Comprehensive Functional Annotation of 77 Prostate Cancer Risk Loci

Hazelett, Dennis J.; Rhie, Suhn Kyong; Gaddis, Malaina; Yan, Chunli; Lakeland, Daniel L.; Coetzee, Simon; Henderson, Brian E.; Noushmehr, Houtan; Cozen, Wendy; Kóte-Jarai, Zsofia; Eeles, Rosalind; Easton, Douglas F.; Haiman, Christopher A.; Lu, Wange; Farnham, Peggy J.; Coetzee, Gerhard A.

doi:10.1371/journal.pgen.1004102

Cited by 172 publications

(166 citation statements)

References 86 publications

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“…Furthermore, this multi-cancer risk locus has also been identified in a smaller scale study of oesophageal squamous cell carcinoma in Chinese populations (Table 1; Zhou et al 2013). Results of an in silico annotation of multiple prostate cancer risk loci indicated MDM4 rs4245739 to be the causal (functional) SNP at this GWAS identified region, though no experimental evidence has yet been provided to demonstrate the prostatic function of this polymorphism (Hazelett et al 2013). In ovarian cancer, Wynendaele et al (2010) previously demonstrated a mechanism of microRNA (miRNA) regulation by which miR-191-5p showed a greater affinity for the MDM4 rs4245739 SNP C-allele, resulting in higher levels of MDM4 expression from the risk-associated A-allele.…”

Section: Introductionmentioning

confidence: 91%

A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer

Stegeman¹,

Moya²,

Selth³

et al. 2015

Endocrine-Related Cancer

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The oncogene MDM4, also known as MDMX or HDMX, contributes to cancer susceptibility and progression through its capacity to negatively regulate a range of genes with tumoursuppressive functions. As part of a recent genome-wide association study it was determined that the A-allele of the rs4245739 SNP (AOC), located in the 3 0 -UTR of MDM4, is associated with an increased risk of prostate cancer. Computational predictions revealed that the rs4245739 SNP is located within a predicted binding site for three microRNAs (miRNAs): miR-191-5p, miR-887 and miR-3669. Herein, we show using reporter gene assays and endogenous MDM4 expression analyses that miR-191-5p and miR-887 have a specific affinity for the rs4245739 SNP C-allele in prostate cancer. These miRNAs do not affect MDM4 mRNA levels, rather they inhibit its translation in C-allele-containing PC3 cells but not in LNCaP cells homozygous for the A-allele. By analysing gene expression datasets from patient cohorts, we found that MDM4 is associated with metastasis and prostate cancer progression and that targeting this gene with miR-191-5p or miR-887 decreases in PC3 cell viability. This study is the first, to our knowledge, to demonstrate regulation of the MDM4 rs4245739 SNP C-allele by two miRNAs in prostate cancer, and thereby to identify a mechanism by which the MDM4 rs4245739 SNP A-allele may be associated with an increased risk for prostate cancer.Key Words " MDM4" microRNA " single nucleotide polymorphism " prostate cancer

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Section: Introductionmentioning

confidence: 91%

A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer

Stegeman¹,

Moya²,

Selth³

et al. 2015

Endocrine-Related Cancer

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“…The numbers shown in the parentheses correspond to the average number of SNPs required to identify 50 and 80% of the causal SNPs, respectively. histone methylation (He et al 2010;Wang et al 2011;Bert et al 2013;Hazelett et al 2014), and transcription factor bindings (Tan et al 2012; The ENCODE Project Consortium 2012; Sharma et al 2013;Hazelett et al 2014;Takayama et al 2014). All annotations were aligned to human genome version 19 coordinates.…”

Section: Annotations Used For Eqtl Datamentioning

confidence: 99%

Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics

et al. 2016

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Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genomewide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.KEYWORDS Bayesian fine mapping; annotations; summary statistics; causal variants A large amount of annotation information of genomic elements has been generated, such as the Encyclopedia of DNA Elements (The ENCODE Project Consortium 2012). Regulatory elements, such as those marked by DNase I hypersensitive sites (DHSs), have been shown to be enriched with associations and explain a large proportion of heritability (Maurano et al. 2012;Gusev et al. 2014). Incorporation of these annotations into statistical association analyses can improve both the power in genome-wide association study (GWAS) discovery (Pickrell 2014) and the accuracy in fine mapping underlying causal variants (Quintana and Conti 2013;Kichaev et al. 2014;Wen et al. 2015). However, the number of annotations is often very large and many of them may not be informative for the underlying causal genetic variants. Currently, there is no systematic and effective way to incorporate a large number of annotations in association analyses, which is crucial to the full use of the available information. In practice, usually only a small number of annotations are considered (Quintana and Conti 2013;Wen et al. 2015). A recent proposed algorithm, PAINTOR, suggests a one-by-one test of each annotation selecting the top 4-5 for inclusion. This has potential to waste the information from the remaining annotations, which may together provide significant predictive power of the causal status. Another approach...

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“…The prevalence of PCa differs significantly among populations, indicating that the host's genetic background may play an important role in susceptibility to PCa (2). Single-nucleotide polymorphisms (SNPs) are the most common type of genetic variations in human genome, and they have been found to be associated with the risk of PCa (3)(4)(5)(6). Genome tiling arrays have indicated that 1% of the human genome is composed of protein-coding sequences and ~4-9% of the sequences of the human genome are transcribed to non-coding RNAs (ncRNAs) (7).…”

Section: Introductionmentioning

confidence: 99%

Association between genetic polymorphisms of long non‑coding RNA PRNCR1 and prostate cancer risk in a sample of the Iranian population

et al. 2017

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Abstract. The aim of the present study was to determine whether there is an association between the long non-coding RNA (lncRNA) prostate cancer-associated non-coding RNA 1 (PRNCR1) polymorphisms and prostate cancer (PCa) risk in a sample of the Iranian population. This case-control study was performed on 178 patients with PCa and 180 subjects with benign prostatic hyperplasia (BPH). Genotyping assay was performed by polymerase chain reaction-restriction fragment length polymorphism. The findings indicated that the GG genotype of the rs13252298 A>G variant significantly increased the risk of PCa (odds ratio=3.49, 95% confidence interval: 1.79-6.81, P=0.0001) compared with AA+AG. As regards the rs1456315 G>A polymorphism, the AG genotype and G allele significantly increased the risk of PCa. As regards the rs7841060 T>G variant, the findings demonstrated that this TG genotype and the G allele significantly increased the risk of PCa. The rs7007694 T>C variant was not found to be associated with the risk of PCa. Haplotype analysis indicated that GTGA and GTGG significantly increased the risk of PCa compared with rs1456315A/rs7007694T/rs7841060T/rs13252298G (ATTG). The PRNCR1 variants were not found to be significantly associated with the clinicopathological characteristics of PCa patients. In conclusion, our findings support an association between PRNCR1 variants and the risk of PCa in a sample of the Iranian population.

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Comprehensive Functional Annotation of 77 Prostate Cancer Risk Loci

Cited by 172 publications

References 86 publications

A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer

A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer

Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics

Association between genetic polymorphisms of long non‑coding RNA PRNCR1 and prostate cancer risk in a sample of the Iranian population

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