基于Cox模型的遺傳方差分量模型研究及應(yīng)用
發(fā)布時間：2018-11-27 07:03
【摘要】：復(fù)雜疾病是環(huán)境因素和遺傳因素共同作用的結(jié)果。為研究復(fù)雜疾病病因,遺傳流行病學(xué)首先需對疾病性狀進(jìn)行家庭聚集性評價。家庭成員間性狀不獨立是家系資料的特點。對常見數(shù)量性狀和質(zhì)量性狀,可采用邊際模型或廣義線性混合效應(yīng)模型解決家系內(nèi)相關(guān)的問題。但如發(fā)病年齡、月經(jīng)初潮等刪失性狀,則需要在生存分析的框架下研究,Cox模型是生存分析中應(yīng)用最廣泛的模型之一。經(jīng)典的遺傳方差分量模型將家系中各成員間復(fù)雜性狀的家庭相關(guān)來源分為加性遺傳隨機(jī)效應(yīng)、顯性遺傳隨機(jī)效應(yīng)和家庭共享環(huán)境隨機(jī)效應(yīng),且通常假設(shè)隨機(jī)效應(yīng)服從多元正態(tài)分布。本文在Cox模型的基礎(chǔ)上引入遺傳和環(huán)境隨機(jī)效應(yīng),構(gòu)造包含多個隨機(jī)效應(yīng)的Cox遺傳方差分量模型。由于隨機(jī)效應(yīng)服從多元正態(tài)分布,給模型參數(shù)估計時求解高維積分帶來極大困難,本課題探討了應(yīng)用Laplace近似法和懲罰性似然理論來解決高維積分的困難;在R和S-Plus軟件中編制程序?qū)崿F(xiàn)了核心家系和擴(kuò)展家系資料的隨機(jī)效應(yīng)方差協(xié)方差矩陣的設(shè)計矩陣,并利用coxme函數(shù)實現(xiàn)了Laplace近似的懲罰性偏似然(PPL)參數(shù)估計;同時將Cox遺傳方差分量模型應(yīng)用于廣東順德的原發(fā)性肝癌核心家系資料和江蘇泰興的原發(fā)性肝癌擴(kuò)展家系資料,利用Laplace近似的PPL參數(shù)估計方法建立原發(fā)性肝癌發(fā)病年齡的Cox遺傳方差分量模型,以說明該模型和參數(shù)估計方法的可行性。本研究旨在為遺傳流行病學(xué)研究者提供一種有效的、靈活的用于評價刪失性狀家庭聚集性的統(tǒng)計分析工具。
[Abstract]:Complex diseases are the result of both environmental and genetic factors. In order to study the etiology of complex diseases, genetic epidemiology needs to evaluate the family aggregation of disease traits. The character of family members is not independent is the characteristics of family data. For common quantitative and qualitative traits, marginal model or generalized linear mixed effect model can be used to solve the related problems in families. However, such as age of onset, menarche and so on, need to be studied in the framework of survival analysis. Cox model is one of the most widely used models in survival analysis. The classical genetic variance component model classifies the family related sources of complex traits among the family members into additive genetic random effect, dominant genetic random effect and family shared environment random effect. And it is usually assumed that the random effect is from the multivariate normal distribution. In this paper, based on the Cox model, genetic and environmental random effects are introduced to construct the Cox genetic variance component model with multiple random effects. Because the random effect is from the multivariate normal distribution, it is very difficult to solve the high dimensional integral in the parameter estimation of the model. In this paper, the difficulty of applying Laplace approximation and punitive likelihood theory to solve the high dimensional integral is discussed. The design matrix of random effect variance covariance matrix of core family and extended family data is realized by programming in R and S-Plus software, and the punitive partial likelihood (PPL) parameter estimation of Laplace approximation is realized by using coxme function. At the same time, the Cox genetic variance component model was applied to the nuclear family data of primary liver cancer in Shunde, Guangdong Province, and the extended family data of primary liver cancer in Taixing, Jiangsu Province. The Laplace approximate PPL parameter estimation method was used to establish the Cox genetic variance component model of the onset age of primary liver cancer to demonstrate the feasibility of the model and the parameter estimation method. The purpose of this study is to provide an effective and flexible statistical analysis tool for genetic epidemiologists to evaluate the family clustering of censored traits.
【學(xué)位授予單位】：廣東藥學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2010
【分類號】：R363
本文編號：2359788
[Abstract]:Complex diseases are the result of both environmental and genetic factors. In order to study the etiology of complex diseases, genetic epidemiology needs to evaluate the family aggregation of disease traits. The character of family members is not independent is the characteristics of family data. For common quantitative and qualitative traits, marginal model or generalized linear mixed effect model can be used to solve the related problems in families. However, such as age of onset, menarche and so on, need to be studied in the framework of survival analysis. Cox model is one of the most widely used models in survival analysis. The classical genetic variance component model classifies the family related sources of complex traits among the family members into additive genetic random effect, dominant genetic random effect and family shared environment random effect. And it is usually assumed that the random effect is from the multivariate normal distribution. In this paper, based on the Cox model, genetic and environmental random effects are introduced to construct the Cox genetic variance component model with multiple random effects. Because the random effect is from the multivariate normal distribution, it is very difficult to solve the high dimensional integral in the parameter estimation of the model. In this paper, the difficulty of applying Laplace approximation and punitive likelihood theory to solve the high dimensional integral is discussed. The design matrix of random effect variance covariance matrix of core family and extended family data is realized by programming in R and S-Plus software, and the punitive partial likelihood (PPL) parameter estimation of Laplace approximation is realized by using coxme function. At the same time, the Cox genetic variance component model was applied to the nuclear family data of primary liver cancer in Shunde, Guangdong Province, and the extended family data of primary liver cancer in Taixing, Jiangsu Province. The Laplace approximate PPL parameter estimation method was used to establish the Cox genetic variance component model of the onset age of primary liver cancer to demonstrate the feasibility of the model and the parameter estimation method. The purpose of this study is to provide an effective and flexible statistical analysis tool for genetic epidemiologists to evaluate the family clustering of censored traits.
【學(xué)位授予單位】：廣東藥學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2010
【分類號】：R363
【參考文獻(xiàn)】
相關(guān)博士學(xué)位論文 前1條
1 郜艷暉;復(fù)雜性狀家庭聚集性統(tǒng)計分析方法的研究[D];復(fù)旦大學(xué);2004年
，本文編號：2359788
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