ESTIMATION & TEST OF MARKER ASSOCIATION IN FAMILY DATA

预估

• 批准号: 3467430
• 负责人: VARGHESE GEORGE
• 金额: $ 6.87万
• 依托单位: LSU HEALTH SCIENCES CENTER
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-09-28 至 1993-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3467430
• 关键词: biological polymorphism; computer data analysis; computer simulation; disease /disorder proneness /risk; gene expression; genetic disorder; genetic markers; genetic models; human age group; human population genetics; mathematical model; model design /development; molecular pathology; statistics /biometry

There is currently an ever-growing number of polymorphic genetic markers available for the study of marker association with human disease traits. Knowledge of such associations provides a basic for genetic counseling. Because the study of familial disease by segregation and linkage analyses requires the collection of data on family and pedigree members, it is becoming more and more necessary to infer marker associations from data that have been collected with these multiple purposes in mind. Therefore, comprehensive, rigorous and practical statistical methodologies will be developed for analyzing from extended human pedigree data the associations between genetic markers and traits characteristic of familial diseases. Separate methodologies for qualitative traits, qualitative traits, and traits with variable age of onset will be developed. In the case of quantitative traits, the usual assumption of normality will be relaxed. The models will allow for a flexible correlation structure among the pedigree individuals. The statistical properties such as the bias, consistency, power and robustness of the estimators and the likelihood ratio tests developed will be studied by mathematical analysis and computer simulations. Extensions to different types of ascertainments will be developed. The methods will be applied to analyse pedigree data obtained from various ongoing studies in order to be sure that the methods are relevant to real data.

目前有越来越多的多态基因