DNA replication is essential for the duplication of a cell and the maintenance of the eukaryotic genome. To replicate the human diploid genome of~3billion base pairs, efficient cellular programs are coordinated to ensure genetic information is accurately copied. In each human cell cycle, replication starts from~50,000 genomic locations called replication origins (Hu and Stillman, 2023). At an origin of replication, the double-stranded DNA is unwound, and primase-DNA polymerase alpha lays down the first RNA primer extended as DNA. Once replication is initiated, the helicase involved in unwinding the origin continues to unwind DNA on either side and the replication proteins, including DNA polymerases, help copy the single-stranded DNA. Unlike yeast origins, human and most eukaryotic origins do not seem to have a clear DNA sequence preference (Hu and Stillman, 2023; Leonard and Méchali, 2013). Yet it becomes clear that chromatin context and other DNA-based activity are important factors for human and non-yeast eukaryotic origin selection. A great amount of effort has been spent to understand how human origins are specified and sometimes different trends are observed regarding genomic features enriched at human origins. For example, G-quadruplex motifs were found to be associated with human origins (Besnard et al., 2012), but can only explain a small subset of origins even after controlling for technical biases (Foulk et al., 2015). To better resolve these issues, we reasoned that a systematic analysis of all available human origin-mapping datasets will help uncover potential technical and biological details that affect origin detection.

To profile replication origins genome-wide, several sequencing-based methods have been developed, including short nascent strand (SNS)-seq (Foulk et al., 2015), Repli-seq (Hansen et al., 2010), Rerep-seq (Menzel et al., 2020), and Bubble-seq (Mesner et al., 2013). SNS-seq assays across different human cell types identified a subset of origins that can explain ~80% of origins in any tested cell type (Akerman et al., 2020); however, no study has systematically examined the consistency within and between different origin-mapping techniques. These methods are based on different molecular capture strategies and therefore are expected to have technical biases. For instance, SNS-seq utilizes lambda exonuclease (λ-exo) to enrich RNA-primed newly replicated DNA by removing parental DNA fragments. However, the products may be contaminated with chromosomal DNA fragments or be affected by the cutting bias of λ-exo against guanine-cytosine (GC)-rich sequences (Foulk et al., 2015), although it has been proposed that one can experimentally correct and control for such biases (Akerman et al., 2020). SNS-seq is the only method that yields origins at a resolution of a few hundred base pairs, with all other methods delineating larger initiation zones (IZs) that may have multiple origins. Repli-seq relies on nucleotide pulse labeling and antibody enrichment of newly replicated DNA superimposed on cells fractionated at different parts of the S phase by flow cytometry. Here the results may be contaminated by DNA non-specifically associated with the antibody (Zhao et al., 2020). Rerep-seq only captures the new synthesized sequences that replicate more than once when replication is dysregulated, and these origins tend to be enriched in the early replicating, epigenetically open parts of the genome (Menzel et al., 2020). Bubble-seq (Mesner et al., 2013) generates long reads because it captures the origin-containing replication intermediates by selection of DNA bubbles, and so may enrich origins that are flanked by pause sites. Okazaki-seq (OK-seq) identifies sites where the direction of the Okazaki fragments changes from leftward to rightward on the chromosome-revealing sites where the two lagging strands diverge from each other (Petryk et al., 2016). Because the technical biases mentioned above are associated with specific techniques, we suspect that origins captured across different techniques are less likely to be affected by those biases. Given the large number of datasets in the public domain (>100 datasets for human origins), this is an opportune time to study the reproducibility between the studies and determine the most reproducible and consistent origins identified by SNS-seq and confirmed by each of the other methods. This most reproducible group of origins (shared origins) from different research groups using different techniques and different cell lines will be far fewer than all the origins reported to date and minimize complications from stochastic (noisy) firing of origins, but they are the best expected to fit the current hypotheses regarding origin specification.

Yeast origin recognition complex (ORC) binds to double-stranded DNA with sequence specificity, helps load minichromosome maintenance protein complex (MCM), and thus prepares the origins for subsequent firing (Bell and Dutta, 2002; Remus et al., 2009; Bell and Stillman, 1992; Costa and Diffley, 2022). Consistent with this, the role of ORC in loading MCM proteins was also described in Xenopus egg extracts (Rowles et al., 1999; Coleman et al., 1996). All of this leads to the expectation that in human cells there will be significant concordance between ORC-binding sites and efficient origins of replication. Efforts to define double-stranded DNA sequences bound specifically by human ORC find very little sequence specificity (Vashee et al., 2003; Hoshina et al., 2013), and this may be responsible for the lack of sequence specificity in human origins. This difference between yeast and human ORC is attributed to sequence features in ORC4, one of the subunits of the ORC (Lee et al., 2021). When a yeast-specific 19 amino acid insert was removed from yeast ORC4 to make it more like the human ORC4, the sequence specificity of ORC binding was lost even in yeast. Despite this loss of sequence specificity, the mutant yeast ORC loaded MCM proteins, and the yeast replicated DNA and survived. Thus, even if human origins of replication do not have specific sequences, one should expect some concordance between ORC-binding sites and the most reproducible origins of replication. An additional complexity is that genome-wide analysis found active origins and dormant origins determined by SNS-seq and OK-seq have little difference in ORC or MCM density (Sugimoto et al., 2018; Kirstein et al., 2021). Finally, a few publications have reported significant MCM2-7 loading and DNA synthesis after genetic mutation of Drosophila ORC1 and human ORC1, ORC2, or ORC5 genes that reduce the expressed proteins to near undetectable levels (Park and Asano, 2008; Shibata et al., 2016; Okano-Uchida et al., 2018; Shibata and Dutta, 2020). In two of the instances, the DNA replication seen was in the context of endo-reduplication, a process that is still believed to require the loading of MCM2-7 and the activation of the same into an active CMG helicase. It should be noted, however, that sgRNA screens revealed that the ORC2 gene was essential for viability in the cancer cell lines mutated for ORC2, suggesting that vanishingly small amounts of the ORC2 gene product are required for some processes essential for cell proliferation (Chou et al., 2021). Definition of the most reproducible and consistent origins identified by different methods from different research groups in different cell lines thus provide a unique opportunity to determine how much overlap is seen between the highly reproducible human origins and the ORC-binding sites reported in the literature from ChIP-seq studies.

To understand the genome-wide distribution patterns of replication origins in an unbiased way, we performed an integrative analysis of 113 DNA replication origin datasets. Because SNS-seq is unique in identifying origins of a few hundred bases long, while the other methods identify larger IZs that contain origins, we first prepared a list of high-resolution SNS-seq origins that have been identified in at least 20 of the 66 SNS-seq datasets in this study. We next determined those reproducible SNS-seq origins that overlap with IZs identified by each of the three techniques (Repli-seq, OK-seq, and Bubble-seq) to identify 20,250 high-confidence origins that are shared between SNS-seq and every other method identifying IZs. Using these shared origins, which overlap significantly with transcription promoters, we tested whether G-quadruplex sites, CTCF-binding sites, ORC-binding sites, or MCM-binding sites help specify origins.

Through integrative analysis of publicly available 113 profiles of replication origins from multiple techniques, we identified ~7.5 million union origins in the human genome, of which only 20,250 shared origins are reproducibly identified by at least 20 SNS-seq datasets and confirmed by each of the three other techniques. The following conclusions were reached: the shared (highly reproducible) origins are only a small subset of all origins identified and are in epigenetically active chromatin with a strong preference for promoters. However, G-quadruplexes, CTCF-binding sites, and ORC-binding sites are also enriched near the promoters, so although these genomic features are enriched near the shared origins, it is not clear whether these genomic features actually specify origins or whether they are simply enriched near origins because the origins are near the promoters. Finally, our results suggest that the co-localization of the shared origins with ORC- or MCM2-7- binding sites is very low, much lower than that in yeast, and so (a) the currently known binding sites of these proteins should not be used as surrogate markers of origins in human cells and (b) there is a great need to improve our methods of identifying isoforms of these proteins that are strictly functional for origin firing. The current models of origin firing require ORC to bind near origins, to load MCM2-7 nearby, and the latter to be converted into active helicase to fire origins. To rigorously test this model, we need examples of reproducible origins where these conditions are met, and our study helps identify 74 such sites where such experimental verification can be carried out.

A careful analysis shows that individual methods produce origin datasets that are more correlated with each other than across methods. The extensive variation of origins between datasets is likely because of background noise in all current techniques and because there is extreme stochasticity of origin selection during DNA replication in individual cells and cell cycles in the same population of cells from a human cell line in culture. There are also differences that are created due to the differences in cell lineage. Despite this, we could cut through those differences to focus on the small subset of shared origins that seem to be active in multiple cell lineages.

The shared origins are enriched with active histone marks and enriched in early replicating parts of the genome that are overwhelmingly in an active epigenetic environment. H3K4me3 has been reported to be enriched at replication origins (Picard et al., 2014; Cayrou et al., 2015), and it has also been reported that demethylation of H3K4me3 by KDM5C/JARID1C promotes origin active (Rondinelli et al., 2015). H3ac/H4ac are also reported to be enriched at replication origins (Cadoret et al., 2008; Sequeira-Mendes et al., 2009), and this is regulated by various histone acetyltransferases and histone deacetylases (Goren et al., 2008; Wang et al., 2009). Interestingly, H3K27me3 has also been reported to be enriched at replication origins (Picard et al., 2014; Cayrou et al., 2015). The higher enrichment of all activating histone modifications relative to the repressive histone modifications (including H3K27me3) (Figure 2j) rather than individual types of modification suggests that the shared, highly reproducible origins are preferentially in epigenetically active euchromatin. This aligns well with the general enrichment of shared origins with TSSs and TF hotspots – which are concentrated in gene-dense, epigenetically open parts of the chromosomes. This also aligns well with the fact that the shared origins were not enriched for H3K9me3. H3K9me3 has been reported to be enriched near late replicating origins (Wang et al., 2017), while the shared origins are biased toward those that are in euchromatin. That origins, like TSS, prefer areas of the chromatin marked by activating epigenetic marks has been reviewed in 2016 (Prioleau and MacAlpine, 2016). We arrive at the same conclusion even though we worked with the origins identified after this review, presumably with significant improvements in method and even though we focused on the most reproducible origins.

We characterized the genomic features of origins and found that the shared origins are more co-localized with TSS. Table 1 shows a summary of the overlap of union origins, shared origins, and shared origins overlapping ORC-binding sites with various genomic features and compares this with the overlap of TSS with the same genomic features. Overall, these results suggest that as we proceed from all origins to shared origins to the highest confidence origins that are proximate to the ORC-binding sites, we see increasing overlap with promoters and early replicating, transcriptionally active, epigenetically open parts of the genome and that features thought to be important for origin selection (like G-quadruplexes, CTCF-binding sites, active chromatin, ORC-binding sites) could simply co-occur with origins because of their enrichment near the promoters (TSS).

To rigorously test whether the overlaps of shared origins that we observe with various genomic features are significantly above the background, we performed a permutation test that evaluates whether the observed overlap is significantly above the mean expected overlap when the experimental dataset is randomized 1000 times (Table 2). The overlap of shared origins with promoters (TSS ±4 kb), G-quadruplexes, R-loops, and CTCF-binding sites are all significantly above the background. However, the overlap of promoters with G-quadruplexes, R loops, and CTCF-binding sites are also all significantly above the background, though the enrichment of G-quadruplexes is minimal. Thus, the genomic features characterized are all significantly enriched in both origins and promoters, and may help specify both, either independently or co-dependently.

G-quadruplexes are of particular interest because they have been experimentally shown to dictate origin specification (Prorok et al., 2019; Valton et al., 2014). Although there is a higher overlap of shared origins than union origins with G-quadruplexes (43.5% vs 15%), this overlap is not higher than that between promoters and G-quadruplexes (48.1%) (Figure 2g). This may suggest that the overlap between origins and G-quadruplexes may be secondary to the overlap between origins and promoters. However, the overlap of shared origins with G-quadruplexes is 3.5-fold above random while that of promoters with G-quadruplexes is only 1.3-fold above random, which would be consistent with the idea that G-quadruplexes have a role in specifying origins and the overlap is not simply due to the proximity of origins with promoters.

Similarly, CTCF-binding sites have been proposed to contribute to origin specification (Emerson et al., 2022). Here again, the 2.2% of shared origins that overlap with constitutive CTCF-binding sites is less than the 6.4% of promoters that overlap with CTCF-binding sites (Figure 2e). Here, though, the permutation test in Table 2 reveals that the enrichment of origins near the CTCF-binding sites (4.3-fold above random background) is comparable to the enrichment of promoters near the same sites (4.5-fold). This may, thus, suggest that the overlap of the CTCF-binding sites with origins is secondary to the overlap of origins with promoters.

Since ORC is an early factor for initiating DNA replication, shared human origins should be proximate to the reproducible ORC-binding sites. The vast majority of ORC-binding sites are not proximate to the shared origins and conversely only about 6.4% of the shared origins are proximate (within 1 kb) to the reproducible (shared) ORC-binding sites (Figure 3f). Even with the most relaxed criteria, nearly 85% of the most reproducible origins in human cells are not proximate to any ORC-binding site (union, Figure 3f). This low level of proximity contrasts with the ~100% of yeast origins that are within 1 kb of a yeast ORC-binding site. Even when we examined cell line-specific origins with the ORC ChIP-seq datasets from the same cell lines, the co-localization between origins and ORC-binding sites remained poor (Figure 3—figure supplement 1f). Another study also noted that only 13% of SNS-seq origins in K562 cells are near the ORC2-binding sites (Miotto et al., 2016), but the authors suggested that this could occur if many SNS-seq sites are not real origins but arose from DNA breaks. Vast contamination of SNS-seq origins by sites of DNA breaks is discounted in our analysis because of the reproducibility of the shared origins in multiple labs, multiple lineages and multiple different techniques.

Instead of an unbiased determination of origins mapped by multiple groups to create a small set of reproducible origins, we could also empirically select a few well-curated origin datasets. Toward that end we used the core origins identified by lambda exonuclease SNS-seq (Akerman et al., 2020) and the Ini-seq2 origins identified by labeling nuclei in vitro to identify the earliest replicated parts of the genome (Guilbaud et al., 2022). The shared origins were only a small subset of these origin datasets (Figure 5—figure supplement 3a and c). The percentage of these origins that were near ORC was still far less than what we observed in yeast: 13.7% for core origins and 30.4% for Ini-seq2 origins (Figure 5—figure supplement 3b and d). The higher overlap with Ini-seq2 origins could be because in vitro initiation of replication in isolated nuclei in Ini-seq preferentially identifies origins in early replicating, euchromatic parts of the genome, areas that have already been reported to be more enriched for the ORC2-binding sites (Miotto et al., 2016).

There could be other explanations for the poor overlap of shared origins with the ORC-binding sites. We tested whether increasing the stringency of reproduction in SNS-seq data will produce more reproducible origins that are better co-localized with the ORC-binding sites. However, when we reduced the shared origins based on their reproduction by 30, 40, or 50 (out of 66) SNS-seq datasets, we did not see a marked improvement of their co-localization with currently known ORC-binding sites (Figure 5—figure supplement 4a and b) and MCM-binding sites (Figure 5—figure supplement 4c and d).

Finally, the permutation test in Table 2 demonstrates that the co-localization of origins with ORC-binding sites was 6.5-fold (p<0.001) greater than random expectation but this was less than the co-localization of promoters with ORC-binding sites, 10.6-fold greater than random (p<0.001). Thus, the co-localization data cannot conclusively say whether ORC binding near an origin is because ORC specifies origins, or whether this is secondary to origins being co-localized near promoters. Note that the co-localization of ORC1-binding sites and origins in HeLa cells with highly active TSS has been noted in the past (Dellino et al., 2013). In human lymphoblast Raji cells, ORC was enriched in promoters and MCM was depleted from gene bodies (Kirstein et al., 2021). ORC1 has also been reported to bind to RNA near promoters and stimulate replication from such sites (Mas et al., 2023). Thus, our observation that ORC is highly enriched near promoters is expected, the main advance being that the random permutation test suggests that co-localization of TSS with ORC is more than the co-localization of shared origins with ORC.

The question arises, are our results contradicting past studies? To the best of our knowledge, no one has taken such a comprehensive and quantitative approach to assess the proximity of origins with the ORC-binding sites genome-wide. We determined the percentage of origins that are within 1 kb of ORC-binding sites genome-wide and did a random permutation test to test whether the observed overlap is greater than random expectation. This showed that only a small percentage of the origins are within 1 kb of the currently known ORC-binding sites on a genome-wide scale, although the selected browser shots as shown in Figure 6 might suggest otherwise. Furthermore, even though origins were enriched near the ORC-binding sites relative to random expectation, this enrichment is not more than that of TSS near the ORC-binding sites.

Taken together, these results suggest three possibilities: (a) the ORC ChIP-seq or origin determination datasets in humans are very noisy and that the ChIP-seq data in particular fail to identify functional ORC-binding sites; (b) the MCM2-7 loaded at the ORC-binding sites move very far to initiate origins, farther than 1 kb from the ORC-binding sites; and (c) there are as yet unexplored mechanisms by which many of the most reproducible origins are specified, mechanisms that might include ORC-independent modes of origin specification, similar to the DnaA-independent modes of origin specification in bacteria that use R-loops or DNA breaks to initiate replication (Itoh and Tomizawa, 1980; Leela et al., 2021; Goswami and Gowrishankar, 2022).

There is significant evidence for possibility (b). MCM2-7 are either loaded far from the ORC-binding sites or move significant distances after loading in the Xenopus egg extracts (Harvey and Newport, 2003). In yeasts, there is evidence that MCM2-7 are pushed far away from the bodies of transcriptionally active genes by the RNA polymerase II and initiate replication at sites distant from where they are loaded on chromatin by ORC (Gros et al., 2015). However, it is worth noting that despite this, nearly 100% of the origins in yeast are within 1 kb of the ORC-binding sites (Figure 3f). Similar observations have been made in Drosophila, where cyclin E/cdk2 kinase activity promotes the loading of a vast excess of MCM2-7 on chromatin relative to ORC, and the MCM2-7 complexes move away from their loading sites due to the activity of the transcriptional apparatus (Powell et al., 2015).

Since most models of replication initiation propose that a stably bound MCM2-7 complex is converted into an active CMG helicase at the time of origin firing, we hoped that even if ORC-binding sites are not necessarily close to the shared origins, MCM2-7-binding sites will be more proximate to the shared origins in human cells. However, only 4.5% of shared origins are near any MCM2-7-binding site in human cells and again this is in contrast to the 96.5% of origins in yeast being near any MCM2-7-binding sites (Figure 5g). Even if we focus on the limited data from the same cell line (K562 or HCT116), only 3.8% (union origins) or 5.5% shared origins overlap with union MCM3-7-binding sites in K562 cells and 19.5% of union origins overlap with any MCM2-binding sites in HCT116 cells (Figure 3—figure supplement 1b, c, and e). As with ORC, we also did the analysis with two selected origin datasets – the core origins (Akerman et al., 2020) and the Ini-seq2 origins (Guilbaud et al., 2022) – and found that still a small percentage of these origins (6.6 and 10.7%, respectively) were near any MCM2-7-binding sites. Finally, a recent paper reported the binding sites of phosphorylated isoforms of MCM2 (Thakur et al., 2022), and since phosphorylation of MCM2 is a prerequisite for MCM2-7 being activated as a helicase, we asked whether any of the phospho-MCM2-binding sites showed better co-localization with the shared origins (Figure 5—figure supplement 2). Phospho-S108 MCM2 showed the best result among the phospho-isoforms tested, with 22% of the shared origins being proximate to the phospho-S108 MCM2-binding sites. Thus, phosphorylation on S108 of MCM2 may indeed mark MCM2-7 complexes that become active helicases that fire origins, but even then the co-localization was seen in a disappointingly low percentage of shared origins.

MCM2-7 ChIP-seq data is biased by a lot of noise as human MCM2-7 slides around after initial loading and due to contamination from the actively replicating CMG helicase that moves all over the genome (and pauses at many sites) during the S phase, but this should increase the number of MCM2-7-binding sites and not decrease the percentage of origins that are near the MCM2-7 sites. Better co-localization of shared origins with phospho-S108 MCM2 suggests that the ChIP methods can be improved to identify a small but critical pool of MCM2-7 that are engaged with the DNA stably in a way that permits origin specification. Alternatively, mapping of the sites bound to the active CMG helicase (MCM2-7, CDC45, and GINS), before the active helicase moves too far from the initiation site, may better enrich sites that are destined to be origins.

A by-product of our analysis of ORC ChIP-seq data was the discovery that ORC1 and ORC2 are not strictly co-located as expected for the subunits of one ORC complex. Only 1363 (21%) of the 6501 ORC1 peaks overlapped with the 29,930 ORC2 peaks (Figure 5—figure supplement 1a), and the vast majority (68%) of ORC1 peaks are far away (≥2 kb) from the closest ORC2 peaks (Figure 5—figure supplement 1b). As a positive control, we checked other proteins that form well-established complexes like SMARCA4 and ARID1A in SWI/SNF complex, SMC1A and SMS63 in cohesion complex, EZH2 and SUZ12 in PRC2 complex, and found that they show a high proportion of overlap between their peaks as expected (Figure 5—figure supplement 1a). A possible explanation of the low overlapping rate between ORC1 and ORC2 peaks could be that the datasets are from different cell types (Figure 5—figure supplement 1c). However, even in ChIP-seq peaks from different cell types, we found continued high overlap between SMC1A and SMC3 peaks as would be expected for complexes that do not have high inter-cell-line variability in binding sites. In contrast, there was a low overlap between EZH2 and SUZ12 peaks taken from different cell lines, suggesting that for other complexes there is significant inter-cell-line variability in binding sites (Figure 5—figure supplement 1d). Thus, the lack of overlap between the ORC1 and ORC2 association sites on chromatin could be explained by inter-cell-line variation of binding sites for ORC (like the PRC2 complex) or could be explained by the ORC subunits binding to chromatin independent of one another. We prefer the latter explanation because of our experimental result that a substantial fraction of ORC2, 3, 4, 5, and 6 bind to chromatin in the absence of ORC1, and ORC2, 3, and 6 bind to chromatin normally in the absence of ORC5 (Park and Asano, 2008; Shibata et al., 2016; Okano-Uchida et al., 2018; Shibata and Dutta, 2020). We and others have also noted that human ORC2, 3, 4, and 5 form a stable subcomplex, with much looser association of the subcomplex with ORC1 and ORC6 (Dhar et al., 2001). Note that lineage-specific variation of where ORC may bind to the chromatin or the binding of ORC subcomplexes to chromatin may explain why so few of the ORC-binding sites overlap with the shared origins but does not explain why only 6.4% of the shared origins (identified reproducibly in different cell lines) is proximate to any ORC-binding sites.

Our analysis provides a characterization of origins in the human genome using multisource data in the public domain. The sequencing-based profiles from different techniques have different biases in detecting DNA replication origins and show different genomic features. As one adds criteria to identify the most reproducible experimentally determined origins that are close to the ORC-binding sites, the overlap with TSS, TF hotspots, constitutive CTCF-binding sites, and G-quadruplexes increases (Table 1), but in general the overlap with the latter three genomic features does not exceed that of the same features with TSS. Thus, although the overlaps are statistically significant, this correlation analysis cannot determine whether the same genomic features independently specify the origins of replication and promoters of transcription. This is a question that should be asked experimentally, and the 74 experimentally reproduced origin datasets with proximity to ORC and MCM2-7 ChIP-seq sites provide a starting list for such experimental queries. Finally, although our datasets for origins were large, as with all integrative data analyses, we are limited by the quality of the data, which can be improved as experimental techniques for both origin identification and ORC- or MCM2-7-binding site determination continue to improve.

The current manuscript is a computational study, so no data have been generated for this manuscript. A summary of the collected public data can be found in Supplementary file 1. Modelling code and origin data are uploaded in: https://github.com/tmx1228/Replication-Origins (copy archived at Tian, 2023).

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Author details

1. Mengxue Tian

   1. Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, United States
   2. Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3707-4225

2. Zhenjia Wang

   Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Zhangli Su

   1. Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, United States
   2. Department of Genetics, University of Alabama at Birmingham, Birmingham, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6495-3351

4. Etsuko Shibata

   1. Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, United States
   2. Department of Genetics, University of Alabama at Birmingham, Birmingham, United States

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Yoshiyuki Shibata

   1. Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, United States
   2. Department of Genetics, University of Alabama at Birmingham, Birmingham, United States

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Anindya Dutta

   1. Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, United States
   2. Department of Genetics, University of Alabama at Birmingham, Birmingham, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   Duttaa@uab.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4319-0073

7. Chongzhi Zang

   1. Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, United States
   2. Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, United States
   3. Department of Public Health Sciences, University of Virginia, Charlottesville, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   zang@virginia.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4812-3627

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