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20 - Optimized Cross-Study Analysis of Microarray-Based Predictors
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- By Xiaogang Zhong, Johns Hopkins University, Luigi Marchionni, Johns Hopkins University, Leslie Cope, Johns Hopkins University, Edwin S. Iversen, Duke University, Elizabeth S. Garrett-Mayer, Medical University of South Carolina, Edward Gabrielson, Johns Hopkins University, Giovanni Parmigiani, Harvard University
- Edited by Kim-Anh Do, Zhaohui Steve Qin, Emory University, Atlanta, Marina Vannucci, Rice University, Houston
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- Book:
- Advances in Statistical Bioinformatics
- Published online:
- 05 June 2013
- Print publication:
- 10 June 2013, pp 398-422
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- Chapter
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Summary
Background
Genome-wide analyses, especially gene expression profiling using microarrays, have been extensively used in medical research and led to the identification of several molecular signatures involved in various aspects of human disease pathogenesis. Individual studies have typically investigated relatively small numbers of samples, making cross-study validation a crucial step for the scientific community. Combined use of gene expression data from public repositories has proved difficult due to inherent differences in microarray platforms, protocols used in independent laboratories, experimental designs, and annotations for both genes and samples. Several methodologies have been proposed to address these issues, depending on the experimental strategies and on the biological and clinical questions. When samples phenotypes are known, statistical methods that handle data sets separately and then apply gene-wise meta-analytic approaches have proven successful, allowing the identification of statistically relevant intersections of molecular signatures from different studies (Rhodes et al., 2002; Ghosh et al., 2003; Rhodes et al., 2004; Wang et al., 2004). Advanced multilevel models are now available for this task (Conlon et al., 2007; Scharpf et al., 2009). As an alternative, the assimilation of gene expression measurements, achieved by merging the data sets, has also been used to evaluate molecular signatures obtained from different studies (Sorlie et al., 2003; Hu et al., 2005; Kapp et al., 2006; Hayes et al., 2006). Finally, we previously developed a method to evaluate cross-platform consistency of expression patterns, using integrative correlation (ICOR).
Variation at 8q24 and 9p24 and Risk of Epithelial Ovarian Cancer
- Kristin L. White, Thomas A. Sellers, Brooke L. Fridley, Robert A. Vierkant, Catherine M. Phelan, Ya-Yu Tsai, Kimberly R. Kalli, Andrew Berchuck, Edwin S. Iversen, Jr, Lynn C. Hartmann, Mark Liebow, Sebastian Armasu, Zachary Fredericksen, Melissa C. Larson, David Duggan, Fergus J. Couch, Joellen M. Schildkraut, Julie M. Cunningham, Ellen L. Goode
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- Journal:
- Twin Research and Human Genetics / Volume 13 / Issue 1 / February 2010
- Published online by Cambridge University Press:
- 21 February 2012, pp. 43-56
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- Article
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- You have access Access
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The chromosome 8q24 region (specifically, 8q24.21.a) is known to harbor variants associated with risk of breast, colorectal, prostate, and bladder cancers. In 2008, variants rs10505477 and rs6983267 in this region were associated with increased risk of invasive ovarian cancer (p < 0.01); however, three subsequent ovarian cancer reports of 8q24 variants were null. Here, we used a multi-site case-control study of 940 ovarian cancer cases and 1,041 controls to evaluate associations between these and other single-nucleotide polymorphisms (SNPs) in this 8q24 region, as well as in the 9p24 colorectal cancer associated-region (specifically, 9p24.1.b). A total of 35 SNPs from previous reports and additional tagging SNPs were assessed using an Illumina GoldenGate array and analyzed using logistic regression models, adjusting for population structure and other potential confounders. We observed no association between genotypes and risk of ovarian cancer considering all cases, invasive cases, or invasive serous cases. For example, at 8q24 SNPs rs10505477 and rs6983267, analyses yielded per-allele invasive cancer odds ratios of 0.95 (95% confidence interval (CI) 0.82–1.09, p trend 0.46) and 0.97 (95% CI 0.84–1.12, p trend 0.69), respectively. Analyses using an approach identical to that of the first positive 8q24 report also yielded no association with risk of ovarian cancer. In the 9p24 region, no SNPs were associated with risk of ovarian cancer overall or with invasive or invasive serous disease (all p values > 0.10). These results indicate that the SNPs studied here are not related to risk of this gynecologic malignancy and that the site-specific nature of 8q24.21.a associations may not include ovarian cancer.
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