Quantitative Trait Loci Identify Functional Noncoding Variation in Cancer

A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease

Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer's Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). The strongest known APOE E4 risk allele was identified for all five phenotypes, and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.

LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements

We describe an updated comprehensive database, LincSNP 3.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), which aims to document and annotate disease or phenotype-associated variants in human long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) or their regulatory elements. LincSNP 3.0 has updated with several novel features, including (i) more types of variants including single nucleotide polymorphisms (SNPs), linkage disequilibrium SNPs (LD SNPs), somatic mutations and RNA editing sites have been expanded; (ii) more regulatory elements including transcription factor binding sites (TFBSs), enhancers, DNase I hypersensitive sites (DHSs), topologically associated domains (TADs), footprintss, methylations and open chromatin regions have been added; (iii) the associations among circRNAs, regulatory elements and variants have been identified; (iv) more experimentally supported variant-lncRNA/circRNA-disease/phenotype associations have been manually collected; (v) the sources of lncRNAs, circRNAs, SNPs, somatic mutations and RNA editing sites have been updated. Moreover, four flexible online tools including Genome Browser, Variant Mapper, Circos Plotter and Functional Annotation have been developed to retrieve, visualize and analyze the data. Collectively, LincSNP 3.0 provides associations among functional variants, regulatory elements, lncRNAs and circRNAs in diseases. It will serve as an important and continually updated resource for investigating functions and mechanisms of lncRNAs and circRNAs in diseases.

Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer

DNA methylation is an important epigenetic mechanism for regulating gene expression. Aberrant DNA methylation has been observed in various human diseases, including cancer. Single-nucleotide polymorphisms can contribute to tumor initiation, progression and prognosis by influencing DNA methylation, and DNA methylation quantitative trait loci (meQTL) have been identified in physiological and pathological contexts. However, no database has been developed to systematically analyze meQTLs across multiple cancer types. Here, we present Pancan-meQTL, a database to comprehensively provide meQTLs across 23 cancer types from The Cancer Genome Atlas by integrating genome-wide genotype and DNA methylation data. In total, we identified 8 028 964 cis-meQTLs and 965 050 trans-meQTLs. Among these, 23 432 meQTLs are associated with patient overall survival times. Furthermore, we identified 2 214 458 meQTLs that overlap with known loci identified through genome-wide association studies. Pancan-meQTL provides a user-friendly web interface (http://bioinfo.life.hust.edu.cn/Pancan-meQTL/) that is convenient for browsing, searching and downloading data of interest. This database is a valuable resource for investigating the roles of genetics and epigenetics in cancer.

A multiplatform approach identifies miR-152-3p as a common epigenetically regulated onco-suppressor in prostate cancer targeting TMEM97

Background

Prostate cancer (PCa) is a major cause of morbidity and mortality in men worldwide. MicroRNAs are globally downregulated in PCa, especially in poorly differentiated tumors. Nonetheless, the underlying mechanisms are still elusive. Herein, using combined analysis of microRNAs expression and genomewide DNA methylation, we aimed to identify epigenetically downregulated microRNAs in PCa.

Results

We found that miR-152-3p was underexpressed in PCa and that lower expression levels were associated with promoter hypermethylation in accordance with TCGA dataset analysis. Functional in vitro assays suggest that miR-152-3p suppresses cell viability and invasion potential, whereas it promotes cell cycle arrest at S and G2/M phases. Additionally, miR-152-3p expression was associated with longer disease-free survival in PCa patients from TCGA. Finally, TMEM97, which is overexpressed in PCa, was identified as a novel miR-152-3p target gene.

Conclusions

Our findings demonstrate the advantages of using a combinatory approach to identify microRNAs downregulated due to aberrant promoter methylation. MiR-152-3p downregulation and promoter methylation was found to be prevalent in primary PCa, which impairs its role in control of cell viability, cell cycle regulation and invasion.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0475-2) contains supplementary material, which is available to authorized users.

Genetic and epigenetic determinants of inter-individual variability in responses to toxicants

It is well established that genetic variability has a major impact on susceptibility to common diseases, responses to drugs and toxicants, and influences disease-related outcomes. The appreciation that epigenetic marks also vary across the population is growing with more data becoming available from studies in humans and model organisms. In addition, the links between genetic variability, toxicity outcomes and epigenetics are being actively explored. Recent studies demonstrate that gene-by-environment interactions involve both chromatin states and transcriptional regulation, and that epigenetics provides important mechanistic clues to connect expression-related quantitative trait loci (QTL) and disease outcomes. However, studies of Gene×Environment×Epigenetics further extend the complexity of the experimental designs and create a challenge for selecting the most informative epigenetic readouts that can be feasibly performed to interrogate multiple individuals, exposures, tissue types and toxicity phenotypes. We propose that among the many possible epigenetic experimental methodologies, assessment of chromatin accessibility coupled with total RNA levels provides a cost-effective and comprehensive option to sufficiently characterize the complexity of epigenetic and regulatory activity in the context of understanding the inter-individual variability in responses to toxicants.