| Title | An entropy-based approach for testing genetic epistasis underlying complex diseases |
| Authors | Kang, Guolian Yue, Weihua Zhang, Jifeng Cui, Yuehua Zuo, Yijun Zhang, Dai |
| Affiliation | Peking Univ, Inst Mental Hlth, Minist Hlth, Key Lab Mental Hlth, Beijing 100083, Peoples R China. Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA. Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100080, Peoples R China. |
| Keywords | case-only design complex diseases entropy genetic epistasis genetic network GENOME-WIDE ASSOCIATION SCHIZOPHRENIA GENES JOINT ANALYSIS POLYMORPHISMS EXPRESSION YEAST |
| Issue Date | 2008 |
| Publisher | 理论生物学杂志 |
| Citation | JOURNAL OF THEORETICAL BIOLOGY.2008,250,(2),362-374. |
| Abstract | The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a chi(2) distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard chi(2) test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design. (c) 2007 Elsevier Ltd. All rights reserved. |
| URI | http://hdl.handle.net/20.500.11897/249582 |
| ISSN | 0022-5193 |
| DOI | 10.1016/j.jtbi.2007.10.001 |
| Indexed | SCI(E) |
| Appears in Collections: | 第六医院 |
