Fernández Maria V, Budde John, Del-Aguila Jorge L, Ibañez Laura, Deming Yuetiva, Harari Oscar, Norton Joanne, Morris John C, Goate Alison M, Cruchaga Carlos
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States.
Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States.
Front Neurosci. 2018 Apr 4;12:209. doi: 10.3389/fnins.2018.00209. eCollection 2018.
Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families ( = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified , a GWAS candidate gene for sporadic AD, along with six novel genes (, and ) as candidate genes for familial LOAD.
基于基因的检测方法用于研究罕见变异对特定表型的综合影响，已广泛应用于病例对照研究，但在家族性研究，尤其是复杂不完全家系研究中的发展和适应性则较为缓慢。在本研究中，我们使用模拟数据集和真实数据集，对所有可用于家族性研究设计的最新基于基因的方法进行了实际检验。我们利用285个患有晚发性阿尔茨海默病（LOAD）的家系（n = 1235），在八个不同软件包和22种模型中，检验了几种合并、方差成分和传递不平衡检验的性能。在对这些检验进行全面检查后，我们提出了一种方法，能够高度自信地识别与测试表型相关的基因，并为基于家族性基因分析选择最佳软件和模型提供建议。此外，在我们的数据集中，我们鉴定出一个散发性AD的全基因组关联研究（GWAS）候选基因，以及六个新基因（、和）作为家族性LOAD的候选基因。
