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http://ir.ncue.edu.tw/ir/handle/987654321/14460
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| 題名: | Extended Bayesian Model Averaging in Generalized Linear Mixed Models Applied to Schizophrenia Family Data |
| 作者: | Tsai, Miao-Yu;Hsiao, Chuhsing K.;Chen, Wei J. |
| 貢獻者: | 統計資訊研究所 |
| 關鍵詞: | Bayesian model averaging;Genotype combination;Heritability;Interactions;Multiplex family |
| 日期: | 2011-01
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| 上傳時間: | 2012-10-25T09:04:04Z
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| 出版者: | Blackwell Publishing Ltd |
| 摘要: | The study of disease etiology and the search for susceptible genes of schizophrenia have attracted scientists’ attention for
decades.Many findings however are inconsistent, possibly due to the higher order interactions involvingmulti-dimensional
genetic and environmental factors or due to the commingling of different ethnic groups. Several studies applied generalized
linear mixed models (GLMMs) with family data to identify the genetic contribution to, and environmental influence
on, schizophrenia, and to clarify the existence and sources of familial aggregation. Based on an extended Bayesian model
averaging (EBMA) procedure, here we estimate the gene-gene (GG) and gene-environment (GE) interactions, and
heritability of schizophrenia via variance components of random-effects in GLMMs. Our proposal takes into account
the uncertainty in covariates and in genetic model structures, where each competing model includes environmental and
genetic covariates, and GE and GG interactions. Simulation studies are conducted to compare the performance of the
EBMA approach, permutation test procedure and GEE method. We also illustrate this approach with data from singleton
and multiplex schizophrenia families. The results indicate that EBMA is a flexible and stable tool in exploring true
candidate genes, and GE and GG interactions, after adjusting for explanatory variables and correlation structures within
family members. |
| 關聯: | Annals of Human Genetics, 75(1): 62-77 |
| 顯示於類別: | [統計資訊研究所] 期刊論文
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