Integrative annotation of variants from 1092 humans: application to cancer genomics

Natural variability of minimotifs in 1092 people indicates that minimotifs are targets of evolution

Since the function of a short contiguous peptide minimotif can be introduced or eliminated by a single point mutation, these functional elements may be a source of human variation and a target of selection. We analyzed the variability of ∼300 000 minimotifs in 1092 human genomes from the 1000 Genomes Project. Most minimotifs have been purified by selection, with a 94% invariance, which supports important functional roles for minimotifs. Minimotifs are generally under negative selection, possessing high genomic evolutionary rate profiling (GERP) and sitewise likelihood-ratio (SLR) scores. Some are subject to neutral drift or positive selection, similar to coding regions. Most SNPs in minimotif were common variants, but with minor allele frequencies generally <10%. This was supported by low substation rates and few newly derived minimotifs. Several minimotif alleles showed different intercontinental and regional geographic distributions, strongly suggesting a role for minimotifs in adaptive evolution. We also note that 4% of PTM minimotif sites in histone tails were common variants, which has the potential to differentially affect DNA packaging among individuals. In conclusion, minimotifs are a source of functional genetic variation in the human population; thus, they are likely to be an important target of selection and evolution.

Whole-Genome Sequencing and Integrative Genomic Analysis Approach on Two 22q11.2 Deletion Syndrome Family Trios for Genotype to Phenotype Correlations

The 22q11.2 deletion syndrome (22q11DS) affects 1:4,000 live births and presents with highly variable phenotype expressivity. In this study, we developed an analytical approach utilizing whole-genome sequencing (WGS) and integrative analysis to discover genetic modifiers. Our pipeline combined available tools in order to prioritize rare, predicted deleterious, coding and noncoding single-nucleotide variants (SNVs), and insertion/deletions from WGS. We sequenced two unrelated probands with 22q11DS, with contrasting clinical findings, and their unaffected parents. Proband P1 had cognitive impairment, psychotic episodes, anxiety, and tetralogy of Fallot (TOF), whereas proband P2 had juvenile rheumatoid arthritis but no other major clinical findings. In P1, we identified common variants in COMT and PRODH on 22q11.2 as well as rare potentially deleterious DNA variants in other behavioral/neurocognitive genes. We also identified a de novo SNV in ADNP2 (NM_014913.3:c.2243G>C), encoding a neuroprotective protein that may be involved in behavioral disorders. In P2, we identified a novel nonsynonymous SNV in ZFPM2 (NM_012082.3:c.1576C>T), a known causative gene for TOF, which may act as a protective variant downstream of TBX1, haploinsufficiency of which is responsible for congenital heart disease in individuals with 22q11DS.

Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci

Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3' UTR regulation. We also showed that (1) 15%-36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci.

Functional annotation of HOT regions in the human genome: implications for human disease and cancer

Advances in genome-wide association studies (GWAS) and large-scale sequencing studies have resulted in an impressive and growing list of disease- and trait-associated genetic variants. Most studies have emphasised the discovery of genetic variation in coding sequences, however, the noncoding regulatory effects responsible for human disease and cancer biology have been substantially understudied. To better characterise the cis-regulatory effects of noncoding variation, we performed a comprehensive analysis of the genetic variants in HOT (high-occupancy target) regions, which are considered to be one of the most intriguing findings of recent large-scale sequencing studies. We observed that GWAS variants that map to HOT regions undergo a substantial net decrease and illustrate development-specific localisation during haematopoiesis. Additionally, genetic risk variants are disproportionally enriched in HOT regions compared with LOT (low-occupancy target) regions in both disease-relevant and cancer cells. Importantly, this enrichment is biased toward disease- or cancer-specific cell types. Furthermore, we observed that cancer cells generally acquire cancer-specific HOT regions at oncogenes through diverse mechanisms of cancer pathogenesis. Collectively, our findings demonstrate the key roles of HOT regions in human disease and cancer and represent a critical step toward further understanding disease biology, diagnosis, and therapy.

Recurrent somatic mutations in regulatory regions of human cancer genomes

Aberrant regulation of gene expression in cancer can promote survival and proliferation of cancer cells. Here we integrate TCGA whole genome sequencing data of 436 patients from eight cancer subtypes with ENCODE and other regulatory annotations to identify point mutations in regulatory regions. We find evidence for positive selection of mutations in transcription factor binding sites, consistent with these sites regulating important cancer cell functions. Using a novel method that adjusts for sample- and genomic locus-specific mutation rate, we identify recurrently mutated sites across cancer patients. Mutated regulatory sites include known sites in the TERT promoter and many novel sites, including a subset in proximity to cancer genes. In reporter assays, two novel sites display decreased enhancer activity upon mutation. These data demonstrate that many regulatory regions contain mutations under selective pressure and suggest a larger role for regulatory mutations in cancer than previously appreciated.

Developments in our understanding of the genetic basis of birth defects

Birth defects are a major cause of morbidity and mortality worldwide. There has been much progress in understanding the genetic basis of familial and syndromic forms of birth defects. However, the etiology of nonsydromic birth defects is not well-understood. Although there is still much work to be done, we have many of the tools needed to accomplish the task. Advances in next-generation sequencing have introduced a sea of possibilities, from disease-gene discovery to clinical screening and diagnosis. These advances have been fruitful in identifying a host of candidate disease genes, spanning the spectrum of birth defects. With the advent of CRISPR-Cas9 gene editing, researchers now have a precise tool for characterizing this genetic variation in model systems. Work in model organisms has also illustrated the importance of epigenetics in human development and birth defects etiology. Here we review past and current knowledge in birth defects genetics. We describe genotyping and sequencing methods for the detection and analysis of rare and common variants. We remark on the utility of model organisms and explore epigenetics in the context of structural malformation. We conclude by highlighting approaches that may provide insight into the complex genetics of birth defects.

A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu

De novo assembly of a haplotype-resolved human genome

The human genome is diploid, and knowledge of the variants on each chromosome is important for the interpretation of genomic information. Here we report the assembly of a haplotype-resolved diploid genome without using a reference genome. Our pipeline relies on fosmid pooling together with whole-genome shotgun strategies, based solely on next-generation sequencing and hierarchical assembly methods. We applied our sequencing method to the genome of an Asian individual and generated a 5.15-Gb assembled genome with a haplotype N50 of 484 kb. Our analysis identified previously undetected indels and 7.49 Mb of novel coding sequences that could not be aligned to the human reference genome, which include at least six predicted genes. This haplotype-resolved genome represents the most complete de novo human genome assembly to date. Application of our approach to identify individual haplotype differences should aid in translating genotypes to phenotypes for the development of personalized medicine.

Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes

Global network modeling of distal regulatory interactions is essential in understanding the overall architecture of gene expression programs. Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model. Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects. Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular. Transcriptional cancer drivers and risk genes were discovered based on the network analysis of somatic and genetic cancer-related DNA variants. Notably, we observed that the risk genes were functionally downstream of the cancer drivers and were selectively susceptible to network perturbation by tumorigenic changes in their upstream drivers. Furthermore, cancer risk alleles tended to increase the susceptibility of the transcription of their associated genes. These findings suggest that transcriptional cancer drivers selectively induce a combinatorial misregulation of downstream risk genes, and that genetic risk factors, mostly residing in distal regulatory regions, increase transcriptional susceptibility to upstream cancer-driving somatic changes.

GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

Quantifying the cumulative effect of low-penetrance genetic variants on breast cancer risk

Many common diseases have a complex genetic basis in which large numbers of genetic variations combine with environmental factors to determine risk. However, quantifying such polygenic effects has been challenging. In order to address these difficulties we developed a global measure of the information content of an individual's genome relative to a reference population, which may be used to assess differences in global genome structure between cases and appropriate controls. Informally this measure, which we call relative genome information (RGI), quantifies the relative "disorder" of an individual's genome. In order to test its ability to predict disease risk we used RGI to compare single-nucleotide polymorphism genotypes from two independent samples of women with early-onset breast cancer with three independent sets of controls. We found that RGI was significantly elevated in both sets of breast cancer cases in comparison with all three sets of controls, with disease risk rising sharply with RGI. Furthermore, these differences are not due to associations with common variants at a small number of disease-associated loci, but rather are due to the combined associations of thousands of markers distributed throughout the genome. Our results indicate that the information content of an individual's genome may be used to measure the risk of a complex disease, and suggest that early-onset breast cancer has a strongly polygenic component.

The role of TREM2 R47H as a risk factor for Alzheimer's disease, frontotemporal lobar degeneration, amyotrophic lateral sclerosis, and Parkinson's disease

A rare variant in TREM2 (p.R47H, rs75932628) was recently reported to increase the risk of Alzheimer's disease (AD) and, subsequently, other neurodegenerative diseases, i.e. frontotemporal lobar degeneration (FTLD), amyotrophic lateral sclerosis (ALS), and Parkinson's disease (PD). Here we comprehensively assessed TREM2 rs75932628 for association with these diseases in a total of 19,940 previously untyped subjects of European descent. These data were combined with those from 28 published data sets by meta-analysis. Furthermore, we tested whether rs75932628 shows association with amyloid beta (Aβ42) and total-tau protein levels in the cerebrospinal fluid (CSF) of 828 individuals with AD or mild cognitive impairment. Our data show that rs75932628 is highly significantly associated with the risk of AD across 24,086 AD cases and 148,993 controls of European descent (odds ratio or OR = 2.71, P = 4.67 × 10(-25)). No consistent evidence for association was found between this marker and the risk of FTLD (OR = 2.24, P = .0113 across 2673 cases/9283 controls), PD (OR = 1.36, P = .0767 across 8311 cases/79,938 controls) and ALS (OR = 1.41, P = .198 across 5544 cases/7072 controls). Furthermore, carriers of the rs75932628 risk allele showed significantly increased levels of CSF-total-tau (P = .0110) but not Aβ42 suggesting that TREM2's role in AD may involve tau dysfunction.

Characterization and identification of hidden rare variants in the human genome

Background

By examining the genotype calls generated by the 1000 Genomes Project we discovered that the human reference genome GRCh37 contains almost 20,000 loci in which the reference allele has never been observed in healthy individuals and around 70,000 loci in which it has been observed only in the heterozygous state.

Results

We show that a large fraction of this rare reference allele (RRA) loci belongs to coding, functional and regulatory elements of the genome and could be linked to rare Mendelian disorders as well as cancer. We also demonstrate that classical germline and somatic variant calling tools are not capable to recognize the rare allele when present in these loci. To overcome such limitations, we developed a novel tool, named RAREVATOR, that is able to identify and call the rare allele in these genomic positions. By using a small cancer dataset we compared our tool with two state-of-the-art callers and we found that RAREVATOR identified more than 1,500 germline and 22 somatic RRA variants missed by the two methods and which belong to significantly mutated pathways.

Conclusions

These results show that, to date, the investigation of around 100,000 loci of the human genome has been missed by re-sequencing experiments based on the GRCh37 assembly and that our tool can fill the gap left by other methods. Moreover, the investigation of the latest version of the human reference genome, GRCh38, showed that although the GRC corrected almost all insertions and a small part of SNVs and deletions, a large number of functionally relevant RRAs still remain unchanged. For this reason, also future resequencing experiments, based on GRCh38, will benefit from RAREVATOR analysis results. RAREVATOR is freely available at http://sourceforge.net/projects/rarevator.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1481-9) contains supplementary material, which is available to authorized users.

Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas

BACKGROUND

With the rapid increase of whole-genome sequencing of human cancers, an important opportunity to analyze and characterize somatic mutations lying within cis-regulatory regions has emerged. A focus on protein-coding regions to identify nonsense or missense mutations disruptive to protein structure and/or function has led to important insights; however, the impact on gene expression of mutations lying within cis-regulatory regions remains under-explored. We analyzed somatic mutations from 84 matched tumor-normal whole genomes from B-cell lymphomas with accompanying gene expression measurements to elucidate the extent to which these cancers are disrupted by cis-regulatory mutations.

RESULTS

We characterize mutations overlapping a high quality set of well-annotated transcription factor binding sites (TFBSs), covering a similar portion of the genome as protein-coding exons. Our results indicate that cis-regulatory mutations overlapping predicted TFBSs are enriched in promoter regions of genes involved in apoptosis or growth/proliferation. By integrating gene expression data with mutation data, our computational approach culminates with identification of cis-regulatory mutations most likely to participate in dysregulation of the gene expression program. The impact can be measured along with protein-coding mutations to highlight key mutations disrupting gene expression and pathways in cancer.

CONCLUSIONS

Our study yields specific genes with disrupted expression triggered by genomic mutations in either the coding or the regulatory space. It implies that mutated regulatory components of the genome contribute substantially to cancer pathways. Our analyses demonstrate that identifying genomically altered cis-regulatory elements coupled with analysis of gene expression data will augment biological interpretation of mutational landscapes of cancers.

ChiNet uncovers rewired transcription subnetworks in tolerant yeast for advanced biofuels conversion

Analysis of rewired upstream subnetworks impacting downstream differential gene expression aids the delineation of evolving molecular mechanisms. Cumulative statistics based on conventional differential correlation are limited for subnetwork rewiring analysis since rewiring is not necessarily equivalent to change in correlation coefficients. Here we present a computational method ChiNet to quantify subnetwork rewiring by statistical heterogeneity that enables detection of potential genotype changes causing altered transcription regulation in evolving organisms. Given a differentially expressed downstream gene set, ChiNet backtracks a rewired upstream subnetwork from a super-network including gene interactions known to occur under various molecular contexts. We benchmarked ChiNet for its high accuracy in distinguishing rewired artificial subnetworks, in silico yeast transcription-metabolic subnetworks, and rewired transcription subnetworks for Candida albicans versus Saccharomyces cerevisiae, against two differential-correlation based subnetwork rewiring approaches. Then, using transcriptome data from tolerant S. cerevisiae strain NRRL Y-50049 and a wild-type intolerant strain, ChiNet identified 44 metabolic pathways affected by rewired transcription subnetworks anchored to major adaptively activated transcription factor genes YAP1, RPN4, SFP1 and ROX1, in response to toxic chemical challenges involved in lignocellulose-to-biofuels conversion. These findings support the use of ChiNet in rewiring analysis of subnetworks where differential interaction patterns resulting from divergent nonlinear dynamics abound.

A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing

Results from numerous linkage and association studies have greatly deepened scientists’ understanding of the genetic basis of many human diseases, yet some important questions remain unanswered. For example, although a large number of disease-associated loci have been identified from genome-wide association studies in the past 10 years, it is challenging to interpret these results as most disease-associated markers have no clear functional roles in disease etiology, and all the identified genomic factors only explain a small portion of disease heritability. With the help of next-generation sequencing (NGS), diverse types of genomic and epigenetic variations can be detected with high accuracy. More importantly, instead of using linkage disequilibrium to detect association signals based on a set of pre-set probes, NGS allows researchers to directly study all the variants in each individual, therefore promises opportunities for identifying functional variants and a more comprehensive dissection of disease heritability. Although the current scale of NGS studies is still limited due to the high cost, the success of several recent studies suggests the great potential for applying NGS in genomic epidemiology, especially as the cost of sequencing continues to drop. In this review, we discuss several pioneer applications of NGS, summarize scientific discoveries for rare and complex diseases, and compare various study designs including targeted sequencing and whole-genome sequencing using population-based and family-based cohorts. Finally, we highlight recent advancements in statistical methods proposed for sequencing analysis, including group-based association tests, meta-analysis techniques, and annotation tools for variant prioritization.

Re-annotation of presumed noncoding disease/trait-associated genetic variants by integrative analyses

Using RefSeq annotations, most disease/trait-associated genetic variants identified by genome-wide association studies (GWAS) appear to be located within intronic or intergenic regions, which makes it difficult to interpret their functions. We reassessed GWAS-Associated single-nucleotide polymorphisms (herein termed as GASs) for their potential functionalities using integrative approaches. 8834 of 9184 RefSeq “noncoding” GASs were reassessed to have potential regulatory functionalities. As examples, 3 variants (rs3130320, rs3806932 and rs6890853) were shown to have regulatory properties in HepG2, A549 and 293T cells. Except rs3130320 as a known expression quantitative trait loci (eQTL), rs3806932 and rs6890853 were not reported as eQTLs in previous reports. 1999 of 9184 “noncoding” GASs were re-annotated to the promoters or intragenic regions using Ensembl, UCSC and AceView gene annotations but they were not annotated into corresponding regions in RefSeq database. Moreover, these GAS-harboring genes were broadly expressed across different tissues and a portion of them was expressed in a tissue-specific manner, suggesting that they could be functional. Collectively, our study demonstrates the benefits of using integrative analyses to interpret genetic variants and may help to predict or explain disease susceptibility more accurately and comprehensively.

Identification of a large set of rare complete human knockouts

Loss-of-function mutations cause many mendelian diseases. Here we aimed to create a catalog of autosomal genes that are completely knocked out in humans by rare loss-of-function mutations. We sequenced the whole genomes of 2,636 Icelanders and imputed the sequence variants identified in this set into 101,584 additional chip-genotyped and phased Icelanders. We found a total of 6,795 autosomal loss-of-function SNPs and indels in 4,924 genes. Of the genotyped Icelanders, 7.7% are homozygotes or compound heterozygotes for loss-of-function mutations with a minor allele frequency (MAF) below 2% in 1,171 genes (complete knockouts). Genes that are highly expressed in the brain are less often completely knocked out than other genes. Homozygous loss-of-function offspring of two heterozygous parents occurred less frequently than expected (deficit of 136 per 10,000 transmissions for variants with MAF <2%, 95% confidence interval (CI) = 10-261).

Large-scale whole-genome sequencing of the Icelandic population

Here we describe the insights gained from sequencing the whole genomes of 2,636 Icelanders to a median depth of 20×. We found 20 million SNPs and 1.5 million insertions-deletions (indels). We describe the density and frequency spectra of sequence variants in relation to their functional annotation, gene position, pathway and conservation score. We demonstrate an excess of homozygosity and rare protein-coding variants in Iceland. We imputed these variants into 104,220 individuals down to a minor allele frequency of 0.1% and found a recessive frameshift mutation in MYL4 that causes early-onset atrial fibrillation, several mutations in ABCB4 that increase risk of liver diseases and an intronic variant in GNAS associating with increased thyroid-stimulating hormone levels when maternally inherited. These data provide a study design that can be used to determine how variation in the sequence of the human genome gives rise to human diversity.

Impact of allele-specific peptides in proteome quantification

MS-based proteome technologies have greatly improved our ability to detect and quantify proteomes across various biological samples. High throughput bottom-up proteome profiling in combination with targeted MS method, e.g. SRM assay, is emerging as a powerful approach in the field of biomarker discovery. In the past few years, increasing number of studies have attempted to integrate genomic and proteomic data for biomarker discovery. Here, we describe how allele-specific peptide can be applied in biomarker discovery and their impact in protein quantification.

In-silico identification and functional validation of allele-dependent AR enhancers

Androgen Receptor (AR) and Estrogen Receptors (ERs) are key nuclear receptors that can cooperate in orchestrating gene expression programs in multiple tissues and diseases, targeting binding elements in promoters and distant enhancers. We report the unbiased identification of enhancer elements bound by AR and ER-α whose activity can be allele-specific depending on the status of nearby Single Nucleotide Polymorphisms (SNP). ENCODE data were computationally mined to nominate genomic loci with: (i) chromatin signature of enhancer activity from activation histone marks, (ii) binding evidence by AR and ER-α, (iii) presence of a SNP. Forty-one loci were identified and two, on 1q21.3 and 13q34, selected for characterization by gene reporter, Chromatin immunoprecipitation (ChIP) and RT-qPCR assays in breast (MCF7) and prostate (PC-3) cancer-derived cell lines. We observed allele-specific enhancer activity, responsiveness to ligand-bound AR, and potentially influence on the transcription of closely located genes (RAB20, ING1, ARHGEF7, ADAM15). The 1q21.3 variant, rs2242193, showed impact on AR binding in MCF7 cells that are heterozygous for the SNP. Our unbiased genome-wide search proved to be an efficient methodology to discover new functional polymorphic regulatory regions (PRR) potentially acting as risk modifiers in hormone-driven cancers and overall nominated SNPs in PRR across 136 transcription factors.

Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer

Background

The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets.

Methods

Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs.

Results

An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line.

Conclusions

A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0135-5) contains supplementary material, which is available to authorized users.

The challenge of small-scale repeats for indel discovery

Repetitive sequences are abundant in the human genome. Different classes of repetitive DNA sequences, including simple repeats, tandem repeats, segmental duplications, interspersed repeats, and other elements, collectively span more than 50% of the genome. Because repeat sequences occur in the genome at different scales they can cause various types of sequence analysis errors, including in alignment, de novo assembly, and annotation, among others. This mini-review highlights the challenges introduced by small-scale repeat sequences, especially near-identical tandem or closely located repeats and short tandem repeats, for discovering DNA insertion and deletion (indel) mutations from next-generation sequencing data. We also discuss the de Bruijn graph sequence assembly paradigm that is emerging as the most popular and promising approach for detecting indels. The human exome is taken as an example and highlights how these repetitive elements can obscure or introduce errors while detecting these types of mutations.

Evolutionary constraint and disease associations of post-translational modification sites in human genomes

Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links.

Enhancer sequence variants and transcription-factor deregulation synergize to construct pathogenic regulatory circuits in B-cell lymphoma

Most B-cell lymphomas arise in the germinal center (GC), where humoral immune responses evolve from potentially oncogenic cycles of mutation, proliferation, and clonal selection. Although lymphoma gene expression diverges significantly from GC B cells, underlying mechanisms that alter the activities of corresponding regulatory elements (REs) remain elusive. Here we define the complete pathogenic circuitry of human follicular lymphoma (FL), which activates or decommissions REs from normal GC B cells and commandeers enhancers from other lineages. Moreover, independent sets of transcription factors, whose expression was deregulated in FL, targeted commandeered versus decommissioned REs. Our approach revealed two distinct subtypes of low-grade FL, whose pathogenic circuitries resembled GC B or activated B cells. FL-altered enhancers also were enriched for sequence variants, including somatic mutations, which disrupt transcription-factor binding and expression of circuit-linked genes. Thus, the pathogenic regulatory circuitry of FL reveals distinct genetic and epigenetic etiologies for GC B-cell transformation.

A method for calculating probabilities of fitness consequences for point mutations across the human genome

We describe a novel computational method for estimating the probability that a point mutation at each position in a genome will influence fitness. These fitness consequence (fitCons) scores serve as evolution-based measures of potential genomic function. Our approach is to cluster genomic positions into groups exhibiting distinct “fingerprints” based on high-throughput functional genomic data, then to estimate a probability of fitness consequences for each group from associated patterns of genetic polymorphism and divergence. We have generated fitCons scores for three human cell types based on public data from ENCODE. Compared with conventional conservation scores, fitCons scores show considerably improved prediction power for cis-regulatory elements. In addition, fitCons scores indicate that 4.2–7.5% of nucleotides in the human genome have influenced fitness since the human-chimpanzee divergence, and they suggest that recent evolutionary turnover has had limited impact on the functional content of the genome.

mirTrios: an integrated pipeline for detection of de novo and rare inherited mutations from trios-based next-generation sequencing

OBJECTIVES

Recently, several studies documented that de novo mutations (DNMs) play important roles in the aetiology of sporadic diseases. Next-generation sequencing (NGS) enables variant calling at single-base resolution on a genome-wide scale. However, accurate identification of DNMs from NGS data still remains a major challenge. We developed mirTrios, a web server, to accurately detect DNMs and rare inherited mutations from NGS data in sporadic diseases.

METHODS

The expectation-maximisation (EM) model was adopted to accurately identify DNMs from variant call files of a trio generated by GATK (Genome Analysis Toolkit). The GATK results, which contain certain basic properties (such as PL, PRT and PART), are iteratively integrated into the EM model to strike a threshold for DNMs detection. Training sets of true and false positive DNMs in the EM model were built from whole genome sequencing data of 64 trios.

RESULTS

With our in-house whole exome sequencing datasets from 20 trios, mirTrios totally identified 27 DNMs in the coding region, 25 of which (92.6%) are validated as true positives. In addition, to facilitate the interpretation of diverse mutations, mirTrios can also be employed in the identification of rare inherited mutations. Embedded with abundant annotation of DNMs and rare inherited mutations, mirTrios also supports known diagnostic variants and causative gene identification, as well as the prioritisation of novel and promising candidate genes.

CONCLUSIONS

mirTrios provides an intuitive interface for the general geneticist and clinician, and can be widely used for detection of DNMs and rare inherited mutations, and annotation in sporadic diseases. mirTrios is freely available at http://centre.bioinformatics.zj.cn/mirTrios/.

ContrastRank: a new method for ranking putative cancer driver genes and classification of tumor samples

Motivation: The recent advance in high-throughput sequencing technologies is generating a huge amount of data that are becoming an important resource for deciphering the genotype underlying a given phenotype. Genome sequencing has been extensively applied to the study of the cancer genomes. Although a few methods have been already proposed for the detection of cancer-related genes, their automatic identification is still a challenging task. Using the genomic data made available by The Cancer Genome Atlas Consortium (TCGA), we propose a new prioritization approach based on the analysis of the distribution of putative deleterious variants in a large cohort of cancer samples.

Results: In this paper, we present ContastRank, a new method for the prioritization of putative impaired genes in cancer. The method is based on the comparison of the putative defective rate of each gene in tumor versus normal and 1000 genome samples. We show that the method is able to provide a ranked list of putative impaired genes for colon, lung and prostate adenocarcinomas. The list significantly overlaps with the list of known cancer driver genes previously published. More importantly, by using our scoring approach, we can successfully discriminate between TCGA normal and tumor samples. A binary classifier based on ContrastRank score reaches an overall accuracy >90% and the area under the curve (AUC) of receiver operating characteristics (ROC) >0.95 for all the three types of adenocarcinoma analyzed in this paper. In addition, using ContrastRank score, we are able to discriminate the three tumor types with a minimum overall accuracy of 77% and AUC of 0.83.

Conclusions: We describe ContrastRank, a method for prioritizing putative impaired genes in cancer. The method is based on the comparison of exome sequencing data from different cohorts and can detect putative cancer driver genes.

ContrastRank can also be used to estimate a global score for an individual genome about the risk of adenocarcinoma based on the genetic variants information from a whole-exome VCF (Variant Calling Format) file. We believe that the application of ContrastRank can be an important step in genomic medicine to enable genome-based diagnosis.

Availability and implementation: The lists of ContrastRank scores of all genes in each tumor type are available as supplementary materials. A webserver for evaluating the risk of the three studied adenocarcinomas starting from whole-exome VCF file is under development.

Contact:emidio@uab.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease

To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intronic and exonic variants revealed widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations that are more than 30 nucleotides from any splice site alter splicing nine times as often as common variants, and missense exonic disease mutations that have the least impact on protein function are five times as likely as others to alter splicing. We detected tens of thousands of disease-causing mutations, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole-genome sequencing of individuals with autism revealed misspliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine.

Mitochondrial genome acquisition restores respiratory function and tumorigenic potential of cancer cells without mitochondrial DNA

We report that tumor cells without mitochondrial DNA (mtDNA) show delayed tumor growth, and that tumor formation is associated with acquisition of mtDNA from host cells. This leads to partial recovery of mitochondrial function in cells derived from primary tumors grown from cells without mtDNA and a shorter lag in tumor growth. Cell lines from circulating tumor cells showed further recovery of mitochondrial respiration and an intermediate lag to tumor growth, while cells from lung metastases exhibited full restoration of respiratory function and no lag in tumor growth. Stepwise assembly of mitochondrial respiratory (super)complexes was correlated with acquisition of respiratory function. Our findings indicate horizontal transfer of mtDNA from host cells in the tumor microenvironment to tumor cells with compromised respiratory function to re-establish respiration and tumor-initiating efficacy. These results suggest pathophysiological processes for overcoming mtDNA damage and support the notion of high plasticity of malignant cells.

Using common variants to indicate cancer genes

The catalogue of tumour-specific somatic mutations (SMs) is growing rapidly owing to the advent of next-generation sequencing. Identifying those mutations responsible for the development and progression of the disease, so-called driver mutations, will increase our understanding of carcinogenesis and provide candidates for targeted therapeutics. The phenotypic consequence(s) of driver mutations cause them to be selected for within the tumour environment, such that many approaches aimed at distinguishing drivers are based on finding significantly somatically mutated genes. Currently, these methods are designed to analyse, or be specifically applied to, nonsynonymous mutations: those that alter an encoded protein. However, growing evidence suggests the involvement of noncoding transcripts in carcinogenesis, mutations in which may also be disease-driving. We wished to test the hypothesis that common DNA variation rates within humans can be used as a baseline from which to score the rate of SMs, irrespective of coding capacity. We preliminarily tested this by applying it to a dataset of 159,498 SMs and using the results to rank genes. This resulted in significant enrichment of known cancer genes, indicating that the approach has merit. As additional data from cancer sequencing studies are made publicly available, this approach can be refined and applied to specific cancer subtypes. We named this preliminary version of our approach PRISMAD (polymorphism rates indicate somatic mutations as drivers) and have made it publicly accessible, with scripts, via a link at www.precancer.leeds.ac.uk/software-and-datasets.

Intrinsic disorder mediates hepatitis C virus core-host cell protein interactions

Viral proteins bind to numerous cellular and viral proteins throughout the infection cycle. However, the mechanisms by which viral proteins interact with such large numbers of factors remain unknown. Cellular proteins that interact with multiple, distinct partners often do so through short sequences known as molecular recognition features (MoRFs) embedded within intrinsically disordered regions (IDRs). In this study, we report the first evidence that MoRFs in viral proteins play a similar role in targeting the host cell. Using a combination of evolutionary modeling, protein-protein interaction analyses and forward genetic screening, we systematically investigated two computationally predicted MoRFs within the N-terminal IDR of the hepatitis C virus (HCV) Core protein. Sequence analysis of the MoRFs showed their conservation across all HCV genotypes and the canine and equine Hepaciviruses. Phylogenetic modeling indicated that the Core MoRFs are under stronger purifying selection than the surrounding sequence, suggesting that these modules have a biological function. Using the yeast two-hybrid assay, we identified three cellular binding partners for each HCV Core MoRF, including two previously characterized cellular targets of HCV Core (DDX3X and NPM1). Random and site-directed mutagenesis demonstrated that the predicted MoRF regions were required for binding to the cellular proteins, but that different residues within each MoRF were critical for binding to different partners. This study demonstrated that viruses may use intrinsic disorder to target multiple cellular proteins with the same amino acid sequence and provides a framework for characterizing the binding partners of other disordered regions in viral and cellular proteomes.

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson's disease

Background

The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson’s disease, the relevance of this cellular model in the context of Parkinson’s disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated.

Results

We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD.

Conclusions

The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson’s disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer’s disease but lower than for Huntington’s disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1154) contains supplementary material, which is available to authorized users.

A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, "Clone-seq", leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.

Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape

The large collections of ChIP-seq data rapidly accumulating in public data warehouses provide genome-wide binding site maps for hundreds of transcription factors (TFs). However, the extent of the regulatory occupancy space in the human genome has not yet been fully apprehended by integrating public ChIP-seq data sets and combining it with ENCODE TFs map. To enable genome-wide identification of regulatory elements we have collected, analysed and retained 395 available ChIP-seq data sets merged with ENCODE peaks covering a total of 237 TFs. This enhanced repertoire complements and refines current genome-wide occupancy maps by increasing the human genome regulatory search space by 14% compared to ENCODE alone, and also increases the complexity of the regulatory dictionary. As a direct application we used this unified binding repertoire to annotate variant enhancer loci (VELs) from H3K4me1 mark in two cancer cell lines (MCF-7, CRC) and observed enrichments of specific TFs involved in biological key functions to cancer development and proliferation. Those enrichments of TFs within VELs provide a direct annotation of non-coding regions detected in cancer genomes. Finally, full access to this catalogue is available online together with the TFs enrichment analysis tool (http://tagc.univ-mrs.fr/remap/).

Cis-regulatory elements and human evolution

Modification of gene regulation has long been considered an important force in human evolution, particularly through changes to cis-regulatory elements (CREs) that function in transcriptional regulation. For decades, however, the study of cis-regulatory evolution was severely limited by the available data. New data sets describing the locations of CREs and genetic variation within and between species have now made it possible to study CRE evolution much more directly on a genome-wide scale. Here, we review recent research on the evolution of CREs in humans based on large-scale genomic data sets. We consider inferences based on primate divergence, human polymorphism, and combinations of divergence and polymorphism. We then consider 'new frontiers' in this field stemming from recent research on transcriptional regulation.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

Enhancer variants: evaluating functions in common disease

Gene enhancer elements are noncoding segments of DNA that play a central role in regulating transcriptional programs that control development, cell identity, and evolutionary processes. Recent studies have shown that noncoding single nucleotide polymorphisms (SNPs) that have been associated with risk for numerous common diseases through genome-wide association studies frequently lie in cell-type-specific enhancer elements. These enhancer variants probably influence transcriptional output, thereby offering a mechanistic basis to explain their association with risk for many common diseases. This review focuses on the identification and interpretation of disease-susceptibility variants that influence enhancer function. We discuss strategies for prioritizing the study of functional enhancer SNPs over those likely to be benign, review experimental and computational approaches to identifying the gene targets of enhancer variants, and highlight efforts to quantify the impact of enhancer variants on target transcript levels and cellular phenotypes. These studies are beginning to provide insights into the mechanistic basis of many common diseases, as well as into how we might translate this knowledge for improved disease diagnosis, prevention and treatments. Finally, we highlight five major challenges often associated with interpreting enhancer variants, and discuss recent technical advances that may help to surmount these challenges.

SuRFing the genomics wave: an R package for prioritising SNPs by functionality

Identifying functional non-coding variants is one of the greatest unmet challenges in genetics. To help address this, we introduce an R package, SuRFR, which integrates functional annotation and prior biological knowledge to prioritise candidate functional variants. SuRFR is publicly available, modular, flexible, fast, and simple to use. We demonstrate that SuRFR performs with high sensitivity and specificity and provide a widely applicable and scalable benchmarking dataset for model training and validation.

Website: http://www.cgem.ed.ac.uk/resources/

Current trend of annotating single nucleotide variation in humans--A case study on SNVrap

As high throughput methods, such as whole genome genotyping arrays, whole exome sequencing (WES) and whole genome sequencing (WGS), have detected huge amounts of genetic variants associated with human diseases, function annotation of these variants is an indispensable step in understanding disease etiology. Large-scale functional genomics projects, such as The ENCODE Project and Roadmap Epigenomics Project, provide genome-wide profiling of functional elements across different human cell types and tissues. With the urgent demands for identification of disease-causal variants, comprehensive and easy-to-use annotation tool is highly in demand. Here we review and discuss current progress and trend of the variant annotation field. Furthermore, we introduce a comprehensive web portal for annotating human genetic variants. We use gene-based features and the latest functional genomics datasets to annotate single nucleotide variation (SNVs) in human, at whole genome scale. We further apply several function prediction algorithms to annotate SNVs that might affect different biological processes, including transcriptional gene regulation, alternative splicing, post-transcriptional regulation, translation and post-translational modifications. The SNVrap web portal is freely available at http://jjwanglab.org/snvrap.

VAS: a convenient web portal for efficient integration of genomic features with millions of genetic variants

Background

High-throughput experimental methods have fostered the systematic detection of millions of genetic variants from any human genome. To help explore the potential biological implications of these genetic variants, software tools have been previously developed for integrating various types of information about these genomic regions from multiple data sources. Most of these tools were designed either for studying a small number of variants at a time, or for local execution on powerful machines.

Results

To make exploration of whole lists of genetic variants simple and accessible, we have developed a new Web-based system called VAS (Variant Annotation System, available at https://yiplab.cse.cuhk.edu.hk/vas/). It provides a large variety of information useful for studying both coding and non-coding variants, including whole-genome transcription factor binding, open chromatin and transcription data from the ENCODE consortium. By means of data compression, millions of variants can be uploaded from a client machine to the server in less than 50 megabytes of data. On the server side, our customized data integration algorithms can efficiently link millions of variants with tens of whole-genome datasets. These two enabling technologies make VAS a practical tool for annotating genetic variants from large genomic studies. We demonstrate the use of VAS in annotating genetic variants obtained from a migraine meta-analysis study and multiple data sets from the Personal Genomes Project. We also compare the running time of annotating 6.4 million SNPs of the CEU trio by VAS and another tool, showing that VAS is efficient in handling new variant lists without requiring any pre-computations.

Conclusions

VAS is specially designed to handle annotation tasks with long lists of genetic variants and large numbers of annotating features efficiently. It is complementary to other existing tools with more specific aims such as evaluating the potential impacts of genetic variants in terms of disease risk. We recommend using VAS for a quick first-pass identification of potentially interesting genetic variants, to minimize the time required for other more in-depth downstream analyses.

Missing heritability of common diseases and treatments outside the protein-coding exome

Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common 'normal' variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

Genome-wide analysis of noncoding regulatory mutations in cancer

Cancer primarily develops due to somatic alterations in the genome. Advances in sequencing have enabled large-scale sequencing studies across many tumor types, emphasizing discovery of alterations in protein-coding genes. However, the protein-coding exome comprises less than 2% of the human genome. Here, we analyze complete genome sequences of 863 human tumors from The Cancer Genome Atlas and other sources to systematically identify non-coding regions that are recurrently mutated in cancer. We utilize novel frequency and sequence-based approaches to comprehensively scan the genome for non-coding mutations with potential regulatory impact. We identified recurrent mutations in regulatory elements upstream of PLEKHS1, WDR74, and SDHD, as well as previously identified mutations in the TERT promoter. SDHD promoter mutations are frequent in melanoma and associated with reduced gene expression and poor patient prognosis. The non-protein-coding cancer genome remains widely unexplored and our findings represent a step towards targeting the entire genome for clinical purposes.

Transcription factors bind negatively selected sites within human mtDNA genes

Transcription of mitochondrial DNA (mtDNA)-encoded genes is thought to be regulated by a handful of dedicated transcription factors (TFs), suggesting that mtDNA genes are separately regulated from the nucleus. However, several TFs, with known nuclear activities, were found to bind mtDNA and regulate mitochondrial transcription. Additionally, mtDNA transcriptional regulatory elements, which were proved important in vitro, were harbored by a deletion that normally segregated among healthy individuals. Hence, mtDNA transcriptional regulation is more complex than once thought. Here, by analyzing ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) data, we identified strong binding sites of three bona fide nuclear TFs (c-Jun, Jun-D, and CEBPb) within human mtDNA protein-coding genes. We validated the binding of two TFs by ChIP-quantitative polymerase chain reaction (c-Jun and Jun-D) and showed their mitochondrial localization by electron microscopy and subcellular fractionation. As a step toward investigating the functionality of these TF-binding sites (TFBS), we assessed signatures of selection. By analyzing 9,868 human mtDNA sequences encompassing all major global populations, we recorded genetic variants in tips and nodes of mtDNA phylogeny within the TFBS. We next calculated the effects of variants on binding motif prediction scores. Finally, the mtDNA variation pattern in predicted TFBS, occurring within ChIP-seq negative-binding sites, was compared with ChIP-seq positive-TFBS (CPR). Motifs within CPRs of c-Jun, Jun-D, and CEBPb harbored either only tip variants or their nodal variants retained high motif prediction scores. This reflects negative selection within mtDNA CPRs, thus supporting their functionality. Hence, human mtDNA-coding sequences may have dual roles, namely coding for genes yet possibly also possessing regulatory potential.