CAUSEL: an epigenome- and genome-editing pipeline for establishing function of noncoding GWAS variants

An Inherited Allele Confers Prostate Cancer Progression and Drug Resistance via RFX6/HOXA10-Orchestrated TGFβ Signaling

Genetic and epigenetic alterations are cancer hallmark characteristics. However, the role of inherited cancer predisposition alleles in co-opting lineage factor epigenetic reprogramming and tumor progression remains elusive. Here the FinnGen cohort phenome-wide analysis, along with multiple genome-wide association studies, has consistently identified the rs339331-RFX6/6q22 locus associated with prostate cancer (PCa) risk across diverse populations. It is uncovered that rs339331 resides in a reprogrammed androgen receptor (AR) binding site in PCa tumors, with the T risk allele enhancing AR chromatin occupancy. RFX6, an AR-regulated gene linked to rs339331, exhibits synergistic prognostic value for PCa recurrence and metastasis. This comprehensive in vitro and in vivo studies demonstrate the oncogenic functions of RFX6 in promoting PCa cell proliferation and metastasis. Mechanistically, RFX6 upregulates HOXA10 that profoundly correlates with adverse PCa outcomes and is pivotal in RFX6-mediated PCa progression, facilitating the epithelial-mesenchymal transition (EMT) and modulating the TGFβ/SMAD signaling axis. Clinically, HOXA10 elevation is associated with increased EMT scores, tumor advancement and PCa recurrence. Remarkably, reducing RFX6 expression restores enzalutamide sensitivity in resistant PCa cells and tumors. This findings reveal a complex interplay of genetic and epigenetic mechanisms in PCa pathogenesis and drug resistance, centered around disrupted prostate lineage AR signaling and abnormal RFX6 expression.

The RNA secondary structure of androgen receptor-FL and V7 transcripts reveals novel regulatory regions

The androgen receptor (AR) is a ligand-dependent nuclear transcription factor belonging to the steroid hormone nuclear receptor family. Due to its roles in regulating cell proliferation and differentiation, AR is tightly regulated to maintain proper levels of itself and the many genes it controls. AR dysregulation is a driver of many human diseases including prostate cancer. Though this dysregulation often occurs at the RNA level, there are many unknowns surrounding post-transcriptional regulation of AR mRNA, particularly the role that RNA secondary structure plays. Thus, a comprehensive analysis of AR transcript secondary structure is needed. We address this through the computational and experimental analyses of two key isoforms, full length (AR-FL) and truncated (AR-V7). Here, a combination of in-cell RNA secondary structure probing experiments (targeted DMS-MaPseq) and computational predictions were used to characterize the static structural landscape and conformational dynamics of both isoforms. Additionally, in-cell assays were used to identify functionally relevant structures in the 5' and 3' UTRs of AR-FL. A notable example is a conserved stem loop structure in the 5'UTR of AR-FL that can bind to Poly(RC) Binding Protein 2 (PCBP2). Taken together, our results reveal novel features that regulate AR expression.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Construction of lncRNA/Pseudogene-miRNA Network Based on In Silico Approaches for Glycolysis Pathway to Identify Prostate Adenocarcinoma-Related Potential Biomarkers

LncRNAs, pseudogenes, and miRNAs participate a fundamental function in tumorigenesis, metabolism, and invasion of cancer cells, although their regulation of tumor glycolysis in prostate adenocarcinoma (PRAD) is thoroughly not well studied. In this study, we applied transcriptomic, proteomic, and medical information to identify glycolysis-related key genes and modules associated with PRAD. Then, the glycolysis-related lncRNA/lncRNAs/pseudogenes-miRNA-mRNA network was constructed. Analysis of DNA methylation status and expression data determined a DNA methylation-dysregulated three-DE-mRNAs signature for predicting diagnosis, ANGPTL4, GNE, and HSPA in PRAD patients and healthy control. Several lncRNAs/pseudogenes, significantly correlated with the overall survival PVT1, CA5BP1, MIRLET7BHG, SNHG12, and ZNF37BP and disease-free survival status, MALAT1, GUSBP11, MIRLET7BHG, and SNHG1, of patients with PRAD were determined. The methylation profile of DE-lncRNA/pseudogenes was significantly proper for predicting PRAD prognostic model. The transcription level of 6 DE-mRNA ANGPTL4, QSOX1, BIK, CLDN3, DDIT4, and TFF3 was correlated with cancer-related fibroblast infiltration in PRAD. The mutated form of 7 mRNAs, COL5A1, IDH1, HK2, DDIT4, GNE, and QSOX1, was associated with PRAD. In addition to the glycolysis pathway, DE-RNAs play regulatory roles on several pathways, including DNA damage, RTK, cell cycle, RAS/MAPK, TSC/mTOR and PI3K/AKT, AR hormone, and EMT. Overall, our study improves our knowledge of the relation between lncRNAs/pseudogenes and miRNA related to glycolysis and PRAD pathogenesis.

A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus

To date, single-nucleotide polymorphisms (SNPs) have been the most intensively investigated class of polymorphisms in genome wide associations studies (GWAS), however, other classes such as insertion-deletion or multiple nucleotide length polymorphism (MNLPs) may also confer disease risk. Multiple reports have shown that the 5p15.33 prostate cancer risk region is a particularly strong expression quantitative trait locus (eQTL) for Iroquois Homeobox 4 (IRX4) transcripts. Here, we demonstrate using epigenome and genome editing that a biallelic (21 and 47 base pairs (bp)) MNLP is the causal variant regulating IRX4 transcript levels. In LNCaP prostate cancer cells (homozygous for the 21 bp short allele), a single copy knock-in of the 47 bp long allele potently alters the chromatin state, enabling de novo functional binding of the androgen receptor (AR) associated with increased chromatin accessibility, Histone 3 lysine 27 acetylation (H3K27ac), and ~3-fold upregulation of IRX4 expression. We further show that an MNLP is amongst the strongest candidate susceptibility variants at two additional prostate cancer risk loci. We estimated that at least 5% of prostate cancer risk loci could be explained by functional non-SNP causal variants, which may have broader implications for other cancers GWAS. More generally, our results underscore the importance of investigating other classes of inherited variation as causal mediators of human traits.

GATA2 co-opts TGFβ1/SMAD4 oncogenic signaling and inherited variants at 6q22 to modulate prostate cancer progression

BACKGROUND

Aberrant somatic genomic alteration including copy number amplification is a hallmark of cancer genomes. We previously profiled genomic landscapes of prostate cancer (PCa), yet the underlying causal genes with prognostic potential has not been defined. It remains unclear how a somatic genomic event cooperates with inherited germline variants contribute to cancer predisposition and progression.

METHODS

We applied integrated genomic and clinical data, experimental models and bioinformatic analysis to identify GATA2 as a highly prevalent metastasis-associated genomic amplification in PCa. Biological roles of GATA2 in PCa metastasis was determined in vitro and in vivo. Global chromatin co-occupancy and co-regulation of GATA2 and SMAD4 was investigated by coimmunoprecipitation, ChIP-seq and RNA-seq assays. Tumor cellular assays, qRT-PCR, western blot, ChIP, luciferase assays and CRISPR-Cas9 editing methods were performed to mechanistically understand the cooperation of GATA2 with SMAD4 in promoting TGFβ1 and AR signaling and mediating inherited PCa risk and progression.

RESULTS

In this study, by integrated genomics and experimental analysis, we identified GATA2 as a prevalent metastasis-associated genomic amplification to transcriptionally augment its own expression in PCa. Functional experiments demonstrated that GATA2 physically interacted and cooperated with SMAD4 for genome-wide chromatin co-occupancy and co-regulation of PCa genes and metastasis pathways like TGFβ signaling. Mechanistically, GATA2 was cooperative with SMAD4 to enhance TGFβ and AR signaling pathways, and activated the expression of TGFβ1 via directly binding to a distal enhancer of TGFβ1. Strinkingly, GATA2 and SMAD4 globally mediated inherited PCa risk and formed a transcriptional complex with HOXB13 at the PCa risk-associated rs339331/6q22 enhancer, leading to increased expression of the PCa susceptibility gene RFX6.

CONCLUSIONS

Our study prioritizes causal genomic amplification genes with prognostic values in PCa and reveals the pivotal roles of GATA2 in transcriptionally activating the expression of its own and TGFβ1, thereby co-opting to TGFβ1/SMAD4 signaling and RFX6 at 6q22 to modulate PCa predisposition and progression.

Computational Assessment of the Expression-modulating Potential for Non-coding Variants

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

CRISPR/Cas9 genome editing demonstrates functionality of the autoimmunity-associated SNP rs12946510

Genome-wide association studies (GWAS) map genetic associations of complex traits with precision limited to a linkage disequilibrium group. To translate GWAS results into new understanding of disease mechanisms, individual causative polymorphisms and their target genes should be identified. CRISPR/Cas9 genome editing can be used to create isogenic cell lines bearing alternative genotypes of candidate single-nucleotide polymorphisms to test their causality and to reveal gene targets. An intergenic polymorphism rs12946510 is associated with multiple sclerosis, inflammatory bowel disease and asthma. We created sublines of the T-helper cell line bearing alternative genotypes of rs12946510 and showed that its risk ("T") allele is associated with lower expression of IKZF3 and ORMDL3 genes and reduced cell activation. Our editing procedure can become an effective tool for discovering new genes involved in pathogenesis of complex diseases.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture

Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation.

SIGNIFICANCE

Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity

Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.

HOXB13 suppresses de novo lipogenesis through HDAC3-mediated epigenetic reprogramming in prostate cancer

HOXB13, a homeodomain transcription factor, critically regulates androgen receptor (AR) activities and androgen-dependent prostate cancer (PCa) growth. However, its functions in AR-independent contexts remain elusive. Here we report HOXB13 interaction with histone deacetylase HDAC3, which is disrupted by the HOXB13 G84E mutation that has been associated with early-onset PCa. Independently of AR, HOXB13 recruits HDAC3 to lipogenic enhancers to catalyze histone deacetylation and suppress lipogenic regulators such as fatty acid synthase. Analysis of human tissues reveals that the HOXB13 gene is hypermethylated and downregulated in approximately 30% of metastatic castration-resistant PCa. HOXB13 loss or G84E mutation leads to lipid accumulation in PCa cells, thereby promoting cell motility and xenograft tumor metastasis, which is mitigated by pharmaceutical inhibition of fatty acid synthase. In summary, we present evidence that HOXB13 recruits HDAC3 to suppress de novo lipogenesis and inhibit tumor metastasis and that lipogenic pathway inhibitors may be useful to treat HOXB13-low PCa.

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

Background

The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants.

Methods

To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles.

Results

We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33).

Conclusions

This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01216-w.

H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility

Genome-wide association studies (GWASs) have identified more than 200 prostate cancer (PrCa) risk regions, which provide potential insights into causal mechanisms. Multiple lines of evidence show that a significant proportion of PrCa risk can be explained by germline causal variants that dysregulate nearby target genes in prostate-relevant tissues, thus altering disease risk. The traditional approach to explore this hypothesis has been correlating GWAS variants with steady-state transcript levels, referred to as expression quantitative trait loci (eQTLs). In this work, we assess the utility of chromosome conformation capture (3C) coupled with immunoprecipitation (HiChIP) to identify target genes for PrCa GWAS risk loci. We find that interactome data confirm previously reported PrCa target genes identified through GWAS/eQTL overlap (e.g., MLPH). Interestingly, HiChIP identifies links between PrCa GWAS variants and genes well-known to play a role in prostate cancer biology (e.g., AR) that are not detected by eQTL-based methods. HiChIP predicted enhancer elements at the AR and NKX3-1 prostate cancer risk loci, and both were experimentally confirmed to regulate expression of the corresponding genes through CRISPR interference (CRISPRi) perturbation in LNCaP cells. Our results demonstrate that looping data harbor additional information beyond eQTLs and expand the number of PrCa GWAS loci that can be linked to candidate susceptibility genes.

Mechanistic insights into genetic susceptibility to prostate cancer

Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.

Identification of Three Novel Susceptibility Loci for Inflammatory Bowel Disease in Koreans in an Extended Genome-Wide Association Study

BACKGROUND AND AIMS

Genome-wide association studies [GWAS] of inflammatory bowel disease [IBD] in multiple populations have identified over 240 susceptibility loci. We previously performed a largest-to-date Asian-specific IBD GWAS to identify two new IBD risk loci and confirm associations with 28 established loci. To identify additional susceptibility loci in Asians, we expanded our previous study design by doubling the case size with an additional dataset of 1726 cases and 378 controls.

METHODS

An inverse-variance fixed-effects meta-analysis was performed between the previous and the new GWAS dataset, comprising a total of 3195 cases and 4419 controls, followed by replication in an additional 1088 cases and 845 controls.

RESULTS

The meta-analysis of Korean GWAS identified one novel locus for ulcerative colitis at rs76227733 on 10q24 [pcombined = 6.56 × 10-9] and two novel loci for Crohn's disease [CD] at rs2240751 on 19p13 [pcombined = 3.03 × 10-8] and rs6936629 on 6q22 [pcombined = 3.63 × 10-8]. Pathway-based analysis of GWAS data using MAGMA showed that the MHC and antigenic stimulus-related pathways were more significant in Korean CD, whereas cytokine and transcription factor-related pathways were more significant in European CD. Phenotype variance explained by the polygenic risk scores derived from Korean data explained up to 14% of the variance of CD whereas those derived from European data explained 10%, emphasizing the need for large-scale genetic studies in this population.

CONCLUSIONS

The identification of novel loci not previously associated with IBD suggests the importance of studying IBD genetics in diverse populations.

Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.

Genome-wide association studies (GWAS) have identified many important loci for common diseases and other traits. However, the loci identified by these studies are almost always many steps away from an understanding of underlying biological mechanisms. Here we develop an approach using hierarchical biological knowledge to identify genes and pathways responsible for phenotypic traits. Variants identified by the new method could explain a substantially greater fraction of heritability than previously reported. Moreover, we identified mechanistic pathways by which each causal variant affects cellular function. For example, we find that sensitivity to hydroxyurea is tied to genetic variants in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. The new approach is a potentially transformative concept for understanding the genetic drivers of phenotypic variance, with potential applications in understanding traits in biomedicine and agriculture.

A transcriptome-wide association study identifies susceptibility genes for Parkinson's disease

Genome-wide association study (GWAS) has seen great strides in revealing initial insights into the genetic architecture of Parkinson's disease (PD). Since GWAS signals often reside in non-coding regions, relatively few of the associations have implicated specific biological mechanisms. Here, we aimed to integrate the GWAS results with large-scale expression quantitative trait loci (eQTL) in 13 brain tissues to identify candidate causal genes for PD. We conducted a transcriptome-wide association study (TWAS) for PD using the summary statistics of over 480,000 individuals from the most recent PD GWAS. We identified 18 genes significantly associated with PD after Bonferroni corrections. The most significant gene, LRRC37A2, was associated with PD in all 13 brain tissues, such as in the hypothalamus (P = 6.12 × 10-22) and nucleus accumbens basal ganglia (P = 5.62 × 10-21). We also identified eight conditionally independent genes, including four new genes at known PD loci: CD38, LRRC37A2, RNF40, and ZSWIM7. Through conditional analyses, we demonstrated that several of the GWAS significant signals on PD could be driven by genetically regulated gene expression. The most significant TWAS gene LRRC37A2 accounts for 0.855 of the GWAS signal at its loci, and ZSWIM7 accounts for all the GWAS signals at its loci. We further identified several phenotypes previously associated with PD by querying the single nucleotide polymorphisms (SNPs) in the final model of the identified genes in phenome databases. In conclusion, we prioritized genes that are likely to affect PD by using a TWAS approach and identified phenotypes associated with PD.

Augmenting and directing long-range CRISPR-mediated activation in human cells

Epigenetic editing is an emerging technology that uses artificial transcription factors (aTFs) to regulate expression of a target gene. Although human genes can be robustly upregulated by targeting aTFs to promoters, the activation induced by directing aTFs to distal transcriptional enhancers is substantially less robust and consistent. Here we show that long-range activation using CRISPR-based aTFs in human cells can be made more efficient and reliable by concurrently targeting an aTF to the target gene promoter. We used this strategy to direct target gene choice for enhancers capable of regulating more than one promoter and to achieve allele-selective activation of human genes by targeting aTFs to single-nucleotide polymorphisms embedded in distally located sequences. Our results broaden the potential applications of the epigenetic editing toolbox for research and therapeutics.

REVA as A Well-curated Database for Human Expression-modulating Variants

More than 90% of disease- and trait-associated human variants are noncoding. By systematically screening multiple large-scale studies, we compiled REVA, a manually curated database for over 11.8 million experimentally tested noncoding variants with expression-modulating potentials. We provided 2424 functional annotations that could be used to pinpoint the plausible regulatory mechanism of these variants. We further benchmarked multiple state-of-the-art computational tools and found their limited sensitivity remains a serious challenge for effective large-scale analysis. REVA provides high-quality experimentally tested expression-modulating variants with extensive functional annotations, which will be useful for users in the noncoding variants community. REVA is available at http://reva.gao-lab.org.

Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease

Prostate cancer (PrCa) is a heterogeneous disease, which presents in individual patients across a diverse phenotypic spectrum ranging from indolent to fatal forms. No robust biomarkers are currently available to enable routine screening for PrCa or to distinguish clinically significant forms, therefore late stage identification of advanced disease and overdiagnosis plus overtreatment of insignificant disease both remain areas of concern in healthcare provision. PrCa has a substantial heritable component, and technological advances since the completion of the Human Genome Project have facilitated improved identification of inherited genetic factors influencing susceptibility to development of the disease within families and populations. These genetic markers hold promise to enable improved understanding of the biological mechanisms underpinning PrCa development, facilitate genetically informed PrCa screening programmes and guide appropriate treatment provision. However, insight remains largely lacking regarding many aspects of their manifestation; especially in relation to genes associated with aggressive phenotypes, risk factors in non-European populations and appropriate approaches to enable accurate stratification of higher and lower risk individuals. This review discusses the methodology used in the elucidation of genetic loci, genes and individual causal variants responsible for modulating PrCa susceptibility; the current state of understanding of the allelic spectrum contributing to PrCa risk; and prospective future translational applications of these discoveries in the developing eras of genomics and personalised medicine.

Leveraging functional annotation to identify genes associated with complex diseases

To increase statistical power to identify genes associated with complex traits, a number of transcriptome-wide association study (TWAS) methods have been proposed using gene expression as a mediating trait linking genetic variations and diseases. These methods first predict expression levels based on inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic effects on diseases by associating phenotypes with predicted expression levels. The success of these methods critically depends on the identification of eQTLs, which may not be functional in the corresponding tissue, due to linkage disequilibrium (LD) and the correlation of gene expression between tissues. Here, we introduce a new method called T-GEN (Transcriptome-mediated identification of disease-associated Genes with Epigenetic aNnotation) to identify disease-associated genes leveraging epigenetic information. Through prioritizing SNPs with tissue-specific epigenetic annotation, T-GEN can better identify SNPs that are both statistically predictive and biologically functional. We found that a significantly higher percentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional by ChromHMM and more are deleterious based on their Combined Annotation Dependent Depletion (CADD) scores. Applying T-GEN to 207 complex traits, we were able to identify more trait-associated genes (ranging from 7.7% to 102%) than those from existing methods. Among the identified genes associated with these traits, T-GEN can better identify genes with high (>0.99) pLI scores compared to other methods. When T-GEN was applied to late-onset Alzheimer's disease, we identified 96 genes located at 15 loci, including two novel loci not implicated in previous GWAS. We further replicated 50 genes in an independent GWAS, including one of the two novel loci.

Transcription factors in epithelial ovarian cancer: histotype-specific drivers and novel therapeutic targets

Transcription factors (TFs) are major contributors to cancer risk and somatic development. In preclinical and clinical studies, direct or indirect inhibition of TF-mediated oncogenic gene expression profiles have proven to be effective in many tumor types, highlighting this group of proteins as valuable therapeutic targets. In spite of this, our understanding of TFs in epithelial ovarian cancer (EOC) is relatively limited. EOC is a heterogeneous disease composed of five major histologic subtypes; high-grade serous, low-grade serous, endometrioid, clear cell and mucinous. Each histology is associated with unique clinical etiologies, sensitivity to therapies, and molecular signatures - including diverse transcriptional regulatory programs. While some TFs are shared across EOC subtypes, a set of TFs are expressed in a histotype-specific manner and likely explain part of the histologic diversity of EOC subtypes. Targeting TFs present with unique opportunities for development of novel precision medicine strategies for ovarian cancer. This article reviews the critical TFs in EOC subtypes and highlights the potential of exploiting TFs as biomarkers and therapeutic targets.

Generation and validation of versatile inducible CRISPRi embryonic stem cell and mouse model

Clustered regularly interspaced short palindromic repeat (CRISPR)-CRISPR-associated (Cas) 9 has been widely used far beyond genome editing. Fusions of deactivated Cas9 (dCas9) to transcription effectors enable interrogation of the epigenome and controlling of gene expression. However, the large transgene size of dCas9-fusion hinders its applications especially in somatic tissues. Here, we develop a robust CRISPR interference (CRISPRi) system by transgenic expression of doxycycline (Dox) inducible dCas9-KRAB in mouse embryonic stem cells (iKRAB ESC). After introduction of specific single-guide RNAs (sgRNAs), the induced dCas9-KRAB efficiently maintains gene inactivation, although it modestly down-regulates the expression of active genes. The proper timing of Dox addition during cell differentiation or reprogramming allows us to study or screen spatiotemporally activated promoters or enhancers and thereby the gene functions. Furthermore, taking the ESC for blastocyst injection, we generate an iKRAB knock-in (KI) mouse model that enables the shutdown of gene expression and loss-of-function (LOF) studies ex vivo and in vivo by a simple transduction of gRNAs. Thus, our inducible CRISPRi ESC line and KI mouse provide versatile and convenient platforms for functional interrogation and high-throughput screens of specific genes and potential regulatory elements in the setting of development or diseases.

Validation of prostate cancer risk variants rs10993994 and rs7098889 by CRISPR/Cas9 mediated genome editing

GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the β-microseminoprotein (MSMB) promoter region, mediates MSMB prostate secretion levels, and is linked to mRNA expression changes in both MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in positive linkage disequilibrium with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions, through which we demonstrate that each of these SNPs independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation studies to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available. AUTHOR SUMMARY: In pursuing the underlying biological mechanism of prostate cancer pathogenesis, scientists utilized the existence of common single nucleotide polymorphisms (SNPs) in the human genome as genetic markers to perform large scale genome wide association studies (GWAS) and have so far identified more than a hundred prostate cancer risk variants. Such variants provide an unbiased and systematic new venue to study the disease mechanism, and the next big challenge is to translate these genetic associations to the causal role of altered gene function in oncogenesis. The majority of these variants are waiting to be studied and lots of them may act in oncogenesis through gene expression regulation. To prove the concept, we took rs10993994 and its linked rs7098889 as an example and engineered single cell clones by allelic-specific CRISPR/Cas9 deletion to separate the effect of each allele. We observed that a single nucleotide difference would lead to surprisingly high level of MSMB gene expression change in a gene specific and cell-type specific manner. Our study strongly supports the notion that differential level of gene expression caused by risk variants and their associated genetic locus play a major role in oncogenesis and also highlights the importance of studying the function of MSMB encoded β-MSP in prostate cancer pathogenesis.

Contributions of HOX genes to cancer hallmarks: Enrichment pathway analysis and review

Homeobox genes function as master regulatory transcription factors during development, and their expression is often altered in cancer. The HOX gene family was initially studied intensively to understand how the expression of each gene was involved in forming axial patterns and shaping the body plan during embryogenesis. More recent investigations have discovered that HOX genes can also play an important role in cancer. The literature has shown that the expression of HOX genes may be increased or decreased in different tumors and that these alterations may differ depending on the specific HOX gene involved and the type of cancer being investigated. New studies are also emerging, showing the critical role of some members of the HOX gene family in tumor progression and variation in clinical response. However, there has been limited systematic evaluation of the various contributions of each member of the HOX gene family in the pathways that drive the common phenotypic changes (or "hallmarks") and that underlie the transformation of normal cells to cancer cells. In this review, we investigate the context of the engagement of HOX gene targets and their downstream pathways in the acquisition of competence of tumor cells to undergo malignant transformation and tumor progression. We also summarize published findings on the involvement of HOX genes in carcinogenesis and use bioinformatics methods to examine how their downstream targets and pathways are involved in each hallmark of the cancer phenotype.

A qPCR method for genome editing efficiency determination and single-cell clone screening in human cells

CRISPR/Cas9 technology has been widely used for targeted genome modification both in vivo and in vitro. However, an effective method for evaluating genome editing efficiency and screening single-cell clones for desired modification is still lacking. Here, we developed this real time PCR method based on the sensitivity of Taq DNA polymerase to nucleotide mismatch at primer 3' end during initiating DNA replication. Applications to CRISPR gRNAs targeting EMX1, DYRK1A and HOXB13 genes in Lenti-X 293 T cells exhibited comprehensive advantages. Just in one-round qPCR analysis using genomic DNA from cells underwent CRISPR/Cas9 or BE4 treatments, the genome editing efficiency could be determined accurately and quickly, for indel, HDR as well as base editing. When applied to single-cell clone screening, the genotype of each cell colony could also be determined accurately. This method defined a rigorous and practical way in quantify genome editing events.

Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations

Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1-4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer-gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer-gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer-gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.

A 16q22.1 variant confers susceptibility to colorectal cancer as a distal regulator of ZFP90

Genome-wide association studies (GWASs) implicate 16q22.1 locus in risk for colorectal cancer (CRC). However, the underlying oncogenic mechanisms remain unknown. Here, through comprehensive filtration, we prioritized rs7198799, a common SNP in the second intron of the CDH1, as the putative causal variant. In addition, we found an association of CRC-risk allele C of rs7198799 with elevated transcript level of biological plausible candidate gene ZFP90 via expression quantitative trait loci analysis. Mechanistically, causal variant rs7198799 resides in an enhancer element and remotely regulate ZFP90 expression by targeting the transcription factor NFATC2. Remarkably, CRISPR/Cas9-guided single-nucleotide editing demonstrated the direct effect of rs7198799 on ZFP90 expression and CRC cellular malignant phenotype. Furthermore, ZFP90 affects several oncogenic pathways, including BMP4, and promotes carcinogenesis in patients and in animal models with ZFP90 specific genetic manipulation. Taken together, these findings reveal a risk SNP-mediated long-range regulation on the NFATC2-ZFP90-BMP4 pathway underlying the initiation of CRC.

CGT-seq: epigenome-guided de novo assembly of the core genome for divergent populations with large genome

Genetic diversity in plants is remarkably high. Recent whole genome sequencing (WGS) of 67 rice accessions recovered 10,872 novel genes. Comparison of the genetic architecture among divergent populations or between crops and wild relatives is essential for obtaining functional components determining crucial traits. However, many major crops have gigabase-scale genomes, which are not well-suited to WGS. Existing cost-effective sequencing approaches including re-sequencing, exome-sequencing and restriction enzyme-based methods all have difficulty in obtaining long novel genomic sequences from highly divergent population with large genome size. The present study presented a reference-independent core genome targeted sequencing approach, CGT-seq, which employed epigenomic information from both active and repressive epigenetic marks to guide the assembly of the core genome mainly composed of promoter and intragenic regions. This method was relatively easily implemented, and displayed high sensitivity and specificity for capturing the core genome of bread wheat. 95% intragenic and 89% promoter region from wheat were covered by CGT-seq read. We further demonstrated in rice that CGT-seq captured hundreds of novel genes and regulatory sequences from a previously unsequenced ecotype. Together, with specific enrichment and sequencing of regions within and nearby genes, CGT-seq is a time- and resource-effective approach to profiling functionally relevant regions in sequenced and non-sequenced populations with large genomes.

De novo pattern discovery enables robust assessment of functional consequences of non-coding variants

MOTIVATION

Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used 'black boxes' methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.

RESULTS

DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes.

AVAILABILITY AND IMPLEMENTATION

The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Role of HOX Transcription Factors in Cancer Predisposition and Progression

Homeobox (HOX) transcription factors, encoded by a subset of homeodomain superfamily genes, play pivotal roles in many aspects of cellular physiology, embryonic development, and tissue homeostasis. Findings over the past decade have revealed that mutations in HOX genes can lead to increased cancer predisposition, and HOX genes might mediate the effect of many other cancer susceptibility factors by recognizing or executing altered genetic information. Remarkably, several lines of evidence highlight the interplays between HOX transcription factors and cancer risk loci discovered by genome-wide association studies, thereby gaining molecular and biological insight into cancer etiology. In addition, deregulated HOX gene expression impacts various aspects of cancer progression, including tumor angiogenesis, cell autophagy, proliferation, apoptosis, tumor cell migration, and metabolism. In this review, we will discuss the fundamental roles of HOX genes in cancer susceptibility and progression, highlighting multiple molecular mechanisms of HOX involved gene misregulation, as well as their potential implications in clinical practice.

Post-GWAS in prostate cancer: from genetic association to biological contribution

Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.

Long non-coding RNA POLR2E gene polymorphisms increased the risk of prostate cancer in a sample of the Iranian population

The current study aimed to examine the impact of POLR2E rs1046040 and rs3787016 polymorphisms on prostate cancer (PCa) risk in a sample of southeast Iranian population. The present case-control study was performed on 178 patients with PCa and 180 benign prostatic hyperplasia (BPH). Genotyping of the variants was done by mismatch PCR-RFLP. The findings showed that the rs3787016 C > T variant significantly increased the risk of PCa in codominant (OR = 1.84, 95% CI = 1.12-3.03, P = 0.018, CT vs CC), dominant (OR = 1.88, 95% CI = 1.63-3.05, P = 0.011, CT + TT vas CC) and allele (OR = 1.77, 95% CI = 1.52-2.72, P = 0.010, T vs C) inheritance model. Regarding rs1046040 C > T polymorphism, the findings revealed that the CT genotype significantly increased the risk of PCa compared to the CC genotype (OR = 1.60, 95% CI = 1.03-2.49, P = 0.043). Furthermore, rs3787016 CT/rs1046040 CC as well as rs3787016 CT/rs1046040 CT increased the risk of PCa compared to the CC/CC genotype (p = 0.029 and p = 0.014, respectively). Haplotype analysis proposed that rs3787016 T/rs1046040 C significantly increased the risk of PCa compared to C/C (p = 0.037). No significant association was observed between POLR2E variants and clinicopathological characteristics of PCa patients. In conclusion, the findings propose that POLR2E variants may be a risk factor for susceptibility to PCa in a sample of Iranian population.

In vivo epigenome editing and transcriptional modulation using CRISPR technology

The rapid advancement of CRISPR technology has enabled targeted epigenome editing and transcriptional modulation in the native chromatin context. However, only a few studies have reported the successful editing of the epigenome in adult animals in contrast to the rapidly growing number of in vivo genome editing over the past few years. In this review, we discuss the challenges facing in vivo epigenome editing and new strategies to overcome the huddles. The biggest challenge has been the difficulty in packaging dCas9 fusion proteins required for manipulation of epigenome into the adeno-associated virus (AAV) delivery vehicle. We review the strategies to address the AAV packaging issue, including small dCas9 orthologues, truncated dCas9 mutants, a split-dCas9 system, and potent truncated effector domains. We discuss the dCas9 conjugation strategies to recruit endogenous chromatin modifiers and remodelers to specific genomic loci, and recently developed methods to recruit multiple copies of the dCas9 fusion protein, or to simultaneous express multiple gRNAs for robust epigenome editing or synergistic transcriptional modulation. The use of Cre-inducible dCas9-expressing mice or a genetic cross between dCas9- and sgRNA-expressing flies has also helped overcome the transgene delivery issue. We provide perspective on how a combination use of these strategies can facilitate in vivo epigenome editing and transcriptional modulation.

Association between three genetic variants in kallikrein 3 and prostate cancer risk

Background: Epidemiological studies have assessed the association between kallikrein 3 (KLK3) polymorphisms and prostate cancer (PCa) susceptibility. However, published data on this association are somewhat inconclusive. Methods: Articles investigating the association between three KLK3 (rs1058205, rs2735839, and rs266882) variants and PCa susceptibility were searched from online databases, which included 35,838 patients and 36,369 control participants. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to demonstrate the strength of the association. We also utilized ELISA to detect serum expression of KLK3. In addition, in silico tools were adopted to evaluate the relationship of KLK3 expression and PCa survival time. Results: The overall results indicated that polymorphism T>C of rs1058205 was associated with decreased risk of PCa (allele contrast: OR = 0.75, 95% CI = 0.64–0.88, P_{heterogeneity} < 0.001; homozygote comparison: OR = 0.58, 95% CI = 0.42–0.81, P_{heterogeneity} < 0.001), particularly in Caucasian population (allele contrast: OR = 0.77, 95% CI = 0.65–0.91, P_{heterogeneity} < 0.001; homozygote comparison: OR = 0.58, 95% CI = 0.41–0.82, P_{heterogeneity} < 0.001). No association was observed between the polymorphism A>G of rs2735839 and risk of PCa. In addition, no association was observed between polymorphism A>G of rs266882 and risk of PCa. Serum KLK3 levels in PCa patients carrying CC/CT genotypes were statistically lower than those carrying TT genotypes. Conclusion: This meta-analysis suggests that rs1058205 polymorphism of KLK3 is a risk factor for PCa development, polymorphism T>C of rs1058205 is associated with decreased susceptibility to PCa particularly in Caucasian population.

Dynamic genome and transcriptional network-based biomarkers and drugs: precision in breast cancer therapy

Despite remarkable progress in medium-term overall survival benefit in the adjuvant, neoadjuvant and metastatic settings, with multiple recent targeted drug approvals, acquired resistance, late relapse, and cancer-related death rates remain challenging. Integrated technological systems have been developed to overcome these unmet needs. The characterization of structural and functional noncoding genome elements through next-generation sequencing (NGS) systems, Hi-C and CRISPR/Cas9, as well as computational models, allows for whole genome and transcriptome analysis. Rapid progress in large-scale single-biopsy genome analysis has identified several novel breast cancer driver genes and oncotargets. The exploration of spatiotemporal tumor evolution has returned exciting while inconclusive data on dynamic intratumor heterogeneity (ITH) through multiregional NGS and single-cell DNA/RNA sequencing and circulating genomic subclones (cGSs) by serial circulating cell-free DNA NGS to predict and overcome intrinsic and acquired therapeutic resistance. This review discusses reliable breast cancer genome analysis data and focuses on two major crucial perspectives. The validation of ITH, cGSs, and intrapatient genetic/genomic heterogeneity as predictive biomarkers, as well as the valid discovery of novel oncotargets within patient-centric genomic trials, encouraging early drug development, could optimize primary and secondary therapeutic decision-making. A longer-term goal is to identify the individualized landscape of both coding and noncoding key mutations. This progress will enable the understanding of molecular mechanisms perturbating regulatory networks, shaping the pharmaceutical controllability of deregulated transcriptional biocircuits.

Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci

Genome-wide association studies (GWASs) have identified thousands of loci associated with hundreds of complex diseases and traits, and progress is being made toward elucidating the causal variants and genes underlying these associations. Functional characterization of mechanisms at GWAS loci is a multi-faceted challenge. Challenges include linkage disequilibrium and allelic heterogeneity at each locus, the noncoding nature of most loci, and the time and cost needed for experimentally evaluating the potential mechanistic contributions of genes and variants. As GWAS sample sizes increase, more loci are identified, and the complexities of individual loci emerge. Loci can consist of multiple association signals, each of which can reflect the influence of multiple variants, inseparable by association analyses. Each signal within a locus can influence the same or different target genes. Experimental studies of genes and variants can differ on the basis of cell type, cellular environment, or other context-specific variables. In this review, we describe the complexity of mechanisms at GWAS loci-including multiple signals, multiple variants, and/or multiple genes-and the implications these complexities hold for experimental study design and interpretation of GWAS mechanisms.

Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2 Mb. 23 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at a pre-defined level; this reduced the list of 217 associations to 109 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

Non-coding RNAs in cardiovascular diseases: diagnostic and therapeutic perspectives

Recent research has demonstrated that the non-coding genome plays a key role in genetic programming and gene regulation during development as well as in health and cardiovascular disease. About 99% of the human genome do not encode proteins, but are transcriptionally active representing a broad spectrum of non-coding RNAs (ncRNAs) with important regulatory and structural functions. Non-coding RNAs have been identified as critical novel regulators of cardiovascular risk factors and cell functions and are thus important candidates to improve diagnostics and prognosis assessment. Beyond this, ncRNAs are rapidly emgerging as fundamentally novel therapeutics. On a first level, ncRNAs provide novel therapeutic targets some of which are entering assessment in clinical trials. On a second level, new therapeutic tools were developed from endogenous ncRNAs serving as blueprints. Particularly advanced is the development of RNA interference (RNAi) drugs which use recently discovered pathways of endogenous short interfering RNAs and are becoming versatile tools for efficient silencing of protein expression. Pioneering clinical studies include RNAi drugs targeting liver synthesis of PCSK9 resulting in highly significant lowering of LDL cholesterol or targeting liver transthyretin (TTR) synthesis for treatment of cardiac TTR amyloidosis. Further novel drugs mimicking actions of endogenous ncRNAs may arise from exploitation of molecular interactions not accessible to conventional pharmacology. We provide an update on recent developments and perspectives for diagnostic and therapeutic use of ncRNAs in cardiovascular diseases, including atherosclerosis/coronary disease, post-myocardial infarction remodelling, and heart failure.

Biology and Clinical Implications of the 19q13 Aggressive Prostate Cancer Susceptibility Locus

Genome-wide association studies (GWAS) have identified rs11672691 at 19q13 associated with aggressive prostate cancer (PCa). Here, we independently confirmed the finding in a cohort of 2,738 PCa patients and discovered the biological mechanism underlying this association. We found an association of the aggressive PCa-associated allele G of rs11672691 with elevated transcript levels of two biologically plausible candidate genes, PCAT19 and CEACAM21, implicated in PCa cell growth and tumor progression. Mechanistically, rs11672691 resides in an enhancer element and alters the binding site of HOXA2, a novel oncogenic transcription factor with prognostic potential in PCa. Remarkably, CRISPR/Cas9-mediated single-nucleotide editing showed the direct effect of rs11672691 on PCAT19 and CEACAM21 expression and PCa cellular aggressive phenotype. Clinical data demonstrated synergistic effects of rs11672691 genotype and PCAT19/CEACAM21 gene expression on PCa prognosis. These results provide a plausible mechanism for rs11672691 associated with aggressive PCa and thus lay the ground work for translating this finding to the clinic.

A Somatically Acquired Enhancer of the Androgen Receptor Is a Noncoding Driver in Advanced Prostate Cancer

Increased androgen receptor (AR) activity drives therapeutic resistance in advanced prostate cancer. The most common resistance mechanism is amplification of this locus presumably targeting the AR gene. Here, we identify and characterize a somatically acquired AR enhancer located 650 kb centromeric to the AR. Systematic perturbation of this enhancer using genome editing decreased proliferation by suppressing AR levels. Insertion of an additional copy of this region sufficed to increase proliferation under low androgen conditions and to decrease sensitivity to enzalutamide. Epigenetic data generated in localized prostate tumors and benign specimens support the notion that this region is a developmental enhancer. Collectively, these observations underscore the importance of epigenomic profiling in primary specimens and the value of deploying genome editing to functionally characterize noncoding elements. More broadly, this work identifies a therapeutic vulnerability for targeting the AR and emphasizes the importance of regulatory elements as highly recurrent oncogenic drivers.

miR-410-3p promotes prostate cancer progression via regulating PTEN/AKT/mTOR signaling pathway

Prostate cancer has become one of commonest urologic tumors in male. In recent years, miRNAs are continually attracting attentions of researchers for their special regulatory function in human cancers. Previous study has revealed that miR-410 acts as a biomarker for the diagnosis of prostate cancer. Whereas, the specific biological function of miR-410-3p in prostate cancer remains unknown. The aim of this study is to explore the function and molecular mechanism of miR-410-3p in prostate cancer. The high expression of miR-410-3p was examined in prostate cancer tissues and cell lines by qRT-PCR. Next, the prognostic value was identified by Kaplan Meier method. High expression of miR-410-3p indicated poor prognosis of prostate cancer patients. To investigate the biological function of miR-410-3p in prostate cancer, loss-of function assays were designed and performed in two prostate cancer cell lines (PC3 and DU145). As a result, downregulated miR-410-3p suppressed cell proliferation, migration and EMT progress. Moreover, flow cytometry analysis was performed to determine that the acceleration effects of miR-410-3p on cell apoptosis. Mechanistically, further analysis demonstrated that the effects of miR-410-3p exert oncogenic functions through downregulating PTEN. All findings in this study revealed that miR-410-3p inhibits prostate cancer progression via downregulating PTEN/AKT/mTOR signaling pathway.

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

The Post-GWAS Era: From Association to Function

During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.

Inferring the effect of genomic variation in the new era of genomics

Accurate and detailed understanding of the effects of variants in the coding and noncoding regions of the genome is the next big challenge in the new genomic era of personalized medicine, especially to tackle newer findings of genetic and phenotypic heterogeneity of diseases. This is necessary to resolve the gene-variant-disease relationship, the pathogenic variant spectrum of genes, pathogenic variants with variable clinical consequences, and multiloci diseases. In turn, this will facilitate patient recruitment for relevant clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics aiming to (a) overcome molecular diagnostic challenges and increase the clinical utility of next-generation sequencing (NGS) platforms, (b) elucidate variants associated with disease, (c) determine overall genomic complexity including epistasis, complex inheritance patterns such as "synergistic heterozygosity," digenic/multigenic inheritance, modifier effect, and rare variant load. We describe the newly emerging field of integrated functional genomics, in vivo or in vitro large-scale functional approaches, statistical bioinformatics algorithms that support NGS genomics data to interpret variants for timely clinical diagnostics and disease management. Thus, facilitating the discovery of new therapeutic or biomarker options, and their roles in the future of personalized medicine.

Emerging applications of genome-editing technology to examine functionality of GWAS-associated variants for complex traits

Over the last decade, genome-wide association studies (GWAS) have propelled the discovery of thousands of loci associated with complex diseases. The focus is now turning toward the function of these association signals, determining the causal variant(s) among those in strong linkage disequilibrium, and identifying their underlying mechanisms, such as long-range gene regulation. Genome-editing techniques utilizing zinc-finger nucleases (ZFN), transcription activator-like effector nucleases (TALENs), and clustered regularly-interspaced short palindromic repeats with Cas9 nuclease (CRISPR-Cas9) are becoming the tools of choice to establish functionality for these variants, due to the ability to assess effects of single variants in vivo. This review will discuss examples of how these technologies have begun to aid functional analysis of GWAS loci for complex traits such as cardiovascular disease, Type 2 diabetes, cancer, obesity, and autoimmune disease. We focus on analysis of variants occurring within noncoding genomic regions, as these comprise the majority of GWAS variants, providing the greatest challenges to determining functionality, and compare editing strategies that provide different levels of evidence for variant functionality. The review describes molecular insights into some of these potentially causal variants and how these may relate to the pathology of the trait and look toward future directions for these technologies in post-GWAS analysis, such as base-editing.

Advancing Stem Cell Models of Alpha-Synuclein Gene Regulation in Neurodegenerative Disease

Alpha-synuclein (non A4 component of amyloid precursor, SNCA, NM_000345.3) plays a central role in the pathogenesis of Parkinson's disease (PD) and related Lewy body disorders such as Parkinson's disease dementia, Lewy body dementia, and multiple system atrophy. Since its discovery as a disease-causing gene in 1997, alpha-synuclein has been a central point of scientific interest both at the protein and gene level. Mutations, including copy number variants, missense mutations, short structural variants, and single nucleotide polymorphisms, can be causative for PD and affect conformational changes of the protein, can contribute to changes in expression of alpha-synuclein and its isoforms, and can influence regulation of temporal as well as spatial levels of alpha-synuclein in different tissues and cell types. A lot of progress has been made to understand both the physiological transcriptional and epigenetic regulation of the alpha-synuclein gene and whether changes in transcriptional regulation could lead to disease and neurodegeneration in PD and related alpha-synucleinopathies. Although the histopathological changes in these neurodegenerative disorders are similar, the temporal and spatial presentation and progression distinguishes them which could be in part due to changes or disruption of transcriptional regulation of alpha-synuclein. In this review, we describe different genetic alterations that contribute to PD and neurodegenerative conditions and review aspects of transcriptional regulation of the alpha-synuclein gene in the context of the development of PD. New technologies, advanced gene engineering and stem cell modeling, are on the horizon to shed further light on a better understanding of gene regulatory processes and exploit them for therapeutic developments.

Common Genetic Variation and Susceptibility to Ovarian Cancer: Current Insights and Future Directions

In this review, we summarize current progress in the genetic epidemiology of epithelial ovarian cancer (EOC), focusing exclusively on elucidating the role of common germline genetic variation in conferring susceptibility to EOC. We provide an overview of the more than 30 EOC risk loci identified to date by genome-wide association studies (GWAS) and describe the contribution of large-scale, cross-cancer type, custom genotyping projects, such as the OncoArray and the Collaborative Oncological Gene-Environment Study, to locus discovery and replication. We discuss the histotype-specific nature of these EOC risk loci, pleiotropy, or overlapping genetic effects between EOC and other hormone-related cancer types, and the application of findings to polygenic risk prediction for EOC. The second part of the article offers a concise review of primarily laboratory-based studies that have led to the identification of several putative EOC susceptibility genes using common variants at the known EOC risk loci as starting points. More global biological insights emerging from network- and pathway-based analyses of GWAS for EOC susceptibility are also highlighted. Finally, we delve into potential future directions, including the need to identify EOC risk loci in non-European populations and the next generation of GWAS functional studies that are likely to involve genome editing to establish the cell type-specific carcinogenic effects of EOC risk variants Cancer Epidemiol Biomarkers Prev; 27(4); 395-404. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

Mammalian Synthetic Biology: Engineering Biological Systems

The programming of new functions into mammalian cells has tremendous application in research and medicine. Continued improvements in the capacity to sequence and synthesize DNA have rapidly increased our understanding of mechanisms of gene function and regulation on a genome-wide scale and have expanded the set of genetic components available for programming cell biology. The invention of new research tools, including targetable DNA-binding systems such as CRISPR/Cas9 and sensor-actuator devices that can recognize and respond to diverse chemical, mechanical, and optical inputs, has enabled precise control of complex cellular behaviors at unprecedented spatial and temporal resolution. These tools have been critical for the expansion of synthetic biology techniques from prokaryotic and lower eukaryotic hosts to mammalian systems. Recent progress in the development of genome and epigenome editing tools and in the engineering of designer cells with programmable genetic circuits is expanding approaches to prevent, diagnose, and treat disease and to establish personalized theranostic strategies for next-generation medicines. This review summarizes the development of these enabling technologies and their application to transforming mammalian synthetic biology into a distinct field in research and medicine.