Laboratory for Statistical Analysis

Medicine, United States

Laboratory for Statistical Analysis

Medicine, United States
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Hachiya T.,Iwate Medical University | Kamatani Y.,Laboratory for Statistical Analysis | Takahashi A.,Laboratory for Statistical Analysis | Takahashi A.,Japan National Cardiovascular Center Research Institute | And 40 more authors.
Stroke | Year: 2017

Background and Purpose-The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk. Methods-We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets). Results-In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33-2.31) and 1.99 (95% confidence interval, 1.19-3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001). Conclusions-The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors. © 2017 American Heart Association, Inc.


Wiklund F.,Karolinska Institutet | Schumacher F.R.,Norris Comprehensive Cancer Center | Stram D.O.,Norris Comprehensive Cancer Center | Berndt S.I.,U.S. National Institutes of Health | And 91 more authors.
Human Molecular Genetics | Year: 2015

Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genomeencoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10-4-5.6 × 10-3) and in 30 regions we identified markers thatwere more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10-6) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation. © The Author 2015. Published by Oxford University Press. All rights reserved.


Nguyen H.H.,RIKEN | Takata R.,RIKEN | Takata R.,Iwate Medical University | Akamatsu S.,RIKEN | And 11 more authors.
Human Molecular Genetics | Year: 2012

Recent genome-wide association studies (GWAS) identified a number of prostate cancer (PC) susceptibility loci, but most of their functional significances are not elucidated. Through our previous GWAS for PC in a Japanese population and subsequent resequencing and fine mapping, we here identified that IRX4 (Iroquois homeobox 4), coding Iroquois homeobox 4, is a causative gene of the PC susceptibility locus (rs12653946) at chromosome 5p15. IRX4 is expressed specifically in the prostate and heart, and quantitative expression analysis revealed a significant association between the genotype of rs12653946 and IRX4 expression in normal prostate tissues. Knockdown of IRX4 in PC cells enhanced their growth and IRX4 overexpression in PC cells suppressed their growth, indicating the functional association of IRX4 with PC and its tumor suppressive effect. Immunoprecipitation confirmed its protein-protein interaction to vitamin D receptor (VDR), and we found a significant interaction between IRX4 and VDR in their reciprocal transcriptional regulation. These findings indicate that the PC-susceptibility locus represented by rs12653946 at 5p15 is likely to regulate IRX4 expression in prostate which could suppress PC growth by interacting with the VDR pathway, conferring to PC susceptibility. © The Author 2012. Published by Oxford University Press. All rights reserved.


Low S.-K.,Laboratory for Statistical Analysis | Takahashi A.,Laboratory for Statistical Analysis | Mushiroda T.,Laboratory for Pharmacogenomics | Kubo M.,RIKEN
Clinical Cancer Research | Year: 2014

In recent years, the utilization of genome-wide association study (GWAS) has proved to be a beneficial method to identify novel common genetic variations not only for disease susceptibility but also for drug efficacy and drug-induced toxicity, creating a field of pharmacogenomics studies. In addition, the findings from GWAS also generate new biologic hypotheses that could improve the understanding of pathophysiology for disease or the mechanism of drug-induced toxicity. This review highlights the implications of GWAS that have been published to date and discusses the successes as well as challenges of using GWAS in cancer pharmacogenomics. The aim of pharmacogenomics is to realize the vision of personalized medicine; it is hoped that through GWAS, novel common genetic variations could be identified to predict clinical outcome and/or toxicity in cancer therapies that subsequently could be implemented to improve the quality of lives of patients with cancer. Nevertheless, given the complexity of cancer therapies, underpowered studies, and large heterogeneity of study designs, collaborative efforts are needed to validate these findings and overcome the limitations of GWA studies before clinical implementation. © 2014 American Association for Cancer Research.

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