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Redwood City, CA, United States

Analyses intended to identify genes with expression that is associated with some clinical outcome or state are often based on ranked p-values from tests of point null hypotheses of no association. Van de Wiel and Kim take the innovative approach of testing the interval null hypotheses that the degree of association for a gene is less than some value of interest against the alternative that it is greater. Combining this idea with the false discovery rate controlling methods of Storey, Taylor and Siegmund gives a computationally simple way to identify true discovery rate degree of association (TDRDA) sets of genes among which a specified proportion are expected to have an absolute association of a specified degree or more. This leads to a gene ranking method that uses the maximum lower bound degree of association for which each gene belongs to a TDRDA set. Estimates of each gene's actual degree of association with approximate correction for 'selection bias' due to regression to the mean (RM) can be derived using simple bivariate normal theory and Efron and Tibshirani's empirical Bayes approach. For a given data set, all possible TDRDA sets can be displayed along with the gene ranking and the RM-corrected estimates of degree of association in a concise graphical summary. Copyright © 2009 John Wiley & Sons, Ltd. Source

Williams M.S.,Genomic Health
American Journal of Medical Genetics, Part C: Seminars in Medical Genetics | Year: 2014

Genomic Medicine is beginning to emerge into clinical practice. The National Human Genome Research Institute's Genomic Medicine Working Group consists of organizations that have begun to implement some aspect of genomic medicine (e.g., family history, systematic implementation of Mendelian disease program, pharmacogenomics, whole exome/genome sequencing). This article concisely reviews the working group and provides a broader context for the articles in the special issue including an assessment of anticipated provider needs and ethical, legal, and social issues relevant to the implementation of genomic medicine. The challenges of implementation of innovation in clinical practice and the potential value of genomic medicine are discussed. © 2014 Wiley Periodicals, Inc. Source

Recent work on prospective power and sample size calculations for analyses of high-dimension gene expression data that control the false discovery rate (FDR) focuses on the average power over all the truly nonnull hypotheses, or equivalently, the expected proportion of nonnull hypotheses rejected. Using another characterization of power, we adapt Efron's ([2007] Ann Stat 35:1351-1377) empirical Bayes approach to post hoc power calculation to develop a method for prospective calculation of the "identification power" for individual genes. This is the probability that a gene with a given true degree of association with clinical outcome or state will be included in a set within which the FDR is controlled at a specified level. An example calculation using proportional hazards regression highlights the effects of large numbers of genes with little or no association on the identification power for individual genes with substantial association. © 2012 Wiley Periodicals, Inc. Source

Genomic Health | Date: 2015-03-11

The invention relates to methods of depleting RNA from a nucleic acid sample. The RNA may be any RNA, including, but not limited to, rRNA, tRNA, and mRNA. The method is useful for depleting RNA from a nucleic acid sample obtained from a fixed paraffin-embedded tissue (FPET) sample. The method may also be used to prepare cDNA, in particular, a cDNA library for further analysis or manipulation.

Genomic Health and NSABP Foundation Inc. | Date: 2014-09-18

The present invention provides gene expression information useful for predicting whether a cancer patient is likely to have a beneficial response to treatment with chemotherapy, comprising measuring, in a biological sample comprising a breast tumor sample obtained from the patient, the expression levels of gene subsets to obtain a risk score associated with a likelihood of a beneficial response to chemotherapy, wherein the score comprises at least one of the following variables: (i) Recurrence Score, (ii) ESRI Group Score; (iii) Invasion Group Score; (iv) Proliferation Group Score; and (v) the expression level of the RNA transcript of at least one of MYBL2 and SCUBE2, or the corresponding expression product. The invention further comprises a molecular assay-based algorithm to calculate the likelihood that the patient will have a beneficial response to chemotherapy based on the risk score.

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