Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

PU.1 controls fibroblast polarization and tissue fibrosis.

Fibroblasts are polymorphic cells with pleiotropic roles in organ morphogenesis, tissue homeostasis and immune responses. In fibrotic diseases, fibroblasts synthesize abundant amounts of extracellular matrix, which induces scarring and organ failure. By contrast, a hallmark feature of fibroblasts in arthritis is degradation of the extracellular matrix because of the release of metalloproteinases and degrading enzymes, and subsequent tissue destruction. The mechanisms that drive these functionally opposing pro-fibrotic and pro-inflammatory phenotypes of fibroblasts remain unknown. Here we identify the transcription factor PU.1 as an essential regulator of the pro-fibrotic gene expression program. The interplay between transcriptional and post-transcriptional mechanisms that normally control the expression of PU.1 expression is perturbed in various fibrotic diseases, resulting in the upregulation of PU.1, induction of fibrosis-associated gene sets and a phenotypic switch in extracellular matrix-producing pro-fibrotic fibroblasts. By contrast, pharmacological and genetic inactivation of PU.1 disrupts the fibrotic network and enables reprogramming of fibrotic fibroblasts into resting fibroblasts, leading to regression of fibrosis in several organs.

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.

gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.

A low-frequency haplotype spanning SLX4/FANCP constitutes a new risk locus for early-onset breast cancer (<60 years) and is associated with reduced DNA repair capacity.

Only a fraction of breast cancer (BC) cases can be yet explained by mutations in genes or genomic variants discovered in linkage, genome-wide association and sequencing studies. The known genes entailing medium or high risk for BC are strongly enriched for a function in DNA double strand repair. Thus, aiming at identifying low frequency variants conferring an intermediate risk, we here investigated 17 variants (MAF: 0.01-0.1) in 10 candidate genes involved in DNA repair or cell cycle control. In an exploration cohort of 437 cases and 1189 controls, we show the variant rs3810813 in the SLX4/FANCP gene to be significantly associated with both BC (≤60 years; OR = 2.6(1.6-3.9), p = 1.6E-05) and decreased DNA repair capacity (≤60 years; beta = 37.8(17.9-57.8), p = 5.3E-4). BC association was confirmed in a verification cohort (N = 2441). Both associations were absent from cases diagnosed >60 years and stronger the earlier the diagnosis. By imputation we show that rs3810813 tags a haplotype with 5 additional variants with the same allele frequency (R2 > 0.9), and a pattern of association very similar for both phenotypes (cases <60 years, p < 0.001, the Bonferroni threshold derived from unlinked variants in the region). In young cases (≤60 years) carrying the risk haplotype, micronucleus test results are predictive for BC (AUC > 0.9). Our findings propose a risk variant with high penetrance on the haplotype spanning SLX4/FANCP to be functionally associated to BC predisposition via decreased repair capacity and suggest this variant is carried by a fraction of these haplotypes that is enriched in early onset BC cases.

Prostate cancer risk regions at 8q24 and 17q24 are differentially associated with somatic TMPRSS2:ERG fusion status.

Molecular and epidemiological differences have been described between TMPRSS2:ERG fusion-positive and fusion-negative prostate cancer (PrCa). Assuming two molecularly distinct subtypes, we have examined 27 common PrCa risk variants, previously identified in genome-wide association studies, for subtype specific associations in a total of 1221 TMPRSS2:ERG phenotyped PrCa cases. In meta-analyses of a discovery set of 552 cases with TMPRSS2:ERG data and 7650 unaffected men from five centers we have found support for the hypothesis that several common risk variants are associated with one particular subtype rather than with PrCa in general. Risk variants were analyzed in case-case comparisons (296 TMPRSS2:ERG fusion-positive versus 256 fusion-negative cases) and an independent set of 669 cases with TMPRSS2:ERG data was established to replicate the top five candidates. Significant differences (P < 0.00185) between the two subtypes were observed for rs16901979 (8q24) and rs1859962 (17q24), which were enriched in TMPRSS2:ERG fusion-negative (OR = 0.53, P = 0.0007) and TMPRSS2:ERG fusion-positive PrCa (OR = 1.30, P = 0.0016), respectively. Expression quantitative trait locus analysis was performed to investigate mechanistic links between risk variants, fusion status and target gene mRNA levels. For rs1859962 at 17q24, genotype dependent expression was observed for the candidate target gene SOX9 in TMPRSS2:ERG fusion-positive PrCa, which was not evident in TMPRSS2:ERG negative tumors. The present study established evidence for the first two common PrCa risk variants differentially associated with TMPRSS2:ERG fusion status. TMPRSS2:ERG phenotyping of larger studies is required to determine comprehensive sets of variants with subtype-specific roles in PrCa.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

This corrects the article DOI: 10.1038/bjc.2016.50.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Motivation: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. Results: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . Contact: hlin1@lsuhsc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies.

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Alcohol consumption and prostate cancer incidence and progression: A Mendelian randomisation study.

Prostate cancer is the most common cancer in men in developed countries, and is a target for risk reduction strategies. The effects of alcohol consumption on prostate cancer incidence and survival remain unclear, potentially due to methodological limitations of observational studies. In this study, we investigated the associations of genetic variants in alcohol-metabolising genes with prostate cancer incidence and survival. We analysed data from 23,868 men with prostate cancer and 23,051 controls from 25 studies within the international PRACTICAL Consortium. Study-specific associations of 68 single nucleotide polymorphisms (SNPs) in 8 alcohol-metabolising genes (Alcohol Dehydrogenases (ADHs) and Aldehyde Dehydrogenases (ALDHs)) with prostate cancer diagnosis and prostate cancer-specific mortality, by grade, were assessed using logistic and Cox regression models, respectively. The data across the 25 studies were meta-analysed using fixed-effect and random-effects models. We found little evidence that variants in alcohol metabolising genes were associated with prostate cancer diagnosis. Four variants in two genes exceeded the multiple testing threshold for associations with prostate cancer mortality in fixed-effect meta-analyses. SNPs within ALDH1A2 associated with prostate cancer mortality were rs1441817 (fixed effects hazard ratio, HRfixed = 0.78; 95% confidence interval (95%CI):0.66,0.91; p values = 0.002); rs12910509, HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.003); and rs8041922 (HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.002). These SNPs were in linkage disequilibrium with each other. In ALDH1B1, rs10973794 (HRfixed = 1.43; 95%CI:1.14,1.79; p values = 0.002) was associated with prostate cancer mortality in men with low-grade prostate cancer. These results suggest that alcohol consumption is unlikely to affect prostate cancer incidence, but it may influence disease progression.

Investigating the possible causal role of coffee consumption with prostate cancer risk and progression using Mendelian randomization analysis.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all-cause and prostate cancer-specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high-grade compared to low-grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all-cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer-specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression.

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

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

Inhibition of phosphodiesterase 4 (PDE4) reduces dermal fibrosis by interfering with the release of interleukin-6 from M2 macrophages.

OBJECTIVES: To investigate the disease-modifying effects of phosphodiesterase 4 (PDE4) inhibition in preclinical models of systemic sclerosis (SSc). METHODS: We studied the effects of PDE4 inhibition in a prevention and a treatment model of bleomycin-induced skin fibrosis, in the topoisomerase mouse model as well as in a model of sclerodermatous chronic graft-versus-host disease. To better understand the mode of action of PDE4 blockade in preclinical models of SSc, we investigated fibrosis-relevant mediators in fibroblasts and macrophages from healthy individuals and patients suffering from diffuse-cutaneous SSc on blockade of PDE4. RESULTS: Specific inhibition of PDE4 by rolipram and apremilast had potent antifibrotic effects in bleomycin-induced skin fibrosis models, in the topoisomerase I mouse model and in murine sclerodermatous chronic graft-versus-host disease. Fibroblasts were not the direct targets of the antifibrotic effects of PDE4 blockade. Reduced leucocyte infiltration in lesional skin on PDE4 blockade suggested an immune-mediated mechanism. Further analysis revealed that PDE4 inhibition decreased the differentiation of M2 macrophages and the release of several profibrotic cytokines, resulting in reduced fibroblast activation and collagen release. Within these profibrotic mediators, interleukin-6 appeared to play a central role. CONCLUSIONS: PDE4 inhibition reduces inflammatory cell activity and the release of profibrotic cytokines from M2 macrophages, leading to decreased fibroblast activation and collagen release. Importantly, apremilast is already approved for the treatment of psoriasis and psoriatic arthritis. Therefore, PDE4 inhibitors might be further developed as potential antifibrotic therapies for patients with SSc. Our findings suggest that particularly patients with inflammation-driven fibrosis might benefit from PDE4 blockade.

Nintedanib inhibits macrophage activation and ameliorates vascular and fibrotic manifestations in the Fra2 mouse model of systemic sclerosis.

BACKGROUND: Nintedanib is an inhibitor targeting platelet-derived growth factor receptor, fibroblast growth factor receptor and vascular endothelial growth factor receptor tyrosine kinases that has recently been approved for the treatment of idiopathic pulmonary fibrosis. The aim of this study was to analyse the effects of nintedanib in the fos-related antigen-2 (Fra2) mouse model of systemic sclerosis (SSc). METHODS: The effects of nintedanib on pulmonary arterial hypertension with proliferation of pulmonary vascular smooth muscle cells (PVSMCs) and luminal occlusion, on microvascular disease with apoptosis of microvascular endothelial cells (MVECs) and on fibroblast activation with myofibroblast differentiation and accumulation of extracellular matrix were analysed. We also studied the effects of nintedanib on the levels of key mediators involved in the pathogenesis of SSc and on macrophage polarisation. RESULTS: Nintedanib inhibited proliferation of PVSMCs and prevented thickening of the vessel walls and luminal occlusion of pulmonary arteries. Treatment with nintedanib also inhibited apoptosis of MVECs and blunted the capillary rarefaction in Fra2-transgenic mice. These effects were associated with a normalisation of the serum levels of vascular endothelial growth factor in Fra2 mice on treatment with nintedanib. Nintedanib also effectively blocked myofibroblast differentiation and reduced pulmonary, dermal and myocardial fibrosis in Fra2-transgenic mice. The antifibrotic effects of nintedanib were associated with impaired M2 polarisation of monocytes and reduced numbers of M2 macrophages. CONCLUSION: Nintedanib targets core features of SSc in Fra2-transgenic mice and ameliorates histological features of pulmonary arterial hypertension, destructive microangiopathy and pulmonary and dermal fibrosis. These data might have direct implications for the ongoing phase III clinical trial with nintedanib in SSc-associated interstitial lung disease.

Pharmacological inhibition of porcupine induces regression of experimental skin fibrosis by targeting Wnt signalling.

OBJECTIVES: Wnt signalling has been implicated in activating a fibrogenic programme in fibroblasts in systemic sclerosis (SSc). Porcupine is an O-acyltransferase required for secretion of Wnt proteins in mammals. Here, we aimed to evaluate the antifibrotic effects of pharmacological inhibition of porcupine in preclinical models of SSc. METHODS: The porcupine inhibitor GNF6231 was evaluated in the mouse models of bleomycin-induced skin fibrosis, in tight-skin-1 mice, in murine sclerodermatous chronic-graft-versus-host disease (cGvHD) and in fibrosis induced by a constitutively active transforming growth factor-ß-receptor I. RESULTS: Treatment with pharmacologically relevant and well-tolerated doses of GNF6231 inhibited the activation of Wnt signalling in fibrotic murine skin. GNF6231 ameliorated skin fibrosis in all four models. Treatment with GNF6231 also reduced pulmonary fibrosis associated with murine cGvHD. Most importantly, GNF6231 prevented progression of fibrosis and showed evidence of reversal of established fibrosis. CONCLUSIONS: These data suggest that targeting the Wnt pathway through inhibition of porcupine provides a potential therapeutic approach to fibrosis in SSc. This is of particular interest, as a close analogue of GNF6231 has already demonstrated robust pathway inhibition in humans and could be available for clinical trials.

Fine-mapping the HOXB region detects common variants tagging a rare coding allele: evidence for synthetic association in prostate cancer.

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10(-14)). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.

Assessing the role of insulin-like growth factors and binding proteins in prostate cancer using Mendelian randomization: Genetic variants as instruments for circulating levels.

Circulating insulin-like growth factors (IGFs) and their binding proteins (IGFBPs) are associated with prostate cancer. Using genetic variants as instruments for IGF peptides, we investigated whether these associations are likely to be causal. We identified from the literature 56 single nucleotide polymorphisms (SNPs) in the IGF axis previously associated with biomarker levels (8 from a genome-wide association study [GWAS] and 48 in reported candidate genes). In ∼700 men without prostate cancer and two replication cohorts (N ∼ 900 and ∼9,000), we examined the properties of these SNPS as instrumental variables (IVs) for IGF-I, IGF-II, IGFBP-2 and IGFBP-3. Those confirmed as strong IVs were tested for association with prostate cancer risk, low (< 7) vs. high (≥ 7) Gleason grade, localised vs. advanced stage, and mortality, in 22,936 controls and 22,992 cases. IV analysis was used in an attempt to estimate the causal effect of circulating IGF peptides on prostate cancer. Published SNPs in the IGFBP1/IGFBP3 gene region, particularly rs11977526, were strong instruments for IGF-II and IGFBP-3, less so for IGF-I. Rs11977526 was associated with high (vs. low) Gleason grade (OR per IGF-II/IGFBP-3 level-raising allele 1.05; 95% CI: 1.00, 1.10). Using rs11977526 as an IV we estimated the causal effect of a one SD increase in IGF-II (∼265 ng/mL) on risk of high vs. low grade disease as 1.14 (95% CI: 1.00, 1.31). Because of the potential for pleiotropy of the genetic instruments, these findings can only causally implicate the IGF pathway in general, not any one specific biomarker.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

BACKGROUND: Germline mutations within DNA-repair genes are implicated in susceptibility to multiple forms of cancer. For prostate cancer (PrCa), rare mutations in BRCA2 and BRCA1 give rise to moderately elevated risk, whereas two of B100 common, low-penetrance PrCa susceptibility variants identified so far by genome-wide association studies implicate RAD51B and RAD23B. METHODS: Genotype data from the iCOGS array were imputed to the 1000 genomes phase 3 reference panel for 21 780 PrCa cases and 21 727 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. We subsequently performed single variant, gene and pathway-level analyses using 81 303 SNPs within 20 Kb of a panel of 179 DNA-repair genes. RESULTS: Single SNP analyses identified only the previously reported association with RAD51B. Gene-level analyses using the SKAT-C test from the SNP-set (Sequence) Kernel Association Test (SKAT) identified a significant association with PrCa for MSH5. Pathway-level analyses suggested a possible role for the translesion synthesis pathway in PrCa risk and Homologous recombination/Fanconi Anaemia pathway for PrCa aggressiveness, even though after adjustment for multiple testing these did not remain significant. CONCLUSIONS: MSH5 is a novel candidate gene warranting additional follow-up as a prospective PrCa-risk locus. MSH5 has previously been reported as a pleiotropic susceptibility locus for lung, colorectal and serous ovarian cancers.

Polyunsaturated fatty acids and prostate cancer risk: a Mendelian randomisation analysis from the PRACTICAL consortium.

BACKGROUND: Prostate cancer is a common cancer worldwide with no established modifiable lifestyle factors to guide prevention. The associations between polyunsaturated fatty acids (PUFAs) and prostate cancer risk have been inconsistent. Using Mendelian randomisation, we evaluated associations between PUFAs and prostate cancer risk. METHODS: We used individual-level data from a consortium of 22 721 cases and 23 034 controls of European ancestry. Externally-weighted PUFA-specific polygenic risk scores (wPRSs), with explanatory variation ranging from 0.65 to 33.07%, were constructed and used to evaluate associations with prostate cancer risk per one standard deviation (s.d.) increase in genetically-predicted plasma PUFA levels using multivariable-adjusted unconditional logistic regression. RESULTS: No overall association was observed between the genetically-predicted PUFAs evaluated in this study and prostate cancer risk. However, risk reductions were observed for short-chain PUFAs, linoleic (ORLA=0.95, 95%CI=0.92, 0.98) and α-linolenic acids (ORALA=0.96, 95%CI=0.93, 0.98), among men <62 years; whereas increased risk was found among men ⩾62 years for LA (ORLA=1.04, 95%CI=1.01, 1.07). For long-chain PUFAs (i.e., arachidonic, eicosapentaenoic, and docosapentaenoic acids), increased risks were observed among men <62 years (ORAA=1.05, 95%CI=1.02, 1.08; OREPA=1.04, 95%CI=1.01, 1.06; ORDPA=1.05, 95%CI=1.02, 1.08). CONCLUSION: Results from this study suggest that circulating ω-3 and ω-6 PUFAs may have a different role in the aetiology of early- and late-onset prostate cancer.