Development of Statistical Approaches Allowing for Genetic Covariates

开发允许遗传协变量的统计方法

• 批准号: 7266172
• 负责人: Chih-Chieh Wu
• 金额: $ 7.7万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7266172
• 关键词: Accounting; Age; Cancer Genetics Network; Complex; Data; Development; Disease; Disease regression; Environmental Risk Factor; Family; Family member; Genes; Genetic; Genetic Models; Genotype; Germ-Line Mutation; Heterogeneity; Joints; Li-Fraumeni Syndrome; Logistic Regressions; Methods; Modeling; Modification; Mutation; Numbers; Onset of illness; Patients; Penetrance; Publishing; Relative (related person); Relative Risks; Residual state; Risk; Statistical Methods; Statistical Models; TP53 gene; Testing; Texas; Variant; Work; anticancer research; base; cancer genetics; cancer risk; hazard; interest; malignant breast neoplasm; mutation carrier; novel; response; segregation; simulation; trait

DESCRIPTION (provided by applicant): To determine the genetic basis of a complex trait, it is necessary to use methods that take account of the joint effects of multiple genetic components underlying the trait. However, this is not possible using a usual segregation analysis, which is often efficient only when the variation of the trait in a family is largely due to a mutation segregating at a single putative locus. In response to this, we propose to incorporate genetic covariates adjusted for mutation carrier status in segregation analysis models that account for the genetic complexity and heterogeneity of a complex trait. Segregation analysis models that include genetic covariates will be more accurate for modeling complex genetic effects than the usual segregation analysis models. Recently, we used an independent genetic covariate for p53 mutation status in the segregation analyses of families with Li-Fraumeni syndrome. This study will be published in Cancer Research in August. However, the use of independent genetic covariates in that study did not take full account of intrafamilial correlation in hereditary mutation distributions. This problem could be more complicated and serious when mutation genotypes are only available for some relatives in a family. The central theme of this proposal is to develop statistical approaches that allow for dependent genetic covariates. It is novel and desirable to develop dependent genetic covariates that account for intrafamilial correlation in hereditary mutation distributions in segregation analysis models. We propose to use simulation-based approaches to quantitatively determine the effects of dependent genetic covariates. Two types of complex segregation analysis models by maximum likelihood will be used as age-specific risk models in this project. The newly developed statistical approaches will be used to determine the genetic basis of breast cancer in association with mutations in BRCA1/2. The Texas Cancer Genetic Consortium, a Cancer Genetic Network regional center, will provide the breast cancer data.

描述（由申请人提供）： 为了确定复杂性状的遗传基础，有必要使用该性状背后的多种遗传成分的联合作用的方法。但是，这是不可能使用通常的隔离分析的，这通常只有当家族中特征的变化很大程度上是由于在单个推定基因座处的突变分离时，这通常是有效的。为此，我们建议在隔离分析模型中纳入针对突变载体状态调整的遗传协变量，以解释复杂性状的遗传复杂性和异质性。与通常的分离分析模型相比，包括遗传协变量的隔离分析模型将更准确地建模复杂的遗传效应。最近，我们在患有Li-Fraumeni综合征的家族的隔离分析中使用了独立的遗传协变量来实现p53突变状态。这项研究将于8月发表在癌症研究中。但是，该研究中独立的遗传协变量的使用并未完全考虑到遗传突变分布中的家族内相关性。当一个家庭中的某些亲属可用时，当突变基因型可用于突变基因型时，这个问题可能会更加复杂和严重。该提案的中心主题是开发允许依赖遗传协变量的统计方法。在隔离分析模型中，开发依赖性的遗传协变量，这是新颖的，并且是可取的。我们建议使用基于模拟的方法定量确定依赖遗传协变量的影响。在该项目中，通过最大似然的两种类型的复杂隔离分析模型将用作特定年龄的风险模型。新开发的统计方法将用于确定与BRCA1/2突变相关的乳腺癌的遗传基础。癌症遗传网络区域中心得克萨斯州癌症遗传联盟将提供乳腺癌数据。