復(fù)雜性狀遺傳風(fēng)險(xiǎn)預(yù)測(cè)的統(tǒng)計(jì)方法研究
發(fā)布時(shí)間:2021-01-24 05:03
這篇博士論文主要研究基于復(fù)雜性狀或疾病的,構(gòu)建風(fēng)險(xiǎn)預(yù)測(cè)模型的統(tǒng)計(jì)遺傳學(xué)方法。隨著復(fù)雜疾病研究的不斷深入,研究者檢測(cè)并收集了大量與復(fù)雜疾病相關(guān)聯(lián)的遺傳變異(如單核苷酸多態(tài)性等)。而基于這些探測(cè)到的遺傳或環(huán)境風(fēng)險(xiǎn)因子而構(gòu)建成的遺傳風(fēng)險(xiǎn)預(yù)測(cè)模型將推進(jìn)醫(yī)學(xué)和臨床的發(fā)展。但是迄今為止,用現(xiàn)有方法構(gòu)建的遺傳風(fēng)險(xiǎn)預(yù)測(cè)模型的精確度都不理想。而與此同時(shí),全基因組關(guān)聯(lián)分析的發(fā)展也激發(fā)了研究者基于高維數(shù)據(jù)構(gòu)建風(fēng)險(xiǎn)預(yù)測(cè)模型的興趣。本論文提出了同時(shí)適用于基于現(xiàn)有的風(fēng)險(xiǎn)遺傳變異或環(huán)境因子的,以及基于全基因組高維序列數(shù)據(jù)的非參數(shù)風(fēng)險(xiǎn)預(yù)測(cè)模型。此外,該風(fēng)險(xiǎn)預(yù)測(cè)模型可以考慮到基因與基因,基因與環(huán)境之間的互作,從而進(jìn)一步提高了風(fēng)險(xiǎn)預(yù)測(cè)的精確度。其中“前向ROC”方法主要適用于病例與對(duì)照的序列數(shù)據(jù)的分析需求,而CORC方法則適用于基于家系產(chǎn)生序列數(shù)據(jù)的遺傳風(fēng)險(xiǎn)預(yù)測(cè)。論文共分四章。第一章引導(dǎo)和概述了復(fù)雜疾病研究的發(fā)展和現(xiàn)狀。特別闡述了用于檢測(cè)在疾病形成過程中具有重要作用的遺傳因子而展開的全基因組關(guān)聯(lián)分析的發(fā)展。在此基礎(chǔ)上,本論文介紹了疾病遺傳風(fēng)險(xiǎn)預(yù)測(cè)的發(fā)展歷史,從最初的孟德爾性狀到如今的常見復(fù)雜疾病,分析了復(fù)雜性狀與孟德爾性...
【文章來源】:浙江大學(xué)浙江省 211工程院校 985工程院校 教育部直屬院校
【文章頁(yè)數(shù)】:82 頁(yè)
【學(xué)位級(jí)別】:博士
【文章目錄】:
致謝
摘要
Abstract
List of Tables
List of Figures
1 INTRODUCTION
1.1 OVERVIEW OF GENOME-WIDE ASSOCIATION STUDIES
1.2 PREDICTIVE GENETIC TESTING
1.3 ROC CURVE AND AUC VALUE
1.3.1 ROC curve
1.3.2 AUC value
1.3.3 Likelihood Ratios and the ROC plot
2 CONSTRUCTION OF PREDICTIVE GENETIC TESTS ON GENOME-WIDECASE-CONTROL DATASETS
2.1 INTRODUCTION
2.2 METHODS
2.2.1 Optimal ROC Curve
2.2.2 The Forward ROC Method
2.2.2.1 Forward selection algorithm
2.2.2.2 Procedure for handling missing data
2.3 RESULTS
2.3.1 Simulation studies
2.3.1.1 Scenario Ⅰ
2.3.1.2 Scenario Ⅱ
2.3.1.3 Scenario Ⅲ
2.3.1.4 Simulations for bagging cross-validation
2.3.1.5 Comparison with Random Forest
2.3.2 Predictive Genetic Tests for Rheumatoid Arthritis
2.3.2.1 Predictive genetic tests based on currently known risk factors
2.3.2.2 A predictive genetic test formed based on whole genome-wide data
2.4 Discussion
3 CONSTRUCTION OF PREDICTIVE GENETIC TESTS ON FAMILIY-BASEDDATASETS
3.1 Introduction
3.2 The CORC Methods
3.3 Simulations Studies
3.3.1 Scenario Ⅰ
3.3.2 ScenarioⅡ
3.4 Data Application for Conduct Disorder
3.5 Discussion
4 SUMMARY AND CONCLUSIONS
PUBLICATIONS
REFERENCES
個(gè)人簡(jiǎn)歷
本文編號(hào):2996627
【文章來源】:浙江大學(xué)浙江省 211工程院校 985工程院校 教育部直屬院校
【文章頁(yè)數(shù)】:82 頁(yè)
【學(xué)位級(jí)別】:博士
【文章目錄】:
致謝
摘要
Abstract
List of Tables
List of Figures
1 INTRODUCTION
1.1 OVERVIEW OF GENOME-WIDE ASSOCIATION STUDIES
1.2 PREDICTIVE GENETIC TESTING
1.3 ROC CURVE AND AUC VALUE
1.3.1 ROC curve
1.3.2 AUC value
1.3.3 Likelihood Ratios and the ROC plot
2 CONSTRUCTION OF PREDICTIVE GENETIC TESTS ON GENOME-WIDECASE-CONTROL DATASETS
2.1 INTRODUCTION
2.2 METHODS
2.2.1 Optimal ROC Curve
2.2.2 The Forward ROC Method
2.2.2.1 Forward selection algorithm
2.2.2.2 Procedure for handling missing data
2.3 RESULTS
2.3.1 Simulation studies
2.3.1.1 Scenario Ⅰ
2.3.1.2 Scenario Ⅱ
2.3.1.3 Scenario Ⅲ
2.3.1.4 Simulations for bagging cross-validation
2.3.1.5 Comparison with Random Forest
2.3.2 Predictive Genetic Tests for Rheumatoid Arthritis
2.3.2.1 Predictive genetic tests based on currently known risk factors
2.3.2.2 A predictive genetic test formed based on whole genome-wide data
2.4 Discussion
3 CONSTRUCTION OF PREDICTIVE GENETIC TESTS ON FAMILIY-BASEDDATASETS
3.1 Introduction
3.2 The CORC Methods
3.3 Simulations Studies
3.3.1 Scenario Ⅰ
3.3.2 ScenarioⅡ
3.4 Data Application for Conduct Disorder
3.5 Discussion
4 SUMMARY AND CONCLUSIONS
PUBLICATIONS
REFERENCES
個(gè)人簡(jiǎn)歷
本文編號(hào):2996627
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