Dataset Information

Generalizability of associations from prostate cancer genome-wide association studies in multiple populations.

ABSTRACT: Genome-wide association studies have identified multiple common alleles associated with prostate cancer risk in populations of European ancestry. Testing these variants in other populations is needed to assess the generalizability of the associations and may guide fine-mapping efforts. We examined 13 of these risk variants in a multiethnic sample of 2,768 incident prostate cancer cases and 2,359 controls from the Multiethnic Cohort (African Americans, European Americans, Latinos, Japanese Americans, and Native Hawaiians). We estimated ethnic-specific and pooled odds ratios and tested for ethnic heterogeneity of effects using logistic regression. In ethnic-pooled analyses, 12 of the 13 variants were positively associated with risk, with statistically significant associations (P < 0.05) noted with six variants: JAZF1, rs10486567 [odds ratio (OR), 1.23; 95% confidence interval (95% CI, 1.12-1.35); Xp11.2, rs5945572 (OR, 1.31; 95% CI, 1.13-1.51); HNF1B, rs4430796 (OR, 1.15; 95% CI, 1.06-1.25); MSMB, rs10993994 (OR, 1.13; 95% CI, 1.04-1.23); 11q13.2, rs7931342 (OR, 1.13; 95% CI, 1.03-1.23); 3p12.1, rs2660753 (OR, 1.11; 95% CI, 1.01-1.21); SLC22A3, rs9364554 (OR, 1.10; 95% CI, 1.00-1.21); CTBP2, rs12769019 (OR, 1.11; 95% CI, 0.99-1.25); HNF1B, rs11649743 (OR, 1.10; 95% CI, 0.99-1.22); EHBP1, rs721048 (OR, 1.08; 95% CI, 0.94-1.25); KLK2/3, rs2735839 (OR, 1.06; 95% CI, 0.97-1.16); 17q24.3, rs1859962 (OR, 1.04; 95% CI, 0.96-1.13); and LMTK2, rs6465657 (OR, 0.99; 95% CI, 0.89-1.09). Significant ethnic heterogeneity of effects was noted for four variants (EHBP1, P(het) = 3.9 x 10(-3); 11q13, P(het) = 0.023; HNF1B (rs4430796), P(het) = 0.026; and KLK2/3, P(het) = 2.0 x 10(-3)). Although power was limited in some ethnic/racial groups due to variation in sample size and allele frequencies, these findings suggest that a large fraction of prostate cancer variants identified in populations of European ancestry are global markers of risk. For many of these regions, fine-mapping in non-European samples may help localize causal alleles and better determine their contribution to prostate cancer risk in the population.

SUBMITTER: Waters KM

PROVIDER: S-EPMC2917607 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Generalizability of associations from prostate cancer genome-wide association studies in multiple populations.

Waters Kevin M KM Le Marchand Loic L Kolonel Laurence N LN Monroe Kristine R KR Stram Daniel O DO Henderson Brian E BE Haiman Christopher A CA

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20090324 4

Genome-wide association studies have identified multiple common alleles associated with prostate cancer risk in populations of European ancestry. Testing these variants in other populations is needed to assess the generalizability of the associations and may guide fine-mapping efforts. We examined 13 of these risk variants in a multiethnic sample of 2,768 incident prostate cancer cases and 2,359 controls from the Multiethnic Cohort (African Americans, European Americans, Latinos, Japanese Americ ...[more]

PMID: 19318432

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Project description:BACKGROUND:Use of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS. METHODS:Phenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line). Imputed 660 K and 80 K genotypes for the LW-line and DL-line, respectively, were imputed to iWGS using Beagle v.4.1. Since only 32 LW-line and 12 DL-line boars were sequenced, 142 animals from eight commercial lines were added. GWAS were performed for each line using the 80 K and 660 K SNPs, the genotype scores of iWGS SNPs that had an imputation accuracy (Beagle R2) higher than 0.6, and the dosage scores of all iWGS SNPs. RESULTS:For the DL-line (LW-line), imputation of 80 K genotypes to iWGS resulted in an average Beagle R2 of 0.39 (0.49). After quality control, 2.5?×?106 (3.5?×?106) SNPs had a Beagle R2 higher than 0.6, resulting in an average Beagle R2 of 0.83 (0.93). Compared to the 80 K and 660 K genotypes, using iWGS led to the identification of 48.9 and 64.4% more QTL regions, for the DL-line and LW-line, respectively, and the most significant SNPs in the QTL regions explained a higher proportion of phenotypic variance. Using dosage instead of genotype scores improved the identification of QTL, because the model accounted for uncertainty of imputation, and all SNPs were used in the analysis. CONCLUSIONS:Imputation to WGS using the multi-line reference population resulted in relatively poor imputation, especially when imputing from 80 K (DL-line). In spite of the poor imputation accuracies, using iWGS instead of a lower density SNP chip increased the number of detected QTL and the estimated proportion of phenotypic variance explained by these QTL, especially when dosage scores were used instead of genotype scores. Thus, iWGS, even with poor imputation accuracy, can be used to identify possible interesting regions for fine mapping.

Dataset Information

Generalizability of associations from prostate cancer genome-wide association studies in multiple populations.

Publications

Generalizability of associations from prostate cancer genome-wide association studies in multiple populations.

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