A sequential threshold cure model for genetic analysis of time-to-event data
A sequential threshold cure model for genetic analysis of time-to-event data
复制标题
DOI：
10.2527/jas.2009-2701
复制
发表时间：
2011-04-01
影响因子：
3.3
通讯作者：
Meuwissen, T. H. E.
中科院分区：
文献类型：
--
作者：
Odegard, J.;Madsen, P.;Meuwissen, T. H. E.
在分析至事件发生时间数据时，经典生存模型忽略了潜在非易感（治愈）个体的存在，如果存在，则推断程序无效。非易感个体的存在在特定病原体的挑战测试中特别相关，这是水产养殖育种计划中的常见程序。治愈模型是一种生存模型，它考虑了群体中一部分非易感个体。本研究提出了一个混合治愈模型的时间到事件的数据，测量顺序二进制记录。在一项模拟研究中，生存数据是通过2个基本特征生成的：易感性和耐力（每时间单位的死亡风险），与2组基本负债相关。尽管存在相当多的表型混杂，但所提出的模型在很大程度上能够区分2个性状。此外，如果选择是为了提高易感性而不是耐力，则应用经典生存模型的错误不可忽略。差异是最明显的情况下，大量潜在的遗传变异的耐力和当2个潜在的特征是低遗传相关。在非易感个体的存在下，该方法提供了一种新颖的和更准确的工具，利用时间到事件的数据，也被证明是成功的，当应用于零膨胀纵向二进制数据。
In analysis of time-to-event data, classical survival models ignore the presence of potential nonsusceptible (cured) individuals, which, if present, will invalidate the inference procedures. Existence of nonsusceptible individuals is particularly relevant under challenge testing with specific pathogens, which is a common procedure in aquaculture breeding schemes. A cure model is a survival model accounting for a fraction of nonsusceptible individuals in the population. This study proposes a mixed cure model for time-to-event data, measured as sequential binary records. In a simulation study survival data were generated through 2 underlying traits: susceptibility and endurance (risk of dying per time-unit), associated with 2 sets of underlying liabilities. Despite considerable phenotypic confounding, the proposed model was largely able to distinguish the 2 traits. Furthermore, if selection is for improved susceptibility rather than endurance, the error of applying a classical survival model was nonnegligible. The difference was most pronounced for scenarios with substantial underlying genetic variation in endurance and when the 2 underlying traits were lowly genetically correlated. In the presence of nonsusceptible individuals, the method provides a novel and more accurate tool for utilization of time-to-event data, and has also been proven successful when applied to zero-inflated longitudinal binary data.