1,721,143 research outputs found
Publisher Correction: Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction
No full textIn the version of this article originally published, the names of the equally contributing authors and jointly supervising authors were switched. The correct affiliations are: “These authors contributed equally: David V. Conti, Burcu F. Darst. These authors jointly supervised this work: David V. Conti, Rosalind A. Eeles, Zsofia Kote-Jarai, Christopher A. Haiman.” The error has been corrected in the HTML and PDF versions of the article
Publisher Correction: Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction (Nature Genetics, (2021), 53, 1, (65-75), 10.1038/s41588-020-00748-0):Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction (Nature Genetics, (2021), 53, 1, (65-75), 10.1038/s41588-020-00748-0)
No full textIn the version of this article originally published, the names of the equally contributing authors and jointly supervising authors were switched. The correct affiliations are: “These authors contributed equally: David V. Conti, Burcu F. Darst. These authors jointly supervised this work: David V. Conti, Rosalind A. Eeles, Zsofia Kote-Jarai, Christopher A. Haiman.” The error has been corrected in the HTML and PDF versions of the article.</p
Abstract 1311: Germline variation at 8q24 and prostate cancer risk
No full textAbstract
The 8q24 region harbors multiple risk variants for distinct cancers including 7 for prostate cancer, the majority of which lie in separate linkage disequilbrium blocks. It is not known whether a common biological mechanism underlies the association of genetic variation with cancer risk at 8q24, or whether there are site-specific functions of regulatory elements that are affected in this region. Given the proximity, the MYC oncogene is a likely candidate as are multiple long non-coding RNAs in the region. To further understand the contribution of germline variation to prostate cancer risk we performed a comprehensive fine-mapping analysis of the region in men of European ancestry from the PRACTICAL/ELLIPSE Consortium. More specifically, we tested 1,731 genotype tag SNPs and 12,221 imputed variants spanning the risk region (127.3-129.0Mb) in 56,363 prostate cancer cases and 37,386 controls of European ancestry that were genotyped with the Illumina OncoArray. We performed stepwise logistic regression and identified 13 variants with risk allele frequencies between 0.006 and 0.998 that surpassed genome-wide statistical significance (p-values between 3.2x10-8 and 8.0 x10-78) and with per allele odds ratios ranging from 1.11(rs5013678) to 2.68(rs183373024). Ongoing analyses that will be presented include incorporating existing GWAS and fine-mapping data (iCOGs) for men of European and African ancestry (35,000 cases and 35,000 controls) using JAM, a Bayesian approach that investigates multi-SNP models using marginal meta-analysis statistics. Leveraging the power from the overall multiethnic meta-analysis (&gt;93,000 cases and &gt;72,000 controls) will provide further insight into the number of independent signals in the region and their contribution to prostate cancer risk in these populations.
Citation Format: Kan Wang, Ali Amin Al Olama, Rosalind Eeles, David Conti, Zsofia Kote-Jarai, Christopher A. Haiman. Germline variation at 8q24 and prostate cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1311. doi:10.1158/1538-7445.AM2017-1311</jats:p
Abstract 4956: Transcriptome-wide association study identifies new prostate cancer susceptibility genes in the OncoArray data
No full textAbstract
Genome-wide association studies (GWAS) have identified over 150 genomic regions harboring risk variants for prostate cancer which explain one third of all familial risk. However, with some notable exceptions, the causal variants and target susceptibility genes at these risk loci have yet to be identified. Recent work has shown a strong overlap between loci associated with gene expression levels (eQTLs) in prostate tissue and GWAS loci, which suggests that the causal mechanism at a significant proportion of risk loci includes causal alleles that regulate expression levels of nearby susceptibility genes. While overlapping eQTLs with GWAS is a powerful method to prioritize susceptibility genes, it is often the case that multiple eQTLs co-localize at the GWAS risk region (due to linkage disequilibrium (LD) and correlations across transcript levels). This prohibits the identification of the true susceptibility gene as opposed to spurious co-localization at the same locus.
We recently leveraged gene expression imputation to perform transcriptome-wide association studies (TWAS) as a principled approach to measure the strength of association between gene expression and disease status. Here, we use imputed expression to identify new susceptibility genes for prostate cancer in the OncoArray GWAS data. We integrate gene expression data from more than 44 tissues across ~4,000 individuals with GWAS of prostate cancer from the OncoArray in ~140,000 individuals. Our approach identified 118 susceptibility genes for prostate cancer that reside in 90 independent loci across the genome. Of these, we report 7 genes located more than 0.5 Megabases away from any previously reported GWAS loci for prostate cancer, thus providing new risk loci. Second, we use TWAS to investigate genes previously reported as susceptibility genes for prostate cancer through overlaps of eQTL and GWAS. We find 36 (out of 86 previously reported genes) to be significant in TWAS. Overall, our findings highlight the power of integrating gene expression data with GWAS and provide testable hypotheses for future functional validation of prostate cancer risk.
Citation Format: Nicholas Mancuso, Wei Zheng, Kathryn Penney, The PRACTICAL Consortium, Zsofia Kote-Jarai, Christopher Haiman, Simon Gayther, Matthew Freedman, Bogdan Pasaniuc. Transcriptome-wide association study identifies new prostate cancer susceptibility genes in the OncoArray data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4956. doi:10.1158/1538-7445.AM2017-4956</jats:p
Abstract 1296: Bayesian fine-mapping using summary data of 145,000 subjects refines common risk associations, discovers secondary signals and novel candidate genes for prostate cancer
No full textAbstract
Genome-wide association studies (GWAS) have identified more than 160 prostate cancer (PrCa) genetic risk loci, however these variants rarely point directly to the true underlying functional variant driving the association. In this fine-mapping study to narrow the credible causal variant set for 80 PrCa regions representing 89 original independent GWAS signals, we performed Bayesian variable selection in combination with functional annotation and quantile regression. We used imputed data for 83,511 PrCa cases and 62,283 controls investigated with high-density genotyping arrays from the OncoArray, iCOGS and 5 previous GWAS studies from the PRACTICAL/ELLIPSE consortia. To facilitate fine-mapping from one-at-a-time SNP associations meta-analyzed over the consortia we first applied JAM, a novel Bayesian algorithm which searches multi-SNP models in summary data by imputing the correlation structure according to a reference panel. JAM provides inference on the number of independent signals, as well as the set of potential SNPs driving those signals. We utilized functional annotation and eQTL analysis (TCGA prostate tumor data) in combination with quantile regression to further prioritize the most likely causal variants within the credible set of SNPs and identify potential candidate genes and functional mechanisms. The median credible set size from JAM was 17 SNPs per region, shrinking the post-QC input set of variants by about 98%. In 13 regions evidence was found for multiple independent signals, up to a maximum of 5 SNPs. Within the single hit regions, almost half had less than 10 variants selected. In 34 regions the credible set included at least one SNP that was co-localized with a significant eQTL. Quantile regression highlighted enrichment for variants in promoters, DNase hypersensitivity site and eQTLs - representing candidate biological mechanisms underpinning disease development. This study has substantially reduced and prioritized the candidate causal PrCa risk variants within previously known GWAS regions, identifying a small subset of variants for further functional investigation and novel candidate genes at a number of loci.
Citation Format: Zsofia Kote-Jarai, Tokhir Dadaev, Ed Saunders, Paul Newcombe, Ezequiel Anokian, Daniel Leongamornlert, Ali Amin Al Olama, Christopher Haiman, Ros Eeles, David Conti, The PRACTICAL/ELLIPSE Consortium. Bayesian fine-mapping using summary data of 145,000 subjects refines common risk associations, discovers secondary signals and novel candidate genes for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1296. doi:10.1158/1538-7445.AM2017-1296</jats:p
Abstract 1301: Identification of novel susceptibility loci and genes for prostate cancer risk: A large transcriptome-wide association study in over 143,000 subjects
No full textAbstract
Common genetic variants in over 150 loci have been found to be associated with prostate cancer (PrCa) risk through GWAS. These variants, however, explain only a small fraction of PrCa heritability, and the genes responsible for the detected associations remain largely unknown. It has been suggested that many GWAS-identified associations may be driven by the regulation of risk variants on the expression of disease causal genes. To identify novel PrCa risk loci and possible causal genes at known risk loci, we performed a transcriptome-wide association study (TWAS) to evaluate associations of genetically predicted gene expressions with PrCa risk.
We used RNA sequencing data from normal prostate tissues and high-density genotyping from 73 European descendants included in the Genotype-Tissue Expression Project and established genetic models to predict gene expression level. Given that the regulatory mechanisms for most genes are similar across most human tissues, we also built cross-tissue models using gene expression data generated in all tissues from 369 European descendants to increase the statistical power. Based on prediction performance, we selected 22,126 genes and conducted association analyses of their predicted expression with PrCa risk using GWAS data obtained from more than 143,000 subjects included in PRACTICAL/ELLIPSE consortia.
We identified 140 genes showing an association of their predicted expression levels with PrCa risk at P &lt; 2.26×10-6, a Bonferroni-corrected significance threshold, including 105 protein-coding genes, 33 long non-coding RNAs, and 2 processed transcripts. Seven of these associated genes are located more than 1Mb away from any of the risk variants identified in PrCa GWAS, representing potential novel risk loci. Of the remaining 133 genes located in known risk loci, 100 have not been reported from previous eQTL analyses. The associations for 25 of these genes remained significant at P &lt; 3.76×10-4 (0.05/133) after adjusting for the risk variants reported in the initial GWAS. Our study also identified 33 genes that were previously reported based on eQTL and fine-mapping analyses. For many of the identified genes, somatic changes of indels, nonsense mutations, splice site variants, translation start site variants, or missense mutations were detected in PrCa patients in the TCGA, including known PrCa driver genes NKX3-1 and PLXNA1. Pathway enrichment analysis showed that cancer related functions were significantly enriched for the identified genes. The top canonical pathways identified included prostate cancer signaling, ATM signaling, AMPK signaling, protein ubiquitination pathway, and antigen presentation pathway.
In summary, we conducted the first large PrCa TWAS and identified multiple novel susceptibility loci and genes for PrCa risk. Our study provided substantial new information towards the understanding of PrCa genetics and biology.
Citation Format: Lang Wu, Jirong Long, Yingchang Lu, Xingyi Guo, Bogdan Pasaniuc, Kathryn L. Penney, Zsofia Kote-Jarai, Christopher A. Haiman, Rosalind A. Eeles, Wei Zheng, the PRACTICAL consortium. Identification of novel susceptibility loci and genes for prostate cancer risk: A large transcriptome-wide association study in over 143,000 subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1301. doi:10.1158/1538-7445.AM2017-1301</jats:p
Abstract 4577: Identification of twenty three novel prostate cancer susceptibility loci using a custom assay (the icogs) in an international consortium: PRACTICAL.
No full textA Review of Prostate Cancer Genome-Wide Association Studies (GWAS)
No full textAbstract
Prostate cancer is the most common cancer in men in Europe and the United States. The genetic heritability of prostate cancer is contributed to by both rarely occurring genetic variants with higher penetrance and moderate to commonly occurring variants conferring lower risks. The number of identified variants belonging to the latter category has increased dramatically in the last 10 years with the development of the genome-wide association study (GWAS) and the collaboration of international consortia that have led to the sharing of large-scale genotyping data. Over 40 prostate cancer GWAS have been reported, with approximately 170 common variants now identified. Clinical utility of these variants could include strategies for population-based risk stratification to target prostate cancer screening to men with an increased genetic risk of disease development, while for those who develop prostate cancer, identifying genetic variants could allow treatment to be tailored based on a genetic profile in the early disease setting. Functional studies of identified variants are needed to fully understand underlying mechanisms of disease and identify novel targets for treatment. This review will outline the GWAS carried out in prostate cancer and the common variants identified so far, and how these may be utilized clinically in the screening for and management of prostate cancer. Cancer Epidemiol Biomarkers Prev; 27(8); 845–57. ©2018 AACR.</jats:p
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