A statistical approach to finding overlooked genetic associations

Background: Complexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects. We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genomewide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus. Our approach...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Andrew K. Rider, Geoffrey Siwo, Nitesh V. Chawla, Michael T. Ferdig, Scott J. Emrich
• Keywords: BMCBI 2010, Expression Quantitative Trait, Expression Traits, False Negatives