MARKOV CHAIN MONTE CARLO FOR VERY LARGE PEDIGREES

适用于大型血统的马尔可夫链蒙特卡罗

• 批准号: 6051591
• 负责人: RICHARD A KERBER
• 金额: $ 7.13万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-03 至 2001-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6051591
• 关键词: United States; breast neoplasms; cancer information system; cancer registry /resource; cancer risk; clinical research; colorectal neoplasms; family genetics; gene frequency; genetic carriers; health surveys; human data; longitudinal human study; neoplasm /cancer epidemiology; ovary neoplasms; phenotype; prostate neoplasms; statistics /biometry

We propose to design, test, and implement new methods for estimating carrier probabilities, penetrance, and allele frequency for cancer predisposition syndromes. We will use Markov Chain Monte Carlo (MCMC) methods and related sample-path methods. Pedigree and disease data will be obtained from the Utah Population Database (UPDB), which consists of more than one million population-based genealogical records linked to cancer and follow-up data. We will begin by simulating the occurrence of disease among members of the pedigrees and attempting to estimate the relevant genetic parameters with MCMC methods. Once we have successfully estimate carrier probabilities, we will extend the methods to estimate penetrance and allele frequency. When the MCMC algorithm is able to correctly estimate all the desired parameters, we will use it to generate estimates of carrier probabilities, penetrance, and allele frequency using actual UPDB data on breast, prostate, colorectal, and ovarian cancer.

我们建议设计、测试和实施新的方法来估计癌症易感综合征的携带者概率、外显率和等位基因频率。我们将使用马尔科夫链蒙特卡罗(MCMC)方法和相关的样本路径方法。谱系和疾病数据将从犹他州人口数据库(UPDB)获得，该数据库由100多万与癌症有关的基于人口的家谱记录和后续数据组成。我们将首先模拟疾病在家系成员中的发生情况，并尝试用MCMC方法估计相关的遗传参数。一旦我们成功地估计了携带者的概率，我们将扩展这些方法来估计外显率和等位基因频率。当MCMC算法能够正确估计所有需要的参数时，我们将使用它来使用乳腺癌、前列腺癌、结直肠癌和卵巢癌的实际UPDB数据来生成携带者概率、外显率和等位基因频率的估计。