Bayesian Variable Selection in Logistic Mixed Models for WTCCC Data Sets / 針對WTCCC資料在羅吉斯混合模型下進行貝氏變數選取

碩士 / 國立彰化師範大學 / 統計資訊研究所 / 100 / In recent years, single nucleotide polymorphism (SNP) is widely used in biological and medical fields. Many scientists and statisticians try to find significant SNPs associated with disease from genome-wide data. In this thesis, we not only detect association between SNPs and disease under logistic mixed models, but also examine heterogeneity among geographical regions by adding random effects. We use stochastic search variable selection (SSVS) to select fixed and random effects simultaneously. Furthermore, we illustrate the SSVS with seven complex human diseases in the WTCCC (Wellcome Trust Case Control Consortium) data.

Identiferoai:union.ndltd.org:TW/100NCUE5506001
Date January 2012
CreatorsLu-Wei Lin, 林祿幃
ContributorsMiao-Yu Tsai, 蔡秒玉
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format56

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