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Identify SNPs associated with type 2 diabetes using self-organizing maps and random forests.

Zhang, Ji. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 100-104). / Abstracts in English and Chinese. / Chapter CHAPTER 1. --- Introduction / Chapter 1.1. --- Introduction of genetic association studies --- p.1 / Chapter 1.1.1. --- Application of genetic association studies in complex diseases --- p.3 / Chapter 1.1.2. --- Application of genetic association studies in type-2 diabetes --- p.4 / Chapter 1.2. --- Study design of genetic association studies --- p.7 / Chapter 1.3. --- Overview of statistical approaches in association studies --- p.10 / Chapter 1.3.1. --- Preliminary analyses --- p.10 / Chapter 1.3.1.1. --- HardýؤWeinberg equilibrium testing --- p.10 / Chapter 1.3.1.2. --- Inference of missing genotype data --- p.12 / Chapter 1.3.1.3. --- SNP tagging --- p.14 / Chapter 1.3.2. --- Single-point and multipoint tests for association --- p.15 / Chapter 1.4. --- Other relevant methods employed in this study --- p.20 / Chapter 1.4.1. --- Self-Organizing Maps (SOM) with further classification by K-means clustering --- p.20 / Chapter 1.4.2. --- Random forests --- p.27 / Chapter 1.5. --- Main objectives of this study --- p.31 / Chapter CHAPTER 2. --- Materials and methods / Chapter 2.1. --- Study cohort --- p.32 / Chapter 2.2. --- Study design --- p.34 / Chapter 2.2.1. --- Construction of sample sets for each stage using SOM and K-means clustering --- p.34 / Chapter 2.2.2. --- Stage 1 analysis by random forests --- p.37 / Chapter 2.2.3. --- Stage 2 analysis by chi-square test --- p.42 / Chapter 2.2.4. --- Two-stage genetic association study by chi-square test --- p.43 / Chapter 2.2.5. --- Comparison of results: random forests plus chi-square test versus chi-square test --- p.43 / Chapter 2.2.6. --- Validation of results in the whole sample set by allelic chi-square test --- p.44 / Chapter 2.2.7. --- Extensions of the study: cumulative effects of candidate SNPs on risk of type-2 diabetes --- p.45 / Chapter CHAPTER 3. --- Results / Chapter 3.1. --- Effects of sample classification by SOM and K-means clustering --- p.50 / Chapter 3.2. --- Genetic associations in stage 1 --- p.64 / Chapter 3.3. --- Genetic associations in stage 2 and validation of results --- p.69 / Chapter 3.4. --- Cumulative effects of candidate SNPs on risk of type-2 diabetes --- p.76 / Chapter CHAPTER 4. --- Discussion / Chapter 4.1. --- Overall strategy --- p.81 / Chapter 4.1.1. --- Effects of SOM and K-means clustering --- p.82 / Chapter 4.1.2. --- Effects of random forests in the first stage of association study --- p.83 / Chapter 4.1.3. --- Comparison of our method with traditional chi-square test --- p.84 / Chapter 4.1.4. --- Joint effects of candidate SNPs selected by the hybrid method --- p.86 / Chapter 4.2. --- Biological significance of candidate SNPs --- p.88 / Chapter 4.2.1. --- Gene CDKAL1 --- p.89 / Chapter 4.2.2. --- Gene KIAA1305 --- p.90 / Chapter 4.2.3. --- Gene DACH1 --- p.91 / Chapter 4.2.4. --- Gene FUCA1 --- p.92 / Chapter 4.2.5. --- Gene KCNQ1 --- p.93 / Chapter 4.2.6. --- Gene SLC27A1 --- p.94 / Chapter 4.3. --- Limits and improvement of this study --- p.96 / Chapter 4.4. --- Conclusion --- p.99 / REFERENCES --- p.100

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_326943
Date January 2009
ContributorsZhang, Ji., Chinese University of Hong Kong Graduate School. Division of Medical Sciences.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xiii, 104 leaves : ill. (chiefly col.) ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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