Evaluating genetic risk for prostate cancer among Japanese and Latinos.

BACKGROUND: There have been few genome-wide association studies (GWAS) of prostate cancer among diverse populations. To search for novel prostate cancer risk variants, we conducted GWAS of prostate cancer in Japanese and Latinos. In addition, we tested prostate cancer risk variants and developed genetic risk models of prostate cancer for Japanese and Latinos. METHODS: Our first-stage GWAS of prostate cancer included Japanese (cases/controls = 1,033/1,042) and Latino (cases/controls = 1,043/1,057) from the Multiethnic Cohort (MEC). Significant associations from stage I (P < 1.0 × 10(-4)) were examined in silico in GWAS of prostate cancer (stage II) in Japanese (cases/controls = 1,583/3,386) and Europeans (cases/controls = 1,854/1,894). RESULTS: No novel stage I single-nucleotide polymorphism (SNP) outside of known risk regions reached genome-wide significance. For Japanese, in stage I, the most notable putative novel association was seen with 10 SNPs (P ≤ 8.0 × 10(-6)) at chromosome 2q33; however, this was not replicated in stage II. For Latinos, the most significant association was observed with rs17023900 at the known 3p12 risk locus (stage I: OR = 1.45; P = 7.01 × 10(-5) and stage II: OR = 1.58; P = 3.05 × 10(-7)). The majority of the established risk variants for prostate cancer, 79% and 88%, were positively associated with prostate cancer in Japanese and Latinos (stage I), respectively. The cumulative effects of these variants significantly influence prostate cancer risk (OR per allele = 1.10; P = 2.71 × 10(-25) and OR = 1.07; P = 1.02 × 10(-16) for Japanese and Latinos, respectively). CONCLUSION AND IMPACT: Our GWAS of prostate cancer did not identify novel genome-wide significant variants. However, our findings show that established risk variants for prostate cancer significantly contribute to risk among Japanese and Latinos

Impact of Dimension and Sample Size on the Performance of Imputation Methods

Real-world data collections often contain missing values, which can bring serious problems for data analysis. Simply discarding records with missing values tend to create bias in analysis. Missing data imputation methods try to fill in the missing values with estimated values. While numerous imputations methods have been proposed, these methods are mostly judged by their imputation accuracy, and little attention has been paid to their efficiency. With the increasing size of data collections, the imputation efficiency becomes an important issue. In this work we conduct an experimental comparison of several popular imputation methods, focusing on their time efficiency and scalability in terms of sample size and record dimension (number of attributes). We believe these results can provide a guide to data analysts when choosing imputation methods.No Full Tex