Fine-mapping the 2q37 and 17q11.2-q22 loci for novel genes and sequence variants associated with a genetic predisposition to prostate cancer

Fine-mapping the 2q37 and 17q11.2-q22 loci for novel genes and sequence variants associated with a genetic predisposition to prostate cancer Virpi H. Laitinen 1 , Tommi Rantapero 1 , Daniel Fischer 2 , Elisa M. Vuorinen 1 , Teuvo L.J. Tammela 3 , PRACTICAL Consortium, Tiina Wahlfors 1 and Johanna Schleutker 1,4 1 BioMediTech, University of Tampere and Fimlab Laboratories, FI-33520, Tampere, Finland 2 School of Health Sciences, University of Tampere, FI-33014 Tampere, Finland 3 Department of Urology, Tampere University Hospital and Medical School, University of Tampere, FI-33520 Tampere, Finland 4 Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland Key words: prostate cancer risk, genetic predisposition, susceptibility loci, 2q37, 17q11.2-q22 Abbreviations: AR: Androgen Receptor; ChIP-seq: Chromatin Immunoprecipitation Combined with Massively Parallel DNA Sequencing; CI: Confidence Interval; DB: Database; DE: Differentially Expressed (gene); eQTL: Expression Quantitative Trait Locus; GWAS: Genome Wide Association Study; HPC: Hereditary Prostate Cancer; HWE: Hardy-Weinberg Equilibrium; Indel: Insertion/Deletion Polymorphism; LD: Linkage Disequilibrium; LincRNA: Large Intergenic Non-Coding RNA; LNCaP: Androgen-Sensitive Human Prostate Adenocarcinoma Cell Line Derived From Lymph Node Metastasis; MAF: Minor Allele Frequency; OR: Odds Ratio; PrCa: Prostate Cancer; PSA: Prostate Specific Antigen; PWM: Position Weight Matrix; RNA-seq: Massively Parallel RNA Sequencing; SNP: Single-Nucleotide Polymorphism; SNV: Single-Nucleotide Variant; TF: Transcription Factor; TSS: Transcription Start Site; UTR: Untranslated Region; VCP: Variant-Calling Pipeline; QC: Quality Control Additional Supporting Information may be found in the online version of this article. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. The genotyping of variants with Sequenom was performed by the Technology Centre, Institute of Molecular Medicine (FIMM), University of Helsinki, Finland The PRACTICAL Consortium: Rosalind Eeles: The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey SM2 5NG, United Kingdom. Royal Marsden NHS Foundation Trust, Fulham and Sutton, London and Surrey, United Kingdom. Doug Easton: Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, United Kingdom. Kenneth Muir: University of Warwick, Coventry, United Kingdom. Graham Giles: Cancer Epidemiology Centre, The Cancer Council Victoria, 1 Rathdowne street, Carlton Victoria, Australia. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Victoria, Australia. Fredrik Wiklund and Henrik Gr€ onberg: Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden. Christopher Haiman: Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA. Johanna Schleutker: Department of Medical Biochemistry and Genetics, University of Turku, Turku, Finland. BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland. Maren Weischer: Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 75, DK-2730 Herlev, Denmark. Ruth C. Travis: Nuffield Department of Clinical Medicine, Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom. David Neal: Surgical Oncology (Uro-Oncology: S4), University of Cambridge, Box 279, Addenbrooke’s Hospital, Hills Road, Cambridge, United Kingdom and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, United Kingdom. Paul Pharoah: Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, United Kingdom. Kay-Tee Khaw: Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom. Janet L. Stanford: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA. Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA. William J. Blot: International Epidemiology Institute, 1455 Research Blvd., Suite 550, Rockville, MD. Stephen Thibodeau: Mayo Clinic, Rochester, MN. Christiane Maier: Department of Urology, University Hospital Ulm, Germany. Institute of Human Genetics University Hospital Ulm, Germany. Adam S. Kibel: Brigham and Women’s Hospital/Dana-Farber Cancer Institute, 45 Francis Street, ASB II-3, Boston, MA. Washington University, St Louis, MO. Cezary Cybulski: Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland. Lisa Cannon-Albright: Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT. Hermann Brenner: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg Germany. Jong Park: Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, FL. Radka Kaneva: Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University—Sofia, 2 Zdrave St, 1431 Sofia, Bulgaria. Jyotnsa Batra: Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and Schools of Life Science and Public Health, Queensland University of Technology, Brisbane, Australia. Manuel R. Teixeira: Department of Genetics, Portuguese Oncology Institute, Porto, Portugal and Biomedical Sciences Institute (ICBAS), Porto University, Porto, Portugal. Zsofia Kote-Jarai: The Institute of Cancer Research,15 Cotswold Road, Sutton, Surrey SM2 5NG, United Kingdom. Ali Amin Al Olama and Sara Benlloch: University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, United Kingdom. Cancer Genetics Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC International Journal of Cancer IJC The 2q37 and 17q12-q22 loci are linked to an increased prostate cancer (PrCa) risk. No candidate gene has been localized at 2q37 and the HOXB13 variant G84E only partially explains the linkage to 17q21-q22 observed in Finland. We screened these regions by targeted DNA sequencing to search for cancer-associated variants. Altogether, four novel susceptibility alleles were identified. Two ZNF652 (17q21.3) variants, rs116890317 and rs79670217, increased the risk of both sporadic and hereditary PrCa (rs116890317: OR 53.3–7.8, p50.003–3.3 310 25 ; rs79670217: OR 51.6–1.9, p50.002–0.009). The HDAC4 (2q37.2) variant rs73000144 (OR 514.6, p50.018) and the EFCAB13 (17q21.3) variant rs118004742 (OR 51.8, p50.048) were overrepresented in patients with familial PrCa. To map the variants within 2q37 and 17q11.2-q22 that may regulate PrCaassociated genes, we combined DNA sequencing results with transcriptome data obtained by RNA sequencing. This expression quantitative trait locus (eQTL) analysis identified 272 single-nucleotide polymorphisms (SNPs) possibly regulating six genes that were differentially expressed between cases and controls. In a modified approach, prefiltered PrCa-associated SNPs were exploited and interestingly, a novel eQTL targeting ZNF652 was identified. The novel variants identified in this study could be utilized for PrCa risk assessment, and they further validate the suggested role of ZNF652 as a PrCa candidate gene. The regulatory regions discovered by eQTL mapping increase our understanding of the relationship between regulation of gene expression and susceptibility to PrCa and provide a valuable starting point for future functional research. A large proportion of familial prostate cancer (PrCa) cases can be explained by genetic risk factors. 1 Despite extensive research, the identification of these factors has proven challenging. In Finland, mutations in hereditary prostate cancer (HPC) risk genes are relatively rare, with the exception of the HOXB13 G84E mutation, 2 which is present in 8.4% of familial PrCa cases and has been significantly associated with an increased PrCa risk in unselected cases. 3 The involvement of chromosomal regions 2q37 and 17q12-q22 with PrCa has been previously reported in numerous linkage 4–6 and genome-wide association studies (GWASs). 7,8 Cropp et al. 9 performed a genome-wide linkage scan of 69 Finnish high-risk HPC families and in the dominant model, the loci on 2q37.3 and 17q21-q22 exhibited the strongest linkage signals. No known PrCa candidate gene resides on 2q37.3, and as demonstrated in our earlier study, the HOXB13 G84E mutation only partially explains the observed linkage to 17q21-q22. 3 Here, we performed targeted resequencing that covered the linkage peaks on 2q37 and 17q11.2-q22. The sequence data were filtered to identify the variants within genes predicted to be involved in PrCa predisposition. These variants were validated in Finnish HPC families and in unselected PrCa patients by Sequenom genotyping, and several novel variants were discovered that were significantly associated with PrCa. To study the impact of single-nucleotide polymorphisms (SNPs) on the regulation of gene expression within the two linked regions, we performed transcriptome sequencing followed by expression quantitative trait loci (eQTL) mapping. eQTLs are known to modify the penetrance of rare What’s new? Prostate cancer runs in families, but its heritability isn’t completely explained by the genetic variants identified to date. In this paper, the authors delve deeper into two loci that have been linked to prostate cancer. Sequencing data revealed four new alleles within these loci that correlate with increased prostate cancer risk. The authors then used the eQTL mapping technique to identify six genes which may be regulated by variants within these two loci, genes which had not previously been associated with prostate cancer. Grant sponsor: Academy of Finland; Grant number: 251074; Grant sponsor: The Finnish Cancer Organisations, the Sigrid Juselius Foundation and the Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital; Grant number: X51003; Grant sponsor: European Commission’s Seventh Framework Programme (The PRACTICAL consortium); Grant number: HEALTH-F2–2009-223175; Grant sponsor: Cancer Research UK; Grant numbers: C5047/A7357, C1287/A10118, C5047/A3354, C5047/ A10692 and C16913/A6135; Grant sponsor: The National Institutes of Health (Cancer Post-Cancer GWAS initiative); Grant number: 1 U19 CA 148537-01 DOI: 10.1002/ijc.29276 History: Received 17 Apr 2014; Accepted 1 Oct 2014; Online 21 Oct 2014 Correspondence to: Johanna Schleutker, Medical Biochemistry and Genetics, Institute of Biomedicine, Kiinamyllynkatu 10, University of Turku, FI-20014 Turku, Finland. Tel.: 1358-2-3337453, Fax: 1358-2-2301280, E-mail: Johanna.Schleutker@utu.fi Cancer Genetics Laitinen et al.2317 Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC deleterious variants and therefore likely contribute to genetic predisposition to complex diseases. New information was obtained on several genes as well as their regulatory elements that generated fresh insights into PrCa susceptibility, especially in HPC. Material and Methods All of the subjects were of Finnish origin. The samples were collected with written and signed informed consent. The cancer diagnoses were confirmed using medical records and the annual update from the Finnish Cancer Registry. The project was approved by the local research ethics committee at Pirkanmaa Hospital District and by the National Supervisory Authority for Welfare and Health. Targeted resequencing of 2q37 and 17q11.2-q22 Based on the linkage analysis results from Cropp et al., 9 63 PrCa patients and five unaffected individuals belonging to 21 Finnish high-risk HPC families 10 were selected for targeted resequencing of the 2q37 and 17q11.2-q22 regions (Supporting Information Table S1). Each family had at least three firstor second-degree relatives diagnosed with PrCa. Pairedend next generation sequencing was performed at the Technology Centre, Institute for Molecular Medicine Finland (FIMM), University of Helsinki. The sequenced fragments spanned approximately 6.8 Mb for chromosome 2q and 21.6 Mb for 17q. The target regions were captured using SeqCap EZ Choice array probes (Roche NimbleGen, Madison, WI) and were sequenced on a Genome Analyzer IIx (Illumina, San Diego, CA) following the manufacturer’s protocol. The read alignment and variant calling were performed according to FIMM’s Variant-Calling Pipeline (VCP). 11 Bioinformatics workflow for variant characterization A schematic overview of our bioinformatics workflow is shown in Figure 1. Only those variants that were present in all the affected family members were selected for subsequent analysis. The variants were annotated using Ensembl V65 gene set retrieved from the UCSC Genome Browser. 12 The phenotypic effects of the variants were studied with three in silico pathogenicity prediction programs. MutationTaster 13 classifies single-nucleotide variants (SNVs) and small insertion/deletion polymorphisms (indels) as polymorphic or pathogenic. PolyPhen-2 14 and PON-P 15 only predict the effects of nonsynonymous SNVs that result in amino acid replacement. PolyPhen-2 classifies the variants as benign, possibly pathogenic or probably pathogenic, whereas PON-P defines them as neutral, unclassified or pathogenic. Variants categorized as pathogenic by at least one tolerance predictor were defined as pathogenic. In addition, minor allele frequencies (MAFs) were obtained from the dbSNP database and information on known PrCa-associated genes was retrieved from the COSMIC 16 and DDPC 17 databases. Pathway data were gathered from Pathway Commons, 18 KEGG 19 and WikiPathways 20 and Gene Ontology data were retrieved from Ensembl BioMart v.65. 21 Higher priority was assigned to rare variants (MAF <0.05), variants located in genes previously linked to PrCa, and variants located in genes functionally similar to PrCa-associated genes. Validation of predicted PrCa-associated variants with Sequenom After filtering, 58 variants in 35 target genes (listed in Supporting Information Tables S2–S4) were selected for validation which was performed on germline DNA from 2,216 subjects, including 1,293 cases and 923 population controls. The majority of the cases (1,105 individuals) represented Figure 1. A flowchart describing the variant characterization pipeline. The targeted resequencing of 2q37 and 17q11.2-q22 from 68 Finnish HPC family members produced a total of 107,479 unique sequence variants. Family-based filtering excluded 66,867 variants that did not cosegregate with affection status. Annotation enabled the selection of 24,813 variants that were located within proteincoding genes. Pathogenicity predictions were performed in silico using MutationTaster, PolyPhen-2 and PON-P. As a result, the number of candidate variants was reduced to 152. The final filtering step exploited diverse information on genes and variants as well as gene ontology and pathway data stored in several public databases. In addition, select HDAC4, ZNF652 and HOXB13 variants, which were predicted to be nonpathogenic, were included in the validation because these genes have been associated with PrCa in previous studies. Cancer Genetics 2318 Fine-mapping the 2q and 17q prostate cancer loci Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC unselected PrCa patients from the Pirkanmaa Hospital District, Tampere, Finland. In addition, 188 index cases from Finnish HPC families 10 were included in the study. The control DNA samples from anonymous male blood donors were provided by the Finnish Red Cross Blood Transfusion Service. Genotyping was performed at the Technology Centre, FIMM using the Sequenom MassARRAY system and iPLEX Gold assays (Sequenom, San Diego, CA). Genotyping reactions were performed with 20 ng of dried genomic DNA according to manufacturer’s recommendations and with their reagents. The genotypes were called using TyperAnalyzer software (Sequenom). For quality control (QC) reasons, the genotype calls were also checked manually. Genotyping quality was examined using a detailed QC procedure that included success rate checks, duplicated samples and water controls. Statistical and bioinformatic analyses of the validated variants Association and Hardy-Weinberg equilibrium (HWE) tests were performed using PLINK. 22 The pvalue threshold for the HWE test was set to 0.05. Samples with low genotyping frequencies (<0.80) were excluded from the association analysis. The statistical significance of the association was evaluated using a two-sided Fisher’s exact test. Odds ratios (OR) were calculated using PLINK with option — fisher. No further model adjustments for confounding factors were made. ENCODE information 23 for noncoding variants was retrieved from the Regulome database (RegulomeDB). 24 The linkage disequilibrium (LD) analysis of the statistically significant variants is described in Supplementary Methods. Genotyping of the top four candidate variants in Finnish HPC families Four variants were chosen for segregation analysis in Finnish HPC families based on a strong association with PrCa, a high OR value and/or predicted pathogenicity. The cosegregation of rs116890317 and rs79670217 in ZNF652 (RefSeq NM_001145365), rs73000144 in HDAC4 (RefSeq NM_006037) and rs118004742 in EFCAB13 (RefSeq NM_152347) with affection status was determined in 41 families whose index cases were mutation-positive in the Sequenom validation. For these families, DNA samples were available from 243 PrCa cases and 204 healthy family members. The variants were genotyped in two to 17 (median: seven) individuals per family by Sanger sequencing. RNA extraction and sequencing Peripheral blood samples collected in PAXgene V R Blood RNA Tubes (PreAnalytiX GmbH, Switzerland) were available from 84 PrCa patients and 15 healthy male relatives belonging to 31 Finnish HPC families. These included 11 families from the targeted resequencing step (Supporting Information Table S1) and additional 20 high-risk families. 10 Total RNA was purified with MagMAX TM for Stabilized Blood Tubes RNA Isolation Kit (Ambion V R /Life Technologies, Carlsbad, CA) and with a PAXgene Blood miRNA Kit (PreAnalytiX GmbH). RNA integrity and quality were analyzed using the Agilent 2100 Bioanalyzer and the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA). The massively parallel paired-end RNA sequencing was performed at Beijing Genomics Institute (BGI Hong Kong Co., Tai Po, Hong Kong) using an Illumina HiSeq2000 sequencing platform (Illumina). RNA sequencing data analysis On average, RNA sequencing produced 45 million reads per sample. The QC check was performed using fastQC (http:// www.bioinformatics.bbsrc.ac.uk/projects/fastqc). The reads were aligned with Tophat2 25 using GRCh37/hg19 as the reference genome. The read counts for the genes were determined using HTSeq (http://www-huber.embl.de/users/anders/ HTSeq/). The raw read counts were transformed into comparable expression values via normalization using the DESeq package for R 26 and the genes with very low or no expression (normalized read counts of <20) were removed. A differential gene expression analysis was then performed using a two-sided Mann–Whitney test with a pvalue cutoff of 0.05. eQTL mapping and data analysis The eQTL analysis was based on the RNA-seq data and on the SNP genotypes obtained from targeted DNA sequencing. This data existed for 19 samples at 2q37 and for 17 samples at 17q11.2-q22. In total, 54,919 SNPs (average 6,865 per gene, see Supporting Information Table S5 for details) were tested for association with their candidate target genes. Only genes with differential expression (DE) patterns between health status groups were included in the eQTL analysis, to increase the probability that found SNP-gene associations also link PrCa with a certain SNP genotype. The eQTL mapping was applied on 2q37 and 17q11.2-q22 to identify cisregulated genes. SNPs associated in cis were defined as variants located within 1 Mb upor downstream of the gene under study. The significance level for SNP-gene associations was set to p0.005. A multiple testing adjustment was omitted because of the large number of tested SNPs and the nature of the permutation type tests, acknowledging that this resulted in compromised resolution. A modified cis-eQTL approach was also utilized, wherein a large genotype dataset from the iCOGS study 27 was used to preidentify possible PrCa-associated SNPs for 2,824 unselected Finnish PrCa patients and 2,440 controls. Here, Fisher’s exact test with a modest significance level of 0.005 was used to study the association. Significant iCOGS variants that were also observed in the targeted DNA sequencing data were then selected for eQTL analysis, which was restricted to the fine-mapped regions. Additional details for the eQTL analysis are presented in Supplementary Methods. RegulomeDB was used to annotate and assess the regulatory potential of the detected eQTLs. 24 The ENCODE Cancer Genetics Laitinen et al.2319 Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC datasets 23 were retrieved from the UCSC Genome Browser website for visualization purposes using the Table Browser tool. 12 As a general indicator of regulatory potential, we used the dataset that contained enriched DNase hypersensitive sites in 125 cell types. To highlight the regulatory potential of eQTLs in PrCa tissue, we used the LNCaP DNase (wgEncodeAwgDnaseUwDukeLncapUniPk) and LNCaP (Andr) DNase (wgEncodeAwgDnaseUwDukeLncapandrogenUniPk) datasets containing DNase hypersensitive sites in LNCaP cells under normal and androgen-induced conditions, respectively. Transcription factor (TF) binding site data were gathered from the Txn Fac ChIP V3 dataset, which contains ChIP-seq experimental data on 91 cell types and 189 TFs. Results Targeted DNA sequencing data analysis The percentage of mapped reads was 95.0 and 95.7% for the samples sequenced for 2q37 and 17q11.2-q22, respectively. The target coverage was 99.8% for 2q37 and 99.5% for 17q11.2-q22. Correspondingly, the percentage of bases having coverage of 203or more was 79.9 and 63.4%. The total number of unique variants across all samples discovered by the utilized VCP was 107,479 (Fig. 1). Among the 41 predicted pathogenic variants in 2q37, there were 20 missense SNVs, 16 noncoding SNVs and five indels. Of all 111 predicted pathogenic variants in 17q11.2-q22, two variants were nonsense SNVs, 49 were missense SNVs, 36 were noncoding SNVs and 24 were indels. PrCa-associated variants identified by Sequenom validation Following prioritization, a total of 58 variants were selected for validation in a larger sample set (Supporting Information Table S2). In the QC analysis, four variants failed the HWE test (p<0.05), and 20 samples were omitted due to low genotyping frequencies (<0.80). In the case-control association analysis, a total of 13 variants in seven different genes were statistically significantly associated with PrCa (p<0.05; Tables 1 and 2 and Supporting Information Tables S3 and S4). Three variants were located in the ZNF652 gene at 17q21.3, and the HDAC4 (2q37.2), HOXB3 (17q21.3), ACACA (17q21) and MYEOV2 (2q37.3) genes harbored two variants each. A single variant was identified in the HOXB13 and EFCAB13 genes at 17q21.3. Only three of these 13 PrCaassociated variants were located within exons, whereas the majority, 10 variants, resided in noncoding regions. Four of the variants with a statistically significant association with PrCa were present in both the familial and the unselected sample sets. These were rs116890317 and rs79670217 in ZNF652, rs10554930 in HOXB3 and rs13411615 in MYEOV2. The two ZNF652 variants had the strongest association with an increased PrCa risk. rs116890317 had the most significant association with the familial cases (OR 57.8, 95% CI 3.0–20.3, p53.3 310 25 ) and the same variant conferred the highest risk of 3.3 (95% CI 1.4–7.5, p50.003) among the unselected cases. rs79670217 had the most significant association with PrCa in the unselected sample set (p50.002) and was the second most significant variant in the familial PrCa patients (OR 51.9, 95% CI 1.2–3.1, p50.009; Tables 1 and 2). The highest OR of 14.6 (95% CI 1.5–140.2, p50.018) was observed for the HDAC4 variant rs73000144 (c.958C>T, p.Val320Ile) among the familial samples (Table 1). Only three familial PrCa patients (1.6%), seven unselected patients (0.6%) and one control individual (0.1%) carried the minor allele in a heterozygous state, and none of the genotyped individuals were homozygous. rs73000144 was predicted to Table 1. Variants significantly associated with prostate cancer based on a comparison of familial cases (n5186) and controls (n5914) SNP Id Function Gene Chr Min/Maj F_A/F_U (%) pvalue OR (95% CI) Pathogenicity prediction rs116890317 Intronic ZNF652 17 A/T 2.96/0.39 3.3 310 25 7.8 (3.0–20.3) Polymorphism/–/– rs79670217 Intronic ZNF652 17 G/T 6.65/3.56 0.009 1.9 (1.2–3.1) Polymorphism/–/– rs10554930 Intronic HOXB3 17 2ACA/ACA 27.5/21.3 0.010 1.4 (1.1–1.8) Pathogenic/–/– rs35384813 50-UTR HOXB3 17 1T/– 26.7/20.8 0.013 1.4 (1.1–1.8) Pathogenic/–/– rs73000144 Missense HDAC4 2 T/C 0.80/0.06 0.018 14.6 (1.5–140.2) Polymorphism/benign/neutral rs13411615 1 Near gene 50MYEOV2 2 C/A 52.1/45.6 0.023 1.3 (1.0–1.6) Polymorphism/–/– rs9899142 Intronic HOXB13 17 T/C 11.2/15.6 0.031 0.7 (0.5–1.0) Polymorphism/–/– rs118004742 Nonsense EFCAB13 17 G/T 4.79/2.73 0.048 1.8 (1.0–3.1) Pathogenic/–/– rs142044482 30-UTR ZNF652 17 1A/– 2.94/1.59 0.087 1.9 (0.9–3.8) Polymorphism/–/– rs140611363 1 Near gene 50ACACA 17 2A/A 28.8/31.1 0.421 0.9 (0.7–1.1) Pathogenic/–/– rs72828246 1 Near gene 50ACACA 17 G/A 28.8/30.9 0.459 0.9 (0.7–1.2) Pathogenic/benign/neutral rs13406410 1 Near gene 50MYEOV2 2 C/T 47.6/46.8 0.817 1.0 (0.8–1.3) Pathogenic/–/– rs61752234 Synonymous HDAC4 2 C/T 7.22/6.83 0.823 1.1 (0.7–1.6) Polymorphism/–/– Bold signifies p<0.05. 1 Variants are in linkage disequilibrium. Abbreviations: Chr: chromosome; Min: minor allele; Maj: major allele; F_A: frequency of the minor allele in cases; F_U: frequency of the minor allele in controls; OR: odds ratio; CI: confidence interval; pathogenicity prediction results from: MutationTaster/PolyPhen-2/Pon-P. Cancer Genetics 2320 Fine-mapping the 2q and 17q prostate cancer loci Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC be benign or neutral by all three in silico pathogenicity prediction algorithms (Supporting Information Table S2). The rs118004742 nonsense mutation (c.1638T>G, p.Tyr546Ter) in the EFCAB13 gene was predicted to be pathogenic by MutationTaster (Supporting Information Table S2). Three familial cases (1.6%) were homozygous for the minor allele. There were 12 heterozygotes among the familial index cases (6.5%) and 66 among the unselected cases (6.0%). A statistically significant association between rs118004742 and PrCa was only observed for the familial patients (Table 1). The OR of 1.8 (95% CI 1.0–3.1) suggested an increased risk of HPC. rs118004742 carriers in the unselected sample set did not have an increased cancer risk (OR 51.1, 95% CI 0.8– 1.6, p50.637; Supporting Information Table S4). Two common noncoding variants in the HOXB3 gene, rs10554930 and rs35384813, had a moderate effect on PrCa risk, with ORs ranging from 1.2 to 1.4 (Tables 1 and 2). MutationTaster predicted both of these variants to be pathogenic (Supporting Information Table S2). For five variants, the ORs were <1.0, indicating a modulatory role in PrCa predisposition. These variants were located near or within the ZNF652, HDAC4, HOXB13 and ACACA genes (Tables 1 and 2). According to the RegulomeDB, three of the 13 statistically significant variants were likely to affect protein binding: rs9899142 in HOXB13 (Regulome score of 1f), rs13406410 in MYEOV2 and rs72828246 in ACACA (both having Regulome score of 2b). In case-case comparisons, none of the identified variants were significantly associated with Gleason score, average age or the serum prostate specific antigen (PSA) level at diagnosis (data not shown). The LD analysis (Supporting Information Fig. S1) revealed that none of our 13 statistically significant variants (Tables 1 and 2) were in linkage disequilibrium with previously reported PrCa-associated variants 27 (see Supplementary Results for details). Segregation analysis of the top four candidate variants Altogether, 41 familial index cases out of 188 genotyped by Sequenom carried at least one of the top four candidate variants. Segregation analysis was performed for these 41 HPC families. rs116890317, rs79670217 and rs118004742 were more common among PrCa patients than healthy family members and provided evidence for cosegregation with affection status in 20 families (Supporting Information Tables S6– S8). However, in 15 of these families, unaffected male mutation carriers were also observed. In seven families, all of the unaffected male carriers were young enough (<55 years) to develop PrCa later in life. rs116890317 segregated completely with affection status in one family (Supporting Information Fig. S2a), as did rs79670217 (Supporting Information Fig. S2b). Complete segregation of rs118004742 was observed in three families (Supporting Information Table S8). The HDAC4 variant rs73000144 was detected in three families, and approximately one-third of the family members were identified as carriers, irrespective of their health status (Supporting Information Table S9). Multiple variants were observed in 16 individuals from 14 families. Two families harbored rs116890317, rs79670217 and rs118004742, whereas one family was positive for rs79670217, rs73000144 and rs118004742. In the remaining families, the most common combination detected was rs79670217 together with rs118004742 (six families). Evidence for segregation with affection status was obtained for a maximum of one variant per family. Table 2. Variants significantly associated with prostate cancer based on a comparison of unselected cases (n51096) and controls (n5914) SNP Id Function Gene Chr Min/Maj F_A/F_U (%) pvalue OR (95% CI) Pathogenicity prediction rs79670217 Intronic ZNF652 17 G/T 5.66/3.56 0.002 1.6 (1.2–2.2) Polymorphism/–/– rs116890317 Intronic ZNF652 17 A/T 1.27/0.39 0.003 3.3 (1.4–7.5) Polymorphism/–/– rs13406410 1 Near gene 50MYEOV2 2 C/T 51.5/46.8 0.006 1.2 (1.1–1.4) Pathogenic/–/– rs61752234 Synonymous HDAC4 2 C/T 4.85/6.83 0.008 0.7 (0.5–0.9) Polymorphism/–/– rs142044482 30-UTR ZNF652 17 1A/- 0.68/1.59 0.009 0.4 (0.2–0.8) Polymorphism/–/– rs140611363 1 Near gene 50ACACA 17 2A/A 27.9/31.1 0.032 0.9 (0.7–1.0) Pathogenic/–/– rs10554930 Intronic HOXB3 17 2ACA/ACA 24.1/21.3 0.034 1.2 (1.0–1.4) Pathogenic/–/– rs13411615 1 Near gene 50MYEOV2 2 C/A 49.0/45.6 0.037 1.1 (1.0–1.3) Polymorphism/–/– rs72828246 1 Near gene 50ACACA 17 G/A 28.0/30.9 0.044 0.9 (0.8–1.0) Pathogenic/benign/neutral rs35384813 50-UTR HOXB3 17 1T/– 23.2/20.8 0.073 1.1 (1.0–1.3) Pathogenic/–/– rs73000144 Missense HDAC4 2 T/C 0.33/0.06 0.078 5.9 (0.7–47.9) Polymorphism/benign/neutral rs118004742 Nonsense EFCAB13 17 G/T 3.0/2.7 0.637 1.1 (0.8–1.6) Pathogenic/–/– rs9899142 Intronic HOXB13 17 T/C 16.1/15.6 0.665 1.0 (0.9–1.2) Polymorphism/–/– Bold signifies p<0.05. 1 Variants are in linkage disequilibrium. Abbreviations: Chr: chromosome; Min: minor allele; Maj: major allele; F_A: frequency of the minor allele in cases; F_U: frequency of the minor allele in controls; OR: odds ratio; CI: confidence interval; pathogenicity prediction results from: MutationTaster/PolyPhen-2/Pon-P. Cancer Genetics Laitinen et al.2321 Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC eQTL mapping results Differential gene expression analysis revealed three genes (of 173 tested) located at 2q37 and five genes (of 761 tested) at 17q11.2-q22 whose expression levels differed significantly between cases and controls (p<0.05). In the targeted ciseQTL analysis, SNPs within 2 Mb windows were tested for association with each of these eight DE genes (Supporting Information Table S5). Altogether, 272 candidate regulatory SNPs were identified for six DE genes only (Supporting Information Table S10). A vast majority, 237 candidate SNPs potentially regulate the expression of AGAP1, SCLY and NDUFA10 at 2q37 (Fig. 2). The remaining 35 candidate SNPs possibly regulate TBKBP1, PNPO and NAGS at 17q11.2-q22 (Fig. 3). Based on the ENCODE data, the strongest evidence for regulatory potential was found for rs11650354 on chromosome 17, which targets the TBKBP1 Figure 2. Cis-eQTLs targeting differentially expressed genes on chromosome 2. All statistically significant eQTLs are indicated with a track of black bars. Selected eQTLs, rs12620966 and rs983221 (targeting AGAP1) and rs1996513 and rs12712297 (targeting NDUFA10) are illustrated in more detail. DNaseI hypersensitive sites from the DNase cluster and LNCaP datasets are indicated with green and red rectangles, respectively. Blue rectangles denote TF binding sites. Cancer Genetics 2322 Fine-mapping the 2q and 17q prostate cancer loci Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC gene. This known eQTL overlaps with an open chromatin region (Mcf7 and Gm12892 cell lines) and its role in the regulation of TBKBP1 expression has been confirmed in a previous study. 28 rs12620966 targeting AGAP1 on chromosome 2 overlaps with several TF binding sites discovered by ChIPseq (HepG2 cell line), position weight matrix (PWM) matching and digital DNaseI footprinting studies (Supporting Information Table S10). None of the coding variants that were identified by targeted DNA sequencing and validated by Sequenom were statistically significant eQTLs (data not shown). The modified cis-eQTL analysis was based on 12 SNPs at 2q37 and 22 SNPs at 17q11.2-q22 that were shared between the iCOGS dataset and our set of variants obtained by targeted resequencing. The regulatory potential of these 34 SNPs was evaluated for 144 genes at 2q37 and for 160 genes Figure 3. Cis-eQTLs targeting differentially expressed genes on chromosome 17. All statistically significant eQTLs are indicated with a track of black bars. Selected eQTLs, rs11650354 (targeting TBKBP1) and rs12951323 (targeting PNPO) are illustrated in more detail. DNaseI hypersensitive sites from the DNase cluster and LNCaP datasets are indicated with green and red rectangles, respectively. Blue rectangles denote TF binding sites. Cancer Genetics Laitinen et al.2323 Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC at 17q11.2-q22. The modified eQTL approach identified only one PrCa-associated candidate eQTL on chromosome 2 and 36 candidate eQTLs on chromosome 17. Selected examples of these eQTLs and their target genes are shown in Supporting Information Table S11. The ENCODE data from RegulomeDB indicated the strongest evidence of regulatory potential for two variants on chromosome 17, rs4796751 and rs4796616, which target the DHX58, MLX and JUP genes. Both variants have previously been reported as eQTLs targeting MGC20781 and NT5C3L 29 and they overlap with open chromatin regions (in 16 and 17 cell lines, respectively). rs4796616 is also located within a TF binding site (U2OS cell line). Two additional chromosome 17 variants, rs4793943 and rs16941107 were defined as likely to affect gene expression. These variants target the ZNF652 and ARL17B genes, respectively, and overlap with open chromatin regions (in 6 and 42 cell lines, respectively) as well as several TF binding sites (Supporting Information Table S11). Of particular interest was the chromosome 17 variant rs4793976 targeting the SPOP gene. Although no data for this eQTL was available in the RegulomeDB, the importance of SPOP in PrCa predisposition has been recognized. 30 Discussion Prior studies have identified a strong relationship between PrCa and linkage to chromosomal regions 2q37 and 17q11.2q22. Inspired by the lack of candidate genes and mutations, we resequenced the linkage peaks and confirmed the sequencing results by validating select variants. As the number of variants provided by the VCP was high, their prioritization for validation was critical. The variants that were statistically significantly associated with PrCa were clustered in two genes on chromosome 2q37, HDAC4 and MYEOV2, and in five genes on chromosome 17q11.2-q22, ZNF652, HOXB3, HOXB13, EFCAB13 and ACACA (Tables 1 and 2). Interestingly, four of these genes, HDAC4, ZNF652, HOXB3 and HOXB13 encode TFs. Transcriptional regulation plays an essential role in maintaining normal gene control, and mutations in genes coding for TFs have been identified in PrCa. Examples of commonly occurring alterations include the fusion of TMPRSS2 with ERG, and mutations in genes coding for the forkhead-box family of TFs. 31 The ZNF652 gene at 17q21.3 codes for a DNA-binding transcriptional repressor protein with seven zinc finger motifs. 32 Highest expression levels have been detected in normal breast, prostate and pancreas, whereas in primary tumors and cancer cell lines, ZNF652 expression is generally lower. 32 However, in PrCa, the coexpression of high levels of ZNF652 and the androgen receptor (AR) has been shown to increase the risk of PSA relapse. 33 In addition, the recently characterized ZNF652 DNA binding site was found in the promoters of several genes that are involved in PrCa development and progression. 34 ZNF652 also interacts with CBFA2T3, a putative breast cancer tumor suppressor, which has been shown to enhance the repressor activity of ZNF652. 32 To date, only a single PrCa-associated risk variant has been identified in the ZNF652 gene. rs7210100 has been reported to predispose men of African descent to PrCa. The risk allele is present at a low frequency (<1%) in nonAfrican populations. 35 A possible European-specific risk variant, rs11650494, is located in a lincRNA just downstream of the ZNF652 gene and was recently described by the PRACTICAL Consortium. 27 The present study identified two novel ZNF652 gene variants, rs116890317 and rs79670217, which were significantly associated with PrCa in both familial and unselected cases. The risk association was particularly apparent in patients with a positive family history of the disease. Correspondingly, both variants showed evidence for at least partial cosegregation with affection status in a substantial portion of Finnish HPC families. Like rs7210100, these two novel variants are located in the first intron of the gene, suggesting that they may play a role in regulating ZNF652 by affecting splicing events and/or tissue-specific expression. The HDAC4 gene at 2q37.2 encodes a well-characterized transcriptional repressor. HDAC4 has been reported to accumulate in the nucleus in hormone-refractory PrCa 36 and to bind to and inhibit the activity of AR by SUMOylation. 37 Here, we determined that the exonic HDAC4 variant rs73000144 (c.958C>T) was significantly associated with familial PrCa (OR 514.6, 95% CI 1.5–140.2, p50.018). The variant also had a high OR (55.8, 95% CI 0.7–47.9) among the unselected cases (Supporting Information Table S4), suggesting an increased cancer risk, but this result was not statistically significant (p50.078). The pathogenicity of rs73000144 is uncertain. The resulting amino acid change, a substitution of isoleucine for valine (p.Val320Ile) is conservative and was not considered pathogenic by any of the in silico predictors used (Supporting Information Table S2). The strikingly high OR for the familial sample set, together with the observation that this variant was detected in only three out of 186 index cases from the Finnish HPC families, suggested that rs73000144 may be a private mutation. The importance of private mutations has been emphasized in many diseases, some of which are associated with specific ethnic groups. The protein encoded by the EFCAB13 (EF-hand calcium binding domain 13) gene at 17q21.3 contains a particular helix-loop-helix domain, the EF-hand, which is required for calcium ion binding. EF-hands are often found in calcium sensor and calcium signal modulator proteins. Ca 21 binding triggers a conformational change in the EF-hand motif, which leads to the activation or inactivation of target proteins. Currently, there is no evidence linking EFCAB13 with PrCa. The nonsense mutation rs118004742 in the EFCAB13 gene introduces a premature stop codon, leading to a significant truncation of the nascent protein. Truncating mutations are generally considered deleterious and, as expected, rs118004742 was predicted pathogenic by MutationTaster (Supporting Information Table S2). The variant segregated Cancer Genetics 2324 Fine-mapping the 2q and 17q prostate cancer loci Int. J. Cancer: 136, 2316–2327 (2015) V C2014 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC