Genome-wide Association Study Of Prostate Cancer Research Articles

The mendelian randomized study revealed the association of prostatitis with prostate cancer risk

In recent observational studies, a potential link between prostatitis and prostate cancer (PCa) has been hinted at, yet the causality remains ambiguous. In our endeavor to scrutinize the conceivable causal nexus between prostatitis and PCa, we embarked upon a Mendelian randomization (MR) study. MR circumvents arbitrary groupings by employing genetic variations that have a strong association with the exposure as instrumental variables to infer causal relationships between exposures and outcomes. The etiology of PCa remains elusive. Given that prostatitis and prostate cancer occupy the same anatomical region, MR can more effectively delineate their relationship by mitigating confounding variables. This method can indirectly elucidate disease correlations, thereby contributing to cancer prevention strategies. FinnGen Consortium data were used for the prostatitis genome-wide association study (GWAS), including 74,658 participants. UK biobank baseline data (ncase = 3436, ncontrol = 459574), European Bioinformatics Institute Database (ncase = 79148, ncontrol = 61106), and IEU openGWAS database (ncase = 79148, ncontrol = 61106) were used for PCa outcomes, mostly for European population samples. Data from the GWSAs for prostatitis were compared with data from the three GWASs for PCa, respectively, in an analysis of an MR. Utilizing the inverse variance weighting (IVW) methodology as our primary analytical framework, we delved into a meticulous exploration of the conceivable causal association between prostatitis and PCa. Furthermore, we deployed supplementary methodologies, including Maximum Likelihood, MR-Egger, weighted median, and MR-PRESSO, to thoroughly assess and scrutinize the causality aspect comprehensively. Cochran’s Q statistic is employed as a metric to quantify the heterogeneity inherent in instrumental variables. The inverse variance weighted analysis revealed no discernible effect of prostatitis on PCa in the three PCa GWAS databases (odds ratio [OR]: 1.001, 95% Confidence Interval [CI]: 0.999–1.002, p = 0.28), (OR: 1.015, 95% CI: 0.981–1.050, p = 0.40), (OR: 1.015, 95% CI: 0.981–1.050, p = 0.40). Similarly, employing MR-Egger did not yield substantial evidence (OR: 0.999, 95% CI: 0.999–1.002, p = 0.89), (OR: 1.103, 95% CI: 1.006–1.209, p = 0.07), (OR: 1.103, 95% CI: 1.006–1.209, p = 0.07). The weighted median analysis also failed to provide convincing support for the impact of prostatitis on the incidence of PCa (OR: 1.001, 95% CI: 1.000-1.002, p = 0.064), (OR: 0.989, 95% CI: 0.946–1.034, p = 0.64), (OR: 0.989, 95% CI: 0.945–1.036, p = 0.65). The results of the MR showed no causality from prostatitis to PCa.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

The Impact of Inherited Genetic Variation on DNA Methylation in Prostate Cancer and Benign Tissues of African American and European American Men.

American men of African ancestry (AA) have higher prostate cancer incidence and mortality rates compared with American men of European ancestry (EA). Differences in genetic susceptibility mechanisms may contribute to this disparity. To gain insights into the regulatory mechanisms of prostate cancer susceptibility variants, we tested the association between SNPs and DNA methylation (DNAm) at nearby CpG sites across the genome in benign and cancer prostate tissue from 74 AA and 74 EA men. Genome-wide SNP data (from benign tissue) and DNAm were generated using Illumina arrays. Among AA men, we identified 6,298 and 2,641 cis-methylation QTLs (meQTL; FDR of 0.05) in benign and tumor tissue, respectively, with 6,960 and 1,700 detected in EA men. We leveraged genome-wide association study (GWAS) summary statistics to identify previously reported prostate cancer GWAS signals likely to share a common causal variant with a detected meQTL. We identified nine GWAS-meQTL pairs with strong evidence of colocalization (four in EA benign, three in EA tumor, two in AA benign, and three in AA tumor). Among these colocalized GWAS-meQTL pairs, we identified colocalizing expression quantitative trait loci (eQTL) impacting four eGenes with known roles in tumorigenesis. These findings highlight epigenetic regulatory mechanisms by which prostate cancer-risk SNPs can modify local DNAm and/or gene expression in prostate tissue. Overall, our findings showed general consistency in the meQTL landscape of AA and EA men, but meQTLs often differ by tissue type (normal vs. cancer). Ancestry-based linkage disequilibrium differences and lack of AA representation in GWAS decrease statistical power to detect colocalization for some regions.

Abstract 5227: Cross-trait genome-wide association meta-analyses of clonal hematopoiesis and solid tumor risk

Abstract Clonal hematopoiesis (CH) is an age-related phenomenon where hematopoietic stem cells and their progeny acquire specific somatic mutations in a small, well-defined set of leukemogenic genes conferring them with fitness advantage leading to clonal proliferation. Mosaic chromosomal alterations (mCAs) are somatic structural variants that are manifestations of genomic instability and a sign of CH when they occur in blood. The recent availability of large-scale cohorts with blood exome sequence and genotype data has powered genome-wide association studies (GWAS) investigating the germline genetic contribution to CH and mCA risk. However, phenotypes of somatic mutation acquisition in driver genes, clonal selection and expansion, and chromosomal mosaicism are processes that are not confined to blood and contribute to neoplastic transformation across tissue types. GWAS of solid tumor susceptibility have so far identified hundreds of germline variants associated with cancer risk and it is likely that a subset of these variants contributes to solid tumor risk by affecting these somatic phenotypes in the corresponding tissues of origin and that some of the variants exert these somatic effects across tissues. We aimed to identify this subset of solid tumor risk variants by undertaking fixed effects inverse-variance weighted meta-analyses combining breast, prostate, ovarian, and endometrial cancer GWAS (total n = 237,483 cases/317,006 controls; no UK Biobank samples included) with UK Biobank-based CH (10,203 “cases”/173,918 controls) and mCA (66,011 “cases”/378,188 controls) risk GWAS in pairs where each pair involved one solid tumor type and one of either CH or mCA (for example, breast cancer and mCA constituted a pair). These analyses collectively identified 61 independent (r2<0.05) lead variants at combined cross-trait genome-wide significance (P<5 × 10−8) across 22 genomic loci where each lead variant was associated with each trait individually at P<10−3, had the same direction of allelic effect across traits, and little evidence of heterogeneity in effect (I2<40%). New loci (>1 Mb away from previously reported risk loci for a given trait) counts were: 14 new loci in the context of mCA risk, 6 new loci for CH risk, 2 for breast cancer risk, and one each for prostate, ovarian, and endometrial cancer risks. In silico functional annotation of these loci suggested lead variant-target genes that could be assigned to the discrete pathways of mitotic spindle assembly (INCENP, CENPN, ZWILCH, MAD1L1), p53 signaling (TP53, MDM4, SPI1), DNA damage repair (ATM, RAD51C, CHEK2, USP28, PARP8), and myeloid oncogenesis (WT1, HOXA9, L3MBTL3). Our novel approach combining the latest and largest blood somatic genomic trait GWAS with large-scale solid tumor risk GWAS thus yielded clear mechanistic insights into the shared inherited genetic basis of solid tumor risk and somatic genomic traits. Citation Format: Victoria Gray, George Richenberg, Pedro Quirós, Matthew Freedman, Paul Pharoah, Simon Gayther, Michelle Jones, George Vassiliou, Kate Lawrenson, Siddhartha Kar. Cross-trait genome-wide association meta-analyses of clonal hematopoiesis and solid tumor risk. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5227.

Abstract B003: Causal effect of type II diabetes on prostate cancer in the East Asian population: A two-sample Mendelian randomization study

Abstract Type II diabetes, a metabolic disease, and prostate cancer are prevalent diseases in the elderly population. Whether type II diabetes has an association with prostate cancer has not been thoroughly studied. Limited research has been performed from a perspective of genetic level, especially among non-European populations. In this study, we used a two-sample Mendelian randomization (MR) approach to evaluate the causal effect between type II diabetes as an exposure and prostate cancer as an outcome in the East Asian population. For type II diabetes, we used 174 risk loci identified in a meta-analysis of 77,418 cases and 356,122 controls from 23 East Asian Genome-wide Association Studies (GWAS). For prostate cancer, summary statistics were extracted from the GWAS from BioBank Japan using 5,408 cases and 103,939 controls. We performed two-sample Mendelian randomization with the inverse variance weighted method while using MR Egger and weighted median methods as sensitivity analysis. For type II diabetes risk SNPs not directly present in the prostate cancer GWAS, we identified proxy SNPs in linkage disequilibrium (LD, r2 > 0.8) using the 1000 genome project. We used P-value < 0.05 as a threshold to determine statistical significance. Based on 152 out of 174 SNPs of type II diabetes in the East Asian population, we found that type II diabetes had a significant negative causal effect on prostate cancer [Odds Ratio (OR) = 0.76, 95% Confidence Interval (CI): 0.76 - 0.89, P = 2.26 x 10-6]. Results from sensitivity analysis using other weighting methods provided similar results. In conclusion, we observed an inverse relationship between type II diabetes and prostate cancer among the East Asian population. Patients with diabetes may have a lower risk of prostate cancer due to a reduced level of insulin and testosterone. To validate this association, further research is needed on those hormones and prostate cancer risk at the metabolic level. Citation Format: Zhongyi Guo. Causal effect of type II diabetes on prostate cancer in the East Asian population: A two-sample Mendelian randomization study [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr B003.

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.

Abstract 6242: Association of prostate cancer risk variants with gene expression in prostate stroma

Abstract Background: Prostate cancer genome-wide association studies have identified >260 germline risk variants which provide strong risk prediction for prostate cancer. Functional interpretation and biological mechanisms of these risk variants are not always clear. Many of the prostate cancer risk variants are expression quantitative trait loci (eQTLs), but studies have mostly been performed in tumor or epithelial-enriched normal prostate tissue. Given emerging evidence supporting that stroma contributes to the carcinogenic process, we examined the associations of prostate cancer risk variants with the expression of genes in prostate stroma. Methods: We generated mRNA expression data for 13,577 genes with the Illumina TruSeq RNA Exome library preparation kit from stromal samples in specimens from participants of the Health Professionals Follow-up Study and Physicians’ Health Study cohorts. Specifically, we profiled 172 stromal samples adjacent to tumor and 86 paired stroma distant from tumor. We included 264 prostate cancer risk variants genotyped data and imputed using a common reference panel (1,000 Genomes Phase I release). We performed cis- (≤1 Mb from the SNP (single nucleotide polymorphism) to the gene) and trans- eQTL analyses using a linear regression model in the R package Matrix eQTL. Results: Thirty-four SNPs were significantly (P < 0.01) associated with cis-expression of 41 nearby genes in prostate stroma adjacent to tumor, and 41 SNPs with 52 nearby genes for stroma distant from tumor. Genome-wide, 86 SNPs were significantly (FDR < 0.10) associated with the expression of 126 genes in stroma adjacent to tumor, and 64 SNPs with 291 genes for stroma distant from tumor. Conclusions: This is the first study to comprehensively examine associations between prostate cancer risk variants and gene expression in prostate stroma. We observed some previous reported associations but also novel associations which may suggest genes that contribute to prostate cancer risk within the stromal tissue. Identifications of eQTLs within prostate stroma may suggest potential mechanisms in specific cell types for their effect on disease. Citation Format: Chaoran Ma, Yinglu Zhou, Anna Plym, Giuseppe Nicolò Fanelli, Massimo Loda, Lorelei Mucci, Svitlana Tyekucheva, Kathryn Penney. Association of prostate cancer risk variants with gene expression in prostate stroma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6242.

Allele-specific epigenetic activity in prostate cancer and normal prostate tissue implicates prostate cancer risk mechanisms

Genome-wide association studies (GWASs) of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk-harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 individuals across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. We identified 7,371 peaks with significant allele specificity (allele-specific quantitative trait locus [asQTL] peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40×), significantly higher than corresponding non-asQTL H3K27ac peaks (14×) or coding regions (14×). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during or after transformation and can be captured by epigenomic profiling of the tumor. Our study demonstrates the power of allele specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.

Abstract NG03: Multiethnic prostate cancer GWAS meta-analysis identifies novel variants, improves genetic risk prediction across populations, and informs biological mechanisms of prostate cancer

Abstract Prostate cancer (PCa) is a highly heritable disease with large disparities in incidence rates across racial and ethnic populations. The inadequate representation of diverse populations in current genome-wide association studies (GWAS) limits the translational potential of findings to the world's populations and could result in biased risk prediction, further exacerbating health disparities. To improve our understanding of genetic risk of PCa, we conducted the largest multiethnic PCa GWAS meta-analysis to date with 107,247 cases and 127,006 controls from the Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome (PRACTICAL) consortium, including 85,554 cases and 91,972 controls of European ancestry, 10,368 cases and 10,986 controls of African ancestry, 8,611 cases and 18,809 controls of Asian ancestry, and 2,714 cases and 5,239 controls of Hispanic ethnicity. We identified 269 genetic risk variants independently associated with PCa risk, 86 of which were novel. Of the 183 previously reported prostate cancer risk variants, we identified stronger markers of risk for 62 variants using fine-mapping. To understand the aggregate effect of the 269 variants, we constructed a genetic risk score (GRS) using the multiethnic weights of the risk variants. Compared to men in the average 40-60% GRS category, the estimated OR for men in the top GRS decile (90-100%) was 5.06 [95% CI 4.84-5.29] for men of European ancestry, 3.74 [95% CI 3.36-4.17] for men of African ancestry, 4.47 [95% CI 3.52-5.68] for men of Asian ancestry, and 4.15 [95% CI 3.33-5.17] for Hispanic men. Men of African ancestry were estimated to have a 2.18-times higher mean GRS [95% CI 2.14-2.22], and men of Asian ancestry 0.73-times lower [95% CI 0.71-0.76], than men of European ancestry. Age significantly modified the GRS association with PCa risk, such that in men of European ancestry, the top decile GRS category was associated with an OR of 6.71 [95 % CI 5.99-7.52] for men ages 55 years or younger and 4.39 [95% CI 4.19-4.60] for men older than 55 years. Similarly, in men of African ancestry, the top GRS decile category was associated with an OR of 4.70 [95 % CI 3.65-6.07] for men ages 55 years or younger and 3.37 [95% CI 2.99-3.80] for men older than 55 years. We found that 51% of aggressive cases of European ancestry and 45% of aggressive cases of African ancestry were within the top 20% of the GRS. The lifetime absolute risk of PCa for men in the top decile of the GRS reached 38% for both African Americans [95% CI 36-41%] and Whites [95% CI 37-39%], 31% [95% CI 27-36%] for Hispanics and 26% [95% CI 22-30%] for Asians. For comparison, we constructed a genome-wide GRS, including the 269 variants and variants associated with PCa with P<1.0 × 10−5. ORs calculated with genome-wide GRS were similar and had nearly identical discriminative ability as the 269 GRS in independent samples of 6,852 cases and 193,117 controls of European ancestry and 1,586 cases and 1,047 controls of African ancestry. This suggests that a genome-wide GRS may not have improved ability to predict PCa risk compared to the 269 GRS. To understand the biological mechanisms impacted by genetic risk of PCa, we conducted integrative analyses of the GRS and serum metabolomics in 611 African ancestry men from the Multiethnic Cohort. We found that a higher GRS was associated with lower sphingolipid levels (beta=-0.08, P=3.1 × 10−4), lower androgenic steroids (beta=-0.06, P=0.003), and higher sarcosine levels (beta=0.11, P=0.002). Further, using a latent clustering approach, we found that these metabolites in conjunction with the GRS contributed to the clustering of men with low-risk, intermediate-risk, and high-risk of PCa. These findings support the role of germline variation contributing to racial and ethnic disparities in PCa risk, with the GRS offering an approach for personalized risk prediction across populations and informing the biological mechanisms underlying PCa risk. Citation Format: Burcu F. Darst, Lilit Moss, Edward J. Saunders, Nicholas Mancuso, Xin Sheng, Alisha Chou, Tokhir Dadaev, Sonja I. Berndt, Stephen K. Van Den Eeden, Stephen J. Chanock, Michael B. Cook, Tracy M. Layne, Demetrius Albanes, Hidewaki Nakagawa, John S. Witte, Practical Consortium, Rosalind A. Eeles, Zsofia Kote-Jarai, David V. Conti, Christopher A. Haiman. Multiethnic prostate cancer GWAS meta-analysis identifies novel variants, improves genetic risk prediction across populations, and informs biological mechanisms of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr NG03.

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 [95% confidence interval (CI) 4.84–5.29] for men of European ancestry to 3.74 [95% CI 3.36–4.17] for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher [95% CI 2.14–2.22], and men of East Asian ancestry 0.73-times lower [95% CI 0.71–0.76], than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

An AR-ERG transcriptional signature defined by long-range chromatin interactomes in prostate cancer cells.

The aberrant activities of transcription factors such as the androgen receptor (AR) underpin prostate cancer development. While the AR cis-regulation has been extensively studied in prostate cancer, information pertaining to the spatial architecture of the AR transcriptional circuitry remains limited. In this paper, we propose a novel framework to profile long-range chromatin interactions associated with AR and its collaborative transcription factor, erythroblast transformation-specific related gene (ERG), using chromatin interaction analysis by paired-end tag (ChIA-PET). We identified ERG-associated long-range chromatin interactions as a cooperative component in the AR-associated chromatin interactome, acting in concert to achieve coordinated regulation of a subset of AR target genes. Through multifaceted functional data analysis, we found that AR-ERG interaction hub regions are characterized by distinct functional signatures, including bidirectional transcription and cotranscription factor binding. In addition, cancer-associated long noncoding RNAs were found to be connected near protein-coding genes through AR-ERG looping. Finally, we found strong enrichment of prostate cancer genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) at AR-ERG co-binding sites participating in chromatin interactions and gene regulation, suggesting GWAS target genes identified from chromatin looping data provide more biologically relevant findings than using the nearest gene approach. Taken together, our results revealed an AR-ERG-centric higher-order chromatin structure that drives coordinated gene expression in prostate cancer progression and the identification of potential target genes for therapeutic intervention.

Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study.

Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer-metabolite associations using two-sample Mendelian randomization (MR). The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

A Review of Prostate Cancer Genome-Wide Association Studies (GWAS).

Prostate cancer is the most common cancer in men in Europe and the United States. The genetic heritability of prostate cancer is contributed to by both rarely occurring genetic variants with higher penetrance and moderate to commonly occurring variants conferring lower risks. The number of identified variants belonging to the latter category has increased dramatically in the last 10 years with the development of the genome-wide association study (GWAS) and the collaboration of international consortia that have led to the sharing of large-scale genotyping data. Over 40 prostate cancer GWAS have been reported, with approximately 170 common variants now identified. Clinical utility of these variants could include strategies for population-based risk stratification to target prostate cancer screening to men with an increased genetic risk of disease development, while for those who develop prostate cancer, identifying genetic variants could allow treatment to be tailored based on a genetic profile in the early disease setting. Functional studies of identified variants are needed to fully understand underlying mechanisms of disease and identify novel targets for treatment. This review will outline the GWAS carried out in prostate cancer and the common variants identified so far, and how these may be utilized clinically in the screening for and management of prostate cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 845-57. ©2018 AACR.

Gene-environment interactions in case-control studies with silent disease.

Genome-wide association studies (GWAS) often measure gene-environment interactions (G × E). We consider the problem of accurately estimating a G × E in a case-control GWAS when a subset of the controls have silent, or undiagnosed, disease and the frequency of the silent disease varies by the environmental variable. We show that using case-control status without accounting for misdiagnosis can lead to biased estimates of the G × E. We further propose a pseudolikelihood approach to remove the bias and accurately estimate how the relationship between the genetic variant and the true disease status varies by the environmental variable. We demonstrate our method in extensive simulations and apply our method to a GWAS of prostate cancer.

Profile of common prostate cancer risk variants in an unscreened Romanian population.

To find sequence variants affecting prostate cancer (PCA) susceptibility in an unscreened Romanian population we use a genome‐wide association study (GWAS). The study population included 990 unrelated pathologically confirmed PCA cases and 1034 male controls. DNA was genotyped using Illumina SNP arrays, and 24.295.558 variants were imputed using the 1000 Genomes data set. An association test was performed between the imputed markers and PCA. A systematic literature review for variants associated with PCA risk identified 115 unique variants that were tested in the Romanian sample set. Thirty of the previously reported SNPs replicated (P‐value < 0.05), with the strongest associations observed at: 8q24.21, 11q13.3, 6q25.3, 5p15.33, 22q13.2, 17q12 and 3q13.2. The replicated variants showing the most significant association in Romania are rs1016343 at 8q24.21 (P = 2.2 × 10−4), rs7929962 at 11q13.3 (P = 2.7 × 10−4) and rs9364554 at 6q25.2 (P = 4.7 × 10−4). None of the variants tested in the Romanian GWAS reached genome‐wide significance (P‐value <5 × 10−8) but 807 markers had P‐values <1 × 10−4. Here, we report the results of the first GWAS of PCA performed in a Romanian population. Our study provides evidence that a substantial fraction of previously validated PCA variants associate with risk in this unscreened Romanian population.

Abstract 1323: Genome-wide association study of energy intake and its relationship to prostate cancer

Abstract Background/Objectives: Previous studies suggested that high energy intake may increase the risk of prostate cancer. However, the mechanisms remain unclear. No genome-wide association studies (GWAS) have been conducted to detect the genetic variations in total energy intake. We aimed to identify genetic variants associated with total energy intake in men and women, and to determine the association between previously identified prostate cancer susceptibility loci and total energy intake. Methods: We conducted a genome-wide study using combined GWAS data from 12,031 European-ancestry women and 6,743 European-ancestry men from the Nurses’ Health Study, Nurses’ Health Study II, Health Professionals Follow-up Study, and Physician’s Health Study. Total energy intake was measured through validated food-frequency questionnaires. Missing genotypes were imputed using MACH with 1000 Genomes Project ALL Phase I Integrated Release Version 3 Haplotypes as the reference panel. We stratified by sex and adjusted for age, weight, height, physical activity and three principal components accounting for subpopulation structure. Meta-analysis based on p-values across sex was implemented. SNP set analyses, including a weighted fixed-effects (Mendelian randomization, MR) and an unweighted random-effects approach (METASOFT), were used to test the association between prostate cancer risk loci and total energy intake using summary statistics of total energy intake from the combined GWAS and the summary statistics for genome-wide significant SNPs (NSNPs = 104) from a GWAS of prostate cancer in 43,303 cases and 43,737 controls [Al Olama (2014) Nat Genet]. Results: Three SNPs near gene ANKRD33 were genome-wide significantly (P &amp;lt; 5.0×10-8) associated with the increase in total energy intake among men (rs10876214 [P = 5.81×10-10], rs9669605 [P = 1.46×10-8], and rs10783478 [P = 2.42×10-8]). No significant genome-wide association was found in women or the meta-analysis combining men and women. SNP set analyses did not find a statistically significant association between established prostate cancer SNPs and energy intake (PMR = 0.11 and PMETASOFT = 0.09, respectively). Conclusions: We identified three significant SNPs near gene ANKRD33 associated with increased total energy intake among men. Genetic alleles related to risk of prostate cancer were not associated with total energy intake. Larger GWAS studies of total energy intake are warranted to explore the genetic basis of energy intake, including possible differences between men and women, and the association between total energy intake, prostate cancer, and other cancers and chronic diseases. Citation Format: Lai Jiang, Peter Kraft, Kathryn M. Wilson. Genome-wide association study of energy intake and its relationship to prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1323. doi:10.1158/1538-7445.AM2017-1323

Abstract 1303: A genome-wide association study of prostate cancer in Latinos

Abstract Recent studies estimate that individuals of African or Latin American ancestry represent less than 4% of samples analyzed to date in genome-wide association studies. The clinical value of genetic information in guiding personalized medicine in populations of non-European ancestry will require additional discovery and risk locus characterization efforts in these populations. Here, we performed a genome-wide association study (GWAS) meta-analysis of prostate cancer in Latino men to search for risk loci that may be important in this population. We combined GWAS data for 1034 cases and 1046 controls genotyped with the Illumina 660 Beadarray with GWAS data for 1235 cases and 1053 controls genotyped with the Illumina Oncoarray as part of the ELLIPSE U19 GAME-ON Consortium. A total of ~11 million genotyped and imputed SNPs of ≥1% frequency were tested for association with prostate cancer risk in logistic regression models controlling for age, study and genetic ancestry. Genome-wide significant associations were observed with 24 variants all located at 8q24 (128.484-128.548), and which capture the first reported prostate cancer susceptibility locus in ‘region 1’ of 8q24. The most significant association genome-wide was with SNP rs7824776 (risk allele frequency, 0.35; OR=1.69, p=3.4x10-11). No novel genome-wide significant associations were noted outside of 8q24. We observed a high degree of generalizability of known prostate cancer risk loci, with 78 (76%) of the 103 known risk variants having effects that were directionally consistent in their association with prostate cancer risk as previously reported, of which 31 (30%) were statistically significant with p &amp;lt; 0.05. In addition to these findings from the largest GWAS of prostate cancer in Latinos conducted to date, we will also present the results investigating effect heterogeneity by local ancestry (i.e. proportion Native American vs. European). In addition, we will present a comparison of polygenic risk models between Latinos, African Africans and men of European ancestry that incorporate the known risk loci to better understand how genetic risk tracks with population differences in prostate cancer incidence. Citation Format: Hannah Hopp, Sue Ingles, Chad Huff, Xin Sheng, Brandi Weaver, Mariana Stern, Sara Strom, Ian Thompson, David Conti, Christopher A. Haiman. A genome-wide association study of prostate cancer in Latinos [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1303. doi:10.1158/1538-7445.AM2017-1303

SeXY: a tool for sex inference from genotype arrays.

Checking concordance between reported sex and genotype-inferred sex is a crucial quality control measure in genome-wide association studies (GWAS). However, limited insights exist regarding the true accuracy of software that infer sex from genotype array data. We present seXY, a logistic regression model trained on both X chromosome heterozygosity and Y chromosome missingness, that consistently demonstrated >99.5% sex inference accuracy in cross-validation for 889 males and 5,361 females enrolled in prostate cancer and ovarian cancer GWAS. Compared to PLINK, one of the most popular tools for sex inference in GWAS that assesses only X chromosome heterozygosity, seXY achieved marginally better male classification and 3% more accurate female classification. https://github.com/Christopher-Amos-Lab/seXY. Christopher.I.Amos@dartmouth.edu. Supplementary data are available at Bioinformatics online.

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

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