Comprehensive functional annotation of 77 prostate cancer risk loci

Abstract

Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations--we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at r(2) ≥ 0.88%. 88% of the 727 SNPs fall within putative enhancers, and many alter critical residues in the response elements of transcription factors known to be involved in prostate biology. We define as risk enhancers those regions with enhancer chromatin biofeatures in prostate-derived cell lines with prostate-cancer correlated SNPs. To aid the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancers, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium (r(2) = 0.91) with an eQTL for NUDT11 (on the X chromosome) in prostate tissue, and rs10486567, the index SNP in intron 3 of the JAZF1 gene on chromosome 7. Rs4907792 is within a critical residue of a strong consensus androgen response element that is interrupted in the protective allele, resulting in a 56% decrease in its androgen sensitivity, whereas rs10486567 affects both NKX3-1 and FOXA-AR motifs where the risk allele results in a 39% increase in basal activity and a 28% fold-increase in androgen stimulated enhancer activity. Identification of such enhancer variants and their potential target genes represents a preliminary step in connecting risk to disease process.

Figure 1. Tag-density profiles of ChIP-seq datasets

‘css’: H3K27Ac ChIP-seq of LNCaP grown in charcoal-stripped serum. ‘dht’: H3K27Ac ChIP-seq of LNCaP exposed to androgen. ‘tcf7l2’: ChIP-seq with anti-TCF7L2 in LNCaP, unstimulated. Top: peak height, is scaled. Middle: peak width, -axis is scaled. Bottom: Peak height vs. width reveals strong correlation.

Figure 2. Results of Funci{SNP} analysis of GWAS correlated SNPs.

Index SNPs with biofeatures and correlated SNPs at are combined and summarized in A–D. A. SNP counts by value. B. SNP counts by biofeature. Some SNPs map to more than one biofeature, hence the total does not sum to 727. C. Classification of 727 SNPs by putative functional category. D. Supervised clustering of SNPs by biofeature.

Figure 3. Genome-wide summary of functional annotations.

Detailed map of the locations and annotations associated with risk for prostate cancer throughout the human genome. Each ring shows, successive from center, the names and locations of proximal genes, the tag- or index-SNPs, and the correlated SNPs. The links in the center highlight known biochemical interactors (e.g. receptor-ligand pairs). Index and correlated SNPs are color-coded by putative functional category (see Legend, center). Potentially disrupted response elements are also indicated for the correlated SNPs. The outermost ring shows the numbered chromosomes to scale with cytological banding patterns. The genome is displayed clockwise from top, with p displayed as the left arm of each chromosome and q as the right arm.

Figure 4. Annotation of the 8q24.21 region.

The intergenic region between FAM84B and MYC is shown with biofeatures indicated as colored hashes in the inside tracks. Index SNPs are black, correlated enhancer snps are in green according to the convention in Figure 3. Chromatin capture 5C data are indicated as links (light blue) in the center, showing interactions between regions. Histogram (inset) indicates the distribution of the dataset, showing the tag density on the -axis vs. number of regions. The dotted line indicates min. tag-density cutoff for the display.

Figure 5. rs1512268 in two populations.

The rs1512268 risk locus is kb downstream of the NKX3-1 gene. An reveals SNPs that are correlated to the index SNP in both populations for which it has been identified as carrying risk. One SNP that is highly correlated in populations of both African and European ancestry is highlighted in red.

Figure 6. Transcription Factor Response Elements are not enriched in PCa GWAS SNPs.

express number of observed response element disruptions as a proportion relative to the standard deviation from the background distribution. The regression line is shown in blue with 95% confidence interval. Transcription factors of interest are highlighted with blue text. The inner box (dotted line) demarcates the 95% C.I. of a bootstrapped distribution for each PWM. A bonferroni box is outside the bounds of the graphic.

Figure 7. Enrichment of Gene Ontology.

Representative ontology clusters from DAVID enrichment analysis of nearby genes given in Table 1. Green boxes indicate membership of the genes (as columns) with the annotations (as rows). A. Transcription factor cluster. B. Male gonad development cluster.

Figure 8. Allelic effects of prostate cancer-correlated SNPs in enhancer-luciferase assays.

A,B,C: alignment of the genomic sequence surrounding the SNP with transcription factor LOGO, highlighting the disruption. Red box indicates the risk allele. Features of interest in the region are highlighted, including the biofeatures from Funci{SNP} analysis. D: enhancer activity in the presence or absence of DHT treatment with 95% C.I. for each allele of SNP and each enhancer (see labels).

Figure 9. Models for association of risk with effector genes.

Red dots indicate the true causal variant position in the genome, as opposed to variants that may be merely correlated with such functional variants (green dots). In panel I. we consider functionality of such variation within a locus. Causal association with risk for disease may be the result of a single variant (A) or multiple correlated variants (B) disrupting regulatory elements in enhancers (white box). In panel II we consider the effector genes of these causal variants. Arrows show regulatory interaction between enhancer and promoter as revealed by chromatin conformation capture experiments. Risk may arise from a damaging hit to a regulatory region that affects the expression of a single key oncogene or tumor suppressor (blue box) (C) or several effector genes that target a disease process or pathway (D).