Mitochondrial heteroplasmy is a risk factor for the development of chronic lymphocytic leukemia

Domain: chemistry
Published: 2026-02-18
Editorial outcome: major_revision

Abstract
Chronic lymphocytic leukemia (CLL) can arise from lymphoid clonal hematopoiesis of indeterminate potential (L-CHIP), but many individuals who develop CLL lack detectable L-CHIP prior to diagnosis. To identify additional predictors of CLL risk, we analyze mitochondrial heteroplasmy in 419,154 individuals from the UK Biobank (UKB). Heteroplasmy is associated with a 1.5-fold increased risk of developing CLL, and this risk rises to 4-fold when accounting for deleterious heteroplasmic variants. These findings are confirmed in an independent cohort, the All of Us Research Program (AoU). Notably, the associations remain significant even in the absence of L-CHIP, highlighting heteroplasmy’s potential utility as an independent biomarker. Moreover, heteroplasmy is enriched in individuals with high-risk L-CHIP genotypes and large clonal burden, suggesting a potential biological role in malignant transformation. Here, we show that mitochondrial heteroplasmy, especially functionally deleterious variants, identifies individuals at increased risk of CLL who would otherwise go undetected by L-CHIP-based assessments.

Introduction
With ageing, stem cells acquire an increasing number of somatic DNA alterations, including mutations and chromosomal aberration (CA). These DNA defects mark a genetically distinct cell population, a condition also known as somatic mosaicism. Occasionally, somatic DNA changes confer functional consequences leading to a relative growth advantage of the mutated cell 1–4. In the context of hematopoiesis, such clonal expansion of a hematopoietic stem cell is known as clonal hematopoiesis (CH) 5–7. Many leukemia-associated genes and mosaic CAs have been found in individuals with CH and have been associated with increased risk of hematologic malignancies 4,6–8. A distinct classification of CH has been proposed based on the association between various genotypes and hematologic malignancy 8. Myeloid clonal hematopoiesis of indeterminate potential (M-CHIP), defined as the expansion of a hematopoietic clone(s) carrying somatic mutation(s) found in myeloid neoplasms (MN) 9,10, is the most frequent. A subset of CH with somatic mutations observed in lymphoid malignancies represents lymphoid CHIP (L-CHIP) 8. Even though L-CHIP is associated with the incidence of lymphoid malignancies, mainly chronic lymphocytic leukemia (CLL), many individuals who develop CLL do not have L-CHIP present years prior to clinical diagnosis 8. Thus, L-CHIP only captures a subset of individuals who will develop CLL.

Because mitochondria have a less efficient DNA repair system compared to the nucleus, mitochondrial DNA (mtDNA) is more prone to mutations 11,12. Heteroplasmy is defined as the presence of these mutations in only a subset of mtDNA. Given the frequent occurrence of mtDNA mutations, heteroplasmy has been commonly used as a clonal marker 13. We have shown that heteroplasmy occurring in the hematopoietic system is associated with incident hematologic malignancy in general 14. In addition, we have demonstrated that likely deleterious heteroplasmies are associated with incident MN 15, suggesting that heteroplasmy is not only a clonal marker, but may play a biological role in the development of hematologic malignancies.

Here, we show that heteroplasmy is an independent predictor of CLL in general and in conjunction with L-CHIP.

Results
Cohort characteristics
The study included 419,154 individuals from the UKB with available whole exome sequencing (WES) and whole genome sequencing (WGS) data. Individuals with prevalent CLL or unexplained lymphocytosis (absolute lymphocyte count over 5000/µL) were excluded from the analysis. The median (Q1, Q3) [range] age was 58 (50, 63) [38, 72] years, 45.8% (n=191,926) were men, and 94.4% (n=395,819) were self-identified as White.

Using WES data restricted to 235 genes involved in lymphoid malignancies (Supplementary Table 1) 8, we identified a total of 12,683 L-CHIP mutations at a variant allele fraction (VAF) ≥ 2%, which were present in 10,746 (2.6%) individuals, similarly low to the previously published 1.3% (Supplementary Table 2) 8. The L-CHIP mutations were evenly distributed among various genes, without the distinct predominance that is typically observed in M-CHIP (Fig. 1A). Similarly to previous reports, DUSP22, FAT1, KMT2D, SYNE1, ATM, KMT2C, PCLO, SPEN, ARID1A, NEB, MGA, RP1L1, SMARCA4, CIITA, DLC1, PTPRD, ATR were most commonly mutated L-CHIP genes 8. Most individuals with L-CHIP had only one mutation, with multiple mutations seen in only 11.5% of those with L-CHIP (n=1232) (Fig. 1B). L-CHIP was significantly associated with older age (P<0.0001) and smoking status (P<0.01) (Table 1 and Fig. 1C). These associations were attenuated but remained significant when comparing individuals with L-CHIP only to those without either L-CHIP or M-CHIP (Table 2).

MtDNA heteroplasmy was identified from WGS data using the MitoHPC pipeline at a VAF threshold of 5% (VAF 5-95%) as previously described 16. Because of this threshold, the observed heteroplasmies likely represented mutations that underwent clonal expansion, rather than the full underlying mtDNA mutational rate, as very small clones would not be detected. For each mtDNA variant, we used a modified mitochondrial DNA local constraint (mMLC) score, which quantifies the local tolerance to base or amino acid substitution for each base pair in the mtDNA genome and thus predicts the likely deleterious effects of each variant 17. To capture the overall impact of multiple heteroplasmies in a given individual, we generated the mMLC score sum (mMSS) by adding all mMLC scores for that individual. Heteroplasmy was present in 118,493 (28.3%) individuals, with Complex I and D-loop as the most frequently mutated complexes/regions (Supplementary Fig. 1A). We observed differences in mMSS across complexes/regions, with the D-loop presenting some of the lowest mMSS values, while tRNA and rRNA presented some of the highest mMSS values (Supplementary Fig. 1B). Among those with heteroplasmy, 22,720 (19.2%) had multiple heteroplasmies (Supplementary Fig. 1C). With regards to phenotype associations, we observed that heteroplasmy was more frequent in older individuals and in smokers (Supplementary Table 3).

Further, we analyzed a random subset of the All of Us Research Program (AoU, n=40,930) to confirm our findings in an independent cohort. Heteroplasmy was present in 11,064 (27.0%) individuals with a distribution of heteroplasmy count and mMSS similar to that observed in the UKB (Supplementary Fig. 2A). More specifically, Complex I and D-loop were the most frequently mutated complex/region. In addition, D-loop and the highest in tRNA and rRNA presented some of the lowest mMSS values (Supplementary Fig. 2B), consistent with the findings in the UKB.

Among those with heteroplasmy, 2086 (18.9%) had multiple heteroplasmies (Supplementary Fig. 2C). Individuals who were older or ever smokers were also more likely to have heteroplasmy (Supplementary Table 4).

Heteroplasmy and the risk of CLL
We have previously reported an association between heteroplasmy and incident hematologic malignancies, particularly MN 14,15. Here, we sought to determine whether the presence of heteroplasmy in the general population was associated with an increased risk of CLL. The incidence of CLL in the UKB was 0.1% (463 cases) with a median follow-up of 13.8 (13.0, 14.5) years. Compared to those without heteroplasmy, individuals with 1 or more heteroplasmies had a higher risk of developing CLL even after adjusting for age, sex, smoking status, and a history of cancer (hazard ratio [HR]=1.5; 95% confidence interval [CI] 1.3–1.8; P<0.0001) (Fig. 2A). All subsequent analyses in this paper included age, sex, smoking status, and a history of cancer as covariates.

Both heteroplasmy count (HR=1.4; 95% CI 1.2–1.5; P<0.0001) and mMSS (HR=4.2; 95% CI 3.2–5.5; P<0.0001) were associated with a higher risk of incident CLL. In addition, when including heteroplasmy count and mMSS in the same model, heteroplasmy count (HR=1.0; 95% CI 0.9–1.2; P=0.76) was not associated with CLL incidence, while mMSS (HR=4.0; 95% CI 2.7–5.9; P<0.0001) remained a significant risk factor for CLL (Fig. 2B). Since higher mMSS indicates the presence of likely deleterious variants, its significant association with CLL incidence, after adjusting for heteroplasmy count, suggests possible causal effect rather than merely a marker of clonal expansion.

Next, we sought to determine if the location of mtDNA mutations had a differential effect on the development of CLL vs. MN. We observed that higher mMSS in complex IV was more strongly associated with MN than CLL (Supplementary Table 5 and Supplementary Fig. 3).

To test the robustness of the heteroplasmy VAF filter used, we reevaluated the association between heteroplasmy count/mMSS and CLL incidence using heteroplasmy at 3% and 10% VAF (Supplementary Table 6). We observed that results remained consistent even when using different VAF thresholds.

Given the role of mtDNA-CN as a marker of hematopoietic aging and the interplay between mtDNA-CN and blood counts, we also evaluated the association between heteroplasmy count/mMSS and CLL incidence when adjusting for mtDNA-CN and for blood counts (absolute neutrophil count, absolute lymphocyte count, absolute monocyte count, red blood cell count and platelet count) (Supplementary Table 7). Even after adjusting for mtDNA-CN and blood counts, heteroplasmy count and mMSS remained associated with CLL incidence.

We observed similar associations between heteroplasmy and CLL in an independent cohort, the AoU, using a case-cohort design. In addition to the random subset of 20% of the eligible participants (n=40,930), we identified all other cases of CLL in AoU (n=56), leading to a total of 87 CLL cases in the case-cohort, with a median follow-up of 3.9 (1.6–4.6) years. After adjusting for age, sex, and smoking status, individuals with ≥1 heteroplasmies had a higher risk of developing CLL compared to those without heteroplasmy (HR 2.0; 95% CI 1.3–3.1; P=0.001) (Supplementary Fig. 4A). Both heteroplasmy count (HR 1.5; 95% CI 1.2–1.8; P=0.001) and mMSS (HR 4.1; 95% CI 2.5–6.8; P<0.0001) were associated with an increased risk of CLL. In addition, when both heteroplasmy and mMSS were included in the same model, mMSS (HR 4.3; 95% CI 1.9–9.4; P<0.001) remained significantly associated with CLL incidence while heteroplasmy count was not (HR 1.0; 95% CI 0.7–1.4; P=0.89) (Supplementary Fig. 4B).

The effect of L-CHIP on CLL incidence
Consistent with previous findings 8, we observed that L-CHIP was associated with incident CLL (HR=5.1; 95% CI 3.9–6.7; P<0.0001) (Fig. 3A). Next, we sought to determine if clonal burden (maximum VAF per individual) and number of mutations were also associated with incident CLL. We observed that, relative to individuals without L-CHIP, both VAF≥10% (HR=6.9; 95% CI 4.9–9.6; P<0.0001) and multiple mutations (HR=12.7; 95% CI 7.7–21.0; P<0.0001) were associated with a higher risk of incident CLL (Fig. 3A).

We then evaluated the association between the recurrent L-CHIP gene mutations and the incidence of CLL. We identified eight high-risk genes: MYD88, IGLL5, NOTCH1, XPO1, LTB, TCL1A, TAF1 and DUSP2 (Fig. 3B and Supplementary Table 8). In contrast, M-CHIP was not associated with CLL incidence in L-CHIP negative individuals (HR=1.3; 95% CI 0.9–1.8; P=0.11).

Association between L-CHIP and heteroplasmy
We and others 14,18–20 observed that a notable number of heteroplasmies are somatic, and thus, mark the clonal expansion of the cells in which they occur. Using WGS, somatic heteroplasmy can only be detected when the mutation-carrying clone is significantly expanded relative to polyclonal counterparts. Previously, we have shown that likely functional heteroplasmy was associated with the presence of M-CHIP 15. Thus, we expected to find a similar association between heteroplasmy and L-CHIP. L-CHIP and heteroplasmy were concurrently present in 0.8% of individuals, while 27.5% and 1.8% of individuals were found to have either heteroplasmy or L-CHIP only, respectively (Fig. 4A). Notably, heteroplasmy was more common in L-CHIP carriers compared to individuals without L-CHIP (31.4% vs 28.2%; P<0.0001) (Fig. 4B). Among L-CHIP carriers, heteroplasmy was significantly associated with large L-CHIP clones (VAF≥10%) (33.4% vs 29.6%; P<0.001) but not with multiple mutations (33.0% vs 31.2%; P=0.14) (Fig. 4B). Heteroplasmy was also significantly more common in individuals with high-risk L-CHIP genotypes (39.0% vs 31.1%; P<0.01) (Fig. 4B and Supplementary Fig. 5). Among individuals with heteroplasmy, those with L-CHIP were more likely to have multiple heteroplasmies compared to those without L-CHIP (21.9% vs 19.1%; P<0.001) (Fig. 4C). Only L-CHIP clonal burden (VAF≥10%) (23.9% vs 20.0%; P=0.01), but not multiple L-CHIP mutations (21.2% vs 22.0%; P=0.80) nor high-risk L-CHIP mutations (23.0% vs 21.9%; P=0.99), was associated with the presence of multiple heteroplasmies (Fig. 4C).

To determine whether the higher prevalence of heteroplasmy in L-CHIP was just a marker of CH (passenger mtDNA mutations) rather than having a functional impact, we assessed the association between L-CHIP and mMSS. Because mMSS comprises not only the functional prediction of each variant but also the sum of all heteroplasmies in an individual, our analysis was adjusted for heteroplasmy count. Among those with heteroplasmy, mMSS was significantly higher in L-CHIP, particularly in individuals with higher clonal burden (VAF≥10%) and with multiple L-CHIP mutations. Even though not statistically significant, there was a trend towards significance for mMSS and high-risk L-CHIP genotypes compared to other L-CHIP (Fig. 4D, Supplementary Fig. 5 and Supplementary Table 9). Overall, these data suggest that the presence of mtDNA mutations may not only be a marker of clonal expansion but may also play an important role in clonal expansion and malignant transformation.

Previously, we have shown that M-CHIP and its high-risk subtypes are associated with heteroplasmy 15. In the current study, we observed that L-CHIP was significantly associated with M-CHIP (P<0.0001). To ensure M-CHIP was not a confounder, we excluded individuals with M-CHIP mutations and found that heteroplasmy was more prevalent in L-CHIP (P<0.0001), large L-CHIP clones (VAF≥10%) (P=0.02) and high-risk L-CHIP mutations (P=0.02). Moreover, we observed that mMSS was higher in individuals with L-CHIP (P<0.0001) and those with multiple L-CHIP mutations (P=0.02) (Supplementary Table 10), indicating its possible functional effect.

The impact of concurrent L-CHIP and heteroplasmy on the risk of CLL
We then explored the potential interaction between L-CHIP and heteroplasmy on CLL risk (Fig. 5A). Both, L-CHIP (HR=5.3; 95% CI 3.7–7.5; P<0.0001) and heteroplasmy alone (HR=1.5; 95% CI 1.2–1.8; P<0.0001) were independent risk factors for CLL (Fig. 5B). The risk of CLL was over 7-fold higher in individuals with concurrent L-CHIP and heteroplasmy (Fig. 5B).

Since heteroplasmy was associated with high-risk L-CHIP, we examined whether heteroplasmy was associated with incident CLL after adjusting for L-CHIP properties (large L-CHIP clones [VAF≥10%], multiple mutations, and high-risk L-CHIP mutations). Among L-CHIP individuals, neither the presence of heteroplasmy (HR=1.2; 95% CI 0.7–2.0; P=0.54), nor mMSS (HR=1.2; 95% CI 0.5–3.2; P=0.66), analyzed in independent models, were significantly associated with an increased risk of CLL after accounting for L-CHIP properties (Fig. 5C). Although these results suggest that heteroplasmy does not provide additional prognostic value in individuals with L-CHIP, heteroplasmy may still be biologically relevant in this population.

Since the majority of UKB individuals who developed CLL did not have L-CHIP (87.3%) (Fig. 5A), we examined whether heteroplasmy was an independent risk factor for CLL in those without L-CHIP. We found that the presence of heteroplasmy was associated 1.5-fold increase in the CLL risk (HR=1.5; 95% CI 1.2–1.8; P<0.0001). In addition, mMSS was associated with CLL incidence even after adjusting for heteroplasmy count (HR=4.1; 95% CI 2.7–6.1; P<0.0001), suggesting that deleterious heteroplasmies may have a direct functional impact on CLL development (Fig. 5D).

Methods
The UK Biobank (UKB) was approved by the UK Biobank Research Ethics Committee, and all participants provided written informed consent before participation. Informed consent was obtained from all individual participants included in the study, and the protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program.

Study population
The UKB is a large population-based prospective study of ~500,000 individuals between the ages of 40–69 years across the United Kingdom, recruited from 2006 to 2010 32. The UKB has extensive information on individual’s genetic and phenotypic data, including demographics and lifestyle factors. The data is linked to the death registry, cancer registry, hospital admissions, and primary care visit data.

We have previously described our filtering approach 15. Briefly, from the standpoint of heteroplasmy we excluded samples that presented either of the following criteria: 1) potential mitochondrial contamination (a contamination level ≥ 3% from Haplocheck (https://github.com/genepi/haplocheck) 34); 2) 2 or more heteroplasmic variants part of different haplogroups; 3) 2 or more variant matching to the same nuclear mitochondrial sequences (NUMT); 4) low minimum base coverage (<100) or low mean base coverage (<500); 5) low mtDNA copy number (mtDNA-CN) (≤40); 6) heteroplasmy count ≥ 6. We also excluded individuals based on missing data (M-CHIP, tobacco smoking, blood counts) or that were ineligible due to a history of MN or presence of a potential myeloproliferative neoplasm 15. In addition to these criteria, we excluded individuals with missing L-CHIP data (n=14,267), those with an excess of L-CHIP variants (n=0), those with a history of CLL (n=201) and those with lymphocytosis (>5000/µL; n=682). Finally, we included a total of 419,154 individuals.

The All of Us Research Program (AoU) is a large population-based cohort study including diverse populations over the age of 18 living in the US 35. It includes physical measurements, a survey of lifestyle factors, and a biospecimen for DNA collected at enrollment, in addition to linkage to electronic health records data. For this study, 317,964 individuals with short-read whole genome sequencing and electronic health records from the Controlled Data Repository v8 (C2024Q3R4, released on February 3, 2025) were considered. From these individuals, we constructed a case-cohort—a valid and efficient study design particularly useful when exposure assessment is resource-intensive or costly 36 using individuals free of cancer, cardiovascular disease, or sepsis at the time of DNA collection (n=248,388). We have further excluded a total of 24,290 individuals for either missing smoking information (n=7172) or having DNA from samples other than blood (n=17,781). Of these participants, 61 were excluded during heteroplasmy filtering steps. We then randomly selected 20% of the individuals as the subcohort (n=40,930), including 31 individuals who developed CLL. We further identified all other cases of CLL in AoU (n=56), leading to a total of 87 CLL cases in the case-cohort.

Definition of lymphocytosis and cytopenias
Lymphocytosis was defined as an absolute lymphocyte count over 5000/µL. Anemia was defined as a hemoglobin concentration under 13 g/dL in men and under 12 g/dL in women. Thrombocytopenia was defined as a platelet count under 150,000/µL 37,38.

Heteroplasmy and mitochondrial DNA copy number analysis in the UKB
Samples for DNA were collected on the baseline visit, and DNA was extracted from buffy coat using the Maxwell® 16 Instrument (Promega) and the Maxwell® 16 Blood DNA Purification Kit (Promega–AS1010X). WGS CRAM files from the UKB were processed on the DNA Nexus server. For heteroplasmy identification, we used the MitoHPC pipeline (version 20230418; all default settings with default random downsampling to use at most 222K reads) 16, implementing GATK Mutect2 39,40. We defined a heteroplasmic SNV at a VAF of 5%, meaning that VAFs of 5–95% within an individual are defined as heteroplasmic. Alleles less than 5% or greater than 95% are classified as homoplasmic. The MitoHPC pipeline incorporates haplogrep (https://github.com/seppinho/haplogrep-cmd/v2.4.0) 41 to identify haplogroups and Haplocheck (https://github.com/genepi/haplocheck) 34 to detect in-sample contamination by detecting two different mitochondrial haplotypes in each sample. For the mtDNA-CN calculation, we used SAMtools 42 embedded in MitoHPC to generate read count and coverage information, using the command ‘samtools idxstats’. A detailed documentation on how to run MitoHPC on the DNA Nexus server is available: https://github.com/ArkingLab/MitoHPC/blob/main/docs/DNAnexus_CLOUD.md.

All 490,355 WGS samples processed using MitoHPC variant calling were successfully completed 16. The median (range) mtDNA coverage was 904x (500x-1887x).

Heteroplasmy filtering for variants
MitoHPC provides various variant annotations to evaluate variant quality, allowing us to remove low-quality variants prior to analysis. We excluded variants with read depth <300 and those flagged as base quality, mapping quality, strand bias, slippage, weak evidence, position, clustered, fragment length, and haplotype flags in the FILTER column of the VCF. We further excluded heteroplasmic variants at poly-C homopolymer regions on the mitochondrial chromosome (the list is provided as HP.bed.gz in https://github.com/dpuiu/MitoHPC). INDELs are also excluded because they are often found in homopolymer regions, where it is challenging to accurately call heteroplasmies 16.

Mitochondrial local constraint (MLC) score
The mitochondrial local constraint (MLC) score is a metric reflecting local tolerance to substitutions of base or amino acid. It is calculated for every possible mtDNA SNV by applying a sliding window method 17. To account for heteroplasmic variants that also present as homoplasmic in the population, we refined the MLC score to account for the frequency of homoplasy in the UKB population by calculating the modified MLC (mMLC) as MLC/(1+log10(number of individuals with homoplasmy in the UKB+1)). mMLC was calculated using the individuals with a given homoplasmic variant in the UKB. To capture the functional impact of multiple heteroplasmies, we calculated an mMLC score sum (mMSS) by summing all mMLC scores within a given individual.

Heteroplasmy and mitochondrial DNA copy number analysis in the AoU
In the AoU, we called mitochondrial heteroplasmy and copy number from short-read WGS data using the same pipeline and filtering steps as we did for the UKB on the All of Us Researcher Workbench. The median (range) mtDNA coverage was 1096x (576x-1288x).

CHIP variant calling
WES CRAM files were aligned to hg38. Variant calling was performed using Genome Analysis Toolkit (GATK) v. 4.2.2. Mutect2 39,43. Mutect2 was run in ‘tumor-only’ mode using non-default parameters: gatk Mutect2… --panel-of-normals ${ref_pon} --germline-resource ${ref_germ}. Raw variants called by Mutect2 were filtered out with FilterMutectCalls using the estimated prior probability of a reading orientation artifact generated by LearnReadOrientationModel.

L-CHIP variant filtering
Variants were annotated using ANNOVAR 44 and filtered based on a pathogenic track and a putative track adapted from a previously published paper 8 and the resulted variants were further filtered in a combined track, as described below. Variants occurring on genes located on the X chromosome in males had their VAFs divided by 2.

Pathogenic track: (1) The presence in a custom L-CHIP gene panel (Supplementary Table 1); (2) a previously published list of variants 8; (3) Mutect2 FILTER of PASS or germline; (4) depth ≥ 20; (5) alternate allele count ≥ 3 in the case of SNVs; (6) alternate allele count ≥ 5 in the case of multinucleotide variants (MNV); (7) ALTF1R2 ≥ 1; (8) ALTF2R1 ≥ 1; (9) VAF ≥ 2% in the case of SNVs; (10) VAF ≥ 10% in the case of MNVs; (11) exclusion of synonymous SNVs; (12) the maximum allele frequency across non-cancer populations (non_cancer_AF_popmax) under 0.001 in the gnomAD exome collection v2.1.1 45.

Putative track: (1) The presence in a custom L-CHIP gene panel (Supplementary Table 1); (2) Mutect2 FILTER of PASS of germline; (3) depth ≥ 20; (4) alternate allele count ≥ 5 regardless of variant type; (5) ALTF1R2 ≥ 2; (6) ALTF2R1 ≥ 2; (7) VAF ≥ 2% in the case of SNVs; (8) VAF ≥ 10% in the case of MNVs; (9) VAF ≤ 20%; (10) exclusion of synonymous SNVs; (11) the maximum allele frequency across non-cancer populations (non_cancer_AF_popmax) under 0.001 in the gnomAD exome collection v2.1.1 45.

Combined track: (1) if a variant was called by both the pathogenic and putative track, only the pathogenic track variant was kept; (2) if a variant occurred > 1% of the cohort, the variant was removed; (3) variants with VAF ≥ 35% were removed, unless they were a known pathogenic nonsynonymous variant; (4) if a gene was mutated in more than 0.05% of the cohort, non-singleton variants in that gene were assessed and were removed all variants that occurred more than the median frequency of variants in that gene as long as the removed variants occurred 10 or more times and the removed variant was not a known pathogenic variant as reported by a previously published variant list 8.

M-CHIP variant calling, and variant filtering were previously described 15. SETDB1 was removed from the M-CHIP panel because it was also present in the L-CHIP panel.

On the sample level, we excluded individuals with an excess of L-CHIP variants considered as more than 3 mutations, all of which are represented by MNVs and individuals with more than 10 CHIP variants. Large CHIP clones were defined as those having a VAF ≥ 10%. Small CHIP clones were defined as having a VAF ≥ 2% and < 10%.

High-risk L-CHIP genes mutations were assessed within individuals with L-CHIP. For a gene to be considered high-risk, it had to present the following criteria: (1) had a significant positive association with incident CLL; (2) presented an HR greater than 1 in the multivariable Cox proportional hazards model where CLL incidence was the dependent variable; (3) was present in two or more individuals that developed CLL.

Definition of CLL
In the UKB, CLL was defined using ICD-9 204.1 and ICD-10 C91.1 codes from the cancer registry linked to the UKB collected on December 22, 2022.

In The AoU CLL was defined using ICD-9 (204.1), ICD-10 (C91.1), and SNOMED (92814006).

Statistical analysis
Categorical data was represented as an absolute count (percent). Frequencies of two categorical variables were analyzed using Fisher’s exact test. Normality of the distribution was assessed using skewness, kurtosis and histogram visualization. Continuous variables were presented as median (Q1, Q3) for variables with a non-Gaussian distribution. Differences between two groups with non-Gaussian distribution were assessed using the Mann-Whitney-Wilcoxon rank sum test. We used multivariable logistic regression models to evaluate the associations between CHIP and a binary variable for heteroplasmy. For the association between CHIP and mMSS, we used multivariable linear regression models. If the dependent variable was right skewed, we added 1 and log10-transformed it. The analyses were adjusted for age modeled as a restricted cubic spline (with 4 degrees of freedom), sex, tobacco smoking status (never or ever), and a history of cancer. Differences between two HR and corresponding 95% CIs were assessed using z-score=(coefficient1 - coefficient2)/(square root(standard error1^2 + standard error2^2)). The z-score was then converted to a p-value using the pnorm function in R.

For survival analysis, we used time from DNA collection to the development of CLL, death, or end of follow-up (administrative censoring on December 22, 2022), whichever occurred first. The cumulative incidence of CLL was presented as Kaplan-Meier curves. Hazard ratios and corresponding 95% confidence intervals were estimated using a multivariable Cox proportional hazards model, adjusting for age (restricted cubic splines with 4 degrees of freedom), sex, tobacco smoking (never or ever), and a history of cancer.

Robustness of the heteroplasmy VAF filter was assessed by evaluating the association between heteroplasmy count/mMSS and CLL incidence using heteroplasmy at 3%, 5% and 10% VAF cut-offs.

To assess if the association between heteroplasmy count/mMSS and CLL incidence is independent of mtDNA-CN and blood counts, we reevaluated the association between heteroplasmy count/mMSS and CLL incidence when adjusting for age, modeled as a restricted cubic spline (with 4 degrees of freedom), sex, tobacco smoking status (never or ever), a history of cancer, mtDNA-CN (natural log-transformed) and blood counts (absolute neutrophil count, absolute lymphocyte count, absolute monocyte count, red blood cell count and platelet count).

In the AoU, to account for case-cohort design, we applied appropriate weights to Cox proportional hazards models 46 to evaluate the association between mitochondrial heteroplasmy measures and incidence of CLL. As we have already excluded individuals with previous cancer, we adjusted for age (restricted cubic spline with 4 degrees of freedom), genetically determined sex, and smoking status (never or never).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Comments\nThe authors in their different manuscripts related to mtDNA heteroplasmy claim some unclear claims:\n1. “ Because mitochondrial DNA (mtDNA) has a less efficient DNA repair system compared\n67 to nuclear DNA, mtDNA is more prone to acquire mutations 11,12” This claim and the references they bring are not\ncorrect. Ref 11 is a review that is not relevant. Ref 12 is unrelated to somatic mutation rate but to germline and only refers to\nthe mtDNA control region.\nMore recent studies that calculated somatic mutation rate per base in the mitochondria identified 5.0 × 10−8 per base pair\n(https://doi.org/10.1038/s41588-024-01838-z) while the per base somatic mutation rate of the nuclear genome in mammals\n(including humans) is 2.8 × 10−7 (https://doi.org/10.1038/ncomms15183)\nBased on these estimates, the authors should provide clear numbers of how many somatic mtDNA mutations they find and\nto see that they match these estimates.\n2. The authors should provide data on the number of mutations and mut rate in the D-Loop control region versus other\nregions of the mtDNA, and their analysis should be adapted accordingly.\nThey should check whether their results are replicated for D-loop-only mutations, which are known to be related to\nreplication history, versus non-D-loop mutations.\n3. This claim needs to be supported by experiments. “Additionally, we have demonstrated that likely deleterious\nheteroplasmies are associated with incident MN, suggesting that heteroplasmy is not only a clonal marker, but may play a\nbiological role in the development of hematologic malignancies.” And the same is true for the same claim in CLL: “Moreover,\nheteroplasmy was enriched in individuals with high-risk L-CHIP features and large clonal burden, suggesting a potential\nbiological role in malignant transformation.” Such claim must be supported by a genetic model demonstrating the functional\nconsequences of such mutations.\n4. Most mtDNA mutations should not be under positive selection and estimating bottlenecks and selection is analytically\nproblematic I would suggest that the authors will embrace the methods presented in\nhttps://doi.org/10.1093/g3journal/jkad068 .\n5. Method of identification of mtDNA heteroplasmy: the references are given but there is not one fig/table/supplementary\nfigure in the paper describing the heteroplasmy itself. This makes the reading tedious. Also, the authors use a single method\nto detect heteroplasmy. In the fields of low VAF somatic mutations, especially from low-depth sequencing, more than 1\nvariant caller should be used and the results should be compared between the different variant callers.\n6. mMSS score sum - This concept seems to be presented for the first time and lacks explanation and validation of the\nassumption that it is a valid measure for the deleterious effects of multiple variants. It is controlled throughout the paper for\nnumber of heteroplasmies, but couldn’t there be biases in this estimation if each variant can appear in a broad and diverse\nrange of copies.\n7. In line 83 the median, Q1 and Q3 values of cohort age are presented, and in the methods the UK biobank is described to\ncontain samples at maximum age of 69. This should be described in the text (giving the full range of ages in addition to Q1-\nQ3), and discussed in the discussion as a limitation of the UKBB dataset in the context of CLL as an age-related disease.\n8. In line 89 (fig 1A) - following the fig, perhaps it should be shortly discussed in comparison with ref 8 which looked for the\nsame variants in a smaller cohort of UKBB. The similarities and differences in variants should be mentioned.\n9. The mutational signature of the heteoplasmic mutations should be presented for the various cohorts. This will help to\nbetter understand the mechanisms related to the mutagenesis and possibly filter noise.\n10. All supplementary tables should be in a CSV or TXT format so they can be analyzed by the reviewers and later by\nreaders.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Pasca et al. have performed an analysis of the UK Biobank in which they analyzes the relationship between mitochondrial\nDNA (mtDNA) heteroplasmy, predicted functional impact of mtDNA mutations (mMSS), and risk of chronic lymphocytic\nleukemia (CLL).\nThey find both heteroplasmy count and mMSS are associated with incident CLL, with mMSS emerging as the stronger\npredictor. L-CHIP is independently associated with CLL risk, particularly in individuals with large clones, multiple mutations,\nor high-risk genotypes. Notably, mtDNA heteroplasmy and mMSS are enriched in individuals with L-CHIP, and even in the\nabsence of L-CHIP, high mMSS remains a significant predictor of CLL.\nI have a number of major concerns that need addressing:\nOne fundamental problem here is establishing causation. Whilst the authors argue that mMSS, a prediction of deleterious\nmutations, is associated with CLL risk both in LCHIP negative patients, these deleterious mutations may also confer a\nselective advantage to clones resulting in expansion (https://pmc.ncbi.nlm.nih.gov/articles/PMC10996716/). This is a\nsignificant concern and the authors will need to address this.\nIf the authors argue that heteroplasmy could be a clinically useful biomarker, the authors should produce data to support\nsuch claims such as sensitivity, specificity, false positive, true positive rates etc. Moreover, in the context of CLL, early\ndiagnosis is not advantageous as therapeutic intervention early does not improve outcomes (in the current landscape) and\nthere are sensitive non-invasive tests that can identify CLL and it’s precursor MBL much more readily\nUnrecognized driver mutations of CHIP are thought to be a major contributor to CHIP\nhttps://www.nature.com/articles/s41588-024-01755-1. Therefore the LCHIP negative cohort may indeed not truly LCHIP\nnegative - this should be discussed as it may affect the interpretation of the results.\nPopulation - UK biobank, although big, has ascertainment issues, with volunteer bias and overrepresentation of individuals\nbelonging to different ancestry affecting the generalizability of findings. This should at least be discussed.\nThe authors should try and replicate their findings in another dataset\nAre there any CLL sequencing datasets where mitochondrial heteroplasmy and predictions of deleterious mitochondrial\nmutations have been studied? Such an analysis may help strength the analysis and interpretation of this study\nIt is possible that the authors have identified cases of MBL (as these will not be excluded by a lymphocyte filter <5,000/µL) in\nthe UK Biobank. If this is the case, what is the impact of this on the authors analysis and conclusions?\nWhat were the effects of age, sex, smoking status, and a history of cancer on CLL risk? And are these features associated\nwith heteroplasmy and mMLC?\nWhat is the distribution of the number of heteroplasmic mitochondrial clones across individuals?\nIs there a relationship between mitochondrial heteroplasmy, mMLC, mMSS and L-CHIP on the time from sampling to\ndiagnosis?", "reviewer_4": "This work neatly demonstrates that deleterious mitochondrial heteroplasmy can be used as a novel predictor of chronic\nlymphocytic leukemia, independent of L-CHIP. This highlights a promising new biomarker associated with CLL, however\nwhile I found the associative results to be compelling, I have concerns about the causal claims made throughout the\nmanuscript.\nThe authors model for heteroplasmy count and mMSS together, but these variables are inherently correlated due to mMSS\nbeing a cumulative sum over heteroplasmies. This dependence makes interpreting the functional importance of mMSS\ndifficult. It may be better to use an average mMLC to separate the functional severity of the mutations from the total burden.\nThe authors might also consider modelling the risk of CLL against average mMLC when restricted to only individuals\ncarrying heteroplasmy (both with and without L-CHIP) to better evaluate the functional importance of heteroplasmy beyond a\nmarker of clonal expansion.\nA further consideration is that mMSS does not account for the VAFs of the contributing mutations. Variants can often have\nheteroplasmy dependent effects, and so a VAF weighted mMSS (or VAF weighted average mMLC) may do better at\ncapturing the functional burden of these mutations.\nThe code provided does not come with a README or equivalent instructions for running. Without the data I cannot verify if\nthe figures would be reproduced, however the analysis supports what is said in the manuscript."}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nComments\nThe authors in their different manuscripts related to mtDNA heteroplasmy claim some\nunclear claims:\n1. “ Because mitochondrial DNA (mtDNA) has a less efficient DNA repair system\ncompared to nuclear DNA, mtDNA is more prone to acquire mutations” This claim and\nthe references they bring are not correct. Ref 11 is a review that is not relevant. Ref 12\nis unrelated to somatic mutation rate but to germline and only refers to the mtDNA\ncontrol region.\nMore recent studies that calculated somatic mutation rate per base in the\nmitochondria identified 5.0 × 10−8 per base pair (https://doi.org/10.1038/s41588-024-\n01838-z) while the per base somatic mutation rate of the nuclear genome in mammals\n(including humans) is 2.8 × 10−7 (https://doi.org/10.1038/ncomms15183)\nBased on these estimates, the authors should provide clear numbers of how many\nsomatic mtDNA mutations they find and to see that they match these estimates.\nThe mutation rates cited by the reviewer originate from two different studies, making the\ncomparison of these two mutation rates challenging. Reference 12 has been replaced with\n(Campbell et al., 2023) to specifically address the rate of somatic mutations within the\nhematopoietic system between nuclear and mitochondrial genomes. In this study, the\nauthors demonstrate that the mtDNA mutation acquisition rate is up to 270 times higher\nthan that of the nuclear genome. Additionally, reference 11 is meant to support the fact\nthat mitochondria have a less efficient DNA repair mechanism compared to the nucleus.\nThe number of mtDNA mutations we see (145,765 across 118,493 individuals) is reflective\nof heteroplasmies detected at a VAF of 5% or higher and should not be directly compared\nto mtDNA mutation rate from single cell colonies, which also captures mutations that do\nnot clonally expand to the point at which we would be able to detect them.\nWe also added the following statement in the text: “Because of this threshold, the\nobserved heteroplasmies likely represented mutations that underwent clonal expansion,\nrather than full underlying mtDNA mutational rate as very small clones would not be\ndetected.”\n2. The authors should provide data on the number of mutations and mut rate in the D-\nLoop control region versus other regions of the mtDNA, and their analysis should be\nadapted accordingly.\nThey should check whether their results are replicated for D-loop-only mutations,\nwhich are known to be related to replication history, versus non-D-loop mutations.\nWe added the following text to the manuscript: “Heteroplasmy was present in 118,493\n(28.3%) individuals, with Complex I and D-loop as the most frequently mutated\ncomplexes/regions (Supplementary Figure 1A). We observed differences in mMSS across\ncomplexes/regions, with the D-loop presenting some of the lowest mMSS values, while\ntRNA and rRNA presented some of the highest mMSS values (Supplementary Figure 1B).\nAmong those with heteroplasmy, 22,720 (19.2%) had multiple heteroplasmies\n(Supplementary Figure 1C). With regards to phenotype associations, we observed that\nheteroplasmy was more frequent in older individuals and in smokers (Supplementary Table\n3).”\nAdditionally, please note that, even though the mutational rate is higher in the D-loop, we\nwill only be able to detect these mutations if they are present at a VAF of 5% or higher.\nThus, we are assessing only mtDNA mutations that have expanded enough to be detected.\nIn Supplementary Table 5 (previously, Supplementary Table 3), we presented the effects of\nmMSS of different complexes/regions on CLL and MN incidence. Mutations in the D-loop\nwere strongly associated with both CLL and MN incidence. In the same table, we have also\nprovided the number of individuals that presented mutations in each of these\ncomplexes/regions.\nOverall, while mutations in the D-loop region are more common, we observe lower mMSS\nvalues in this region. However, the mMSS of these mutations were still positively\nassociated with the incidence of CLL and MN.\nSupplementary Figure 1. Description of heteroplasmy in the UKB. A) Distribution of the\nnumber of individuals with heteroplasmy stratified by complex/region. B) Distribution of\nmMSS stratified by complex/region. Only individuals with heteroplasmy in the evaluated\ncomplex/region are shown. Graphical representation of mMSS is truncated at mMSS = 2.\nC) Number of heteroplasmies per individual, among those that have heteroplasmy.\nSupplementary Table 3. Individual characteristics by presence of heteroplasmy in the\nUKB.\nContinuous variables are displayed as median (Q1, Q3). Individual characteristics were\nbased on the time of DNA collection.\nAbbreviations: L-CHIP, lymphoid clonal hematopoiesis of indeterminate potential.\nSupplementary Table 5. Risk of CLL and MN by complex/region-specific mMSS and the\ndifference between the CLL and MN risk by each complex in the UKB.\nmMSS values for individual complexes/regions were included in the same model. These\nanalyses were adjusted for age modeled as a restricted cubic spline, sex, smoking status\nand the presence of prevalent cancer. Hazard ratios and 95% CI were not determined for\ngroups with fewer than 10 events.\nAbbreviations: mMSS, modified mitochondrial DNA local constraint (MLC) score sum;\nCLL, chronic lymphocytic leukemia; MN, myeloid neoplasm; HR, hazard ratio; CI, confidence\ninterval.\n3. This claim needs to be supported by experiments. “Additionally, we have\ndemonstrated that likely deleterious heteroplasmies are associated with incident\nMN, suggesting that heteroplasmy is not only a clonal marker, but may play a\nbiological role in the development of hematologic malignancies.” And the same is\ntrue for the same claim in CLL: “Moreover, heteroplasmy was enriched in individuals\nwith high-risk L-CHIP features and large clonal burden, suggesting a potential\nbiological role in malignant transformation.” Such claim must be supported by a\ngenetic model demonstrating the functional consequences of such mutations.\nWe agree with the reviewer that association does not imply causation, and that the\nobserved enrichment of likely deleterious mutations in individuals with incident CLL or\nhigh-risk L-CHIP features does not establish a direct biological role in malignant\ntransformation. As such, we have been careful throughout the manuscript to present these\nfindings as associative rather than causal. Demonstrating functional consequences of\nspecific heteroplasmic mutations through genetic modeling is an important next step and\nis part of our future research plans but is beyond the scope of the current study.\nTo address this, we have revised the “Discussion” to read: “Further, the hypothesis that\nlikely deleterious heteroplasmies have biological consequences is supported by our\nprevious study demonstrating that deleterious heteroplasmies are more likely to increase\nover time and such increase is associated with overall mortality 21. Nonetheless, functional\nstudies are needed to confirm the causal role of heteroplasmy in CLL development.”\nTo further support the likely biological consequences of altered mitochondrial function in\nmalignant transformation, we added the following to the “Discussion” section:\n“Even though the current results only demonstrate an association between heteroplasmy\nand leukemogenesis, there has been emerging literature suggesting that altered\nmitochondrial function contributes to leukemia pathogenesis. Specifically, studies in\nacute myeloid leukemia (AML), have shown intrinsic limitations in the oxidative\nphosphorylation flux 22. To circumvent this bioenergetic stress, complex V can consume\nATP rather than generate it pumping protons into the mitochondrial intermembrane space\nto sustain inner membrane polarization. This adaptation has been causally linked to\ntherapy resistance 23. Given the deleterious effects of mtDNA mutations on oxidative\nphosphorylation, it is plausible that dysfunctional mitochondria rely on ATP consumption\nto maintain their integrity.”\n4. Most mtDNA mutations should not be under positive selection and estimating\nbottlenecks and selection is analytically problematic I would suggest that the authors\nwill embrace the methods presented in https://doi.org/10.1093/g3journal/jkad068 .\nWe appreciate the reviewer's suggestion, and we have carefully read over the paper\nreferenced by the reviewer, and do not see how it directly applies to our study. The main\nissue is that the methods referenced focused on estimating mitochondrial bottleneck size\nand heteroplasmy selection using samples from mother/offspring, with the estimates they\ngenerate relying on frequency distributions of a specific variant and then comparing\nobserved to the expected distribution under a neutral model (Kimura distribution) for that\nvariant. Conversely, our study focuses on thousands of different likely somatic/acquired\nmtDNA mutations that arise in the context of clonal hematopoiesis and malignant\ntransformation. These mutations are subject to very different selective pressures than\nthose operating during normal development. In particular, malignant transformation is\ncharacterized by bioenergetic reprogramming, altered metabolic dependencies, and\nevasion of apoptosis—all of which may create a permissive or even selective environment\nfor certain mtDNA variants. While we acknowledge the limitations of current models, we\nbelieve that the selection pressures acting on mtDNA in hematologic malignancies merit\ndistinct consideration.\n5. Method of identification of mtDNA heteroplasmy: the references are given but there\nis not one fig/table/supplementary figure in the paper describing the heteroplasmy\nitself. This makes the reading tedious.\nWe agree with the reviewer that a description of heteroplasmy would be valuable in this\npaper. Please see our response to point #2.\nAlso, the authors use a single method to detect heteroplasmy. In the fields of low VAF\nsomatic mutations, especially from low-depth sequencing, more than 1 variant caller\nshould be used and the results should be compared between the different variant\ncallers.\nWe would like to note that the median mtDNA coverage in the UKB was 904. Additionally,\nwe are excluding mtDNA variants with a read depth < 300. For clarity, we added the\nfollowing sentence in the “Methods” section: “The median (range) mtDNA coverage was\n904x (500x-1887x).”. Pertaining to the variant caller, we have previously assessed multiple\nvariant calling methods (Mutect2, Mutserve, Mitoverse and MToolBox) and Mutect2, which\nis incorporated in MitoHPC, was proven to have the lowest number of false positive and\nfalse negative calls (Battle et al., 2022). Additionally, we have shown the reproducibility of\nMitoHPC between two different longitudinal measures of heteroplasmy (Kuiper et al.,\n2025).\n6. mMSS score sum - This concept seems to be presented for the first time and lacks\nexplanation and validation of the assumption that it is a valid measure for the\ndeleterious effects of multiple variants. It is controlled throughout the paper for\nnumber of heteroplasmies, but couldn’t there be biases in this estimation if each\nvariant can appear in a broad and diverse range of copies.\nEven though we agree that mMSS has been introduced recently, this is not the first time\nthat it has been described or used. This concept was first introduced by Lake et al. (Lake et\nal., 2024) and was shown to be associated with mortality and MN in the UK Biobank,\nAtherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis and\nRotterdam Study (Hong et al., 2023, 2024; Kuiper et al., 2025).\nTo assess if the percent of mutated mtDNA copies would have an impact in our analyses,\nwe have assessed the association between the maximum heteroplasmy VAF per sample\nand CLL incidence. No significant association was observed in all individuals with\nheteroplasmy (HR = 1.0; 95% CI 0.5–1.7; P = 0.89), individuals with only one heteroplasmy\n(HR = 0.8; 95% CI 0.4–1.6; P = 0.46) and in individuals with a single heteroplasmy with VAF\n< 50% (HR = 0.7; 95% CI 0.1–3.6; P = 0.70). We have also assessed the correlation between\nL-CHIP VAF and heteroplasmy VAF in individuals harboring one L-CHIP mutation and a\nsingle heteroplasmy with a VAF < 50%. Using both a multivariable linear model (Beta-\nestimate = 0.0005214; P = 0.12) and a Spearman’s rank correlation (rho = 0.03; P = 0.16)\nand found no evidence of significant correlation.\nThus, the percent of mtDNA copies mutated offers limited additional prognostic value.\n7. In line 83 the median, Q1 and Q3 values of cohort age are presented, and in the\nmethods the UK biobank is described to contain samples at maximum age of 69. This\nshould be described in the text (giving the full range of ages in addition to Q1-\nQ3), and discussed in the discussion as a limitation of the UKBB dataset in the context of CLL as an age-related disease.\nWe have replaced “The median (Q1, Q3) age was 58 (50, 63) years, 45.8% (n = 191,926)\nwere men and 94.4% (n = 395,819) were self-identified as White.” with “The median (Q1,\nQ3) [range] age was 58 (50, 63) [38, 72] years, 45.8% (n = 191,926) were men and 94.4% (n\n= 395,819) were self-identified as White.”\nPlease note that the range between 40 and 69 years is quoted from (Sudlow et al., 2015).\nThis refers to the majority of individuals included. In the current cohort, there were 1987\n(0.47%) individuals over 69 years, and 5 (0.001%) individuals under 40 years. In addition,\nthis refers to the age at baseline and individuals were followed for approximately 15 years\nsince enrollment, providing enough time for a large number of the individuals to be old\nenough to be diagnosed with CLL.\nWe have added the following statement in the limitation section of Discussions: “Third, the\nUKB presents volunteer bias and overrepresentation of individuals belonging to one\nancestry. Specifically, socio-behavioral traits, like education, are known to have an effect\non UKB participation. Additionally, because CLL is typically a disease of older adults, the\nrelatively young age of UKB individuals at enrollment (as young as 40 years, with most\nunder 69) may limit the ability to fully capture incident cases of CLL. Some individuals may\nnot have developed CLL during the study period due to their younger age at baseline 31,32.”\n8. In line 89 (fig 1A) - following the fig, perhaps it should be shortly discussed in\ncomparison with ref 8 which looked for the same variants in a smaller cohort of UKBB.\nThe similarities and differences in variants should be mentioned.\nWe agree with the reviewer that discussing these similarities would add value to the paper.\nThus, we added/modified the following segments:\n“Using WES data restricted to 235 genes involved in lymphoid malignancies\n(Supplementary Table 1) 8, we identified a total of 12,683 L-CHIP mutations at a variant\nallele fraction (VAF) ≥ 2%, which were present in 10,746 (2.6%) individuals, similarly low to\nthe previously published 1.3% (Supplementary Table 2) 8.”\n“Similarly to previous reports, DUSP22, FAT1, KMT2D, SYNE1, ATM, KMT2C, PCLO, SPEN,\nARID1A, NEB, MGA, RP1L1, SMARCA4, CIITA, DLC1, PTPRD, ATR were most commonly\nmutated L-CHIP genes 8.”.\n9. The mutational signature of the heteoplasmic mutations should be presented for\nthe various cohorts. This will help to better understand the mechanisms related to the\nmutagenesis and possibly filter noise.\nWe assessed the mutational signatures of heteroplasmy and their association with CLL\nand MN incidence. We restricted the cohort to only individuals with one heteroplasmy or\nno heteroplasmy (n = 396,434; L-CHIP n = 10,007; M-CHIP n = 29,471; incident CLL events\n= 419; incident MN events = 968). In the current subset of the cohort, we observe 92,532\nindividuals carrying transitions and 3241 individuals carrying transversions (Ti/Tv = 28.6).\nWe used multivariable logistic regression models adjusted for age modeled as a restricted\ncubic spline (with 4 degrees of freedom), sex, tobacco smoking status (never or ever), and\na history of cancer to evaluate the associations between mMSS of different bases and the\npresence of either L-CHIP or M-CHIP. Transition mMSS tended to have a higher point-\nestimate compared to transversions in the case of M-CHIP (P = 0.10). Further, transition\nmMSS was associated with M-CHIP more than with L-CHIP (P < 0.0001). This supports the\nfunctional aspect of heteroplasmies, as, if these mutations were random, we would not\nsee differences between M-CHIP and L-CHIP.\nRegarding the use of mutational signatures to filter noise: We assume that the reviewer\nrefers to filtering out sequencing errors, specifically pertaining to some approaches that\naim to consider transversions as errors in the mtDNA. To evaluate whether transversions\nare errors or not, we assessed the association between mMSS of transitions, transversions\nand individual base changes on MN and CLL incidence. Survival analysis was performed\nusing a multivariable Cox-proportional hazards model adjusted for age modeled as a\nrestricted cubic spline (with 4 degrees of freedom), sex, tobacco smoking status (never or\never), and a history of cancer. In this analysis, we see a positive association between\ntransversion mMSS and the incidence of MN. Considering the small number of individuals\ncarrying transversions and the lower incidence of CLL compared to MN, it is likely that the\napparent lack of association of transversion mMSS with CLL incidence is due to power\nconsiderations.\n10. All supplementary tables should be in a CSV or TXT format so they can be analyzed\nby the reviewers and later by readers.\nWe appreciate the reviewer’s suggestion and have now provided a Supplementary Data\nFile.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nReviewer #3 (Remarks to the Author):\nPasca et al. have performed an analysis of the UK Biobank in which they analyzes the\nrelationship between mitochondrial DNA (mtDNA) heteroplasmy, predicted functional\nimpact of mtDNA mutations (mMSS), and risk of chronic lymphocytic leukemia (CLL).\nThey find both heteroplasmy count and mMSS are associated with incident CLL, with\nmMSS emerging as the stronger predictor. L-CHIP is independently associated with\nCLL risk, particularly in individuals with large clones, multiple mutations, or high-risk\ngenotypes. Notably, mtDNA heteroplasmy and mMSS are enriched in individuals with\nL-CHIP, and even in the absence of L-CHIP, high mMSS remains a significant predictor\nof CLL.\nI have a number of major concerns that need addressing:\nOne fundamental problem here is establishing causation. Whilst the authors argue\nthat mMSS, a prediction of deleterious mutations, is associated with CLL risk both in\nLCHIP negative patients, these deleterious mutations may also confer a selective\nadvantage to clones resulting in expansion\n(https://pmc.ncbi.nlm.nih.gov/articles/PMC10996716/). This is a significant concern\nand the authors will need to address this.\nWe appreciate the reviewer highlighting this important limitation of epidemiological\nstudies- namely, that association does not imply causation. We have addressed this\nconcern above-please see our response to Reviewer’s 1 point 3.\nWe would like to thank the reviewer for noting our paper on “Deleterious mitochondrial\nheteroplasmies exhibit increased longitudinal change in variant allele fraction”. In it, we\nhave shown that deleterious heteroplasmies are more likely to increase in VAF over time\nand that an increase in heteroplasmy VAF is associated with overall mortality. This is\nsupportive of the link between heteroplasmy and malignancy development. Thus, we are\nnot sure we completely understand the reviewer’s concern.\nTo address this, we have revised the “Discussion” to read: “Further, the hypothesis that\nlikely deleterious heteroplasmies have biological consequences is supported by our\nprevious study demonstrating that deleterious heteroplasmies are more likely to increase\nover time and such increase is associated with overall mortality 21. Nonetheless, functional\nstudies are needed to confirm the causal role of heteroplasmy in CLL development.”\nIf the authors argue that heteroplasmy could be a clinically useful biomarker, the\nauthors should produce data to support such claims such as sensitivity, specificity,\nfalse positive, true positive rates etc.\nWe appreciate the reviewer’s insight. We have decided that the main focus of this paper\nrevolves around the biological insights offered by heteroplasmy, rather than its clinical\nutility. Thus, we have decided to remove: “and that it may present clinical utility in\ncapturing individuals at risk for CLL that would have otherwise been missed by L-CHIP\nassessment only”.\nMoreover, in the context of CLL, early diagnosis is not advantageous as therapeutic\nintervention early does not improve outcomes (in the current landscape) and there are\nsensitive non-invasive tests that can identify CLL and it’s precursor MBL much more\nreadily\nWe agree with the reviewer that, in the context of CLL, therapeutic intervention early does\nnot improve outcomes and that there are sensitive non-invasive tests that can identify CLL\nand it’s precursor MBL much more readily. However, the comprehensive evaluation of L-\nCHIP and heteroplasmy brings additional insight into CLL precursor states. For example,\nthe presence of high-risk L-CHIP gene mutations, the number of L-CHIP mutations and\npredicted deleterious effect of heteroplasmy can better identify individuals at risk for CLL.\nFurther, even though it may not be acted on clinically, at least not now, the evaluation of\neither nuclear or mtDNA mutations is still biologically important. It is possible that this will\nprovide further insight into the biology of CLL precursor states, transformation to CLL and\nthe role of mitochondria in this process which may have both preventive and therapeutic\nimplications.\nAcknowledging the reviewer's concerns, we added the following statement in the\n“Discussion” section: “Monoclonal B-cell lymphocytosis (MBL), a precursor state for CLL,\nis present in up to 12% of general population and can be readily identified using flow\ncytometry 28–30. However, only 1-2% of individuals with MBL progress to clinically apparent\nCLL. The assessment of L-CHIP and heteroplasmy may further risk stratify persons with\nMBL. It may also provide additional insight into the mechanism of progression and the role\nof mitochondria in this process.”\nUnrecognized driver mutations of CHIP are thought to be a major contributor to CHIP\nhttps://www.nature.com/articles/s41588-024-01755-1. Therefore the LCHIP negative\ncohort may indeed not truly LCHIP negative - this should be discussed as it may affect\nthe interpretation of the results.\nWe agree with the reviewer that there might be unrecognized driver mutations in the L-\nCHIP negative individuals, and added the following statement in the Discussion section:\n“ Although we assessed mutations in all common drivers of lymphoid malignancies,\nclonal expansion in L-CHIP negative individuals may still be driven by currently unidentified\ndrivers 24.”\nPopulation - UK biobank, although big, has ascertainment issues, with volunteer bias\nand overrepresentation of individuals belonging to different ancestry affecting the\ngeneralizability of findings. This should at least be discussed.\nWe agree with the reviewer that the UK biobank does have limitations regarding volunteer\nbias and overrepresentation of individuals belonging to different ancestry. Also, please\nnote the response to Reviewer’s 1 point #7. Thus, we added the following statement to the\nDiscussion section:\n“Third, the UKB presents volunteer bias and overrepresentation of individuals belonging to\none ancestry. Specifically, socio-behavioral traits, like education, are known to have an\neffect on UKB participation. Additionally, because CLL is typically a disease of older\nadults, the relatively young age of UKB individuals at enrollment (as young as 40 years, with\nmost under 69) may limit the ability to fully capture incident cases of CLL. Some individuals may\nnot have developed CLL during the study period due to their younger age at baseline 31,32.”\nThe authors should try and replicate their findings in another dataset\nWe have replicated our findings in the All of Us Research Program, a large population-\nbased cohort study including diverse populations living in the US (All of Us Research\nProgram Investigators et al., 2019). From the 317,964 individuals with short-read whole\ngenome sequencing and electronic health records data, we implemented a case-cohort\nstudy design, which is a cost-effective design while providing valid estimates from an\nexisting cohort. The case-cohort includes a subcohort (20%) of individuals randomly\nselected from the eligible cohort of individuals who did not have a history of cancer and all\nindividuals who developed CLL. Of the 40,986 individuals included in the case-cohort\ndesign, 87 (31 from the random sample and 56 from the remaining AoU participants\neligible for this study) developed CLL with a median (Q1, Q3) follow up of 3.9 (1.6–4.6)\nyears. In the random subcohort (n = 40,930), heteroplasmy was identified in 11,064\n(27.0%) individuals. The distribution of heteroplasmy count (median 0; range 0 – 5) and\nmMSS (median 0; range 0–2.58) were similar to those observed in the UK Biobank. After\nadjusting for age (restricted cubic splines with 4 degrees of freedom), sex, and smoking\nstatus (never vs. ever), the HR (95% CI) between mMSS and incident CLL was 4.1 (95% CI\n2.5–6.8; P = 4.5x10-8), similar to what we observed in the UK Biobank. We also found an\nincreased risk of CLL among people with any heteroplasmy (HR 2.0; 95% CI 1.3–3.1; P =\n0.001) or a higher heteroplasmy count (HR 1.5; 95% CI 1.2–1.8; P = 0.001) compared to\nthose who did not have heteroplasmy. As whole exome sequencing was not performed in\nthe All of Us Research Program to call CHIP, we performed the replication only for the\nassociation between heteroplasmy and CLL. In response, we have added the description\nof relevant results and methods as follows:\nResults\n“Further, we analyzed a random subset of the All of Us Research Program (AoU, n = 40,930)\nto confirm our findings in an independent cohort. Heteroplasmy was present in 11,064\n(27.0%) individuals with a distribution of heteroplasmy count and mMSS similar to that\nobserved in the UKB (Supplementary Figure 2A). More specifically, Complex I and D-loop\nwere the most frequently mutated complex/region. Additionally, mMSS was the lowest in\nD-loop and the highest in tRNA and rRNA (Supplementary Figure 2B), consistent with the\nfindings in the UKB. Among those with heteroplasmy, 2,086 (18.9%) had multiple\nheteroplasmies (Supplementary Figure 2C). Individuals who were older or ever smokers\nwere also more likely to have heteroplasmy (Supplementary Table 4).”\n“We observed similar associations between heteroplasmy and CLL in an independent\ncohort, the AoU, using a case-cohort design. In addition to the random subset of 20% of\nthe eligible participants (n = 40,930), we identified all other cases of CLL in AoU (n = 56),\nleading to a total of 87 CLL cases in the case-cohort, with a median follow up of 3.9 (1.6–\n4.6) years. After adjusting for age, sex, and smoking status, individuals with ≥1\nheteroplasmies had a higher risk of developing CLL compared to those without\nheteroplasmy (HR 2.0; 95% CI 1.3–3.1; P = 0.001) (Supplementary Figure 4A). Both\nheteroplasmy count (HR 1.5; 95% CI 1.2–1.8; P = 0.001) and mMSS (HR 4.1; 95% CI 2.5–\n6.8; P < 0.0001) were associated with an increased risk of CLL. Additionally, when both\nheteroplasmy and mMSS were included in the same model, mMSS (HR 4.3; 95% CI 1.9–\n9.4; P < 0.001) remained significantly associated with CLL incidence while heteroplasmy\ncount was not (HR 1.0; 95% CI 0.7–1.4; P = 0.89) (Supplementary Figure 4B).”\nMethods\nStudy population\n“The All of Us Research Program (AoU) is a large population-based cohort study including\ndiverse populations over the age of 18 living in the US 34. It includes physical\nmeasurements, survey of lifestyle factors, and biospecimen for DNA collected at\nenrollment, in addition to linkage to electronic health records data. For this study, 317,964\nindividuals with short-read whole genome sequencing and electronic health records from\nthe Controlled Data Repository v8 (C2024Q3R4, released on February 3, 2025) were\nconsidered. From these individuals, we constructed a case-cohort—a valid and efficient\nstudy design particularly useful when exposure assessment is resource-intensive or costly\n35 using individuals free of cancer, cardiovascular disease, or sepsis at the time of DNA\ncollection (n = 248,388). We have further excluded a total of 24,290 individuals for either\nmissing smoking information (n = 7,172) or having DNA from samples other than blood (n =\n17,781). Of these participants, 61 were excluded during heteroplasmy filtering steps. We\nthen randomly selected 20% of the individuals as the subcohort (n = 40,930), including 31\nindividuals who developed CLL. We further identified all other cases of CLL in AoU (n = 56),\nleading to a total of 87 CLL cases in the case-cohort.”\n“Heteroplasmy and mitochondrial DNA copy number analysis in the AoU\nIn the AoU, we called mitochondrial heteroplasmy and copy number from short-read WGS\ndata using the same pipeline and filtering steps as we did for the UKB on the All of Us\nResearcher Workbench. The median (range) mtDNA coverage was 1096x (576x-1288x).”\nDefinition of CLL\n“In The AoU CLL was defined using ICD-9 (204.1), ICD-10 (C91.1), and SNOMED\n(92814006).”\nStatistical analysis\n“In the AoU, to account for case-cohort design, we applied appropriate weights to Cox\nproportional hazards models 45 to evaluate the association between mitochondrial\nheteroplasmy measures and incidence of CLL. As we have already excluded individuals\nwith previous cancer, we adjusted for age (restricted cubic spline with 4 degrees of\nfreedom), genetically determined sex, and smoking status (never or never).”\nSupplementary Figure 2. Description of heteroplasmy in the random sample of AoU. A)\nDistribution of the number of individuals with heteroplasmy stratified by complex/region.\nB) Distribution of mMSS stratified by complex/region. Only individuals that presented\nheteroplasmy in the evaluated complex/region were shown. Graphical representation of\nmMSS was truncated at mMSS = 2. C) Number of heteroplasmies per individual among\nthose who have heteroplasmy.\nSupplementary Figure 4. The effect of heteroplasmy on CLL incidence in AoU. A)\nWeighted Kaplan-Meier curves and hazard ratios from the adjusted Cox proportional\nhazards models comparing the risk of CLL development between individuals with\nheteroplasmy and those without heteroplasmy accounting for case-cohort design. This\nanalysis was adjusted for age modeled as a restricted cubic spline, sex and smoking\nstatus. B) Hazard ratios from the adjusted Cox proportional hazards model showing the\neffect of heteroplasmy count and mMSS on the risk of CLL development accounting for\ncase-cohort design. We have also shown the change in the associations of heteroplasmy\ncount and mMSS when they are mutually adjusted. These analyses were also adjusted for\nage modeled as a restricted cubic spline, sex and smoking status. HR = hazard ratio; CI =\nconfidence interval.\nSupplementary Table 4. Individual characteristics by presence of heteroplasmy in AoU.\nAre there any CLL sequencing datasets where mitochondrial heteroplasmy and\npredictions of deleterious mitochondrial mutations have been studied? Such an\nanalysis may help strength the analysis and interpretation of this study\nGiven that mMLC is a relatively newly developed score, it has not been applied to CLL\ncohorts. Thus, we would need to reanalyze previously published CLL cohorts with whole\ngenome sequencing data available. Even though this is part of our roadmap moving\nforward, it is not feasible at the current time due to funding and time constraints.\nIt is possible that the authors have identified cases of MBL (as these will not be\nexcluded by a lymphocyte filter <5,000/µL) in the UK Biobank. If this is the case, what\nis the impact of this on the authors analysis and conclusions?\nWe agree with the reviewer that information on MBL would add value to this study.\nHowever, given the absence of flow cytometry data, we were not able to identify cases of\nMBL. This limitation is stated in the Discussion section:\n“Finally, because we did not have flow cytometry data to diagnose prevalent CLL, we had\nto rely on lymphocytosis to eliminate potentially undiagnosed CLL, thus likely excluding\nsome individuals with polyclonal secondary lymphocytosis. On the other hand, we likely\nincluded some individuals with prevalent MBL.”\nConsidering the clonal nature of MBL, heteroplasmy and L-CHIP, it is possible that MBL\nwould be associated with both heteroplasmy and L-CHIP, especially in cases that\ndeveloped CLL. Nonetheless, the presence of high-risk L-CHIP gene mutations, the\nnumber of L-CHIP mutations and predicted deleterious effect of heteroplasmy are likely to\noffer more information on CLL risk, compared to MBL alone. Thus, even if all individuals\nwith L-CHIP would also present MBL, we expect that genetic information would further\nhelp to define individuals at a higher risk of developing CLL.\nWhat were the effects of age, sex, smoking status, and a history of cancer on CLL risk?\nAnd are these features associated with heteroplasmy and mMLC?\nUsing a univariable Cox-proportional hazards model, we observed that age (used as a 10-\nyear unit), male sex, ever smoker status and history of cancer were associated with an\nincreased incidence of CLL.\nWe observed that heteroplasmy was more prevalent in older individuals, men, ever\nsmokers and those having a history of cancer. P-values were generated using Fisher’s\nexact test for categorical variables and Mann-Whitney-Wilcoxon rank sum for age.\nWithin individuals having heteroplasmy, we observed higher mMSS values in older\nindividuals, ever smokers and those who have a history of cancer.\nWhat is the distribution of the number of heteroplasmic mitochondrial clones across\nindividuals?\nWe added Supplementary Figure 1.\nSupplementary Figure 1. Description of heteroplasmy in the UKB. A) Distribution of the\nnumber of individuals with heteroplasmy stratified by complex/region. B) Distribution of\nmMSS stratified by complex/region. Only individuals with heteroplasmy in the evaluated\ncomplex/region are shown. Graphical representation of mMSS is truncated at mMSS = 2.\nC) Number of heteroplasmies per individual, among those that have heteroplasmy.\nIs there a relationship between mitochondrial heteroplasmy, mMLC, mMSS and L-\nCHIP on the time from sampling to diagnosis?\nWe restricted the cohort to only individuals who developed CLL. For the association\nbetween L-CHIP, heteroplasmy, mMSS and time between sampling to diagnosis of CLL, we\nused multivariable linear regression models. The analyses were adjusted for age modeled\nas a restricted cubic spline (with 4 degrees of freedom), sex, tobacco smoking status\n(never or ever), and a history of cancer.\nWe did see an association between L-CHIP and a shorter time to CLL diagnosis (P = 0.02)\nbut did not see an association between any heteroplasmy and the time to CLL diagnosis (P\n= 0.37).\nWhen assessing the correlation between mMSS and time to CLL diagnosis we found that\nindividuals with higher mMSS were more likely to have a shorter time to CLL diagnosis\namong all individuals who developed CLL (P < 0.01) and restricted to individuals with\nheteroplasmy (P = 0.01). Conversely, when individuals with high mMSS (>2) were excluded,\nthe association was no longer significant (P = 0.26).\nReviewer #4 (Remarks to the Author):\nThis work neatly demonstrates that deleterious mitochondrial heteroplasmy can be\nused as a novel predictor of chronic lymphocytic leukemia, independent of L-CHIP.\nThis highlights a promising new biomarker associated with CLL, however while I found\nthe associative results to be compelling, I have concerns about the causal claims\nmade throughout the manuscript.\nWe agree with the reviewer that causality cannot be inferred solely from association data.\nAccordingly, our statements regarding potential biological consequences of heteroplasmy\nwere intentionally non-definitive and we have revised the manuscript to clarify this point.\nAlso, please see our repones to Reviewer’s 1 point #3 and Reviewer 2.\nThe authors model for heteroplasmy count and mMSS together, but these variables\nare inherently correlated due to mMSS being a cumulative sum over heteroplasmies.\nThis dependence makes interpreting the functional importance of mMSS difficult. It\nmay be better to use an average mMLC to separate the functional severity of the\nmutations from the total burden. The authors might also consider modelling the risk of\nCLL against average mMLC when restricted to only individuals carrying heteroplasmy\n(both with and without L-CHIP) to better evaluate the functional importance of\nheteroplasmy beyond a marker of clonal expansion.\nWe appreciate the reviewer’s thoughtful suggestion. While we recognize the rationale\nbehind using an average mMLC score (mMSA) to disentangle the functional severity of\nmutations from total heteroplasmy burden, we note that this approach may not be\nsuperior to our currently used model. As an example, mMSA would assign the same score\nto an individual with two heteroplasmies (both with an mMLC of 0.5) as to someone with\none heteroplasmy of the same mMLC.\nThat said, in response to the reviewer’s comment, for individuals with heteroplasmy, we\ndefined the mMSA as mMSS/heteroplasmy count. For individuals without heteroplasmy,\nthe score was set to 0. We then performed survival analysis using a multivariable Cox-\nproportional hazards model for risk of CLL adjusting for age (modeled as a restricted cubic\nspline with 4 degrees of freedom), sex, tobacco smoking status (never or ever), and a\nhistory of cancer. The results of this analysis, including heteroplasmy count, mMSS and\nmMSA are presented in the table below. We observe that, in the whole cohort and in\nanalyses restricted to L-CHIP negative individuals, the Z-score for mMSA is lower than that\nof mMSS, showing that mMSS better captures the risk for CLL compared to mMSA.\nAdditionally, please note that, in the manuscript, we evaluated mMSS with and without\nadjusting for heteroplasmy count.\nThus, we believe that using mMSS offers more information on CLL risk compared to mMSA.\nA further consideration is that mMSS does not account for the VAFs of the contributing\nmutations. Variants can often have heteroplasmy dependent effects, and so a VAF\nweighted mMSS (or VAF weighted average mMLC) may do better at capturing the\nfunctional burden of these mutations.\nWe agree with the reviewer that incorporating heteroplasmy VAF could, in theory, provide\nadditional insight into the functional consequences of mitochondrial mutation both at the\nindividual cell level and the clone level. We initially considered including VAF as a variable\nfor our predictive models. However, the interpretation of VAF-based metrics was\nconfounded by the inherent complexity of mtDNA mutational burden assessment in bulk\nDNA samples. The significant variability in mtDNA copy number per cell, the proportion of\nmutant mtDNA per cell and the size of hematopoietic clone affected make the\ninterpretation of the impact of VAF practically infeasible in this context.\nIn response to reviewer’s suggestion, we have assessed the association between the\nmaximum heteroplasmy VAF per sample and CLL incidence. No significant association\nwas observed in all individuals with heteroplasmy (HR = 1.0; 95% CI 0.5–1.7; P = 0.89),\nindividuals with only one heteroplasmy (HR = 0.8; 95% CI 0.4–1.6; P = 0.46) and in\nindividuals with a single heteroplasmy with VAF < 50% (HR = 0.7; 95% CI 0.1–3.6; P = 0.70).\nWe have also assessed the correlation between L-CHIP VAF and heteroplasmy VAF in\nindividuals harboring one L-CHIP mutation and a single heteroplasmy with a VAF < 50%.\nUsing both a multivariable linear model (Beta-estimate = 0.0005214; P = 0.12) and a\nSpearman’s rank correlation (rho = 0.03; P = 0.16) and found no evidence of significant\ncorrelation.\nTogether, these findings suggest that heteroplasmy VAF, as derived from bulk sequencing\ndata, offers limited additional prognostic value and provides little insight into the\nfunctional effect of these mutations.\nReviewer #4 (Remarks on code availability):\nThe code provided does not come with a README or equivalent instructions for\nrunning. Without the data I cannot verify if the figures would be reproduced, however\nthe analysis supports what is said in the manuscript.\nWe cannot make raw UK Biobank data available. This is available through application to\nthe UK Biobank. Nonetheless, we added a supplementary data file with summary\nstatistics. Additionally, we added the following statement under “Data availability”: “Code\nfor data analysis is available on our GitHub repository:\nhttps://github.com/ArkingLab/Chronic_Lymphocytic_Leukemia.”\nReferences\nAll of Us Research Program Investigators, Denny, J. C., Rutter, J. L., Goldstein, D. B.,\nPhilippakis, A., Smoller, J. W., Jenkins, G., & Dishman, E. (2019). The “All of Us”\nResearch Program. The New England Journal of Medicine, 381(7), 668–676.\nhttps://doi.org/10.1056/NEJMsr1809937\nBattle, S. L., Puiu, D., TOPMed mtDNA Working Group, Verlouw, J., Broer, L., Boerwinkle,\nE., Taylor, K. D., Rotter, J. I., Rich, S. S., Grove, M. L., Pankratz, N., Fetterman, J. L.,\nLiu, C., & Arking, D. E. (2022). A bioinformatics pipeline for estimating mitochondrial\nDNA copy number and heteroplasmy levels from whole genome sequencing data.\nNAR Genomics and Bioinformatics, 4(2), lqac034.\nhttps://doi.org/10.1093/nargab/lqac034\nCampbell, P., Chapman, M. S., Przybilla, M., Lawson, A., Mitchell, E., Dawson, K.,\nWilliams, N., Harvey, L., Ranzoni, A. M., Cvejic, A., Mahbubani, K., Saeb-Parsy, K.,\nGreen, A., Nangalia, J., Laurenti, E., & Martincorena, I. (2023). Mitochondrial\nmutation, drift and selection during human development and ageing.\nhttps://doi.org/10.21203/rs.3.rs-3083262/v1\nHong, Y. S., Battle, S. L., Shi, W., Puiu, D., Pillalamarri, V., Xie, J., Pankratz, N., Lake, N. J.,\nLek, M., Rotter, J. I., Rich, S. S., Kooperberg, C., Reiner, A. P., Auer, P. L., Heard-\nCosta, N., Liu, C., Lai, M., Murabito, J. M., Levy, D., … Arking, D. E. (2023).\nDeleterious heteroplasmic mitochondrial mutations are associated with an\nincreased risk of overall and cancer-specific mortality. Nature Communications,\n14(1), 6113. https://doi.org/10.1038/s41467-023-41785-7\nHong, Y. S., Pasca, S., Shi, W., Puiu, D., Lake, N. J., Lek, M., Ru, M., Grove, M., Prizment, A.,\nJoshu, C. E., Platz, E. A., Guallar, E., Arking, D. E., & Gondek, L. P. (2024).\nMitochondrial heteroplasmy improves risk prediction for myeloid neoplasms.\nhttps://doi.org/10.1101/2024.04.07.24305454\nKuiper, L. M., Shi, W., Verlouw, J. A. M., Hong, Y. S., Arp, P., Puiu, D., Broer, L., Xie, J.,\nNewcomb, C., Rich, S. S., Taylor, K. D., Rotter, J. I., Bader, J. S., Guallar, E., van\nMeurs, J. B. J., & Arking, D. E. (2025). Deleterious mitochondrial heteroplasmies\nexhibit increased longitudinal change in variant allele fraction. iScience, 28(6),\n112590. https://doi.org/10.1016/j.isci.2025.112590\nLake, N. J., Ma, K., Liu, W., Battle, S. L., Laricchia, K. M., Tiao, G., Puiu, D., Ng, K. K., Cohen,\nJ., Compton, A. G., Cowie, S., Christodoulou, J., Thorburn, D. R., Zhao, H., Arking, D.\nE., Sunyaev, S. R., & Lek, M. (2024). Quantifying constraint in the human\nmitochondrial genome. Nature, 635(8038), 390–397.\nhttps://doi.org/10.1038/s41586-024-08048-x\nSudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P.,\nGreen, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A.,\nSprosen, T., Peakman, T., & Collins, R. (2015). UK Biobank: An Open Access\nResource for Identifying the Causes of a Wide Range of Complex Diseases of Middle\nand Old Age. PLOS Medicine, 12(3), e1001779.\nhttps://doi.org/10.1371/journal.pmed.1001779"}, {"round": 2, "reviewer_comments": {"reviewer_4": "The authors have satisfactorily altered to manuscript to address my concerns.", "reviewer_5": "In this manuscript, Pasca et al. conduct an analysis of the UK Biobank to examine the relationship between mitochondrial\nDNA (mtDNA) heteroplasmy and the risk of chronic lymphocytic leukemia (CLL). Their study demonstrates that deleterious\nmitochondrial heteroplasmy associates with increased incident risk of CLL and may serve as a novel predictor of CLL risk,\nhighlighting a promising new biomarker for CLL.\nThe authors have adequately addressed the prior comments from Reviewers 1 and 3. However, several important issues\nremain, and the following comments will help improve the clarity of the manuscript.\nHere is my additional major concern on this Manuscript:\n• It is unclear why the 5% VAF threshold was set for mitochondrial heteroplasmy. Lower-frequency variants may reflect lower\nlevel heteroplasmy that is still important biologically. By using a 5% VAF cutoff, you may be missing some of these very early\nclonal events that are biologically important. Many recent mtDNA studies now use methods that can detect variants below\n1% (https://www.neurology.org/doi/10.1212/WNL.0000000000200745). If the reason for using the 5% threshold was to limit\nanalysis to larger clones, then it would be helpful to know whether patients with heteroplasmy and VAF <5% have similar or\nabrogated clinical associations.\n• Variant filtering included mtDNA-CN thresholds, but mtDNA-CN is itself a strong marker of hematopoietic aging and may\nbe associated with CH and leukemia risk. Because both heteroplasmy detection and mMSS may be influenced by mtDNA-\nCN, failing to control for mtDNA-CN in the regression models may confound the observed associations. Please provide\nmodels adjusted for mtDNA-CN and test for interaction between mtDNA-CN and heteroplasmy/mMSS. It would be helpful to\nknow whether mtDNA-CN differs between L-CHIP positive and negative individuals.\n• The authors state that many heteroplasmies are somatic, but this is assumed rather than demonstrated. Because the\nprevalence of germline mtDNA heteroplasmy varies substantially across mitochondrial haplogroups, ancestry groups, and\nmaternal age, the current analyses may be confounded by background population structure. The authors should provide a\nsensitivity analysis excluding common germline heteroplasmies.\nMinor comment\n• “Considering that a notable number of heteroplasmies have been shown to be somatic, it is reasonable to regard them as a\nmarker of clonal expansion that occurs with age” — cite recent single-cell mtDNA studies to strengthen these statements\n(https://www.science.org/doi/10.1126/sciadv.adi4038).", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.’"}, "author_response": "RESPONSE TO REVIEW ERS' COMMENTS\nReviewer #4 (Remarks to the Author):\nThe authors have satisfactorily altered to manuscript to address my concerns.\nReviewer #5 (Remarks to the Author):\nIn this manuscript, Pasca et al. conduct an analysis of the UK Biobank to examine the\nrelationship between mitochondrial DNA (mtDNA) heteroplasmy and the risk of chronic\nlymphocytic leukemia (CLL). Their study demonstrates that deleterious mitochondrial\nheteroplasmy associates with increased incident risk of CLL and may serve as a novel\npredictor of CLL risk, highlighting a promising new biomarker for CLL.\nThe authors have adequately addressed the prior comments from Reviewers 1 and 3.\nHowever, several important issues remain, and the following comments will help improve the\nclarity of the manuscript.\nHere is my additional major concern on this Manuscript:\n• It is unclear why the 5% VAF threshold was set for mitochondrial heteroplasmy. Lower-\nfrequency variants may reflect lower level heteroplasmy that is still important biologically. By\nusing a 5% VAF cutoff, you may be missing some of these very early clonal events that are\nbiologically important. Many recent mtDNA studies now use methods that can detect\nvariants below 1% (https://www.neurology.org/doi/10.1212/WNL.0000000000200745). If the\nreason for using the 5% threshold was to limit analysis to larger clones, then it would be\nhelpful to know whether patients with heteroplasmy and VAF <5% have similar or abrogated\nclinical associations.\nWe agree that very early clonal events marked by heteroplasmy can be informative,\nespecially when assessing these events at a single cell level. However, in the context of bulk\nsequencing, it has been shown that the number of false positive heteroplasmies increases\nwhen a VAF threshold below 3% is used (C. Liu et al., 2021). We have now repeated our\nanalysis for CLL incidence using VAF filters of 3%, 5% and 10% (Supplementary Table 6).\nWe noted some attenuation when using a heteroplasmy VAF filter of 3%, likely due to the\nincrease in false positives. However, the point estimates we observed with different VAF\nfilters did not lead to a change in our conclusions, demonstrating the robustness of our\nresults to VAF threshold. We have added the following statement in the manuscript:\n“Results\nTo test the robustness of the heteroplasmy VAF filter used, we reevaluated the association\nbetween heteroplasmy count/mMSS and CLL incidence using heteroplasmy at 3% and 10%\nVAF (Supplementary Table 6). We observed that results remained consistent even when\nusing different VAF thresholds.”\n“Methods\nStatistical analysis\nRobustness of the heteroplasmy VAF filter was assessed by evaluating the association\nbetween heteroplasmy count/mMSS and CLL incidence using heteroplasmy at 3%, 5% and\n10% VAF cut-offs.”\nSupplementary Table 6. Comparison of hazard ratios and 95% CI for the association\nbetween heteroplasmy count/mMSS and CLL incidence by different heteroplasmy VAF\nfilters.\nAbbreviations: VAF, variant allele frequency; HR, hazard ratio; CI, confidence interval;\nmMSS, modified mitochondrial local constraint (MLC) score sum.\nFurther, even though there have been important advancements in heteroplasmy detection\nlimit with the implementation of mity (Davis et al., 2022; Puttick et al., 2024), the authors of\nmity acknowledge that a higher number of false positives occur when using lower VAF\nthresholds (Puttick et al., 2024). In the context of mitochondrial disease diagnosis, low VAF\nvariants can be validated in a secondary tissue and evaluated in conjunction with the clinical\nphenotype (Puttick et al., 2024). However, such validation is not possible in the UKB cohort\ngiven the information is only available from blood samples and we are mainly focusing on\nsomatic heteroplasmies.\n• Variant filtering included mtDNA-CN thresholds, but mtDNA-CN is itself a strong marker of\nhematopoietic aging and may be associated with CH and leukemia risk. Because both\nheteroplasmy detection and mMSS may be influenced by mtDNA-CN, failing to control for\nmtDNA-CN in the regression models may confound the observed associations. Please\nprovide models adjusted for mtDNA-CN and test for interaction between mtDNA-CN and\nheteroplasmy/mMSS. It would be helpful to know whether mtDNA-CN differs between L-\nCHIP positive and negative individuals.\nWe agree that mtDNA-CN is an important facet of mitochondrial assessment, so we\nprovided the requested analyses bellow.\nMtDNA-CN is affected by blood counts. Thus, in addition to adjusting for age, modelled as a\nrestricted cubic spline (with 4 degrees of freedom), sex, tobacco smoking status (never or\never), and a history of cancer as in the original models (referred to as “baseline” in\nSupplementary Table 7), we also adjusted for mtDNA-CN and blood counts (absolute\nneutrophil count, absolute lymphocyte count, absolute monocyte count, red blood cell count\nand platelet count). For all analysis, mtDNA-CN was natural log-transformed.\nWhen assessing the association between heteroplasmy count/mMSS and CLL, the\nadditional adjustments did not change the conclusions drawn from the initial model, with both\nheteroplasmy count and mMSS remaining associated with CLL incidence.\nWe have added the following statement in the manuscript:\n“Results\nGiven the role of mtDNA-CN as a marker of hematopoietic aging and the interplay between\nmtDNA-CN and blood counts, we also evaluated the association between heteroplasmy\ncount/mMSS and CLL incidence when adjusting for mtDNA-CN and for blood counts\n(absolute neutrophil count, absolute lymphocyte count, absolute monocyte count, red blood\ncell count and platelet count) (Supplementary Table 7). Even after adjusting for mtDNA-CN\nand blood counts, heteroplasmy count and mMSS remained associated with CLL incidence.”\n“Methods\nStatistical analysis\nTo assess if the association between heteroplasmy count/mMSS and CLL incidence is\nindependent of mtDNA-CN and blood counts, we reevaluated the association between\nheteroplasmy count/mMSS and CLL incidence when adjusting for age, modelled as a\nrestricted cubic spline (with 4 degrees of freedom), sex, tobacco smoking status (never or\never), a history of cancer, mtDNA-CN (natural log-transformed) and blood counts (absolute\nneutrophil count, absolute lymphocyte count, absolute monocyte count, red blood cell count\nand platelet count).“\nSupplementary Table 7. Risk of CLL when adjusting for mtDNA-CN and for blood counts.\nBaseline model was adjusted for age, modelled as a restricted cubic spline (with 4 degrees\nof freedom), sex, tobacco smoking status (never or ever), and a history of cancer. We further\nadjusted for mtDNA-CN and blood counts (absolute neutrophil count, absolute lymphocyte\ncount, absolute monocyte count, red blood cell count and platelet count). For all analyses,\nmtDNA-CN was natural log-transformed.Abbreviations: CLL, chronic lymphocytic leukemia;\nmtDNA-CN, mtDNA copy number; HR, hazard ratio; CI, confidence interval; mMSS, modified\nmitochondrial DNA local constraint (MLC) score sum.\nWe also tested for an interaction between mtDNA-CN and heteroplasmy count/mMSS, and\ndid not observe any significant interactions (all P>0.05). Further, we assessed if mtDNA-CN\ndiffers between L-CHIP+ and LCHIP- individuals and did not see a significant difference.\nSimilarly, no notable differences were found between various L-CHIP genotypes.\n• The authors state that many heteroplasmies are somatic, but this is assumed rather than\ndemonstrated. Because the prevalence of germline mtDNA heteroplasmy varies\nsubstantially across mitochondrial haplogroups, ancestry groups, and maternal age, the\ncurrent analyses may be confounded by background population structure. The authors\nshould provide a sensitivity analysis excluding common germline heteroplasmies.\nThe assumption that a notable number of heteroplasmies are somatic was based both on\npublished literature (C. Liu et al., 2021; Q. Liu et al., 2022; Sondheimer et al., 2011) and on\nour previous analysis of UKB on monozygotic twins, mother/child pairs and full sibling pairs\n(Hong et al., 2023).\nTo address reviewer’s concerns, we fitted heteroplasmy count as a function of age\n(generalized additive model) and observed that heteroplasmy increased from 0.293 at 40\nyears of age to 0.393 at 65 years of age. Considering that this increase in heteroplasmy is\ndue to the acquisition of somatic heteroplasmies, we can infer that at least 25% of\nheteroplasmies are somatic by 65 years of age. Please note that this is an underestimate, as\nsome of the heteroplasmies seen at 40 years of age are likely to be somatic.\nWith respect to accounting for common germline heteroplasmies, MSS was derived by\ncomparing observed mitochondrial variation in gnomAD with the variation expected under\nneutrality (Lake et al., 2024). We subsequently refined this metric by developing mMSS,\nwhich adjusts MSS based on the number of homoplasmic variants observed in the UKB\n(Hong et al., 2024). As a result, mMSS effectively accounts for common germline\nheteroplasmies, assigning scores close to zero to common germline heteroplasmies.\nMinor comment\n• “Considering that a notable number of heteroplasmies have been shown to be somatic, it is\nreasonable to regard them as a marker of clonal expansion that occurs with age” — cite\nrecent single-cell mtDNA studies to strengthen these statements\n(https://www.science.org/doi/10.1126/sciadv.adi4038).\nWe have added (Glynos et al., 2023) as an additional reference for the sentence:\n“Considering that a notable number of heteroplasmies have been shown to be somatic, it is\nreasonable to regard them as a marker of clonal expansion that occurs with age”.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.’\nReferences\nDavis, R. L., Kumar, K. R., Puttick, C., Liang, C., Ahmad, K. E., Edema-Hildebrand, F., Park,\nJ.-S., Minoche, A. E., Gayevskiy, V., Mallawaarachchi, A. C., Christodoulou, J.,\nSchofield, D., Dinger, M. E., Cowley, M. J., & Sue, C. M. (2022). Use of Whole-\nGenome Sequencing for Mitochondrial Disease Diagnosis. Neurology, 99(7).\nhttps://doi.org/10.1212/WNL.0000000000200745\nGlynos, A., Bozhilova, L. V., Frison, M., Burr, S., Stewart, J. B., & Chinnery, P. F. (2023).\nHigh-throughput single-cell analysis reveals progressive mitochondrial DNA\nmosaicism throughout life. Science Advances, 9(43), eadi4038.\nhttps://doi.org/10.1126/sciadv.adi4038\nHong, Y. S., Battle, S. L., Shi, W., Puiu, D., Pillalamarri, V., Xie, J., Pankratz, N., Lake, N. J.,\nLek, M., Rotter, J. I., Rich, S. S., Kooperberg, C., Reiner, A. P., Auer, P. L., Heard-\nCosta, N., Liu, C., Lai, M., Murabito, J. M., Levy, D., … Arking, D. E. (2023).\nDeleterious heteroplasmic mitochondrial mutations are associated with an increased\nrisk of overall and cancer-specific mortality. Nature Communications, 14(1), 6113.\nhttps://doi.org/10.1038/s41467-023-41785-7\nHong, Y. S., Pasca, S., Shi, W., Puiu, D., Lake, N. J., Lek, M., Ru, M., Grove, M. L.,\nPrizment, A., Joshu, C. E., Platz, E. A., Guallar, E., Arking, D. E., & Gondek, L. P.\n(2024). Mitochondrial heteroplasmy improves risk prediction for myeloid neoplasms.\nNature Communications, 15(1), 10133. https://doi.org/10.1038/s41467-024-54443-3\nLake, N. J., Ma, K., Liu, W., Battle, S. L., Laricchia, K. M., Tiao, G., Puiu, D., Ng, K. K.,\nCohen, J., Compton, A. G., Cowie, S., Christodoulou, J., Thorburn, D. R., Zhao, H.,\nArking, D. E., Sunyaev, S. R., & Lek, M. (2024). Quantifying constraint in the human\nmitochondrial genome. Nature, 635(8038), 390–397. https://doi.org/10.1038/s41586-\n024-08048-x\nLiu, C., Fetterman, J. L., Qian, Y., Sun, X., Blackwell, T. W., Pitsillides, A., Cade, B. E.,\nWang, H., Raffield, L. M., Lange, L. A., Anugu, P., Abecasis, G., Adrienne Cupples,\nL., Redline, S., Correa, A., Vasan, R. S., Wilson, J. G., Ding, J., Levy, D., & NHLBI\nTrans-Omics for Precision Medicine TOPMed Consortium. (2021). Presence and\ntransmission of mitochondrial heteroplasmic mutations in human populations of\nEuropean and African ancestry. Mitochondrion, 60, 33–42.\nhttps://doi.org/10.1016/j.mito.2021.07.004\nLiu, Q., Iqbal, M. F., Yaqub, T., Firyal, S., Zhao, Y., Stoneking, M., & Li, M. (2022). The\ntransmission of human mitochondrial DNA in four-generation pedigrees. Human\nMutation, 43(9), 1259–1267. https://doi.org/10.1002/humu.24390\nPuttick, C., L Davis, R., R Kumar, K., Mw Quinn, J., Zeng, T., Fares, C., Pinese, M., M\n, M\nThomas, D., E Dinger, M., M Sue, C., & J Cowley, M. (2024). mity: A Highly Sensitive\nMitochondrial Variant Analysis Pipeline for Whole Genome Sequencing Data. Journal\nof Bioinformatics and Systems Biology, 07(01).\nhttps://doi.org/10.26502/jbsb.5107074\nSondheimer, N., Glatz, C. E., Tirone, J. E., Deardorff, M. A., Krieger, A. M., & Hakonarson,\nH. (2011). Neutral mitochondrial heteroplasmy and the influence of aging. Human\nMolecular Genetics, 20(8), 1653–1659. https://doi.org/10.1093/hmg/ddr043"}, {"round": 3, "reviewer_comments": {"reviewer_5": "The authors have addressed our concerns.", "reviewer_6": "The authors have adequately revised the manuscript to address all of my concerns."}, "author_response": ""}]

Decision letter