Journal Article

Identification of epistatic effects using a protein–protein interaction database

Yan V. Sun and Sharon L.R. Kardia

in Human Molecular Genetics

Volume 19, issue 22, pages 4345-4352
Published in print November 2010 | ISSN: 0964-6906
Published online August 2010 | e-ISSN: 1460-2083 | DOI: http://dx.doi.org/10.1093/hmg/ddq356
Identification of epistatic effects using a protein–protein interaction database

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Epistasis (i.e. gene–gene interaction) has long been recognized as an important mechanism underlying the complexity of the genetic architecture of human traits. Definitions of epistasis range from the purely molecular to the traditional statistical measures of interaction. The statistical detection of epistasis usually does not map onto or easily relate to the biological interactions between genetic variations through their combined influence on gene expression or through their interactions at the gene product (i.e. protein) or DNA level. Recently, greater high-dimensional data on protein–protein interaction (PPI) and gene expression profiles have been collected that enumerates sets of biological interactions. To better align statistical and molecular models of epistasis, we present an example of how to incorporate the PPI information into the statistical analysis of interactions between copy number variations (CNVs). Among the 23 640 pairs of known human PPIs and the 1141 common CNVs detected among HapMap samples, we identified 37 pairs of CNVs overlapping with both genes of a PPI pair. Two CNV pairs provided sufficient genotype variation to search for epistatic effects on gene expression. Using 47 294 probe-specific gene expression levels as the outcomes, five epistatic effects were identified with P-value less than 10−6. We found a CNV–CNV interaction significantly associated with gene expression of TP53TG3 (P-value of 2 × 10−20). The proteins associated with the CNV pair also bind TP53 which regulates the transcription of TP53TG3. This study demonstrates that using PPI data can assist in targeting statistical hypothesis testing to biological plausible epistatic interaction that reflects molecular mechanisms.

Journal Article.  4677 words.  Illustrated.

Subjects: Genetics and Genomics

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