Sonja Grill1, Mahdi Fallah2, Robin J Leach3, Ian M Thompson4, Kari Hemminki5, Donna P Ankerst6. 1. Department of Life Sciences of the Technical University Munich, Liesel-Beckmann-Str. 2, 85354 Freising, Germany. Electronic address: sonja.grill@tum.de. 2. Division of Molecular Genetic Epidemiology, German Cancer Research Centre, Im Neuenheimer Feld 580, Im Technologiepark, 69120 Heidelberg, Germany. 3. Department of Urology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA; Department of Cellular and Structural Biology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA. 4. Department of Urology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA. 5. Division of Molecular Genetic Epidemiology, German Cancer Research Centre, Im Neuenheimer Feld 580, Im Technologiepark, 69120 Heidelberg, Germany; Center for Primary Health Care Research, Lund University, Box 117, 221 00 LUND, Sweden. 6. Department of Life Sciences of the Technical University Munich, Liesel-Beckmann-Str. 2, 85354 Freising, Germany; Department of Urology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA; Department of Mathematics of the Technical University Munich, Boltzmannstr. 3, 85748 Garching b. München, Germany; Department of Epidemiology and Biostatistics of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA.
Abstract
OBJECTIVES: To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC). STUDY DESIGN AND SETTING: A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds. RESULTS: In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively). CONCLUSION: The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.
OBJECTIVES: To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC). STUDY DESIGN AND SETTING: A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds. RESULTS: In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively). CONCLUSION: The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.
Authors: Wenting Cheng; Jeremy M G Taylor; Tian Gu; Scott A Tomlins; Bhramar Mukherjee Journal: J R Stat Soc Ser C Appl Stat Date: 2018-08-13 Impact factor: 1.864
Authors: Donna P Ankerst; Martin Goros; Scott A Tomlins; Dattatraya Patil; Ziding Feng; John T Wei; Martin G Sanda; Jonathan Gelfond; Ian M Thompson; Robin J Leach; Michael A Liss Journal: Eur Urol Focus Date: 2018-02-13