Publications by authors named "Shannon K McDonnell"

102 Publications

Rare Germline Variants in ATM Predispose to Prostate Cancer: A PRACTICAL Consortium Study.

Eur Urol Oncol 2021 Jan 9. Epub 2021 Jan 9.

Institute of Biomedicine, University of Turku, Turku, Finland.

Background: Germline ATM mutations are suggested to contribute to predisposition to prostate cancer (PrCa). Previous studies have had inadequate power to estimate variant effect sizes.

Objective: To precisely estimate the contribution of germline ATM mutations to PrCa risk.

Design, Setting, And Participants: We analysed next-generation sequencing data from 13 PRACTICAL study groups comprising 5560 cases and 3353 controls of European ancestry.

Outcome Measurements And Statistical Analysis: Variant Call Format files were harmonised, annotated for rare ATM variants, and classified as tier 1 (likely pathogenic) or tier 2 (potentially deleterious). Associations with overall PrCa risk and clinical subtypes were estimated.

Results And Limitations: PrCa risk was higher in carriers of a tier 1 germline ATM variant, with an overall odds ratio (OR) of 4.4 (95% confidence interval [CI]: 2.0-9.5). There was also evidence that PrCa cases with younger age at diagnosis (<65 yr) had elevated tier 1 variant frequencies (p = 0.04). Tier 2 variants were also associated with PrCa risk, with an OR of 1.4 (95% CI: 1.1-1.7).

Conclusions: Carriers of pathogenic ATM variants have an elevated risk of developing PrCa and are at an increased risk for earlier-onset disease presentation. These results provide information for counselling of men and their families.

Patient Summary: In this study, we estimated that men who inherit a likely pathogenic mutation in the ATM gene had an approximately a fourfold risk of developing prostate cancer. In addition, they are likely to develop the disease earlier.
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http://dx.doi.org/10.1016/j.euo.2020.12.001DOI Listing
January 2021

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

Nat Genet 2021 01 4;53(1):65-75. Epub 2021 Jan 4.

Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

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.
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http://dx.doi.org/10.1038/s41588-020-00748-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8148035PMC
January 2021

Two-stage Study of Familial Prostate Cancer by Whole-exome Sequencing and Custom Capture Identifies 10 Novel Genes Associated with the Risk of Prostate Cancer.

Eur Urol 2021 Mar 14;79(3):353-361. Epub 2020 Aug 14.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Background: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease.

Objective: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa.

Design, Setting, And Participants: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls.

Outcome Measurements And Statistical Analysis: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls.

Results And Limitations: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa.

Conclusions: Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease.

Patient Summary: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.
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http://dx.doi.org/10.1016/j.eururo.2020.07.038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881048PMC
March 2021

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Cancer Epidemiol Biomarkers Prev 2020 09 24;29(9):1731-1738. Epub 2020 Jun 24.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-1527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483627PMC
September 2020

The effect of sample size on polygenic hazard models for prostate cancer.

Eur J Hum Genet 2020 10 8;28(10):1467-1475. Epub 2020 Jun 8.

Humangenetik Tuebingen, Paul-Ehrlich-Str 23, D-72076, Tuebingen, Germany.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.
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http://dx.doi.org/10.1038/s41431-020-0664-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608255PMC
October 2020

An expanded variant list and assembly annotation identifies multiple novel coding and noncoding genes for prostate cancer risk using a normal prostate tissue eQTL data set.

PLoS One 2019 8;14(4):e0214588. Epub 2019 Apr 8.

Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, SW, Rochester, Minnesota, United States of America.

Prostate cancer (PrCa) is highly heritable; 284 variants have been identified to date that are associated with increased prostate cancer risk, yet few genes contributing to its development are known. Expression quantitative trait loci (eQTL) studies link variants with affected genes, helping to determine how these variants might regulate gene expression and may influence prostate cancer risk. In the current study, we performed eQTL analysis on 471 normal prostate epithelium samples and 249 PrCa-risk variants in 196 risk loci, utilizing RNA sequencing transcriptome data based on ENSEMBL gene definition and genome-wide variant data. We identified a total of 213 genes associated with known PrCa-risk variants, including 141 protein-coding genes, 16 lncRNAs, and 56 other non-coding RNA species with differential expression. Compared to our previous analysis, where RefSeq was used for gene annotation, we identified an additional 130 expressed genes associated with known PrCa-risk variants. We detected an eQTL signal for more than half (n = 102, 52%) of the 196 loci tested; 52 (51%) of which were a Group 1 signal, indicating high linkage disequilibrium (LD) between the peak eQTL variant and the PrCa-risk variant (r2>0.5) and may help explain how risk variants influence the development of prostate cancer.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214588PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453468PMC
December 2019

Familial recurrence risk with varying amount of family history.

Genet Epidemiol 2019 06 11;43(4):440-448. Epub 2019 Feb 11.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

The familial recurrence risk is the probability a person will have disease, given a reported family history. When family histories are obtained as simple counts of disease among family members, as often obtained in cancer registries or surveys, we propose methods to estimate recurrence risks based on truncated binomial distributions. By this approach, we are able to obtain unbiased estimates of risk for a person with at least k-affected relatives, where k can be specified to determine how risk varies with k. We also derive robust variances of the recurrence risk estimate, to account for correlations within families, such as those induced by shared genes or shared environment, without explicitly modeling the factors that cause familial correlations. Furthermore, we illustrate how mixture models can be used to account for a sample composed of low- and high-risk families. Using simulations, we illustrate the properties of the proposed methods. Application of our methods to a family history survey of prostate cancer shows that the recurrence risk for prostate cancer increased from 16%, when there was at least one affected relative, to 52%, when there was at least five affected relatives.
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http://dx.doi.org/10.1002/gepi.22193DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6561475PMC
June 2019

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD.

Acta Neuropathol 2019 06 9;137(6):879-899. Epub 2019 Feb 9.

German Center for Neurodegenerative Diseases (DZNE), 18147, Rostock, Germany.

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e - 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e - 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e - 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis.
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http://dx.doi.org/10.1007/s00401-019-01962-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533145PMC
June 2019

Author Correction: Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

Nat Genet 2019 02;51(2):363

Dame Roma Mitchell Cancer Research Centre, University of Adelaide, Adelaide, South Australia, Australia.

In the version of this article initially published, the name of author Manuela Gago-Dominguez was misspelled as Manuela Gago Dominguez. The error has been corrected in the HTML and PDF version of the article.
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http://dx.doi.org/10.1038/s41588-018-0330-6DOI Listing
February 2019

Association of mitochondrial DNA copy number with self-rated health status.

Appl Clin Genet 2018 25;11:121-127. Epub 2018 Oct 25.

Department of health sciences research.

Purpose: In aging adults, mitochondrial dysfunction may be an important contributor. We evaluated the association between mitochondrial DNA (mtDNA) copy number, which is a biomarker for mitochondrial function, and self-rated health status.

Patients And Methods: We conducted a cross-sectional study of patients enrolled within the Mayo Clinic Biobank. We utilized the questionnaire and sequence data from 944 patients. We examined the association between mtDNA copy number and self-rated health status with 3 collapsed categories for the latter variable (excellent/very good, good, and fair/poor). For analysis, we used proportional odds models after log-transforming mtDNA copy number, and we adjusted for age and sex.

Results: We found the median age at enrollment was 61 years (25th-75th percentile: 51-71), and 64% reported excellent or very good health, 31% reported good health, and 6% reported fair/poor health. Overall, the median mtDNA copy number was 88.9 (25th-75th percentile: 77.6-101.1). Higher mtDNA copy number was found for subjects reporting better self-rated health status after adjusting for age, sex, and comorbidity burden (OR =2.3 [95% CI: 1.2-4.5] for having better self-rated health for a one-unit increase in log-transformed mtDNA copy number).

Conclusion: We found that a higher mtDNA copy number is associated with better self-rated health status after adjustment for age, sex, and comorbidity burden. The current study implies that mtDNA copy number may serve as a biomarker for self-reported health. Further studies, potentially including cohort studies, may be required.
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http://dx.doi.org/10.2147/TACG.S167640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207265PMC
October 2018

Risk SNP-Mediated Promoter-Enhancer Switching Drives Prostate Cancer through lncRNA PCAT19.

Cell 2018 07 19;174(3):564-575.e18. Epub 2018 Jul 19.

Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada. Electronic address:

The prostate cancer (PCa) risk-associated SNP rs11672691 is positively associated with aggressive disease at diagnosis. We showed that rs11672691 maps to the promoter of a short isoform of long noncoding RNA PCAT19 (PCAT19-short), which is in the third intron of the long isoform (PCAT19-long). The risk variant is associated with decreased and increased levels of PCAT19-short and PCAT19-long, respectively. Mechanistically, the risk SNP region is bifunctional with both promoter and enhancer activity. The risk variants of rs11672691 and its LD SNP rs887391 decrease binding of transcription factors NKX3.1 and YY1 to the promoter of PCAT19-short, resulting in weaker promoter but stronger enhancer activity that subsequently activates PCAT19-long. PCAT19-long interacts with HNRNPAB to activate a subset of cell-cycle genes associated with PCa progression, thereby promoting PCa tumor growth and metastasis. Taken together, these findings reveal a risk SNP-mediated promoter-enhancer switching mechanism underlying both initiation and progression of aggressive PCa.
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http://dx.doi.org/10.1016/j.cell.2018.06.014DOI Listing
July 2018

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants.

Nat Commun 2018 06 11;9(1):2256. Epub 2018 Jun 11.

Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia.

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.
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http://dx.doi.org/10.1038/s41467-018-04109-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995836PMC
June 2018

Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

Nat Genet 2018 07 11;50(7):928-936. Epub 2018 Jun 11.

Dame Roma Mitchell Cancer Research Centre, University of Adelaide, Adelaide, South Australia, Australia.

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P < 5.0 × 10) with PrCa and one locus significantly associated with early-onset PrCa (≤55 years). Our findings include missense variants rs1800057 (odds ratio (OR) = 1.16; P = 8.2 × 10; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa.
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http://dx.doi.org/10.1038/s41588-018-0142-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568012PMC
July 2018

Identification of missing variants by combining multiple analytic pipelines.

BMC Bioinformatics 2018 04 16;19(1):139. Epub 2018 Apr 16.

Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, 32224, USA.

Background: After decades of identifying risk factors using array-based genome-wide association studies (GWAS), genetic research of complex diseases has shifted to sequencing-based rare variants discovery. This requires large sample sizes for statistical power and has brought up questions about whether the current variant calling practices are adequate for large cohorts. It is well-known that there are discrepancies between variants called by different pipelines, and that using a single pipeline always misses true variants exclusively identifiable by other pipelines. Nonetheless, it is common practice today to call variants by one pipeline due to computational cost and assume that false negative calls are a small percent of total.

Results: We analyzed 10,000 exomes from the Alzheimer's Disease Sequencing Project (ADSP) using multiple analytic pipelines consisting of different read aligners and variant calling strategies. We compared variants identified by using two aligners in 50,100, 200, 500, 1000, and 1952 samples; and compared variants identified by adding single-sample genotyping to the default multi-sample joint genotyping in 50,100, 500, 2000, 5000 and 10,000 samples. We found that using a single pipeline missed increasing numbers of high-quality variants correlated with sample sizes. By combining two read aligners and two variant calling strategies, we rescued 30% of pass-QC variants at sample size of 2000, and 56% at 10,000 samples. The rescued variants had higher proportions of low frequency (minor allele frequency [MAF] 1-5%) and rare (MAF < 1%) variants, which are the very type of variants of interest. In 660 Alzheimer's disease cases with earlier onset ages of ≤65, 4 out of 13 (31%) previously-published rare pathogenic and protective mutations in APP, PSEN1, and PSEN2 genes were undetected by the default one-pipeline approach but recovered by the multi-pipeline approach.

Conclusions: Identification of the complete variant set from sequencing data is the prerequisite of genetic association analyses. The current analytic practice of calling genetic variants from sequencing data using a single bioinformatics pipeline is no longer adequate with the increasingly large projects. The number and percentage of quality variants that passed quality filters but are missed by the one-pipeline approach rapidly increased with sample size.
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http://dx.doi.org/10.1186/s12859-018-2151-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5902939PMC
April 2018

Network-directed cis-mediator analysis of normal prostate tissue expression profiles reveals downstream regulatory associations of prostate cancer susceptibility loci.

Oncotarget 2017 Oct 8;8(49):85896-85908. Epub 2017 Sep 8.

Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

Large-scale genome-wide association studies have identified multiple single-nucleotide polymorphisms associated with risk of prostate cancer. Many of these genetic variants are presumed to be regulatory in nature; however, follow-up expression quantitative trait loci (eQTL) association studies have to-date been restricted largely to -acting associations due to study limitations. While -eQTL scans suffer from high testing dimensionality, recent evidence indicates most -eQTL associations are mediated by -regulated genes, such as transcription factors. Leveraging a data-driven gene co-expression network, we conducted a comprehensive -mediator analysis using RNA-Seq data from 471 normal prostate tissue samples to identify downstream regulatory associations of previously identified prostate cancer risk variants. We discovered multiple -eQTL associations that were significantly mediated by -regulated transcripts, four of which involved risk locus 17q12, proximal transcription factor , and target -genes with known HNF response elements (, , , ). We additionally identified evidence of -acting down-regulation of via rs10993994 corresponding to reduced co-expression of . The majority of these -mediator relationships demonstrated -eQTL replicability in 87 prostate tissue samples from the Gene-Tissue Expression Project. These findings provide further biological context to known risk loci and outline new hypotheses for investigation into the etiology of prostate cancer.
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http://dx.doi.org/10.18632/oncotarget.20717DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5689655PMC
October 2017

FIRE: functional inference of genetic variants that regulate gene expression.

Bioinformatics 2017 Dec;33(24):3895-3901

Department of Health Research & Policy.

Motivation: Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies.

Results: We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types.

Availability And Implementation: FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/.

Contact: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx534DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860093PMC
December 2017

Mapping Complex Traits in a Diversity Outbred F1 Mouse Population Identifies Germline Modifiers of Metastasis in Human Prostate Cancer.

Cell Syst 2017 01 1;4(1):31-45.e6. Epub 2016 Dec 1.

Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA. Electronic address:

It is unclear how standing genetic variation affects the prognosis of prostate cancer patients. To provide one controlled answer to this problem, we crossed a dominant, penetrant mouse model of prostate cancer to Diversity Outbred mice, a collection of animals that carries over 40 million SNPs. Integration of disease phenotype and SNP variation data in 493 F1 males identified a metastasis modifier locus on Chromosome 8 (LOD = 8.42); further analysis identified the genes Rwdd4, Cenpu, and Casp3 as functional effectors of this locus. Accordingly, analysis of over 5,300 prostate cancer patient samples revealed correlations between the presence of genetic variants at these loci, their expression levels, cancer aggressiveness, and patient survival. We also observed that ectopic overexpression of RWDD4 and CENPU increased the aggressiveness of two human prostate cancer cell lines. In aggregate, our approach demonstrates how well-characterized genetic variation in mice can be harnessed in conjunction with systems genetics approaches to identify and characterize germline modifiers of human disease processes.
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http://dx.doi.org/10.1016/j.cels.2016.10.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5269474PMC
January 2017

Whole exome sequencing in 75 high-risk families with validation and replication in independent case-control studies identifies TANGO2, OR5H14, and CHAD as new prostate cancer susceptibility genes.

Oncotarget 2017 Jan;8(1):1495-1507

National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.

Prostate cancer (PCa) susceptibility is defined by a continuum from rare, high-penetrance to common, low-penetrance alleles. Research to date has concentrated on identification of variants at the ends of that continuum. Taking an alternate approach, we focused on the important but elusive class of low-frequency, moderately penetrant variants by performing disease model-based variant filtering of whole exome sequence data from 75 hereditary PCa families. Analysis of 341 candidate risk variants identified nine variants significantly associated with increased PCa risk in a population-based, case-control study of 2,495 men. In an independent nested case-control study of 7,121 men, there was risk association evidence for TANGO2 p.Ser17Ter and the established HOXB13 p.Gly84Glu variant. Meta-analysis combining the case-control studies identified two additional variants suggestively associated with risk, OR5H14 p.Met59Val and CHAD p.Ala342Asp. The TANGO2 and HOXB13 variants co-occurred in cases more often than expected by chance and never in controls. Finally, TANGO2 p.Ser17Ter was associated with aggressive disease in both case-control studies separately. Our analyses identified three new PCa susceptibility alleles in the TANGO2, OR5H14 and CHAD genes that not only segregate in multiple high-risk families but are also of importance in altering disease risk for men from the general population. This is the first successful study to utilize sequencing in high-risk families for the express purpose of identifying low-frequency, moderately penetrant PCa risk mutations.
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http://dx.doi.org/10.18632/oncotarget.13646DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5341753PMC
January 2017

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

Am J Hum Genet 2016 Oct 22;99(4):877-885. Epub 2016 Sep 22.

Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA.

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.
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http://dx.doi.org/10.1016/j.ajhg.2016.08.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065685PMC
October 2016

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

Genetics 2016 11 21;204(3):933-958. Epub 2016 Sep 21.

Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic, Rochester, Minnesota 55905

Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide 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.
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http://dx.doi.org/10.1534/genetics.116.188953DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5105870PMC
November 2016

Germline miRNA DNA variants and the risk of colorectal cancer by subtype.

Genes Chromosomes Cancer 2017 03 30;56(3):177-184. Epub 2016 Nov 30.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.

MicroRNAs (miRNAs) regulate up to one-third of all protein-coding genes including genes relevant to cancer. Variants within miRNAs have been reported to be associated with prognosis, survival, response to chemotherapy across cancer types, in vitro parameters of cell growth, and altered risks for development of cancer. Five miRNA variants have been reported to be associated with risk for development of colorectal cancer (CRC). In this study, we evaluated germline genetic variation in 1,123 miRNAs in 899 individuals with CRCs categorized by clinical subtypes and in 204 controls. The role of common miRNA variation in CRC was investigated using single variant and miRNA-level association tests. Twenty-nine miRNAs and 30 variants exhibited some marginal association with CRC in at least one subtype of CRC. Previously reported associations were not confirmed (n = 4) or could not be evaluated (n = 1). The variants noted for the CRCs with deficient mismatch repair showed little overlap with the variants noted for CRCs with proficient mismatch repair, consistent with our evolving understanding of the distinct biology underlying these two groups. © 2016 The Authors Genes, Chromosomes & Cancer Published by Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/gcc.22420DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5245119PMC
March 2017

Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types.

Cancer Discov 2016 09 17;6(9):1052-67. Epub 2016 Jul 17.

Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California.

Unlabelled: Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.

Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
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http://dx.doi.org/10.1158/2159-8290.CD-15-1227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010513PMC
September 2016

Genome-wide association of familial prostate cancer cases identifies evidence for a rare segregating haplotype at 8q24.21.

Hum Genet 2016 08 4;135(8):923-38. Epub 2016 Jun 4.

Brady Urological Institute, Johns Hopkins University, Baltimore, MD, 21287, USA.

Previous genome-wide association studies (GWAS) of prostate cancer risk focused on cases unselected for family history and have reported over 100 significant associations. The International Consortium for Prostate Cancer Genetics (ICPCG) has now performed a GWAS of 2511 (unrelated) familial prostate cancer cases and 1382 unaffected controls from 12 member sites. All samples were genotyped on the Illumina 5M+exome single nucleotide polymorphism (SNP) platform. The GWAS identified a significant evidence for association for SNPs in six regions previously associated with prostate cancer in population-based cohorts, including 3q26.2, 6q25.3, 8q24.21, 10q11.23, 11q13.3, and 17q12. Of note, SNP rs138042437 (p = 1.7e(-8)) at 8q24.21 achieved a large estimated effect size in this cohort (odds ratio = 13.3). 116 previously sampled affected relatives of 62 risk-allele carriers from the GWAS cohort were genotyped for this SNP, identifying 78 additional affected carriers in 62 pedigrees. A test for an excess number of affected carriers among relatives exhibited strong evidence for co-segregation of the variant with disease (p = 8.5e(-11)). The majority (92 %) of risk-allele carriers at rs138042437 had a consistent estimated haplotype spanning approximately 100 kb of 8q24.21 that contained the minor alleles of three rare SNPs (dosage minor allele frequencies <1.7 %), rs183373024 (PRNCR1), previously associated SNP rs188140481, and rs138042437 (CASC19). Strong evidence for co-segregation of a SNP on the haplotype further characterizes the haplotype as a prostate cancer predisposition locus.
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http://dx.doi.org/10.1007/s00439-016-1690-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5020907PMC
August 2016

Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation.

Nat Commun 2016 Apr 7;7:10979. Epub 2016 Apr 7.

Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan 48202, USA.

Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.
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http://dx.doi.org/10.1038/ncomms10979DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4829663PMC
April 2016

Genome-wide association study of prostate cancer-specific survival.

Cancer Epidemiol Biomarkers Prev 2015 Nov 25;24(11):1796-800. Epub 2015 Aug 25.

Mayo Clinic, Rochester, Minnesota.

Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical.

Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR).

Results: We observed no significant association between genetic variants and prostate cancer survival.

Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study.

Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes.
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http://dx.doi.org/10.1158/1055-9965.EPI-15-0543DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5674990PMC
November 2015

How well do whole exome sequencing results correlate with medical findings? A study of 89 Mayo Clinic Biobank samples.

Front Genet 2015 24;6:244. Epub 2015 Jul 24.

Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA ; Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, MN, USA.

Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype-phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype-phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES.
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http://dx.doi.org/10.3389/fgene.2015.00244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513238PMC
August 2015

Prediction of individual genetic risk to prostate cancer using a polygenic score.

Prostate 2015 Sep 14;75(13):1467-74. Epub 2015 Jul 14.

Mayo Clinic, Rochester, Minnesota.

Background: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction.

Methods: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls.

Results: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk.

Conclusions: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.
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http://dx.doi.org/10.1002/pros.23037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745998PMC
September 2015

A large multiethnic genome-wide association study of prostate cancer identifies novel risk variants and substantial ethnic differences.

Cancer Discov 2015 Aug 1;5(8):878-91. Epub 2015 Jun 1.

National Cancer Institute, NIH, Bethesda, Maryland.

Unlabelled: A genome-wide association study (GWAS) of prostate cancer in Kaiser Permanente health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) revealed a new independent risk indel rs4646284 at the previously identified locus 6q25.3 that replicated in PEGASUS (N = 7,539) and the Multiethnic Cohort (N = 4,679) with an overall P = 1.0 × 10(-19) (OR, 1.18). Across the 6q25.3 locus, rs4646284 exhibited the strongest association with expression of SLC22A1 (P = 1.3 × 10(-23)) and SLC22A3 (P = 3.2 × 10(-52)). At the known 19q13.33 locus, rs2659124 (P = 1.3 × 10(-13); OR, 1.18) nominally replicated in PEGASUS. A risk score of 105 known risk SNPs was strongly associated with prostate cancer (P < 1.0 × 10(-8)). Comparing the highest to lowest risk score deciles, the OR was 6.22 for non-Hispanic whites, 5.82 for Latinos, 3.77 for African-Americans, and 3.38 for East Asians. In non-Hispanic whites, the 105 risk SNPs explained approximately 7.6% of disease heritability. The entire GWAS array explained approximately 33.4% of heritability, with a 4.3-fold enrichment within DNaseI hypersensitivity sites (P = 0.004).

Significance: Taken together, our findings of independent risk variants, ethnic variation in existing SNP replication, and remaining unexplained heritability have important implications for further clarifying the genetic risk of prostate cancer. Our findings also suggest that there may be much promise in evaluating understudied variation, such as indels and ethnically diverse populations.
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http://dx.doi.org/10.1158/2159-8290.CD-15-0315DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527942PMC
August 2015

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

Hum Mol Genet 2015 Oct 29;24(19):5589-602. Epub 2015 May 29.

Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Laboratory, Department of Applied Health Research, University College London, London, UK.

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.
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http://dx.doi.org/10.1093/hmg/ddv203DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572072PMC
October 2015