Bayesian methods for structural equation models in quantitative genetics with applications to the study of mammary gland disease

定量遗传学结构方程模型的贝叶斯方法及其在乳腺疾病研究中的应用

• 批准号: 0443771
• 负责人: Daniel Gianola
• 金额: --
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0443771&HistoricalAwards=false
• 关键词: Bayesian; methods; structural; equation; models

This project will develop quantitative genetic theory and statistical methods for studying complex genetic systems where recursive or feedback relationships between variables exist. Methods researched will be used to study mastitis, a disease of the mammary gland in breast-feeding women (mastitis), employing the cow as an animal model. Extensive health, milk production and milk somatic cell cow data from the Norwegian cattle health registry will be used to model relationships between variables. The 3-year project includes: 1) development of Bayesian Markov chain Monte Carlo algorithms for quantitative genetic systems under Gaussian assumptions. 2) Extension to systems in which some phenotypes are limited-dependent (e.g., binary). 3) Modeling of relationships between presence/absence of clinical mastitis, somatic cell concentration in milk, milk yield, genotype and several explanatory variables, using information from cow records. A fully pedigreed data set (complete medical treatment history of 33,453 first-lactation cows from 4961 herds, daughters of 245 sires) will be used for pilot studies.The research focuses on a new framework for the study of multivariate systems in quantitative genetics, using the cow as a model for a disease of the mammary gland, mastitis. Relationships between milk output, somatic cell concentration, presence or absence of clinical mastitis and several potential explanatory data will be investigated using data from the Norwegian cattle health registry system. Knowledge will be drawn and integrated from econometrics, structural equation modeling and statistical genetics. An educational component in a novel area in the interface between statistics, genetics, disease modeling, econometrics and sociometrics will be included. Software developed will be made available to the scientific community through the Internet.

该项目将开发用于研究变量之间存在递归或反馈关系的复杂遗传系统的定量遗传理论和统计方法。研究方法将用于研究乳腺炎，这是一种哺乳期妇女的乳腺疾病（乳腺炎），采用奶牛作为动物模型。挪威牛健康登记处的大量健康、产奶量和奶牛体细胞数据将用于建立变量之间的关系模型。这个为期3年的项目包括：1）在高斯假设下开发定量遗传系统的贝叶斯马尔可夫链蒙特卡罗算法。2)扩展到某些表型是有限依赖的系统（例如，二进制）。3)利用奶牛记录中的信息，对临床乳腺炎的存在/不存在、牛奶中体细胞浓度、牛奶产量、基因型和几个解释变量之间的关系进行建模。一个完整的血统数据集（来自4961个牛群的33,453头第一泌乳期奶牛的完整医疗史，245个父系的女儿）将用于试点研究。研究重点是一个新的框架，用于数量遗传学中的多变量系统的研究，使用奶牛作为乳腺疾病，乳腺炎的模型。将使用挪威牛健康登记系统的数据研究产奶量、体细胞浓度、临床乳腺炎的存在或不存在与几个潜在解释性数据之间的关系。知识将从计量经济学，结构方程模型和统计遗传学中提取和整合。将包括统计学，遗传学，疾病建模，计量经济学和社会计量学之间的接口的一个新领域的教育组成部分。所开发的软件将通过互联网提供给科学界。