Cell-type-specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants affecting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the receiver operating characteristic curve of 0.99 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. We demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Identifying Strong Neoantigen MHC-I/II Binding Candidates for Targeted Immunotherapy with SINE

The discovery of tumor-derived neoantigens which elicit an immune response through major histocompatibility complex (MHC-I/II) binding has led to significant advancements in immunotherapy. While many neoantigens have been discovered through the identification of non-synonymous mutations, the rate of these is low in some cancers, including head and neck squamous cell carcinoma. Therefore, the identification of neoantigens through additional means, such as aberrant splicing, is necessary. To achieve this, we developed the splice isoform neoantigen evaluator (SINE) pipeline. Our tool documents peptides present on spliced or inserted genomic regions of interest using Patient Harmonic-mean Best Rank scores, calculating the MHC-I/II binding affinity across the complete human leukocyte antigen landscape. Here, we found 125 potentially immunogenic events and 9 principal binders in a cohort of head and neck cancer patients where the corresponding wild-type peptides display no MHC-I/II affinity. Further, in a melanoma cohort of patients treated with anti-PD1 therapy, the expression of immunogenic splicing events identified by SINE predicted response, potentially indicating the existence of immune editing in these tumors. Overall, we demonstrate SINE's ability to identify clinically relevant immunogenic neojunctions, thus acting as a useful tool for researchers seeking to understand the neoantigen landscape from aberrant splicing in cancer.

Genome-Wide European Ancestry Study Identifies Coronary Artery Disease-Associated Loci Through Gene-Sex Hormone Interaction

BACKGROUND

Although sex differences in coronary artery disease (CAD) risk have been observed, little is known about the role of sex hormones in CAD genetics. Accounting for sex hormone levels may help identify CAD-risk loci and extend our knowledge of its genetic architecture.

METHODS AND RESULTS

A total of 365 662 individuals of European ancestry enrolled in the UK Biobank were considered. Genetic interaction of total testosterone, bioavailable testosterone, and SHBG (sex hormone-binding globulin) were evaluated. Gene-environment interactions in millions of samples software was used to conduct sex-stratified genome-wide interaction analysis with prevalent CAD as the outcome. Participant age at enrollment and principal components 1 to 10 were adjusted as covariates. We identified 45 loci in men and 8 loci in women that reached genome-wide significance (P < 5 × 10-8) for CAD. Ten of the loci identified (5 loci in both men and women) were through joint effects and would not have been picked up using a traditional genome-wide association study. Two of the joint effect loci in women were independently identified with significant single nucleotide polymorphism-total testosterone interactions.

CONCLUSIONS

This genome-wide gene-sex hormone interaction study identified genomic-risk loci that may contribute to the differential CAD risk between men and women, which otherwise would not have been discovered in a traditional genome-wide association study solely including marginal genetic effects.

Mapping chromatin interactions at melanoma susceptibility loci and cell-type specific dataset integration uncovers distant gene targets of cis-regulation

Genome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma susceptibility genes. Overall, chromatin interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with cell-type specific epigenomic (chromatin state, accessibility), gene expression (eQTL/TWAS), DNA methylation (meQTL/MWAS), and massively parallel reporter assay (MPRA) data to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized candidate causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Transcriptome-Wide Association Study Reveals New Molecular Interactions Associated with Melanoma Pathogenesis

A transcriptome-wide association study (TWAS) was conducted on genome-wide association study (GWAS) summary statistics of malignant melanoma of skin (UK Biobank dataset) and The Cancer Genome Atlas-Skin Cutaneous Melanoma (TCGA-SKCM) gene expression weights to identify melanoma susceptibility genes. The GWAS included 2465 cases and 449,799 controls, while the gene expression testing was conducted on 103 cases. Afterward, a gene enrichment analysis was applied to identify significant TWAS associations. The melanoma's gene-microRNA (miRNA) regulatory network was constructed from the TWAS genes and their corresponding miRNAs. At last, a disease enrichment analysis was conducted on the corresponding miRNAs. The TWAS detected 27 genes associated with melanoma with p-values less than 0.05 (the top three genes are LOC389458 (RBAK), C16orf73 (MEIOB), and EIF3CL). After the joint/conditional test, one gene (AMIGO1) was dropped, resulting in 26 significant genes. The Gene Ontology (GO) biological process associated the extended gene set (76 genes) with protein K11-linked ubiquitination and regulation of cell cycle phase transition. K11-linked ubiquitin chains regulate cell division. Interestingly, the extended gene set was related to different skin cancer subtypes. Moreover, the enriched pathways were nsp1 from SARS-CoV-2 that inhibit translation initiation in the host cell, cell cycle, translation factors, and DNA repair pathways full network. The gene-miRNA regulatory network identified 10 hotspot genes with the top three: TP53, BRCA1, and MDM2; and four hotspot miRNAs: mir-16, mir-15a, mir-125b, and mir-146a. Melanoma was among the top ten diseases associated with the corresponding (106) miRNAs. Our results shed light on melanoma pathogenesis and biologically significant molecular interactions.

Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

Single-cell profiling of MC1R-inhibited melanocytes

The human red hair color (RHC) trait is caused by increased pheomelanin (red-yellow) and reduced eumelanin (black-brown) pigment in skin and hair due to diminished melanocortin 1 receptor (MC1R) function. In addition, individuals harboring the RHC trait are predisposed to melanoma development. While MC1R variants have been established as causative of RHC and are a well-defined risk factor for melanoma, it remains unclear mechanistically why decreased MC1R signaling alters pigmentation and increases melanoma susceptibility. Here, we use single-cell RNA sequencing (scRNA-seq) of melanocytes isolated from RHC mouse models to define a MC1R-inhibited Gene Signature (MiGS) comprising a large set of previously unidentified genes which may be implicated in melanogenesis and oncogenic transformation. We show that one of the candidate MiGS genes, TBX3, a well-known anti-senescence transcription factor implicated in melanoma progression, binds both E-box and T-box elements to regulate genes associated with melanogenesis and senescence bypass. Our results provide key insights into further mechanisms by which melanocytes with reduced MC1R signaling may regulate pigmentation and offer new candidates of study toward understanding how individuals with the RHC phenotype are predisposed to melanoma.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants impacting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the Receiver Operating Characteristic curve of 0.98 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. Importantly, we demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

eQTL studies: from bulk tissues to single cells

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

A genome-wide genetic screen uncovers determinants of human pigmentation

Skin color, one of the most diverse human traits, is determined by the quantity, type, and distribution of melanin. In this study, we leveraged the light-scattering properties of melanin to conduct a genome-wide screen for regulators of melanogenesis. We identified 169 functionally diverse genes that converge on melanosome biogenesis, endosomal transport, and gene regulation, of which 135 represented previously unknown associations with pigmentation. In agreement with their melanin-promoting function, the majority of screen hits were up-regulated in melanocytes from darkly pigmented individuals. We further unraveled functions of KLF6 as a transcription factor that regulates melanosome maturation and pigmentation in vivo, and of the endosomal trafficking protein COMMD3 in modulating melanosomal pH. Our study reveals a plethora of melanin-promoting genes, with broad implications for human variation, cell biology, and medicine.

Haplotype-based analysis resolves missing heritability in oculocutaneous albinism type 1B

Oculocutaneous albinism (OCA) is a rare disorder of pigment production. Affected individuals have variably decreased global pigmentation and visual-developmental changes that lead to low vision. OCA is notable for significant missing heritability, particularly among individuals with residual pigmentation. Tyrosinase (TYR) is the rate-limiting enzyme in melanin pigment biosynthesis and mutations that decrease enzyme function are one of the most common causes of OCA. We present the analysis of high-depth short-read TYR sequencing data for a cohort of 352 OCA probands, ∼50% of whom were previously sequenced without yielding a definitive diagnostic result. Our analysis identified 66 TYR single-nucleotide variants (SNVs) and small insertion/deletions (indels), 3 structural variants, and a rare haplotype comprised of two common frequency variants (p.Ser192Tyr and p.Arg402Gln) in cis-orientation, present in 149/352 OCA probands. We further describe a detailed analysis of the disease-causing haplotype, p.[Ser192Tyr; Arg402Gln] ("cis-YQ"). Haplotype analysis suggests that the cis-YQ allele arose by recombination and that multiple cis-YQ haplotypes are segregating in OCA-affected individuals and control populations. The cis-YQ allele is the most common disease-causing allele in our cohort, representing 19.1% (57/298) of TYR pathogenic alleles in individuals with type 1 (TYR-associated) OCA. Finally, among the 66 TYR variants, we found several additional alleles defined by a cis-oriented combination of minor, potentially hypomorph-producing alleles at common variant sites plus a second, rare pathogenic variant. Together, these results suggest that identification of phased variants for the full TYR locus are required for an exhaustive assessment for potentially disease-causing alleles.

Expression quantitative trait locus studies in the era of single-cell omics

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

Loss of MC1R signaling implicates TBX3 in pheomelanogenesis and melanoma predisposition

The human Red Hair Color (RHC) trait is caused by increased pheomelanin (red-yellow) and reduced eumelanin (black-brown) pigment in skin and hair due to diminished melanocortin 1 receptor (MC1R) function. In addition, individuals harboring the RHC trait are predisposed to melanoma development. While MC1R variants have been established as causative of RHC and are a well-defined risk factor for melanoma, it remains unclear mechanistically why decreased MC1R signaling alters pigmentation and increases melanoma susceptibility. Here, we use single-cell RNA-sequencing (scRNA-seq) of melanocytes isolated from RHC mouse models to reveal a Pheomelanin Gene Signature (PGS) comprising genes implicated in melanogenesis and oncogenic transformation. We show that TBX3, a well-known anti-senescence transcription factor implicated in melanoma progression, is part of the PGS and binds both E-box and T-box elements to regulate genes associated with melanogenesis and senescence bypass. Our results provide key insights into mechanisms by which MC1R signaling regulates pigmentation and how individuals with the RHC phenotype are predisposed to melanoma.

eQTL Studies: from Bulk Tissues to Single Cells

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Epigenetic and pharmacological control of pigmentation via Bromodomain Protein 9 (BRD9)

Lineage-specific differentiation programs are activated by epigenetic changes in chromatin structure. Melanin-producing melanocytes maintain a gene expression program ensuring appropriate enzymatic conversion of metabolites into the pigment, melanin, and transfer to surrounding cells. During neuroectodermal development, SMARCA4 (BRG1), the catalytic subunit of SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complexes, is essential for lineage specification. SMARCA4 is also required for development of multipotent neural crest precursors into melanoblasts, which differentiate into pigment-producing melanocytes. In addition to the catalytic domain, SMARCA4 and several SWI/SNF subunits contain bromodomains which are amenable to pharmacological inhibition. We investigated the effects of pharmacological inhibitors of SWI/SNF bromodomains on melanocyte differentiation. Strikingly, treatment of murine melanoblasts and human neonatal epidermal melanocytes with selected bromodomain inhibitors abrogated melanin synthesis and visible pigmentation. Using functional genomics, iBRD9, a small molecule selective for the bromodomain of BRD9 was found to repress pigmentation-specific gene expression. Depletion of BRD9 confirmed a requirement for expression of pigmentation genes in the differentiation program from melanoblasts into pigmented melanocytes and in melanoma cells. Chromatin immunoprecipitation assays showed that iBRD9 disrupts the occupancy of BRD9 and the catalytic subunit SMARCA4 at melanocyte-specific loci. These data indicate that BRD9 promotes melanocyte pigmentation whereas pharmacological inhibition of BRD9 is repressive.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

Functional characterization of 5p15.33 risk locus in uveal melanoma reveals rs452384 as a functional variant and NKX2.4 as an allele-specific interactor

The TERT/CLPTM1L risk locus on chromosome 5p15.33 is a pleiotropic cancer risk locus in which multiple independent risk alleles have been identified, across well over ten cancer types. We previously conducted a genome-wide association study in uveal melanoma (UM), which uncovered a role for the TERT/CLPTM1L risk locus in this intraocular tumor and identified multiple highly correlated risk alleles. Aiming to unravel the biological mechanisms in UM of this locus, which contains a domain enriched in active chromatin marks and enhancer elements, we demonstrated the allele-specific enhancer activity of this risk region using reporter assays. In UM, we identified the functional variant rs452384, of which the C risk allele is associated with higher gene expression, increased CLPTM1L expression in UM tumors, and a longer telomere length in peripheral blood mononuclear cells. Electrophoretic mobility shift assays and quantitative mass spectrometry identified NKX2.4 as an rs452384-T-specific binding protein, whereas GATA4 preferentially interacted with rs452384-C. Knockdown of NKX2.4 but not GATA4 resulted in increased TERT and CLPTM1L expression. In summary, the UM risk conferred by the 5p locus is at least partly due to rs452384, for which NKX2.4 presents strong differential binding activity and regulates CLPTM1L and TERT expression. Altogether, our work unraveled some of the complex regulatory mechanisms at the 5p15.33 susceptibility region in UM, and this might also shed light on shared mechanisms with other tumor types affected by this susceptibility region.

Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity

The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR < 1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Integrated Analysis of Coexpression and Exome Sequencing to Prioritize Susceptibility Genes for Familial Cutaneous Melanoma

The application of whole-exome sequencing has led to the identification of high- and moderate-risk variants that contribute to cutaneous melanoma susceptibility. However, confirming disease-causing variants remains challenging. We applied a gene coexpression network analysis to prioritize the candidate genes identified from whole-exome sequencing of 34 melanoma-prone families, with at least three affected members sequenced per family (N = 119 cases). A coexpression network was constructed from genotype-tissue expression project, skin melanoma from the cancer genome atlas, and primary melanocyte cultures. We performed module-specific enrichment and focused on modules associated with pigmentation processes because they are the best-studied and most well-known risk factors for melanoma susceptibility. We found that pigmentation-associated modules across the four expression datasets examined were enriched for well-known melanoma susceptibility genes plus genes associated with pigmentation. We also used network properties to prioritize genes within pigmentation modules as candidate susceptibility genes. Integrating information from coexpression network analysis and variant prioritization, we identified 36 genes (such as DCT, TPCN2, TRPM1, ATP10A, and EPHA5) as potential melanoma risk genes in the families. Our approach also allowed us to link families with private gene mutations on the basis of gene coexpression patterns and thereby may provide an innovative perspective in gene identification in high-risk families.

Defining novel causal SNPs and linked phenotypes at melanoma-associated loci

A number of genomic regions have been associated with melanoma risk through genome-wide association studies; however, the causal variants underlying the majority of these associations remain unknown. Here, we sequenced either the full locus or the functional regions including exons of 19 melanoma-associated loci in 1959 British melanoma cases and 737 controls. Variant filtering followed by Fisher's exact test analyses identified 66 variants associated with melanoma risk. Sequential conditional logistic regression identified the distinct haplotypes on which variants reside, and massively parallel reporter assays provided biological insights into how these variants influence gene function. We performed further analyses to link variants to melanoma risk phenotypes and assessed their association with melanoma-specific survival. Our analyses replicate previously known associations in the melanocortin 1 receptor (MC1R) and tyrosinase (TYR) loci, while identifying novel potentially causal variants at the MTAP/CDKN2A and CASP8 loci. These results improve our understanding of the architecture of melanoma risk and outcome.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk

Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.

Investigating the genetic architecture of eye colour in a Canadian cohort

Eye color is highly variable in populations with European ancestry, ranging from low to high quantities of melanin in the iris. Polymorphisms in the HERC2/OCA2 locus have the largest effect on eye color in these populations, although other genomic regions also influence eye color. We performed genome-wide association studies of eye color in a Canadian cohort of European ancestry (N = 5,641) and investigated candidate causal variants. We uncovered several candidate causal signals in the HERC2/OCA2 region, whereas other loci likely harbor a single causal signal. We observed colocalization of eye color signals with the expression or methylation profiles of cultured primary melanocytes. Genetic correlations of eye and hair color suggest high genome-wide pleiotropy, but locus-level differences in the genetic architecture of both traits. Overall, we provide a better picture of the polymorphisms underpinning eye color variation, which may be a consequence of specific molecular processes in the iris melanocytes.

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Genome-wide association studies of eye color in 5,641 participants

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Multiple independent candidate causal variants were identified across HERC2/OCA2

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Single candidate causal variants observed on or near IRF4, SLC24A4, TYR, and TYRP1

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Colocalization of eye color signals with expression and methylation profiles

Characterization and clustering of kinase isoform expression in metastatic melanoma

Mutations to the human kinome are known to play causal roles in cancer. The kinome regulates numerous cell processes including growth, proliferation, differentiation, and apoptosis. In addition to aberrant expression, aberrant alternative splicing of cancer-driver genes is receiving increased attention as it could lead to loss or gain of functional domains, altering a kinase's downstream impact. The present study quantifies changes in gene expression and isoform ratios in the kinome of metastatic melanoma cells relative to primary tumors. We contrast 538 total kinases and 3,040 known kinase isoforms between 103 primary tumor and 367 metastatic samples from The Cancer Genome Atlas (TCGA). We find strong evidence of differential expression (DE) at the gene level in 123 kinases (23%). Additionally, of the 468 kinases with alternative isoforms, 60 (13%) had significant difference in isoform ratios (DIR). Notably, DE and DIR have little correlation; for instance, although DE highlights enrichment in receptor tyrosine kinases (RTKs), DIR identifies altered splicing in non-receptor tyrosine kinases (nRTKs). Using exon junction mapping, we identify five examples of splicing events favored in metastatic samples. We demonstrate differential apoptosis and protein localization between SLK isoforms in metastatic melanoma. We cluster isoform expression data and identify subgroups that correlate with genomic subtypes and anatomic tumor locations. Notably, distinct DE and DIR patterns separate samples with BRAF hotspot mutations and (N/K/H)RAS hotspot mutations, the latter of which lacks effective kinase inhibitor treatments. DE in RAS mutants concentrates in CMGC kinases (a group including cell cycle and splicing regulators) rather than RTKs as in BRAF mutants. Furthermore, isoforms in the RAS kinase subgroup show enrichment for cancer-related processes such as angiogenesis and cell migration. Our results reveal a new approach to therapeutic target identification and demonstrate how different mutational subtypes may respond differently to treatments highlighting possible new driver events in cancer.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Plasma Extracellular Vesicle Long RNAs Have Potential as Biomarkers in Early Detection of Colorectal Cancer

Background

Early detection of colorectal cancer (CRC) is crucial to the treatment and prognosis of patients. Traditional screening methods have disadvantages.

Methods

231 blood samples were collected from 86 CRC, 56 colorectal adenoma (CRA), and 89 healthy individuals, from which extracellular vesicle long RNAs (exLRs) were isolated and sequenced. An CRC diagnostic signature (d-signature) was established, and prognosis-associated cell components were evaluated.

Results

The exLR d-signature for CRC was established based on 17 of the differentially expressed exLRs. The d-signature showed high diagnostic efficiency of CRC and control (CRA and healthy) samples with an area under the curve (AUC) of 0.938 in the training cohort, 0.943 in the validation cohort, and 0.947 in an independent cohort. The d-signature could effectively differentiate early-stage (stage I–II) CRC from healthy individuals (AUC 0.990), as well as differentiating CEA-negative CRC from healthy individuals (AUC 0.988). A CRA d-signature was also generated and could differentiate CRA from healthy individuals both in the training (AUC 0.993) and validation (AUC 0.978) cohorts. The enrichment of class-switched memory B-cells, B-cells, naive B-cells, and mast cells showed increasing trends between CRC, CRA, and healthy cohorts. Class-switched memory B-cells, mast cells, and basophils were positively associated with CRC prognosis while natural killer T-cells, naive B-cells, immature dendritic cells, and lymphatic endothelial cells were negatively associated with prognosis.

Conclusions

Our study identified that the exLR d-signature could differentiate CRC from CRA and healthy individuals with high efficiency and exLR profiling also has potential in CRA screening and CRC prognosis prediction.

Prognostic and Functional Analysis of NPY6R in Uveal Melanoma Using Bioinformatics

Neuropeptides can mediate tumor cell proliferation and differentiation through autocrine, paracrine, neurosecretory, and endocrine mechanisms. This study investigated the expression and prognostic significance of neuropeptide Y receptor Y6 (NPY6R) in uveal melanoma (UVM) and preliminarily investigated the biological function of NPY6R in UVM. NPY6R was poorly expressed in most tumors and was associated with better prognosis in UVM. Among the clinicopathological features of UVM, NPY6R expression was lower in male patients. The area under the curve (AUC) value of NPY6R for the diagnosis of UVM was 0.676 (95% CI: 0.556–0.795). A nomogram including four clinical predictors was constructed. NPY6R expression was significantly associated with features of the UVM immune microenvironment. ESTIMATE and CIBERSORT algorithms were used to calculate the fraction of immune cells and the percentage of infiltration in each patient, respectively. NPY6R expression-related gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analyses were performed. GO and KEGG enrichment analyses revealed that NPY6R-related genes are mainly enriched in pathways and functions related to visual light perception. Gene set enrichment analysis suggested that NPY6R is associated with tumor progression in UVM. NPY6R is involved in the tumor progression of UVM and has a good predictive value as a prognostic marker of UVM.

Efficient Isolation and Functional Characterization of Niche Cells from Human Corneal Limbus

The fate decision of limbal epithelial progenitor cells (LEPC) at the human corneal limbus is determined by the surrounding microenvironment with limbal niche cells (LNC) as one of its essential components. Research on freshly isolated LNC which mainly include limbal mesenchymal stromal cells (LMSC) and limbal melanocytes (LM) has been hampered by a lack of efficient protocols to isolate and purify these cells. We devised a protocol for rapid retrieval of pure LMSC, LM and LEPC populations by collagenase digestion of limbal tissue and subsequent fluorescence-activated cell sorting (FACS) using antibodies against CD90 and CD117. The sorted cells were characterized by immunophenotyping and functional assays. The effects of LMSC and LM on LEPC were studied in 3D co-cultures and LEPC differentiation status was assessed by immunohistochemistry. Enzymatic digestion and flow sorting yielded pure populations of LMSC (CD117⁻CD90⁺), LM (CD117⁺CD90⁻), and LEPC (CD117⁻CD90⁻). The LMSC exhibited self-renewal capacity (55.0 ± 4.6 population doublings), expressed mesenchymal stem cell markers (CD73, CD90, CD105, and CD44), and transdifferentiated to adipocytes, osteocytes, or chondrocytes. The LM exhibited self-renewal capacity and sustained melanin production. The sorted LEPC expressed epithelial progenitor markers (CK14, CK19, and CK15) and showed a colony-forming ability. Co-cultivation of LMSC and LM with LEPC resulted in a 4–5-layered stratified epithelium and supported the preservation of a LEPC phenotype, as reflected by increased p63⁺ and Ki67⁺ cells and decreased CK12⁺ cells compared with LEPC monocultures. A highly efficient isolation of pure LM, LMSC, and LEPC populations from a single preparation may allow for direct transcriptomic and proteomic profiling as well as functional studies on native unpassaged LNC, which can be considered as proper equivalents of LNC in vivo. The developed biomimetic 3D co-culture method could provide an experimental model for investigating the functional role of LNC in the limbal stem cell niche.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment

As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting PrediXcan to increase robustness to incomplete data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes proposed to be involved in diet-related local adaptation, and the predicted effects on regulation often suggest explanations for known signals of selection, for example, at FADS1, GPX1, and LEPR. In contrast, skin pigmentation genes show little regulatory change over a 38,000-year time series of 2,999 ancient Europeans, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates that combining aDNA with present-day genomes is informative about the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between genetic diversity and complex traits.

A large Canadian cohort provides insights into the genetic architecture of human hair colour

Hair colour is a polygenic phenotype that results from differences in the amount and ratio of melanins located in the hair bulb. Genome-wide association studies (GWAS) have identified many loci involved in the pigmentation pathway affecting hair colour. However, most of the associated loci overlap non-protein coding regions and many of the molecular mechanisms underlying pigmentation variation are still not understood. Here, we conduct GWAS meta-analyses of hair colour in a Canadian cohort of 12,741 individuals of European ancestry. By performing fine-mapping analyses we identify candidate causal variants in pigmentation loci associated with blonde, red and brown hair colour. Additionally, we observe colocalization of several GWAS hits with expression and methylation quantitative trait loci (QTLs) of cultured melanocytes. Finally, transcriptome-wide association studies (TWAS) further nominate the expression of EDNRB and CDK10 as significantly associated with hair colour. Our results provide insights on the mechanisms regulating pigmentation biology in humans.

A UVB-responsive common variant at chromosome band 7p21.1 confers tanning response and melanoma risk via regulation of the aryl hydrocarbon receptor, AHR

Genome-wide association studies (GWASs) have identified a melanoma-associated locus on chromosome band 7p21.1 with rs117132860 as the lead SNP and a secondary independent signal marked by rs73069846. rs117132860 is also associated with tanning ability and cutaneous squamous cell carcinoma (cSCC). Because ultraviolet radiation (UVR) is a key environmental exposure for all three traits, we investigated the mechanisms by which this locus contributes to melanoma risk, focusing on cellular response to UVR. Fine-mapping of melanoma GWASs identified four independent sets of candidate causal variants. A GWAS region-focused Capture-C study of primary melanocytes identified physical interactions between two causal sets and the promoter of the aryl hydrocarbon receptor (AHR). Subsequent chromatin state annotation, eQTL, and luciferase assays identified rs117132860 as a functional variant and reinforced AHR as a likely causal gene. Because AHR plays critical roles in cellular response to dioxin and UVR, we explored links between this SNP and AHR expression after both 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and ultraviolet B (UVB) exposure. Allele-specific AHR binding to rs117132860-G was enhanced following both, consistent with predicted weakened AHR binding to the risk/poor-tanning rs117132860-A allele, and allele-preferential AHR expression driven from the protective rs117132860-G allele was observed following UVB exposure. Small deletions surrounding rs117132860 introduced via CRISPR abrogates AHR binding, reduces melanocyte cell growth, and prolongs growth arrest following UVB exposure. These data suggest AHR is a melanoma susceptibility gene at the 7p21.1 risk locus and rs117132860 is a functional variant within a UVB-responsive element, leading to allelic AHR expression and altering melanocyte growth phenotypes upon exposure.

Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.

Computational Investigation of the pH Dependence of Stability of Melanosome Proteins: Implication for Melanosome formation and Disease

Intravesicular pH plays a crucial role in melanosome maturation and function. Melanosomal pH changes during maturation from very acidic in the early stages to neutral in late stages. Neutral pH is critical for providing optimal conditions for the rate-limiting, pH-sensitive melanin-synthesizing enzyme tyrosinase (TYR). This dramatic change in pH is thought to result from the activity of several proteins that control melanosomal pH. Here, we computationally investigated the pH-dependent stability of several melanosomal membrane proteins and compared them to the pH dependence of the stability of TYR. We confirmed that the pH optimum of TYR is neutral, and we also found that proteins that are negative regulators of melanosomal pH are predicted to function optimally at neutral pH. In contrast, positive pH regulators were predicted to have an acidic pH optimum. We propose a competitive mechanism among positive and negative regulators that results in pH equilibrium. Our findings are consistent with previous work that demonstrated a correlation between the pH optima of stability and activity, and they are consistent with the expected activity of positive and negative regulators of melanosomal pH. Furthermore, our data suggest that disease-causing variants impact the pH dependence of melanosomal proteins; this is particularly prominent for the OCA2 protein. In conclusion, melanosomal pH appears to affect the activity of multiple melanosomal proteins.

A custom capture sequence approach for oculocutaneous albinism identifies structural variant alleles at the OCA2 locus

Oculocutaneous albinism (OCA) is a heritable disorder of pigment production that manifests as hypopigmentation and altered eye development. Exon sequencing of known OCA genes is unsuccessful in producing a complete molecular diagnosis for a significant number of affected individuals. We sequenced the DNA of individuals with OCA using short-read custom capture sequencing that targeted coding, intronic, and noncoding regulatory regions of known OCA genes, and genome-wide association study-associated pigmentation loci. We identified an OCA2 complex structural variant (CxSV), defined by a 143 kb inverted segment reintroduced in intron 1, upstream of the native location. The corresponding CxSV junctions were observed in 11/390 probands screened. The 143 kb CxSV presents in one family as a copy number variant duplication for the 143 kb region. In the remaining 10/11 families, the 143 kb CxSV acquired an additional 184 kb deletion across the same region, restoring exons 3-19 of OCA2 to a copy-number neutral state. Allele-associated haplotype analysis found rare SNVs rs374519281 and rs139696407 are linked with the 143 kb CxSV in both OCA2 alleles. For individuals in which customary molecular evaluation does not reveal a biallelic OCA diagnosis, we recommend preliminary screening for these haplotype-associated rare variants, followed by junction-specific validation for the OCA2 143 kb CxSV.

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

MiR-3130-5p is an intermediate modulator of 2q33 and influences the invasiveness of lung adenocarcinoma by targeting NDUFS1

Genome‐wide association studies (GWAS) have reported a handful of loci associated with lung cancer risk, of which the pathogenic pathways are largely unknown. We performed cis‐expression quantitative trait loci (eQTL) mapping for 376 lung cancer related GWAS loci in 227 TCGA lung adenocarcinoma (LUAD) and reported two risk loci as eQTL of miRNA. Among the miRNAs in association with lung cancer risk, we further predicted and validated miR‐3130‐5p as an intermediate modulator of risk loci 2q33 and the tumor suppressor NDUFS1. We assessed the phenotypic impacts of the interaction between miR‐3130‐5p and NDUFS1 in both lung cancer cell lines and mice xenograft models. As a result, miR‐3130‐5p directly regulates the expression of NDUFS1 and the corresponding tumor invasiveness, migration and epithelial‐mesenchymal transition (EMT). Our findings provide important clues for the pathogenic mechanism of 2q33 in lung carcinogenesis which informs clinical diagnosis and prognosis of LUAD.

We performed a cis‐eQTL analysis for 376 lung cancer risk loci based on the expression profiles of 251 miRNAs in a cohort of 227 TCGA lung adenocarcinoma. We report a novel pathogenic pathway of 2q33 via miR‐3130‐5p and NDUFS1.

Evolutionary genetics of skin pigmentation in African populations

Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.

Cell type-aware analysis of RNA-seq data

Most tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a computational framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). CARseq employs a negative binomial distribution that appropriately models the count data from RNA-seq experiments. Simulation studies show that CARseq has substantially higher power than a linear model-based approach and it also provides more accurate estimate of the rankings of differentially expressed genes. We have applied CARseq to compare gene expression of schizophrenia/autism subjects versus controls, and identified the cell types underlying the difference and similarities of these two neuron-developmental diseases. Our results are consistent with the results from differential expression analysis using single cell RNA-seq data.

Whole-organism eQTL mapping at cellular resolution with single-cell sequencing

Genetic regulation of gene expression underlies variation in disease risk and other complex traits. The effect of expression quantitative trait loci (eQTLs) varies across cell types; however, the complexity of mammalian tissues makes studying cell-type eQTLs highly challenging. We developed a novel approach in the model nematode Caenorhabditis elegans that uses single-cell RNA sequencing to map eQTLs at cellular resolution in a single one-pot experiment. We mapped eQTLs across cell types in an extremely large population of genetically distinct C. elegans individuals. We found cell-type-specific trans eQTL hotspots that affect the expression of core pathways in the relevant cell types. Finally, we found single-cell-specific eQTL effects in the nervous system, including an eQTL with opposite effects in two individual neurons. Our results show that eQTL effects can be specific down to the level of single cells.

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (INHBA, SMC2, ABO, PDX1, RCCD1, CFDP1, and PGAP3) and six novel genes not yet reported for pancreatic cancer risk [6q27: SFT2D1 OR (95% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: MTMR6 OR (95% CI), 0.78 (0.70-0.88); 14q24.3: ACOT2 OR (95% CI), 1.35 (1.17-1.56); 17q12: STARD3 OR (95% CI), 6.49 (2.96-14.27); 17q21.1: GSDMB OR (95% CI), 1.94 (1.45-2.58); and 20p13: ADAM33 OR (95% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (SFT2D1, MTMR6, ACOT2, STARD3, GSDMB, ADAM33, SMC2, RCCD1, CFDP1, and PGAP3) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

A Transcriptome-Wide Association Study Identifies Novel Candidate Susceptibility Genes for Pancreatic Cancer

BACKGROUND

Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown.

METHODS

To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).

RESULTS

We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.

CONCLUSIONS

By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

Germline and somatic albinism variants in amelanotic/hypomelanotic melanoma: Increased carriage of TYR and OCA2 variants

Amelanotic/hypomelanotic melanoma is a clinicopathologic subtype with absent or minimal melanin. This study assessed previously reported coding variants in albinism genes (TYR, OCA2, TYRP1, SLC45A2, SLC24A5, LRMDA) and common intronic, regulatory variants of OCA2 in individuals with amelanotic/hypomelanotic melanoma, pigmented melanoma cases and controls. Exome sequencing was available for 28 individuals with amelanotic/hypomelanotic melanoma and 303 individuals with pigmented melanoma, which were compared to whole exome data from 1144 Australian controls. Microarray genotyping was available for a further 17 amelanotic/hypomelanotic melanoma, 86 pigmented melanoma, 147 melanoma cases (pigmentation unknown) and 652 unaffected controls. Rare deleterious variants in TYR/OCA1 were more common in amelanotic/hypomelanotic melanoma cases than pigmented melanoma cases (set mixed model association tests P = 0.0088). The OCA2 hypomorphic allele p.V443I was more common in melanoma cases (1.8%) than controls (1.0%, X2 P = 0.02), and more so in amelanotic/hypomelanotic melanoma (4.4%, X2 P = 0.007). No amelanotic/hypomelanotic melanoma cases carried an eye and skin darkening haplotype of OCA2 (including rs7174027), present in 7.1% of pigmented melanoma cases (P = 0.0005) and 9.4% controls. Variants in TYR and OCA2 may play a role in amelanotic/hypomelanotic melanoma susceptibility. We suggest that somatic loss of function at these loci could contribute to the loss of tumor pigmentation, consistent with this we found a higher rate of somatic mutation in TYR/OCA2 in amelanotic/hypomelanotic melanoma vs pigmented melanoma samples (28.6% vs 3.0%; P = 0.021) from The Cancer Genome Atlas Skin Cutaneous Melanoma collection.

Complexities of Understanding Function from CKD-Associated DNA Variants

Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type-specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype-phenotype association.

Massively parallel reporter assays of melanoma risk variants identify MX2 as a gene promoting melanoma

Genome-wide association studies (GWAS) have identified ~20 melanoma susceptibility loci, most of which are not functionally characterized. Here we report an approach integrating massively-parallel reporter assays (MPRA) with cell-type-specific epigenome and expression quantitative trait loci (eQTL) to identify susceptibility genes/variants from multiple GWAS loci. From 832 high-LD variants, we identify 39 candidate functional variants from 14 loci displaying allelic transcriptional activity, a subset of which corroborates four colocalizing melanocyte cis-eQTL genes. Among these, we further characterize the locus encompassing the HIV-1 restriction gene, MX2 (Chr21q22.3), and validate a functional intronic variant, rs398206. rs398206 mediates the binding of the transcription factor, YY1, to increase MX2 levels, consistent with the cis-eQTL of MX2 in primary human melanocytes. Melanocyte-specific expression of human MX2 in a zebrafish model demonstrates accelerated melanoma formation in a BRAF^V600E background. Our integrative approach streamlines GWAS follow-up studies and highlights a pleiotropic function of MX2 in melanoma susceptibility.

There are more than 20 known melanoma susceptibility genes. Here, using a massively parallel reporter assay, the authors identify risk-associated variants that alter gene transcription, and demonstrate that expression of one such gene, MX2, leads to the promotion of melanoma in a zebrafish model.