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

本项目将发展定量遗传理论和统计方法，用于研究变量之间存在递归或反馈关系的复杂遗传系统。所研究的方法将用于研究乳腺炎，乳腺炎是哺乳妇女的一种乳腺疾病（乳腺炎），以牛为动物模型。来自挪威牛健康登记的大量健康、产奶量和乳体细胞牛数据将用于模拟变量之间的关系。该项目为期3年，主要包括：1)开发高斯假设下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)利用奶牛记录的信息，建立临床乳腺炎存在/不存在、乳中体细胞浓度、产奶量、基因型和几个解释变量之间的关系模型。一个完整的系谱数据集（来自4961个畜群的33,453头首次泌乳奶牛的完整医疗史，245头母猪）将用于试点研究。该研究的重点是在定量遗传学中研究多元系统的新框架，使用奶牛作为乳腺疾病乳腺炎的模型。将利用挪威牛健康登记系统的数据调查产奶量、体细胞浓度、是否存在临床乳腺炎和几个潜在的解释性数据之间的关系。知识将从计量经济学、结构方程建模和统计遗传学中汲取和整合。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一个新领域的教育组成部分。开发的软件将通过互联网提供给科学界。

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.