Research Article

Inferring the functional effects of mutation through clusters of mutations in homologous proteins

Peng Yue

Corresponding Author

E-mail address: [email protected]

Department of Bioinformatics, Genentech Inc., South San Francisco, California

Genentech Inc., Bioinformatics, 1 DNA Way, South San Francisco, CA 94080Search for more papers by this author
William F. Forrest

Department of Biostatistics, Genentech Inc., South San Francisco, California

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Joshua S. Kaminker

Department of Bioinformatics, Genentech Inc., South San Francisco, California

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Scott Lohr

Department of Bioinformatics, Genentech Inc., South San Francisco, California

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Zemin Zhang

Department of Bioinformatics, Genentech Inc., South San Francisco, California

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Guy Cavet

Department of Bioinformatics, Genentech Inc., South San Francisco, California

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First published: 05 January 2010

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Citations: 28

Communicated by Marc S. Greenblatt

Abstract

Inferring functional consequences is a bottleneck in high‐throughput cancer mutation discovery and genetic association studies. Most polymorphisms and germline mutations are unlikely to have functionally significant consequences. Most cancer somatic mutations do not contribute to tumorigenesis and are not under selective pressure. Identifying and understanding functionally important mutations can clarify disease biology and lead to new therapeutic and diagnostic opportunities. We investigated the extent to which protein mutations with functional consequences are enriched in clusters at conserved positions across related proteins. We found that disease‐causing mutations form clusters more than random mutations or single nucleotide polymorphisms, confirming that mutation hotspots occur at the domain level. In addition to helping to identify functionally significant mutations, analysis of clustered mutations can indicate the mechanism and consequences for protein function. Our analysis focused on somatic cancer mutations suggests functional impact for many, including singleton mutations in FGFR1, FGFR3, GFI1B, PIK3CG, RALB, RAP2B, and STK11. This provides evidence and generates mechanistic hypotheses for the contribution of such mutations to cancer. The same approach can be applied to mutations suspected of involvement in other diseases. An interactive Web application for browsing mutation clusters is available at http://www.mcluster.org. Hum Mutat 30:1–9, 2010. © 2010 Wiley‐Liss, Inc.

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