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A statistical approach to finding overlooked genetic associations |
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| Authors: | Andrew K Rider Geoffrey Siwo Nitesh V Chawla Michael Ferdig Scott J Emrich |
| Institution: | 1.Department of Computer Science and Engineering,University of Notre Dame,Notre Dame,USA;2.Department of Biological Sciences,University of Notre Dame,Notre Dame,USA;3.Eck Institute for Global Health,University of Notre Dame,Notre Dame,USA;4.Interdisciplinary Center for Network Science and Applications,University of Notre Dame,Notre Dame,USA |
| Abstract: | BackgroundComplexity 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. |
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