用于家系研究遗传关联分析的相关脆弱模型
Correlated frailty model for analysis of genetic association in family studies
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中文总结 AI 辅助
本文提出相关脆弱模型，用于分析家系研究中与癌症相关的生存结局的遗传关联，经模拟评估显示该模型可用于识别与癌症发病时间显著相关的基因组区域。
中文摘要 AI 辅助
家系研究设计可通过考虑相关家庭成员，探究基因突变对疾病风险的影响。目前已开发出一些用于测试家系研究中遗传变异集的方法，但能处理右删失时间-事件数据的方法极少。本文提出一种相关脆弱模型，用于分析存在家系相关性时与癌症相关的生存结局。这些家系相关性由亲缘系数矩阵指定的残留家系成分，以及通过同源同一（IBD）概率矩阵建模的区域或基因特异性相关结构来解释。该方法用于量化和评估同一基因组区域的一组常见单核苷酸多态性（SNPs）或罕见变异（或两者）与生存结局（如疾病发病时间）之间的关联。模型的边际似然使用Marquardt算法最大化。我们通过在不同场景下进行模拟来评估该方法，这些场景中我们改变了家系大小、多个罕见变异的遗传关联强度以及是否存在残留家系相关性。结果表明，相关脆弱模型在家系癌症研究中具有重要价值，例如可用于识别与癌症发病时间显著相关的基因组区域。
英文摘要
Family-based study designs allow the investigation of gene mutation effects on a disease risk by considering related family members. Some methods have been developed for testing sets of genetic variants in family studies but only very few can handle right-censored time-to-event data. We propose here a correlated frailty model for the analysis of a survival outcome related to cancer in presence of familial correlations. These familial correlations are explained by a residual familial component specified by a kinship matrix and a region- or gene-based specific correlation structure modeled via identical-by-descent (IBD) probability matrix. The proposed approach is used to quantify and evaluate the association between a set of common single nucleotide polymorphism (SNPs) or rare variants (or both) from the same genomic region and a survival outcome, e.g. time to disease onset. The model's marginal likelihood is maximized using the Marquardt algorithm. We evaluated the method by simulations under various scenarios where we varied the family size, the strength of genetic associations from multiple rare variants and the presence or not of residual familial correlation. The results indicate that the correlated frailty model can be valuable in family cancer studies, for example to identify genomic regions significantly associated with the time to cancer onset.
