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(Circulation Research. 2005;96:605.)
© 2005 American Heart Association, Inc.

Editorials

Candidate Gene Discovery in Cardiovascular Disease

Raimond L. Winslow, Zhong Gao

From The Center for Cardiovascular Bioinformatics and Modeling and The Whitaker Biomedical Engineering Institute, Johns Hopkins University School of Medicine and Whiting School of Engineering, Baltimore, MD.

Correspondence to Raimond L. Winslow, Rm 201B Clark Hall, The Johns Hopkins University, 3400 N. Charles St., Baltimore MD 21218. E-mail rwinslow@bme.jhu.edu

See related article, pages 617–625

Key Words: microarrays • gene expression

An extract of the first 250 words of the full text is provided, because this article has no abstract.

One of the most exciting new experimental technologies to emerge in recent years has been methods for obtaining genome-wide mRNA expression data using oligonucleotide¹ and cDNA microarrays² (for review, see Cook and Rosenzweig³). Application of gene expression profiling was limited initially by cost, which in turn imposed severe constraints on the number of hybridizations that could be performed and thus the statistical significance of experimental results. This barrier has been reduced as array fabrication techniques have advanced and it is now common for studies to investigate differential gene expression at relatively large sample numbers, see for example, Singh et al⁴ and Margulis et al⁵. Improved statistical methods for inferring regulated genes⁶ as well as for grouping genes on the basis of related expression patterns⁷ have also been developed and refined.

Application of these methods to analysis of differential gene expression in cardiovascular disease typically reveals large numbers of genes with altered expression. These numbers can range from hundreds⁸ to thousands⁵ and they pose a new challenge—how best do we sift through these lists to identify those genes having relevance to disease development and progression (referred to as "candidate genes")? The most common approach is to perform a manual or semi-automated⁹ search of publicly available databases to evaluate gene function on the basis of annotation, and to then select as candidate genes those with function deemed most relevant to the disease process. While useful, such approaches are subjective and limited by the availability and accuracy of functional annotations. The . . . [Full Text of this Article]

Related Article:

Identification of Hypertension-Related Genes Through an Integrated Genomic-Transcriptomic Approach

Chana Yagil, Norbert Hubner, Jan Monti, Herbert Schulz, Marina Sapojnikov, Friedrich C. Luft, Detlev Ganten, and Yoram Yagil
Circ. Res. 2005 96: 617-625. [Abstract] [Full Text] [PDF]

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