L
    127. Holmes C
    128. de Bruijn RF
    129. Passmore P
    130. Montine TJ
    131. Bettens K
    132. Rotter JI
    133. Brice A
    134. Morgan K
    135. Foroud TM
    136. Kukull WA
    137. Hannequin D
    138. Powell JF
    139. Nalls MA
    140. Ritchie K
    141. Lunetta KL
    142. Kauwe JS
    143. Boerwinkle E
    144. Riemenschneider M
    145. Boada M
    146. Hiltuenen M
    147. Martin ER
    148. Schmidt R
    149. Rujescu D
    150. Wang LS
    151. Dartigues JF
    152. Mayeux R
    153. Tzourio C
    154. Hofman A
    155. Nöthen MM
    156. Graff C
    157. Psaty BM
    158. Jones L
    159. Haines JL
    160. Holmans PA
    161. Lathrop M
    162. Pericak-Vance MA
    163. Launer LJ
    164. Farrer LA
    165. van Duijn CM
    166. Van Broeckhoven C
    167. Moskvina V
    168. Seshadri S
    169. Williams J
    170. Schellenberg GD
    171. Amouyel P
    172. Cohorts for Heart and Aging Research in Genomic Epidemiology
    (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease
    Nature Genetics 45:1452–1458.
    https://doi.org/10.1038/ng.2802

Decision letter

  1. Magnus Nordborg
    Reviewing Editor; Austrian Academy of Sciences, Austria
  2. YM Dennis Lo
    Senior Editor; The Chinese University of Hong Kong, Hong Kong
  3. Magnus Nordborg
    Reviewer; Austrian Academy of Sciences, Austria

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "GWAS and ExWAS of blood Mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Magnus Nordborg as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Y M Dennis Lo as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Your method for estimating mtDNA copy number needs to be better described and explored. We need: a more detailed description of the approach; a more thorough benchmarking (comparing to standard approaches and against estimates from sequencing data); a more thorough description of the phenotype and its distribution; comparisons with age, sex and other UK Biobank phenotypes; description of samples removed and the rationale for removing them.

2. More thorough Mendelian Randomisation analysis: test for pleiotropy using Heidi; perform comparative analysis using all genome wide significant hits (with accompanying discussion of any differing results).

3. Place work in context of other two highly similar recent studies (Longchamps 2021 and Hagg 2021).

4. Data release: code should be on GitHub, raw data for mtDNA-CN estimates and covariates provided where possible, summary statistics uploaded to GWAS catalogue.

5. A bit more discussion of potential population structure confounding? You focus is on individuals of European decent, with meta-analysis including non-Europeans giving similar results.

Reviewer #1:

This is an original human GWAS study that treats mitochondrial copy number variation as a trait, and investigates its genetic basis, as well as its association with (and possible causal role in) various human diseases. The main strength of the study is the development of a method for estimating mitochondrial number from standard array data. This makes it possible to reuse the vast UK Biobank data (n = 383,476) and work with much larger sample sizes than previous studies. The estimated phenotypes are sensibly QC-ed using various data.

They then carry out a standard GWAS, identify 71 significant loci, show that these are significantly over-represented in a priori candidates, and argue convincingly that this could help us understand how mitochondrial copy number is regulated at a cellular level. They show that rare SAMHD1 variants associated with high copy number are also associated with increased cancer risk, and, finally, that mitochondrial copy number may play a role in dementia.

The study represents clever re-use of data, and the findings appear to be well supported and should be interesting to a broad audience. Unlike many human GWAS, there is clearly real biology here.

Your analyses appear admirably solid to me, but this need to be verified by an expert on human GWAS. The same is true for the novelty of the results.

The only improvement I would like to see lies in the description of the estimated phenotype. There is little detail about its distribution, and I would also like to learn more about the factors influencing it, including, obviously, an estimate of much of the variation your 71 loci explain, and a discussion of what explains the rest of the variation. The UK Biobank is full of data…

Reviewer #2:

Mitochondrial DNA copy number is a significant aspect of mitochondrion function, both of which have been linked to disease states. This is an interesting assessment of the genetic basis for variation in mitochondrial DNA copy number. The authors describe a new method for mtDNA CN estimation, although additional benchmarking and validation is warranted in this reviewer's opinion. The GWAS/ExWAS analyses are fairly standard and identify genetic variants likely influencing mtDNA CN. The implementation of the MR analyses is more superficial; it remains unclear in this reviewer's opinion that pleiotropy has been effectively ruled out. The estimates of mtDNA CN and other non-other non-identifiable information (eg, sex, age, ethnic group, etc) necessary to replicate the author's findings should be made publicly available as easily accessible supplementary files.

1. Regarding the method for mtDNA estimation. The method is barely described in the main body of the manuscript. More information should be provided in the main manuscript given that the method it is so essential for this work. Greater clarity about the reproducibility would have helped. Also, it appears that the estimates of mtDNA CN were not tested against a standard with known CN, could this be performed? Some benchmark was done with qPCR estimates, while the results of the methods here developed and qPCR are positively correlated, the magnitude of the correlation is moderate. Finally, wow does the new method compare with sequencing based estimates? All in all, more benchmarking of the method in the main manuscript would greatly improve the paper.

2. Besides better description of the methods for mtDNA CN estimation, a much more detailed description of the mtDNA CN data should be provided in the main manuscript. It is not possible to properly understand/appreciate the reported associations and MR results without such description of the underlying mtDNA CN data.

3. In this reviewer's opinion the pre-computed mtDNA estimates and other non-identifiable information (eg, sex, age, ethnic group, etc) necessary to replicate the author's findings should be made publicly available in supplementary text format files and that could be easily accessible to readers. If the text files are too large for eLife the authors should upload the files to Dryad, dbGaP, etc.

4. The author's state that "After quality control, 359,689 British, 10,598 Irish, 13,189 Other White, 6,172 South Asian, and 6,133 African samples had suitable array-based mtDNA-CN estimates for subsequent GWAS testing. It is unclear what the authors mean by "suitable" in this case. It would also be good to describe in the main text how many were unsuitable in each case and the reasons why these estimates did not match the requirement for suitability.

5. It is unclear if the authors detected ethnic and/or population differences that might confound the other downstream analyses. It is also unclear what are the error and reproducibility of the CN estimates.

6. The influence of sex and age are not described (or superficially described).

7. The presentation of the MR results is unconvincing. For the analysis, the authors have not ruled out pleiotropy in my opinion. Heterogeneity tests such as Heidi should be used Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481-487 (2016) in addition to MREgger. Also, dementia seems to have the largest error bars around its OR estimates. More detailed analyses and discussion could help here.

Reviewer #3:

This work develops and presents an adapted approach to quantify mitochondrial DNA copy number (mtDNA-CN) from DNA genotyping arrays (named AutoMitoC) by using the intensity of probes emanating from the mitochondrial genome and then using a series of control and normalisation steps that can be applied within a standard framework across data from different populations. They use their estimates of mtDNA-CN as a quantitative trait within a genome wide association study to identify genes and genetic variants that are associated with variation in this phenotype, using both rare and common polymorphisms. Through multiple complementary approaches they then identify a large number of genes that are linked to mtDNA-CN, including those that are involved in mtDNA depletion disorders and various different components of mitochondrial function. Within this, using rare variant analysis they find SAMHD1 as a potential regulator of mtDNA-CN (and possibly breast cancer risk), and the authors suggest that the product of this gene may make a good draggable target to control the downstream effects of altered mtDNA-CN. Finally, the study uses Mendelian Randomisation analyses between genetically determined mtDNA-CN and a selection of disease phenotypes that have been linked to mitochondrial processes previously, and in doing so they find a link between mtDNA-CN and dementia.

In general, the work appears robust and achieves the aims of uncovering the genetic architecture of a disease relevant phenotype, and its potential downstream implications. Readers of this article should be aware of two further studies that have performed similar analyses on largely the same data as those described in this paper. The first by Hagg et al. (2021) considers largely the same set of individuals and identifies 50 genetic loci associated with variation in mtDNA-CN across individuals. The second by Longchamps et al. (2021), finds 129 independent genetic variant associations and includes additional data from a different cohort. The study presented here discusses the findings of Hagg et al. (although a direct comparison of genes is not given) but does not interrogate and compare results from the Longchamps study, which is necessary to truly understand the genetic architecture of this trait and to place this work in context. For the comparison with Hagg et al., the authors suggest that their improved method for mtDNA-CN quantification leads to a higher number of genetic associations, although a more formal comparison of AutoMitoC and standard approaches against control data like that generated from qPCR would make this statement more robust (there is a comparison between AutoMitoC and Hagg et al. quantification in the Discussion section, although it is not clear whether this comparison is like-for-like using the same set of samples). In general, many of the bigger picture biological findings are shared across the three studies (many shared loci and enrichment of genes involved in mtDNA depletion syndrome and mitochondrial processes), which is an excellent advert for reproducible science.

The additional strength of the work by Kong et al. are two extra pieces of analyses not found in other works – an analysis of rare variants and the implementation of Mendelian Randomisation to link variation in mtDNA-CN to disease risk. For the Mendelian Randomisation analyses, authors select genetic variants associated with mtDNA-CN based on their likely role in mitochondrial processes (those variants falling within or close to a mitocarta gene). This is rational given the assumption in Mendelian Randomisation analyses, that genetic instruments should be causal, but it would also be useful to also consider the full set of genetic variants that are associated with mtDNA-CN at genome wide significance to test the relevance of selecting a subset of genetic variants.

References:

RJ Longchamps et al., Genetic analysis of mitochondrial DNA copy number and associated traits identifies loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation, and reveals a causal association of mitochondrial function with mortality. bioRxiv 2021.01.25.428086; doi: https://doi.org/10.1101/2021.01.25.428086

Hägg S, Jylhävä J, Wang Y, Czene K, Grassmann F. Deciphering the genetic and epidemiological landscape of mitochondrial DNA abundance. Hum Genet. 2021 Jun;140(6):849-861. doi: 10.1007/s00439-020-02249-w.

1. Page 3, lines 3-8 (but also throughout the text): Whilst it is appreciated that there is a vast body of literature pointing to links between mtDNA-CN and complex disease, I think it is worth briefly discussing caveats to these results in order to properly frame the relevance of the work. For instance, there are many studies showing that links between blood derived mtDNA-CN and age-related diseases may at least in part be driven by cell type composition. There is also plenty of debate around whether mtDNA-CN in blood is indicative of processes in other tissues which might be more relevant for each particular disease. Much of these caveats have been recently neatly summarised in Picard 2021.

2. Page 9, lines 16-17: "We postulated that mtDNA-CN loci may regulate copy number by inducing changes in expression of genes that are directly transcribed from mtDNA.": I don't understand this rationale – are you postulating that lower/higher expression from the MT genome may trigger changes in copy number (and thus these may be modulated by variants influencing expression)? If so, this should be stated more clearly, if not, please explain the thinking here. I also think that it is worth rewording your conclusions from this analysis, as there is no test of directionality.

3. Page 9: lines 31-41: Similar to point 2, it would be good to be a clear rationale for the comparison of loci associated with mtDNA-CN and heteroplasmy level.

4. Page 10: lines 29-35: It would be good to see statistical tests comparing the fractions of genes that fall into the mitochondrial process categories described here, versus proportions of genes in these categories in Mitocarta as a whole – are these categories enriched for genes identified in this study?

5. Page 25: lines 20-31: Links should be provided in any revised manuscript, including the GitHub repository for the method, and summary statistics should be uploaded to the GWAS catalogue.

6. Figures: In general, the figures are not particularly clear and should be improved for publication. For example, axes text is often too small and difficult to read, and text labels on figures 1 and 2 are not aligned properly.

References:

Picard, M., Blood Mitochondrial DNA Copy Number: What Are We Counting? Mitochondrion (2021), doi: https://doi.org/10.1016/j.mito.2021.06.010

https://doi.org/10.7554/eLife.70382.sa1

Author response

Essential revisions:

1. Your method for estimating mtDNA copy number needs to be better described and explored. We need: a more detailed description of the approach; a more thorough benchmarking (comparing to standard approaches and against estimates from sequencing data); a more thorough description of the phenotype and its distribution; comparisons with age, sex and other UK Biobank phenotypes; description of samples removed and the rationale for removing them.

We wholly agree that a more detailed description of the method in the main text is warranted to improve the readability of the manuscript. We apologize for not realizing that the Materials and methods section does not contribute to the manuscript word count, hence why we originally provided only a general overview of the method in the main manuscript. Accordingly, we have added more detail to nearly all sections in the Materials and methods.

To specifically address the issue of further clarity regarding the AutoMitoC method, we added a figure describing the methodology step-by-step (Results, page 5, lines 1-21; page 6, lines 1-4).

We also provide an extensive description of the rationale behind AutoMitoC and details on how it was developed in the UKBiobank by adding a new subsection to the Materials and methods subheading “The Automatic Mitochondrial Copy (AutoMitoC) Number Pipeline”.

With respect to more clarity on sample exclusions, further details have now been added to the Materials and Methods section, for the GWAS:

“Initial quality control of 488,264 samples and 784,256 directly genotyped variants was executed in PLINK followed that recommended by the MitoPipeline (i.e. sample call rate > 0.96; variant call rate > 0.98; HWE p-value > 1x10-5; PLINK mishap P-value > 1x10-4; genotype association with sex p-value > 0.00001; LD-pruning r2 < 0.30; MAF > 0.01) (Purcell et al., 2007). Variants within 1 Mb of immunoglobulin, T-cell receptor genes, and centromeric regions were removed. After this quality control procedure, 466,093 samples and 86,677 common variants remained. Next, genomic waves were corrected according to Diskin et al. (2008) using the PennCNV “genomic_wave.pl” script (https://github.com/WGLab/PennCNV/blob/master/genomic_wave.pl) (Wang et al., 2007; Diskin et al., 2008). Samples with high genomic waviness (L2R SD > 0.35) before and after GC-correction were removed resulting in 431,501 samples with array L2R values corresponding to 86,677 common autosomal variants. Lastly, we excluded samples representing blood cell count outliers (> 3SDs) as per Longchamps et al. (2019) which led to 395,781 participants (Longchamps, 2019). Finally, we took the intersection of European samples with both suitable array and whole-exome sequencing data resulting in a final testing dataset of 34,436 European participants. To evaluate the possibility of replacing common autosomal variant signal normalization with rare variants, we also analyzed a set of 79,611 rare variants with a MAF<0.01. ”

Exome sequencing analysis:

“Population-level whole-exome sequencing (WES) variant genotypes (UKB data field: 23155) for 200,643 UKBiobank participants corresponding to 17,975,236 variants were downloaded using the gfetch utility. […] Finally, 12,394,404 non-coding variants were removed, and 5,176,300 protein-altering variants (stopgain, stoploss, startloss, splicing, missense, frameshift and in-frame indels) were retained in 173,688 samples.”

Regarding the request for further details about the mtDNA-CN phenotype and its determinants by the Reviewers, we performed an analysis of key determinants of mtDNA-CN including age, sex, ethnicity, and blood cell traits and presented these in the Results section:

“In the larger UKBiobank dataset of 395,781 with suitable array-based estimates, the distribution of mtDNA-CN was approximately normal (Figure 2A). […] Collectively, total white blood cell, platelet, and neutrophil counts explained 12.3% variance in mtDNA-CN levels.”

In response to Reviewer #2’s recommendation to perform further benchmarking against sequencing datasets, we attempted to compare array-based estimates to WES-based estimates in a subset of 412 INTERSTROKE participants who had been exome sequenced. Unfortunately, the exome capture method strongly depleted for the mitochondrial genome thus leading to insufficient coverage to make mtDNA-CN estimates. Furthermore, we hope the reviewers will appreciate that not only will additional experimentation require more time and costs, but also INTERSTROKE samples have undergone extensive molecular testing and thus limited DNA quantities remain for these precious samples. Moreover, we note that our method has been benchmarked to the same, if not higher, standard than other array-based methods. Whereas AutoMitoC was compared to both qPCR and exome sequencing-based estimates in two independent datasets, the MitoPipeline was benchmarked against qPCR only, and the method by Hagg et al. (2021) was benchmarked against whole-exome sequencing-based calls in the UKBiobank only. To our knowledge, neither competing method has been benchmarked in different ethnic groups individually as we had done, so the extensibility of their methods outside of European participants remains unproven. Granted, we agree that further benchmarking could be performed and thus have amended the manuscript Discussion (page 18, lines 25-29): accordingly:

“AutoMitoC was benchmarked using multiple comparative mtDNA-CN estimation modalities, in independent cohorts, and in various ethnic groups, but further work can be done to assess the reproducibility and error of mtDNA-CN estimates, calibration to absolute counts, and the robustness to different genotyping platforms.”

2. More thorough Mendelian Randomisation analysis: test for pleiotropy using Heidi; perform comparative analysis using all genome wide significant hits (with accompanying discussion of any differing results).

We greatly appreciate the suggestion to perform additional sensitivity MR analyses. Accordingly, we have repeated MR analyses using the GSMR method which not only incorporates HEIDI outlier removal (HEIDI P < 0.05) but also accounts for the uncertainty in SNP-outcome effect estimates (unlike IVW, weighted median, and MR-EGGER) to improve statistical power. GSMR yielded highly consistent results to our previous analyses; among the 10 phenotypes tested, only dementia was statistically significant (OR=1.95; 95% CI, 1.34-2.84; P=0.0005). This effect estimate was highly concordant with that previously obtained from IVW (OR=1.93; 95% CI, 1.32-2.84; p=0.00008). Furthermore, as per the reviewer’s recommendation, we also performed GSMR using the broader set of genome-wide significant SNPs (irrespective of being located proximal to or within a mitochondrial protein-encoding gene). The association with dementia remained directionally concordant and statistically significant (OR=1.31; 95% CI, 1.02-1.68; P=0.035). Consequently, we have amended the Results section (page 15, lines 17-23) to reflect this addition:

“Findings were robust across several different MR methods including the Weighted Median (OR=2.47; 95% CI, 1.93-3.00; P=0.001), MR-EGGER (OR=2.41; 95% CI, 1.71-3.11; P=0.02), and GSMR-HEIDI (OR=1.95; 95% CI, 1.34-2.84; P=0.001) methods. Results also remained statistically significant when using a broader set of genetic instruments including all genome-wide significant loci irrespective of whether variants were located proximally to MitoCarta3 genes (GSMR-HEIDI OR=1.31; 95% CI, 1.02-1.68; P=0.04).”

Regarding the reviewer’s comment about larger confidence intervals for dementia, this is likely explained by lower prevalence of dementia cases as compared to other phenotypes assessed. Although the replication of this result using an independent set of outcome summary statistics provides further support that this association is unlikely due to chance alone, we further acknowledge this important limitation in the Discussion (page 18, lines 40-42; page 19, lines 1-3).

“Fourth, Mendelian Randomization analyses were underpowered to conduct a broad survey of diseases in which mitochondrial dysfunction may play a causal role, and even among the ten phenotypes tested, statistical power varied by disease prevalence as evidenced by relatively large confidence intervals for the dementia association. Accordingly, future studies including larger case sample sizes are necessary to provide more precise effect estimates.”

3. Place work in context of other two highly similar recent studies (Longchamps 2021 and Hagg 2021).

We thank the reviewers and editorial team for raising this very important consideration. The major ways in which our work distinguishes itself from the other studies is as follows:

1. AutoMitoC represents a methodological advance in array-based mtDNA-CN estimation over the MitoPipeline and the novel method proposed by Hagg et al. (2021) in several aspects summarized below:

a. Superior performance as quantified by correlation coefficient with alternative mtDNA-CN measurement techniques:

i. qPCR-based (MitoPipeline R=0.51 vs. AutoMitoC R=0.64)

ii. WES-based (Hagg R=0.33 vs. AutoMitoC R=0.45)

b. Ease-of-implementation, performance, and evaluation in ethnically diverse studies

c. The removal of problematic probes is streamlined through empirical filtering as opposed to manual curation which is time-consuming and subjective (as in the MitoPipeline).

d. Unlike the method by Hagg et al. but in the same manner as the MitoPipeline, AutoMitoC only necessitates array genotyping data to derive mtDNA-CN estimates. In contrast, Hagg et al. also requires secondary molecular testing (e.g. exome sequencing as in Hagg et al.) in a subset of array-genotyped samples for calibration purposes.

e. AutoMitoC has undergone more intensive benchmarking than either method including comparison to mtDNA-CN estimates from two different modalities and in two independent datasets (as mentioned above in the response to Essential Revision #2).

2. We undertook additional sensitivity testing to verify the robustness of identified genetic associations to NUMT interference. Following the scheme developed by Nandeyakumar et al. (2021), we rederived mtDNA-CN estimates using sliding window tertiles of MT SNPs and then repeated the GWAS to assess whether genetic associations remained consistent across the three mitochondrial genome segments. If genetic signals were specific to a single region, then this may suggest that NUMTs may underlie the association. Notably, such sensitivity analyses were not performed by either group which leaves opens the possibility that some of these reported associations may be driven by NUMTs.

3. As acknowledged by Reviewer #3, we present several unique downstream analyses not present in the other works:

a. Mendelian Randomization analyses to assess contexts where genetically determined mtDNA-CN levels may influence disease risk

b. Rare variant analyses to identify genes in which rare protein-altering variation impacts mtDNA-CN levels.

c. Phenome-wide association testing for rare variants in SAMHD1 to understand associated phenotypic consequences.

We also performed a direct comparison of the genome-wide significant associations identified by all three studies by searching hits from the other two studies in our own GWAS analyses and is now reflected in the Results section (page 9, lines 1-31):

“We also compared our findings to genome-wide significant results from two recent GWAS of mtDNA-CN by Hagg et al. (2020) and Longchamps et al. (2021) to better contextualize our findings. […] Notably, these 11 associations were also not reported in Hagg et al. (2020).”

We also note that the work by Hagg et al. (2020) has gone through peer review and is published by Human Genetics, whereas to our knowledge, the work by Longchamps et al. (2021) remains in pre-print. Given that the findings from Longchamps et al. (2021) has not gone through due process (albeit appears to be of excellent quality), we hope the reviewers and editorial team can understand our decision to focus the discussion of the comparison of papers on the peer-reviewed work by Hagg et al., out of respect for the peer review process. Granted, our work will still add to the mtDNA-CN narrative by Longchamps et al. (2021) when published for the aforementioned reasons. Accordingly, we have amended the following Discussion paragraph (page 16, lines 17-39) comparing our work to Hagg et al. (2020):

“While several investigations for mtDNA-CN have been published, the present study represents the most comprehensive genetic assessment published to date(Cai et al., 2015; Longchamps, 2019; Hägg et al., 2020). […] Finally, in the present study we included complementary explorations of the role of rare variants through ExWAS and PheWAS, as well as, Mendelian Randomization analyses to assess disease contexts whereby mtDNA-CN may represent a causal mediator and a potential therapeutic target.“

Lastly, given the stronger focus of the revised manuscript on the methodology and as a way to further differentiate our work, with the permission of the editorial team and reviewers, we propose to amend the title of the work to the following: "AutoMitoC: Novel pipeline to estimate mitochondrial DNA copy number from genotyping arrays identifies 71 loci and potential role in dementia."

4. Data release: code should be on GitHub, raw data for mtDNA-CN estimates and covariates provided where possible, summary statistics uploaded to GWAS catalogue.

We have released the code on Github (https://github.com/GMELab/AutoMitoC). An example dataset comprising an anonymized subset of the validation samples including age, sex, and qPCR-based mtDNA-CN estimates is made publicly available for testing.

Raw data for mtDNA-CN estimates for the 395,781 participants have been returned to the UKBiobank data show case (as per the screenshots below) and can be accessed by any registered UKBiobank researcher.

Full genome-wide summary statistics have been uploaded to the GWAS catalogue and are available. The Data and Materials availability statement has been updated to reflect the current statuses:

5. A bit more discussion of potential population structure confounding? You focus is on individuals of European decent, with meta-analysis including non-Europeans giving similar results.

We undertook several precautions to mitigate confounding from population structure (and substructure). Firstly, in all GWAS analyses, association testing was initially performed within each subpopulation separately (British, Irish, Other Caucasian). These stratified analyses limit the potential for SNPs associated with British ancestry, for example, to drive GWAS associations with mtDNA-CN if mtDNA-CN levels systematically differ between subpopulations. Notably, most UKB-based GWAS analyses consolidate these European subpopulations together which would render it more susceptible to this exact type of bias, which is why we opted for this stratified approach. Secondly, to address issues of population substructure within these ethnic groups, we also adjusted for a relatively high number (20) of intra-ethnic genetic principal components. Thirdly, to mitigate the risk of confounding from inter-ethnic differences in downstream analyses as much as possible (e.g. Mendelian Randomization analysis), we also restricted the use of mtDNA-CN GWAS summary statistics to those derived from European participants only to better match the predominantly European ancestry comprising external databases. Lastly, population structure confounding typically manifests as a systematic and strong inflation in genome-wide P-values which was not observed (LD-score intercept=1.036 which is close to the null expectation of 1.00). Accordingly, we added the following to the Discussion (page 18, lines 34-40):

“Third, we did not detect strong evidence of confounding from population structure (or substructure) in our genetic analyses, and we undertook several precautions to mitigate such biases including (i) stratified GWAS analyses by ethnicity, (ii) adjustment for a large number of intra-ethnic principal components, and (iii) restriction of downstream analyses to use of European GWAS summary statistics to harmonize with external databases, however, it is still plausible that population substructure may have influenced our findings.”

Reviewer #1:

[…]

The only improvement I would like to see lies in the description of the estimated phenotype. There is little detail about its distribution, and I would also like to learn more about the factors influencing it, including, obviously, an estimate of much of the variation your 71 loci explain, and a discussion of what explains the rest of the variation. The UK Biobank is full of data…

We greatly thank the reviewer for these comments and the suggestion. Most of the reviewer’s comments have been relayed as part of Essential Revision #1. Accordingly, please see our detailed response to Essential Revision #1.

As per the reviewer’s additional suggestion to mention the amount of variance explained by identified loci, we have now commented on this in the Results section.

Common loci (page 8, lines 20-21): “These 80 independent genetic signals explained 1.48% variance in mtDNA-CN levels”.

Rare loci (page 13, lines 17-18): “Rare variants in SAMHD1 and TFAM accounted for 0.06% of the variance in mtDNA-CN levels. Collectively, rare and common loci accounted for 1.55%.”

Reviewer #2:

Mitochondrial DNA copy number is a significant aspect of mitochondrion function, both of which have been linked to disease states. This is an interesting assessment of the genetic basis for variation in mitochondrial DNA copy number. The authors describe a new method for mtDNA CN estimation, although additional benchmarking and validation is warranted in this reviewer's opinion. The GWAS/ExWAS analyses are fairly standard and identify genetic variants likely influencing mtDNA CN. The implementation of the MR analyses is more superficial; it remains unclear in this reviewer's opinion that pleiotropy has been effectively ruled out. The estimates of mtDNA CN and other non-other non-identifiable information (eg, sex, age, ethnic group, etc) necessary to replicate the author's findings should be made publicly available as easily accessible supplementary files.

1. Regarding the method for mtDNA estimation. The method is barely described in the main body of the manuscript. More information should be provided in the main manuscript given that the method it is so essential for this work. Greater clarity about the reproducibility would have helped. Also, it appears that the estimates of mtDNA CN were not tested against a standard with known CN, could this be performed? Some benchmark was done with qPCR estimates, while the results of the methods here developed and qPCR are positively correlated, the magnitude of the correlation is moderate. Finally, wow does the new method compare with sequencing based estimates? All in all, more benchmarking of the method in the main manuscript would greatly improve the paper.

We thank the reviewer for their important comments regarding better description and benchmarking of the AutoMitoC method. This concern was relayed as Essential Revision #1 by the Editorial team. Accordingly, please see our response above to Essential Revision #1.

2. Besides better description of the methods for mtDNA CN estimation, a much more detailed description of the mtDNA CN data should be provided in the main manuscript. It is not possible to properly understand/appreciate the reported associations and MR results without such description of the underlying mtDNA CN data.

The reviewer raises an excellent point which was relayed by the editorial team as a component of Essential Revision #1. Accordingly, please see our response above to this Essential Revision #1.

3. In this reviewer's opinion the pre-computed mtDNA estimates and other non-identifiable information (eg, sex, age, ethnic group, etc) necessary to replicate the author's findings should be made publicly available in supplementary text format files and that could be easily accessible to readers. If the text files are too large for eLife the authors should upload the files to Dryad, dbGaP, etc.

Unfortunately, we are prohibited by the UKBiobank to share individual-level data even if de-identified. The UKBiobank Material Transfer Agreement states that, “The Applicant shall not share, sub-license, disclose, transfer, sell, gift or supply the Materials to any other person or unauthorised third party” (https://www.ukbiobank.ac.uk/media/5cclro0y/applicant-mta-data-only-2021.pdf). However, we have returned the derived individual-level data to the UKBiobank data showcase to enable other approved UKBiobank researchers to access mtDNA-CN estimates. For further details, please see our above response to “Essential Revision #4”. Also, an example dataset derived from our validation cohort with age, sex, and qPCR mtDNA-CN measurements are made available through our GitHub page (https://github.com/GMELab/AutoMitoC ).

4. The author's state that "After quality control, 359,689 British, 10,598 Irish, 13,189 Other White, 6,172 South Asian, and 6,133 African samples had suitable array-based mtDNA-CN estimates for subsequent GWAS testing. It is unclear what the authors mean by "suitable" in this case. It would also be good to describe in the main text how many were unsuitable in each case and the reasons why these estimates did not match the requirement for suitability.

Thank you very much for this suggestion which was relayed by the editorial team as part of Essential Revision #1. As such, we have provided a description of sample exclusion scheme in the Materials and methods section of the main manuscript. For further details, please see above our response to Essential Revision #1.

5. It is unclear if the authors detected ethnic and/or population differences that might confound the other downstream analyses. It is also unclear what are the error and reproducibility of the CN estimates.

We thank the reviewer for pointing out this important confounder. As such, we have further explored the impact of ethnicity on mtDNA-CN levels and added the following to the Results (page 6, lines 35-37):

“MtDNA-CN levels also differed between ethnicities with South Asians (β=-0.18; 95% CI, -0.16 to -0.21; P=2.93x10-47) and Africans (β=-0.18; 95% CI, -0.16 to -0.21; P=8.39x10-48) having significantly lower levels than Europeans.”

Notably, in the independent INTERSTROKE dataset with qPCR-based mtDNA-CN measurements, we replicated the lower AutoMitoC-based mtDNA-CN levels observed in South Asians (β=-0.18; 95% CI, -0.33 to -0.03; P=0.02) and Africans (β = -0.11; 95% CI, -0.20 to -0.02; P=0.01) relative to Europeans.

To mitigate potential for detecting spurious associations due to inter-ethnic differences, we employed several precautions including (i) performing stratified GWAS analysis within each ethnic group, (ii) adjustment for the top 20 intra-ethnic genetic principal components to capture population substructure within ethnic groups, and (iii) the use of ancestrally matched datasets for downstream analyses. Further details are available above in our response to Essential Revision #5.

To the reviewer’s latter point, indeed, the error and reproducibility of AutoMitoC was not evaluated, and accordingly, we have acknowledged this limitation in the Discussion (page 18, lines 27-29):

“… further work can be done to assess the reproducibility and error of mtDNA-CN estimates, calibration to absolute counts, and the robustness to different genotyping platforms.”

6. The influence of sex and age are not described (or superficially described).

The reviewer raises an excellent point which was relayed by the editorial team as a component of Essential Revision #1. Accordingly, please see our response above to this essential revision.

7. The presentation of the MR results is unconvincing. For the analysis, the authors have not ruled out pleiotropy in my opinion. Heterogeneity tests such as Heidi should be used Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481-487 (2016) in addition to MREgger. Also, dementia seems to have the largest error bars around its OR estimates. More detailed analyses and discussion could help here.

We thank the reviewer for their suggestions to further scrutinize the robustness of the MR findings with alternative methods and sensitivity analyses. These important considerations were relayed by the editorial team as “Essential Revision #2”. Accordingly, please see our response above to Essential Revision #2.

Reviewer #3:

[…]

1. Page 3, lines 3-8 (but also throughout the text): Whilst it is appreciated that there is a vast body of literature pointing to links between mtDNA-CN and complex disease, I think it is worth briefly discussing caveats to these results in order to properly frame the relevance of the work. For instance, there are many studies showing that links between blood derived mtDNA-CN and age-related diseases may at least in part be driven by cell type composition. There is also plenty of debate around whether mtDNA-CN in blood is indicative of processes in other tissues which might be more relevant for each particular disease. Much of these caveats have been recently neatly summarised in Picard 2021.

We tremendously appreciate the reviewer’s comment that the positioning of mtDNA-CN as a mitochondrial biomarker was too simplified and should have been more nuanced. Also, thank you very much for providing the very informative reference by Picard (2021). Accordingly, we have better framed the relevance of the work by changing the language around mtDNA as a mitochondrial biomarker throughout the text and by adding the following exposition to the Introduction (page 3, lines 20-37):

“… In contrast to marked drops in mtDNA-CN by 60-80% as seen in those with rare mtDNA depletion syndromes, the relevance of subtler perturbations in mtDNA-CN in disease risk remain to be determined (Basel, 2020).

Granted, the connection between blood mtDNA-CN and aspects of mitochondrial biology remains unclear with many studies showing only moderate correlation between mtDNA-CN and markers of mitochondrial function or biogenesis (Frahm et al., 2005; Wachsmuth et al., 2016). Further complicating the interpretation of epidemiological associations between blood mtDNA-CN and disease risk is the fact that (i) blood mtDNA-CN is strongly confounded by blood cell composition, particularly, platelets that are devoid of nuclei and that (ii) blood mtDNA-CN does not correlate well with mtDNA-CN measured in other tissues in which mitochondrial dysfunction may be more relevant (Picard, 2021). Accordingly, understanding the genetic determinants of blood mtDNA-CN may provide a better understanding of the etiological processes reflected by this poorly understood mitochondrial biomarker in the blood.

To interrogate mtDNA-CN as a potential determinant of human diseases and to better understand its biological relevance to mitochondria, we performed extensive genetic investigations in up to 395,781 participants from the UKBiobank study(Sudlow et al., 2015).”

2. Page 9, lines 16-17: "We postulated that mtDNA-CN loci may regulate copy number by inducing changes in expression of genes that are directly transcribed from mtDNA.": I don't understand this rationale – are you postulating that lower/higher expression from the MT genome may trigger changes in copy number (and thus these may be modulated by variants influencing expression)? If so, this should be stated more clearly, if not, please explain the thinking here. I also think that it is worth rewording your conclusions from this analysis, as there is no test of directionality.

We apologize for the confusion here and have rephrased the rationale more clearly (page 9, lines 38-40):

“We postulated that differential expression of genes encoded by the mitochondrial genome may trigger changes in copy number, and thus a subset of identified loci may affect mtDNA-CN through mitochondrial genome transcription.”

3. Page 9: lines 31-41: Similar to point 2, it would be good to be a clear rationale for the comparison of loci associated with mtDNA-CN and heteroplasmy level.

Thank you for this comment. For further clarity, we have added a rationale for these analyses (page 10, lines 14-20):

“Heteroplasmy refers to the coexistence of multiple mtDNA alleles within an individual for a particular variant, which is a function of the multicopy nature of the mitochondrial genome. A recent GWAS by Nandakumar et al. (2021) for mean heteroplasmy levels in saliva specimen provided initial evidence supporting a shared genetic basis for heteroplasmy and copy number. To further explore the overlap in genetic determinants of these traits, we searched for the previously reported heteroplasmy loci within our mtDNA-CN GWAS.”

4. Page 10: lines 29-35: It would be good to see statistical tests comparing the fractions of genes that fall into the mitochondrial process categories described here, versus proportions of genes in these categories in Mitocarta as a whole – are these categories enriched for genes identified in this study?

The reviewer raises an excellent point and in response, we have performed enrichment analyses comparing the observed frequency of process categories to the null expectation based on the broader database of 1120 nuclear genes. Using a binomial test, we observe a significant enrichment in mitochondrial central dogma genes and have integrated these additions into the Results section (page 11, lines 19-24) accordingly:

“Most (16; 57%) genes were members of the “Mitochondrial central dogma” pathway, which represents a nearly 3-fold enrichment as compared to the frequency of this pathway in the whole MitoCarta3 database (null expectation = 20.7%; P=1.3x10-5). Other implicated (albeit not significantly enriched) pathways included “Metabolism”, “Mitochondrial dynamics and surveillance”, “Oxidative phosphorylation”, and “Protein import, sorting and homeostasis” (Figure 3D).”

5. Page 25: lines 20-31: Links should be provided in any revised manuscript, including the GitHub repository for the method, and summary statistics should be uploaded to the GWAS catalogue.

Thank you very much for this suggestion which was relayed by the editorial team as an essential revision. Accordingly, please see our detailed response above to essential revisions comment #4.

6. Figures: In general, the figures are not particularly clear and should be improved for publication. For example, axes text is often too small and difficult to read, and text labels on figures 1 and 2 are not aligned properly.

Thank you for this suggestion – we agree that the figure quality could have been improved. Accordingly, we provided higher resolution images for all figures, increased axes text size, and re-aligned text labels for Figures 1 and 2 (now Figures 3 and 4).

https://doi.org/10.7554/eLife.70382.sa2

Article and author information

Author details

  1. Michael Chong

    1. Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada
    2. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
    3. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0555-4622
  2. Pedrum Mohammadi-Shemirani

    1. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
    2. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    3. Thrombosis and Atherosclerosis Research Institute, Hamilton, Canada
    Contribution
    Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6740-7858
  3. Nicolas Perrot

    Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    Contribution
    Formal analysis, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Walter Nelson

    Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada
    Contribution
    Conceptualization, Methodology, Software, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Robert Morton

    1. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
    2. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    Contribution
    Conceptualization, Supervision, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0099-4167
  6. Sukrit Narula

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Ricky Lali

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
    Contribution
    Data curation, Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Irfan Khan

    1. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
    2. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Mohammad Khan

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Conor Judge

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. National University of Ireland, Galway, Galway, Ireland
    Contribution
    Conceptualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Tafadzwa Machipisa

    1. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
    2. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    3. Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa
    4. Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA) & Cape Heart Institute (CHI), Department of Medicine, University of Cape Town, Cape Town, South Africa
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Nathan Cawte

    Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    Contribution
    Data curation, Validation, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Martin O'Donnell

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. National University of Ireland, Galway, Galway, Ireland
    Contribution
    Conceptualization, Data curation, Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Marie Pigeyre

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
    Contribution
    Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Loubna Akhabir

    1. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    2. Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
    Contribution
    Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Guillaume Paré

    1. Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada
    2. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
    3. Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences, Hamilton, Canada
    4. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
    5. Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
    Contribution
    Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review and editing
    For correspondence
    pareg@mcmaster.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6795-4760

Funding

No external funding was received for this work.

Acknowledgements

We want to acknowledge the participants and investigators of the UKBiobank and FinnGen studies.

Ethics

Approval was received to use UKBiobank study data in this work under application ID # 15,255 ("Identification of the shared biological and sociodemographic factors underlying cardiovascular disease and dementia risk"). The UKBiobank study obtained ethics approval from the North West Multi-centre Research Ethics Committee which encompasses the UK (REC reference: 11/NW/0382). All research participants provided informed consent.

Senior Editor

  1. YM Dennis Lo, The Chinese University of Hong Kong, Hong Kong

Reviewing Editor

  1. Magnus Nordborg, Austrian Academy of Sciences, Austria

Reviewer

  1. Magnus Nordborg, Austrian Academy of Sciences, Austria

Publication history

  1. Preprint posted: April 14, 2021 (view preprint)
  2. Received: May 14, 2021
  3. Accepted: January 11, 2022
  4. Accepted Manuscript published: January 13, 2022 (version 1)
  5. Version of Record published: February 23, 2022 (version 2)

Copyright

© 2022, Chong et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,586
    Page views
  • 298
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Michael Chong
  2. Pedrum Mohammadi-Shemirani
  3. Nicolas Perrot
  4. Walter Nelson
  5. Robert Morton
  6. Sukrit Narula
  7. Ricky Lali
  8. Irfan Khan
  9. Mohammad Khan
  10. Conor Judge
  11. Tafadzwa Machipisa
  12. Nathan Cawte
  13. Martin O'Donnell
  14. Marie Pigeyre
  15. Loubna Akhabir
  16. Guillaume Paré
(2022)
GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia
eLife 11:e70382.
https://doi.org/10.7554/eLife.70382

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Jayashree Kumar et al.
    Research Article Updated

    Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.

    1. Cell Biology
    2. Genetics and Genomics
    Heyun Guo et al.
    Research Article

    In the first meiotic cell division, proper segregation of chromosomes in most organisms depends on chiasmata, exchanges of continuity between homologous chromosomes that originate from the repair of programmed double-strand breaks (DSBs) catalyzed by the Spo11 endonuclease. Since DSBs can lead to irreparable damage in germ cells, while chromosomes lacking DSBs also lack chiasmata, the number of DSBs must be carefully regulated to be neither too high nor too low. Here, we show that in Caenorhabditis elegans, meiotic DSB levels are controlled by the phosphoregulation of DSB-1, a homolog of the yeast Spo11 cofactor Rec114, by the opposing activities of PP4PPH-4.1 phosphatase and ATRATL-1 kinase. Increased DSB-1 phosphorylation in pph-4.1 mutants correlates with reduction in DSB formation, while prevention of DSB-1 phosphorylation drastically increases the number of meiotic DSBs both in pph-4.1 mutants as well as in the wild type background. C. elegans and its close relatives also possess a diverged paralog of DSB-1, called DSB-2, and loss of dsb-2 is known to reduce DSB formation in oocytes with increasing age. We show that the proportion of the phosphorylated, and thus inactivated, form of DSB-1 increases with age and upon loss of DSB-2, while non-phosphorylatable DSB-1 rescues the age-dependent decrease in DSBs in dsb-2 mutants. These results suggest that DSB-2 evolved in part to compensate for the inactivation of DSB-1 through phosphorylation, to maintain levels of DSBs in older animals. Our work shows that PP4PPH-4.1, ATRATL-1, and DSB-2 act in concert with DSB-1 to promote optimal DSB levels throughout the reproductive lifespan.