Comprehensive functional annotation of 77 prostate cancer risk loci

Genetics and Genomics of Coronary Artery Disease

Coronary artery disease (or coronary heart disease), is the leading cause of mortality in many of the developing as well as the developed countries of the world. Cholesterol-enriched plaques in the heart's blood vessels combined with inflammation lead to the lesion expansion, narrowing of blood vessels, reduced blood flow, and may subsequently cause lesion rupture and a heart attack. Even though several environmental risk factors have been established, such as high LDL-cholesterol, diabetes, and high blood pressure, the underlying genetic composition may substantially modify the disease risk; hence, genome composition and gene-environment interactions may be critical for disease progression. Ongoing scientific efforts have seen substantial advancements related to the fields of genetics and genomics, with the major breakthroughs yet to come. As genomics is the most rapidly advancing field in the life sciences, it is important to present a comprehensive overview of current efforts. Here, we present a summary of various genetic and genomics assays and approaches applied to coronary artery disease research.

Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics

Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10(-20)), ER-negative BC (P=1.1 × 10(-13)), BRCA1-associated BC (P=7.7 × 10(-16)) and triple negative BC (P-diff=2 × 10(-5)). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10(-3)) and ABHD8 (P<2 × 10(-3)). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3'-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk.

Familial prostate cancer

Prostate cancer is the most commonly diagnosed cancer among men in the United States as well as most Western countries. A significant proportion of men report having a positive family history of prostate cancer in a first-degree relative (father, brother, son), which is important in that family history is one of the only established risk factors for the disease and plays a role in decision-making for prostate cancer screening. Familial aggregation of prostate cancer is considered a surrogate marker of genetic susceptibility to developing the disease, but shared environment cannot be excluded as an explanation for clustering of cases among family members. Prostate cancer is both a clinically and genetically heterogeneous disease with inherited factors predicted to account for 40%-50% of cases, comprised of both rare highly to moderately penetrant gene variants, as well as common genetic variants of low penetrance. Most notably, HOXB13 and BRCA2 mutations have been consistently shown to increase prostate cancer risk, and are more commonly observed among patients diagnosed with early-onset disease. A recurrent mutation in HOXB13 has been shown to predispose to hereditary prostate cancer (HPC), and BRCA2 mutations to hereditary breast and ovarian cancer (HBOC). Genome-wide association studies (GWAS) have also identified approximately 100 loci that associate with modest (odds ratios <2.0) increases in prostate cancer risk, only some of which have been replicated in subsequent studies. Despite these efforts, genetic testing in prostate cancer lags behind other common tumors like breast and colorectal cancer. To date, National Comprehensive Cancer Network (NCCN) guidelines have highly selective criteria for BRCA1/2 testing for men with prostate cancer based on personal history and/or specific family cancer history. Tumor sequencing is also leading to the identification of germline mutations in prostate cancer patients, informing the scope of inheritance. Advances in genetic testing for inherited and familial prostate cancer (FPC) are needed to inform personalized cancer risk screening and treatment approaches.

Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer

Long noncoding RNAs (lncRNAs) represent an attractive class of candidates to mediate cancer risk. Through integrative analysis of the lncRNA transcriptome with genomic data and SNP data from prostate cancer genome-wide association studies (GWAS), we identified 45 candidate lncRNAs associated with risk to prostate cancer. We further evaluated the mechanism underlying the top hit, PCAT1, and found that a risk-associated variant at rs7463708 increases binding of ONECUT2, a novel androgen receptor (AR)-interacting transcription factor, at a distal enhancer that loops to the PCAT1 promoter, resulting in upregulation of PCAT1 upon prolonged androgen treatment. In addition, PCAT1 interacts with AR and LSD1 and is required for their recruitment to the enhancers of GNMT and DHCR24, two androgen late-response genes implicated in prostate cancer development and progression. PCAT1 promotes prostate cancer cell proliferation and tumor growth in vitro and in vivo. These findings suggest that modulating lncRNA expression is an important mechanism for risk-associated SNPs in promoting prostate transformation.

Defining the Influence of Germline Variation on Metastasis Using Systems Genetics Approaches

Cancer is estimated to be responsible for 8 million deaths worldwide and over half a million deaths every year in the United States. The majority of cancer-related deaths in solid tumors is directly associated with the effects of metastasis. While the influence of germline factors on cancer risk and development has long been recognized, the contribution of hereditary variation to tumor progression and metastasis has only gained acceptance more recently. A variety of approaches have been used to define how hereditary variation influences tumor progression and metastasis. One approach that garnered much early attention was epidemiological studies of cohorts of cancer patients, which demonstrated that specific loci within the human genome are associated with a differential propensity for aggressive tumor development. However, a powerful, and somewhat underutilized approach has been the use of systems genetics approaches in transgenic mouse models of human cancer. Such approaches are typically multifaceted, and involve integration of multiple lines of evidence derived, for example, from genetic and transcriptomic screens of genetically diverse mouse models of cancer, coupled with bioinformatics analysis of human cancer datasets, and functional analysis of candidate genes. These methodologies have allowed for the identification of multiple hereditary metastasis susceptibility genes, with wide-ranging cellular functions including regulation of gene transcription, cell proliferation, and cell-cell adhesion. In this chapter, we review how each of these approaches have facilitated the identification of these hereditary metastasis modifiers, the molecular functions of these metastasis-associated genes, and the implications of these findings upon patient survival.

The importance of detailed epigenomic profiling of different cell types within organs

The human body consists of hundreds of kinds of cells specified from a single genome overlaid with cell type-specific epigenetic information. Comprehensively profiling the body's distinct epigenetic landscapes will allow researchers to verify cell types used in regenerative medicine and to determine the epigenetic effects of disease, environmental exposures and genetic variation. Key marks/factors that should be investigated include regions of nucleosome-free DNA accessible to regulatory factors, histone marks defining active enhancers and promoters, DNA methylation levels, regulatory RNAs, and factors controlling the three-dimensional conformation of the genome. Here we use the lung to illustrate the importance of investigating an organ's purified cell epigenomes, and outline the challenges and promise of realizing a comprehensive catalog of primary cell epigenomes.

TRIM24 Is an Oncogenic Transcriptional Activator in Prostate Cancer

Androgen receptor (AR) signaling is a key driver of prostate cancer (PC). While androgen-deprivation therapy is transiently effective in advanced disease, tumors often progress to a lethal castration-resistant state (CRPC). We show that recurrent PC-driver mutations in speckle-type POZ protein (SPOP) stabilize the TRIM24 protein, which promotes proliferation under low androgen conditions. TRIM24 augments AR signaling, and AR and TRIM24 co-activated genes are significantly upregulated in CRPC. Expression of TRIM24 protein increases from primary PC to CRPC, and both TRIM24 protein levels and the AR/TRIM24 gene signature predict disease recurrence. Analyses in CRPC cells reveal that the TRIM24 bromodomain and the AR-interacting motif are essential to support proliferation. These data provide a rationale for therapeutic TRIM24 targeting in SPOP mutant and CRPC patients.

Common genetic variation associated with increased susceptibility to prostate cancer does not increase risk of radiotherapy toxicity

BACKGROUND

Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy.

METHODS

The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity (overall, urinary frequency, decreased stream, rectal bleeding). Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods.

RESULTS

A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity.

CONCLUSION

Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens.

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation

Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.

Association between single nucleotide polymorphism in miR-499, miR-196a2, miR-146a and miR-149 and prostate cancer risk in a sample of Iranian population

MicroRNAs (miRNAs) play an important role in regulating gene expression at the post-transcriptional level and are involved in numerous physiological processes. Accumulating evidence suggests that single-nucleotide polymorphisms (SNPs) in human miRNA genes may affect miRNA biogenesis pathway and influence the susceptibility to several diseases such as cancer. The present study aimed to evaluate the impact of miR-499 rs3746444, miR-196a2 rs11614913, miR-149 rs2292832, and miR-146a rs2910164 polymorphisms on prostate cancer (PCa) risk in a sample of Iranian population. This case-control study was done on 169 patients with pathologically confirmed PCa and 182 benign prostatic hyperplasia (BPH). The genotyping assays were done using T-ARMS-PCR or PCR-RFLP methods. The findings indicated that CC genotype of miR-499 rs3746444 polymorphism increased the risk of PCa (OR = 1.76, 95% CI = 1.12-2.79, P = 0.019) compared to TT genotype. No statistically significant association was found between miR-196a2 rs11614913, miR-149 rs2292832, and miR-146a rs2910164 polymorphisms and PCa risk. In summary, the findings indicated that miR-499 rs3746444 polymorphism increased the risk of PCa in an Iranian population. Further studies with larger sample sizes and different ethnicities are necessary to verify the findings of the present study.

Gene regulatory mechanisms underpinning prostate cancer susceptibility

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

4C-seq revealed long-range interactions of a functional enhancer at the 8q24 prostate cancer risk locus

Genome-wide association studies (GWAS) have identified >100 independent susceptibility loci for prostate cancer, including the hot spot at 8q24. However, how genetic variants at this locus confer disease risk hasn’t been fully characterized. Using circularized chromosome conformation capture (4C) coupled with next-generation sequencing and an enhancer at 8q24 as “bait”, we identified genome-wide partners interacting with this enhancer in cell lines LNCaP and C4-2B. These 4C-identified regions are distributed in open nuclear compartments, featuring active histone marks (H3K4me1, H3K4me2 and H3K27Ac). Transcription factors NKX3-1, FOXA1 and AR (androgen receptor) tend to occupy these 4C regions. We identified genes located at the interacting regions, and found them linked to positive regulation of mesenchymal cell proliferation in LNCaP and C4-2B, and several pathways (TGF beta signaling pathway in LNCaP and p53 pathway in C4-2B). Common genes (e.g. MYC and POU5F1B) were identified in both prostate cancer cell lines. However, each cell line also had exclusive genes (e.g. ELAC2 and PTEN in LNCaP and BRCA2 and ZFHX3 in C4-2B). In addition, BCL-2 identified in C4-2B might contribute to the progression of androgen-refractory prostate cancer. Overall, our work reveals key genes and pathways involved in prostate cancer onset and progression.

Genetics of Common Endocrine Disease: The Present and the Future

CONTEXT

In honor of the 75th issue of the Journal of Clinical Endocrinology and Metabolism, the author was invited to present his perspectives on genetics in human endocrinology. This paper reviews what the field has achieved in the genetics of common endocrine disease, and offers predictions on where the field will move in the future and its impact on endocrine clinical practice.

EVIDENCE ACQUISITION

The October 2015 data release of the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) Catalog of Published Genome-wide Association Studies was queried regarding endocrinologic diseases and traits. PubMed searches were focused on genetic prediction of disease, genetic findings and drug targets, functional interrogation of genetic loci, use of genetics to subtype disease, missing heritability, systems genomics, and higher order chromatin structures as regulators of gene function.

EVIDENCE SYNTHESIS

Nearly a quarter of genome wide association study findings concern endocrinologic diseases and traits. While these findings have not yet dramatically altered clinical care, genetics will have a major impact by providing the drug targets of tomorrow, facilitated by experimental and bioinformatic advances that will shorten the time from gene discovery to drug development. Use of genetic findings to subtype common endocrine disease will allow more precise prevention and treatment efforts. Future advances will allow us to move away from the common view of DNA as a string of letters, allowing exploration of higher order structure that likely explains much "missing heritability."

CONCLUSIONS

The future will see a greater role of genetics at the bedside, with genetic epidemiologic discoveries leading not only to new treatments of endocrine disease, but also helping us prescribe the right drug to the right patients by allowing subclassification of common heterogeneous endocrine conditions. Future technological breakthroughs will reveal the heritable mysteries hidden in chromatin structure, leading to a more complete characterization of heritability that will propel our ability to translate genetic findings into new preventions and treatments.

Germline genetic profiling in prostate cancer: latest developments and potential clinical applications

Familial and twin studies have demonstrated a significant inherited component to prostate cancer predisposition. Genome wide association studies have shown that there are 100 single nucleotide polymorphisms which have been associated with the development of prostate cancer. This review aims to discuss the scientific methods used to identify these susceptibility loci. It will also examine the current clinical utility of these loci, which include the development of risk models as well as predicting treatment efficacy and toxicity. In order to refine the clinical utility of the susceptibility loci, international consortia have been developed to combine statistical power as well as skills and knowledge to further develop models that could be used to predict risk and treatment outcomes.

Reducing GWAS Complexity

Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015 (1,2) from our groups.

The contribution of rare variation to prostate cancer heritability

We report targeted sequencing of 63 known prostate cancer risk regions in a multi-ancestry study of 9,237 men and use the data to explore the contribution of low-frequency variation to disease risk. We show that SNPs with minor allele frequencies (MAFs) of 0.1-1% explain a substantial fraction of prostate cancer risk in men of African ancestry. We estimate that these SNPs account for 0.12 (standard error (s.e.) = 0.05) of variance in risk (∼42% of the variance contributed by SNPs with MAF of 0.1-50%). This contribution is much larger than the fraction of neutral variation due to SNPs in this class, implying that natural selection has driven down the frequency of many prostate cancer risk alleles; we estimate the coupling between selection and allelic effects at 0.48 (95% confidence interval [0.19, 0.78]) under the Eyre-Walker model. Our results indicate that rare variants make a disproportionate contribution to genetic risk for prostate cancer and suggest the possibility that rare variants may also have an outsize effect on other common traits.

Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome

Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs.

Enhancer scanning to locate regulatory regions in genomic loci

This protocol provides a rapid, streamlined and scalable strategy to systematically scan genomic regions for the presence of transcriptional regulatory regions that are active in a specific cell type. It creates genomic tiles spanning a region of interest that are subsequently cloned by recombination into a luciferase reporter vector containing the simian virus 40 promoter. Tiling clones are transfected into specific cell types to test for the presence of transcriptional regulatory regions. The protocol includes testing of different single-nucleotide polymorphism (SNP) alleles to determine their effect on regulatory activity. This procedure provides a systematic framework for identifying candidate functional SNPs within a locus during functional analysis of genome-wide association studies. This protocol adapts and combines previous well-established molecular biology methods to provide a streamlined strategy, based on automated primer design and recombinational cloning, allowing one to rapidly go from a genomic locus to a set of candidate functional SNPs in 8 weeks.

Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set

Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP–gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa.

Single nucleotide polymorphisms—SNPs—have been identified for prostate cancer but whether these SNPs alter the expression of genes is largely unknown. In this study, the authors search for genes located within 2 Mb of the SNPs and identify SNPs that influence gene expression, so called expression quantitative trait loci.

Common variants at the CHEK2 gene locus and risk of epithelial ovarian cancer

Genome-wide association studies have identified 20 genomic regions associated with risk of epithelial ovarian cancer (EOC), but many additional risk variants may exist. Here, we evaluated associations between common genetic variants [single nucleotide polymorphisms (SNPs) and indels] in DNA repair genes and EOC risk. We genotyped 2896 common variants at 143 gene loci in DNA samples from 15 397 patients with invasive EOC and controls. We found evidence of associations with EOC risk for variants at FANCA, EXO1, E2F4, E2F2, CREB5 and CHEK2 genes (P ≤ 0.001). The strongest risk association was for CHEK2 SNP rs17507066 with serous EOC (P = 4.74 x 10(-7)). Additional genotyping and imputation of genotypes from the 1000 genomes project identified a slightly more significant association for CHEK2 SNP rs6005807 (r (2) with rs17507066 = 0.84, odds ratio (OR) 1.17, 95% CI 1.11-1.24, P = 1.1×10(-7)). We identified 293 variants in the region with likelihood ratios of less than 1:100 for representing the causal variant. Functional annotation identified 25 candidate SNPs that alter transcription factor binding sites within regulatory elements active in EOC precursor tissues. In The Cancer Genome Atlas dataset, CHEK2 gene expression was significantly higher in primary EOCs compared to normal fallopian tube tissues (P = 3.72×10(-8)). We also identified an association between genotypes of the candidate causal SNP rs12166475 (r (2) = 0.99 with rs6005807) and CHEK2 expression (P = 2.70×10(-8)). These data suggest that common variants at 22q12.1 are associated with risk of serous EOC and CHEK2 as a plausible target susceptibility gene.

Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes

Enhancers are short regulatory sequences bound by sequence-specific transcription factors and play a major role in the spatiotemporal specificity of gene expression patterns in development and disease. While it is now possible to identify enhancer regions genomewide in both cultured cells and primary tissues using epigenomic approaches, it has been more challenging to develop methods to understand the function of individual enhancers because enhancers are located far from the gene(s) that they regulate. However, it is essential to identify target genes of enhancers not only so that we can understand the role of enhancers in disease but also because this information will assist in the development of future therapeutic options. After reviewing models of enhancer function, we discuss recent methods for identifying target genes of enhancers. First, we describe chromatin structure-based approaches for directly mapping interactions between enhancers and promoters. Second, we describe the use of correlation-based approaches to link enhancer state with the activity of nearby promoters and/or gene expression. Third, we describe how to test the function of specific enhancers experimentally by perturbing enhancer–target relationships using high-throughput reporter assays and genome editing. Finally, we conclude by discussing as yet unanswered questions concerning how enhancers function, how target genes can be identified, and how to distinguish direct from indirect changes in gene expression mediated by individual enhancers.

Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions

Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genome-encoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10(-4)-5.6 × 10(-3)) and in 30 regions we identified markers that were more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10(-6)) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Cis-eQTL analysis and functional validation of candidate susceptibility genes for high-grade serous ovarian cancer

Genome-wide association studies have reported 11 regions conferring risk of high-grade serous epithelial ovarian cancer (HGSOC). Expression quantitative trait locus (eQTL) analyses can identify candidate susceptibility genes at risk loci. Here we evaluate cis-eQTL associations at 47 regions associated with HGSOC risk (P≤10(-5)). For three cis-eQTL associations (P<1.4 × 10(-3), FDR<0.05) at 1p36 (CDC42), 1p34 (CDCA8) and 2q31 (HOXD9), we evaluate the functional role of each candidate by perturbing expression of each gene in HGSOC precursor cells. Overexpression of HOXD9 increases anchorage-independent growth, shortens population-doubling time and reduces contact inhibition. Chromosome conformation capture identifies an interaction between rs2857532 and the HOXD9 promoter, suggesting this SNP is a leading causal variant. Transcriptomic profiling after HOXD9 overexpression reveals enrichment of HGSOC risk variants within HOXD9 target genes (P=6 × 10(-10) for risk variants (P<10(-4)) within 10 kb of a HOXD9 target gene in ovarian cells), suggesting a broader role for this network in genetic susceptibility to HGSOC.

Systematic enrichment analysis of potentially functional regions for 103 prostate cancer risk-associated loci

BACKGROUND

More than 100 prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) have been identified by genome wide association studies (GWAS). However, the molecular mechanisms are unclear for most of these SNPs.

METHODS

All reported PCa risk-associated SNPs reaching the genome-wide significance level of P < 1 × 10(-7) (index SNPs), as well as SNPs in linkage disequilibrium (LD, r(2) ≥ 0.5) with them were cataloged. Genomic regions with potentially functional impact were also identified, including UCSC annotated coding regions (exon and snoRNA/miRNA) and regulatory regions, as well as binding regions for transcription factors (TFs), histone modifications (HMs), DNase I hypersensitivity (DHSs), and RNA Polymerase IIA (POLR2A) defined by ChIP-Seq in prostate cell lines and tissues. Enrichment analysis was performed to test whether PCa risk-associated SNPs are located in these functional regions more than expected.

RESULTS

A total of 103 PCa risk-associated index SNPs and 7,244 SNPs in LD with these index SNPs were cataloged. Genomic regions with potentially functional impact, grouped in 30 different categories of functionalities, were identified. Enrichment analysis indicated that genomic regions in the following 15 categories were enriched for the PCa risk-associated SNPs: exons, CpG regions, 6 TFs (AR, ERG, FOXA1, HOXB13, CTCF, and NR3C1), 5 HMs (H3K4me1, H3K4me2, H3K4me3, H3K27AC, and H3T11P), DHSs and POLR2A. In contrast, significantly fewer PCa risk SNPs were mapped to binding regions for H3K27me3, a repressive chromatin marker.

CONCLUSIONS

The PCa risk-associated SNPs discovered to date may affect PCa risk through multiple different mechanisms, especially by affecting binding regions of TFs/HMs.

Analysis of Prostate Cancer Susceptibility Variants in South African Men: Replicating Associations on Chromosomes 8q24 and 10q11

Genome-wide association studies (GWAS) have implicated single nucleotide polymorphisms (SNPs) on chromosomes 2p15, 6q25, 7p15.2, 7q21, 8q24, 10q11, 10q26, 11q13, 17q12, 17q24, 19q13, and Xp11, with prostate cancer (PCa) susceptibility and/or tumour aggressiveness, in populations of African, European, and Asian ancestry. The objective of this study was to confirm these associations in South African Mixed Ancestry and White men. We evaluated 17 prioritised GWAS SNPs in South African cases (331 Mixed Ancestry and 155 White) and controls (178 Mixed Ancestry and 145 White). The replicated SNP associations for the different South African ethnic groups were rs7008482 (8q24) (p = 2.45 × 10⁻⁵), rs6983267 (8q24) (p = 4.48 × 10⁻⁷), and rs10993994 (10q11) (p = 1.40 × 10⁻³) in Mixed Ancestry men and rs10993994 (p = 1.56 × 10⁻⁹) in White men. No significant associations were observed for the analyses stratified by disease aggressiveness in the individual and the combined population group analysis. The present study demonstrates that a number of known PCa susceptibility variants may contribute to disease susceptibility in South African men. Larger genetic investigations extended to other South African population groups are warranted to confirm the role of these and other SNPs in disease susceptibility.

motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites

Summary: Functional annotation represents a key step toward the understanding and interpretation of germline and somatic variation as revealed by genome-wide association studies (GWAS) and The Cancer Genome Atlas (TCGA), respectively. GWAS have revealed numerous genetic risk variants residing in non-coding DNA associated with complex diseases. For sequences that lie within enhancers or promoters of transcription, it is not straightforward to assess the effects of variants on likely transcription factor binding sites. Consequently we introduce motifbreakR, which allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism or mutation relative to the other. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from many public sources that users can choose from. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within bioconductor.

Availability and implementation:https://github.com/Simon-Coetzee/MotifBreakR, www.bioconductor.org.

Contact:dennis.hazelett@cshs.org

Comprehensive assembly of novel transcripts from unmapped human RNA-Seq data and their association with cancer

Crucial parts of the genome including genes encoding microRNAs and noncoding RNAs went unnoticed for years, and even now, despite extensive annotation and assembly of the human genome, RNA-sequencing continues to yield millions of unmappable and thus uncharacterized reads. Here, we examined > 300 billion reads from 536 normal donors and 1,873 patients encompassing 21 cancer types, identified ∼300 million such uncharacterized reads, and using a distinctive approach de novo assembled 2,550 novel human transcripts, which mainly represent long noncoding RNAs. Of these, 230 exhibited relatively specific expression or non-expression in certain cancer types, making them potential markers for those cancers, whereas 183 exhibited tissue specificity. Moreover, we used lentiviral-mediated expression of three selected transcripts that had higher expression in normal than in cancer patients and found that each inhibited the growth of HepG2 cells. Our analysis provides a comprehensive and unbiased resource of unmapped human transcripts and reveals their associations with specific cancers, providing potentially important new genes for therapeutic targeting.

Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies

Localization of causal variants underlying known risk loci is one of the main research challenges following genome-wide association studies. Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations. More recent works have shown that genomic functional annotations (i.e., localization of tissue-specific regulatory marks) can be integrated for increasing fine-mapping performance within single-population studies. Here, we introduce methods that integrate the strength of association between genotype and phenotype, the variability in the genetic backgrounds across populations, and the genomic map of tissue-specific functional elements to increase trans-ethnic fine-mapping accuracy. Through extensive simulations and empirical data, we have demonstrated that our approach increases fine-mapping resolution over existing methods. We analyzed empirical data from a large-scale trans-ethnic rheumatoid arthritis (RA) study and showed that the functional genetic architecture of RA is consistent across European and Asian ancestries. In these data, we used our proposed methods to reduce the average size of the 90% credible set from 29 variants per locus for standard non-integrative approaches to 22 variants.

Can genome engineering be used to target cancer-associated enhancers?

Transcriptional misregulation is involved in the development of many diseases, especially neoplastic transformation. Distal regulatory elements, such as enhancers, play a major role in specifying cell-specific transcription patterns in both normal and diseased tissues, suggesting that enhancers may be prime targets for therapeutic intervention. By focusing on modulating gene regulation mediated by cell type-specific enhancers, there is hope that normal epigenetic patterning in an affected tissue could be restored with fewer side effects than observed with treatments employing relatively nonspecific inhibitors such as epigenetic drugs. New methods employing genomic nucleases and site-specific epigenetic regulators targeted to specific genomic regions, using either artificial DNA-binding proteins or RNA-DNA interactions, may allow precise genome engineering at enhancers. However, this field is still in its infancy and further refinements that increase specificity and efficiency are clearly required.

Cell-type-specific enrichment of risk-associated regulatory elements at ovarian cancer susceptibility loci

Understanding the regulatory landscape of the human genome is a central question in complex trait genetics. Most single-nucleotide polymorphisms (SNPs) associated with cancer risk lie in non-protein-coding regions, implicating regulatory DNA elements as functional targets of susceptibility variants. Here, we describe genome-wide annotation of regions of open chromatin and histone modification in fallopian tube and ovarian surface epithelial cells (FTSECs, OSECs), the debated cellular origins of high-grade serous ovarian cancers (HGSOCs) and in endometriosis epithelial cells (EECs), the likely precursor of clear cell ovarian carcinomas (CCOCs). The regulatory architecture of these cell types was compared with normal human mammary epithelial cells and LNCaP prostate cancer cells. We observed similar positional patterns of global enhancer signatures across the three different ovarian cancer precursor cell types, and evidence of tissue-specific regulatory signatures compared to non-gynecological cell types. We found significant enrichment for risk-associated SNPs intersecting regulatory biofeatures at 17 known HGSOC susceptibility loci in FTSECs (P = 3.8 × 10(-30)), OSECs (P = 2.4 × 10(-23)) and HMECs (P = 6.7 × 10(-15)) but not for EECs (P = 0.45) or LNCaP cells (P = 0.88). Hierarchical clustering of risk SNPs conditioned on the six different cell types indicates FTSECs and OSECs are highly related (96% of samples using multi-scale bootstrapping) suggesting both cell types may be precursors of HGSOC. These data represent the first description of regulatory catalogues of normal precursor cells for different ovarian cancer subtypes, and provide unique insights into the tissue specific regulatory variation with respect to the likely functional targets of germline genetic susceptibility variants for ovarian cancer.

Comprehensively evaluating cis-regulatory variation in the human prostate transcriptome by using gene-level allele-specific expression

The identification of cis-acting regulatory variation in primary tissues has the potential to elucidate the genetic basis of complex traits and further our understanding of transcriptomic diversity across cell types. Expression quantitative trait locus (eQTL) association analysis using RNA sequencing (RNA-seq) data can improve upon the detection of cis-acting regulatory variation by leveraging allele-specific expression (ASE) patterns in association analysis. Here, we present a comprehensive evaluation of cis-acting eQTLs by analyzing RNA-seq gene-expression data and genome-wide high-density genotypes from 471 samples of normal primary prostate tissue. Using statistical models that integrate ASE information, we identified extensive cis-eQTLs across the prostate transcriptome and found that approximately 70% of expressed genes corresponded to a significant eQTL at a gene-level false-discovery rate of 0.05. Overall, cis-eQTLs were heavily concentrated near the transcription start and stop sites of affected genes, and effects were negatively correlated with distance. We identified multiple instances of cis-acting co-regulation by using phased genotype data and discovered 233 SNPs as the most strongly associated eQTLs for more than one gene. We also noted significant enrichment (25/50, p = 2E-5) of previously reported prostate cancer risk SNPs in prostate eQTLs. Our results illustrate the benefit of assessing ASE data in cis-eQTL analyses by showing better reproducibility of prior eQTL findings than of eQTL mapping based on total expression alone. Altogether, our analysis provides extensive functional context of thousands of SNPs in prostate tissue, and these results will be of critical value in guiding studies examining disease of the human prostate.

Identification of a Novel Mucin Gene HCG22 Associated With Steroid-Induced Ocular Hypertension

PURPOSE

The pathophysiology of ocular hypertension (OH) leading to primary open-angle glaucoma shares many features with a secondary form of OH caused by treatment with glucocorticoids, but also exhibits distinct differences. In this study, a pharmacogenomics approach was taken to discover candidate genes for this disorder.

METHODS

A genome-wide association study was performed, followed by an independent candidate gene study, using a cohort enrolled from patients treated with off-label intravitreal triamcinolone, and handling change in IOP as a quantitative trait.

RESULTS

An intergenic quantitative trait locus (QTL) was identified at chromosome 6p21.33 near the 5' end of HCG22 that attained the accepted statistical threshold for genome-level significance. The HCG22 transcript, encoding a novel mucin protein, was expressed in trabecular meshwork cells, and expression was stimulated by IL-1, and inhibited by triamcinolone acetate and TGF-β. Bioinformatic analysis defined the QTL as an approximately 4 kilobase (kb) linkage disequilibrium block containing 10 common single nucleotide polymorphisms (SNPs). Four of these SNPs were identified in the National Center for Biotechnology Information (NCBI) GTEx eQTL browser as modifiers of HCG22 expression. Most are predicted to disrupt or improve motifs for transcription factor binding, the most relevant being disruption of the glucocorticoid receptor binding motif. A second QTL was identified within the predicted signal peptide of the HCG22 encoded protein that could affect its secretion. Translation, O-glycosylation, and secretion of the predicted HCG22 protein was verified in cultured trabecular meshwork cells.

CONCLUSIONS

Identification of two independent QTLs that could affect expression of the HCG22 mucin gene product via two different mechanisms (transcription or secretion) is highly suggestive of a role in steroid-induced OH.

In silico functional pathway annotation of 86 established prostate cancer risk variants

Heritability is one of the strongest risk factors of prostate cancer, emphasizing the importance of the genetic contribution towards prostate cancer risk. To date, 86 established prostate cancer risk variants have been identified by genome-wide association studies (GWAS). To determine if these risk variants are located near genes that interact together in biological networks or pathways contributing to prostate cancer initiation or progression, we generated gene sets based on proximity to the 86 prostate cancer risk variants. We took two approaches to generate gene lists. The first strategy included all immediate flanking genes, up- and downstream of the risk variant, regardless of distance from the index variant, and the second strategy included genes closest to the index GWAS marker and to variants in high LD (r2 ≥0.8 in Europeans) with the index variant, within a 100 kb window up- and downstream. Pathway mapping of the two gene sets supported the importance of the androgen receptor-mediated signaling in prostate cancer biology. In addition, the hedgehog and Wnt/β-catenin signaling pathways were identified in pathway mapping for the flanking gene set. We also used the HaploReg resource to examine the 86 risk loci and variants high LD (r2 ≥0.8) for functional elements. We found that there was a 12.8 fold (p = 2.9 x 10-4) enrichment for enhancer motifs in a stem cell line and a 4.4 fold (p = 1.1 x 10-3) enrichment of DNase hypersensitivity in a prostate adenocarcinoma cell line, indicating that the risk and correlated variants are enriched for transcriptional regulatory motifs. Our pathway-based functional annotation of the prostate cancer risk variants highlights the potential regulatory function that GWAS risk markers, and their highly correlated variants, exert on genes. Our study also shows that these genes may function cooperatively in key signaling pathways in prostate cancer biology.

Association of prostate cancer risk variants with gene expression in normal and tumor tissue

BACKGROUND

Numerous germline genetic variants are associated with prostate cancer risk, but their biologic role is not well understood. One possibility is that these variants influence gene expression in prostate tissue. We therefore examined the association of prostate cancer risk variants with the expression of genes nearby and genome-wide.

METHODS

We generated mRNA expression data for 20,254 genes with the Affymetrix GeneChip Human Gene 1.0 ST microarray from normal prostate (N = 160) and prostate tumor (N = 264) tissue from participants of the Physicians' Health Study and Health Professionals Follow-up Study. With linear models, we tested the association of 39 risk variants with nearby genes and all genes, and the association of each variant with canonical pathways using a global test.

RESULTS

In addition to confirming previously reported associations, we detected several new significant (P < 0.05) associations of variants with the expression of nearby genes including C2orf43, ITGA6, MLPH, CHMP2B, BMPR1B, and MTL5. Genome-wide, five genes (MSMB, NUDT11, RBPMS2, NEFM, and KLHL33) were significantly associated after accounting for multiple comparisons for each SNP (P < 2.5 × 10(-6)). Many more genes had an FDR <10%, including SRD5A1 and PSCA, and we observed significant associations with pathways in tumor tissue.

CONCLUSIONS

The risk variants were associated with several genes, including promising prostate cancer candidates and lipid metabolism pathways, suggesting mechanisms for their impact on disease. These genes should be further explored in biologic and epidemiologic studies.

IMPACT

Determining the biologic role of these variants can lead to improved understanding of prostate cancer etiology and identify new targets for chemoprevention.

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.

A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer

Genome-wide association studies (GWAS) have identified 76 variants associated with prostate cancer risk predominantly in populations of European ancestry. To identify additional susceptibility loci for this common cancer, we conducted a meta-analysis of > 10 million SNPs in 43,303 prostate cancer cases and 43,737 controls from studies in populations of European, African, Japanese and Latino ancestry. Twenty-three new susceptibility loci were identified at association P < 5 × 10(-8); 15 variants were identified among men of European ancestry, 7 were identified in multi-ancestry analyses and 1 was associated with early-onset prostate cancer. These 23 variants, in combination with known prostate cancer risk variants, explain 33% of the familial risk for this disease in European-ancestry populations. These findings provide new regions for investigation into the pathogenesis of prostate cancer and demonstrate the usefulness of combining ancestrally diverse populations to discover risk loci for disease.

Functional annotation of colon cancer risk SNPs

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for CRC. A molecular understanding of the functional consequences of this genetic variation has been complicated because each GWAS SNP is a surrogate for hundreds of other SNPs, most of which are located in non-coding regions. Here we use genomic and epigenomic information to test the hypothesis that the GWAS SNPs and/or correlated SNPs are in elements that regulate gene expression, and identify 23 promoters and 28 enhancers. Using gene expression data from normal and tumour cells, we identify 66 putative target genes of the risk-associated enhancers (10 of which were also identified by promoter SNPs). Employing CRISPR nucleases, we delete one risk-associated enhancer and identify genes showing altered expression. We suggest that similar studies be performed to characterize all CRC risk-associated enhancers.

Previous studies identified genetic variants associated with colorectal cancer (CRC), but the functional consequences of these genetic risk factors remain poorly understood. Here, the authors report that CRC risk variants reside in promoters and enhancers and could increase colon cancer risk through gene expression regulation.

Prostate cancer risk locus at 8q24 as a regulatory hub by physical interactions with multiple genomic loci across the genome

Chromosome 8q24 locus contains regulatory variants that modulate genetic risk to various cancers including prostate cancer (PC). However, the biological mechanism underlying this regulation is not well understood. Here, we developed a chromosome conformation capture (3C)-based multi-target sequencing technology and systematically examined three PC risk regions at the 8q24 locus and their potential regulatory targets across human genome in six cell lines. We observed frequent physical contacts of this risk locus with multiple genomic regions, in particular, inter-chromosomal interaction with CD96 at 3q13 and intra-chromosomal interaction with MYC at 8q24. We identified at least five interaction hot spots within the predicted functional regulatory elements at the 8q24 risk locus. We also found intra-chromosomal interaction genes PVT1, FAM84B and GSDMC and inter-chromosomal interaction gene CXorf36 in most of the six cell lines. Other gene regions appeared to be cell line-specific, such as RRP12 in LNCaP, USP14 in DU-145 and SMIN3 in lymphoblastoid cell line. We further found that the 8q24 functional domains more likely interacted with genomic regions containing genes enriched in critical pathways such as Wnt signaling and promoter motifs such as E2F1 and TCF3. This result suggests that the risk locus may function as a regulatory hub by physical interactions with multiple genes important for prostate carcinogenesis. Further understanding genetic effect and biological mechanism of these chromatin interactions will shed light on the newly discovered regulatory role of the risk locus in PC etiology and progression.

Prostate Cancer Related JAZF1 Gene is Associated with Schizophrenia

BACKGROUND

Epidemiological studies have shown that there is a reduced risk of prostate cancer among persons diagnosed with schizophrenia (SCZ). However, the mechanism of such relationship is not clear. The reduced incidence of cancer observed in SCZ patients may be related to differences in genetic background. Recently, the JAZF1 gene is found to be associated with prostate cancer and type 2 diabetes. However, no study has focused on the association of JAZF1 with the risk of SCZ.

METHODS

We examined genetic associations of 118 single-nucleotide polymorphisms (SNPs) within the JAZF1 gene with SCZ using one European American (EA) sample of 1,149 cases and 1,347 controls. Logistic regression analysis of SCZ as a binary trait was performed using PLINK software.

RESULTS

The most significant association with SCZ was observed with rs10258132 (p = 0.0011); while the next best signal was rs17156259 (p = 0.0031). The third best associated SNP was rs7791865 (p = 0.00889). In addition, haplotype analyses revealed that the A-C haplotype from rs10244184 and rs10258132 was associated with SCZ (p = 0.00093); and the G-G haplotype from rs17156238 and rs17156259 was associated with SCZ (p = 0.00455).

CONCLUSION

These findings provide evidence of several genetic variants in JAZF1 gene influencing the risk of SCZ and will serve as a resource for replication in other populations.

Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer

Despite much evidence on epigenetic abnormalities in cancer, it is currently unclear to what extent epigenetic alterations can be associated with tumors' clonal genetic origins. Here, we show that the prostate intratumor heterogeneity in DNA methylation and copy-number patterns can be explained by a unified evolutionary process. By assaying multiple topographically distinct tumor sites, premalignant lesions, and lymph node metastases within five cases of prostate cancer, we demonstrate that both DNA methylation and copy-number heterogeneity consistently reflect the life history of the tumors. Furthermore, we show cases of genetic or epigenetic convergent evolution and highlight the diversity in the evolutionary origins and aberration spectrum between tumor and metastatic subclones. Importantly, DNA methylation can complement genetic data by serving as a proxy for activity at regulatory domains, as we show through identification of high epigenetic heterogeneity at androgen-receptor-bound enhancers. Epigenome variation thereby expands on the current genome-centric view on tumor heterogeneity.