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One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study.

ABSTRACT: Genotype imputation substantially increases available markers for analysis in genome-wide association studies (GWAS) by leveraging linkage disequilibrium from a reference panel. We sought to (i) investigate the performance of imputation from the August 2010 release of the 1000 Genomes Project (1000GP) in an existing GWAS of prostate cancer, (ii) look for novel associations with prostate cancer risk, (iii) fine-map known prostate cancer susceptibility regions using an approximate Bayesian framework and stepwise regression, and (iv) compare power and efficiency of imputation and de novo sequencing.We used 2,782 aggressive prostate cancer cases and 4,458 controls from the NCI Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer GWAS to infer 5.8 million well-imputed autosomal single nucleotide polymorphisms (SNPs).Imputation quality, as measured by correlation between imputed and true allele counts, was higher among common variants than rare variants. We found no novel prostate cancer associations among a subset of 1.2 million well-imputed low-frequency variants. At a genome-wide sequencing cost of $2,500, imputation from SNP arrays is a more powerful strategy than sequencing for detecting disease associations of SNPs with minor allele frequencies (MAF) above 1%.1000GP imputation provided dense coverage of previously identified prostate cancer susceptibility regions, highlighting its potential as an inexpensive first-pass approach to fine mapping in regions such as 5p15 and 8q24. Our study shows 1000GP imputation can accurately identify low-frequency variants and stresses the importance of large sample size when studying these variants.

SUBMITTER: Machiela MJ

PROVIDER: S-EPMC3962143 | biostudies-other | 2013 May

REPOSITORIES: biostudies-other

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One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study.

Machiela Mitchell J MJ Chen Constance C Liang Liming L Diver W Ryan WR Stevens Victoria L VL Tsilidis Konstantinos K KK Haiman Christopher A CA Chanock Stephen J SJ Hunter David J DJ Kraft Peter P

The Prostate 20121219 7

<h4>Background</h4>Genotype imputation substantially increases available markers for analysis in genome-wide association studies (GWAS) by leveraging linkage disequilibrium from a reference panel. We sought to (i) investigate the performance of imputation from the August 2010 release of the 1000 Genomes Project (1000GP) in an existing GWAS of prostate cancer, (ii) look for novel associations with prostate cancer risk, (iii) fine-map known prostate cancer susceptibility regions using an approxima ...[more]

PMID: 23255287

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Project description:BackgroundScreening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM).ObjectiveTo examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM.Design, setting, and participantsWe included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred.Outcome measurements and statistical analysisThe main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls.Results and limitationsAmong the cases, we found that 8 of the 47 SNPs were significantly associated (p<0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p<0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only.ConclusionsEight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa.Patient summaryIn this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction.

Dataset Information

One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study.

Publications

One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study.

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