Department of Medical Statistics and BioInformatics, Leiden University Medical Center, Post Zone S5-P, PO Box 9600, 2300 RC Leiden, The Netherlands.
Biostatistics. 2013 Apr;14(2):220-31. doi: 10.1093/biostatistics/kxs032. Epub 2012 Sep 18.
Family studies are often used in genetic research to explore associations between genetic markers and various phenotypes. A commonly used design oversamples families enriched with the disease under study for efficient data collection and estimation. For instance, in a multiple cases family study, families are selected based on the number of affected relatives. In such cases, valid inference for the model parameters relies on the proper modeling of both the within family correlations and the outcome-dependent sampling, also known as ascertainment. A flexible modeling approach is the ascertainment-corrected mixed-effects model, but it is known to only be asymptotically identifiable, because in small samples the available data do not provide sufficient information to estimate both the intercept and the genetic variance. To deal with this issue, we propose a penalized maximum likelihood estimation procedure which reliably estimates the model parameters in small family studies by using external population-based information.
家族研究常用于遗传研究中，以探索遗传标记与各种表型之间的关联。一种常用的设计方法是对富含研究疾病的家族进行过采样，以便有效地进行数据收集和估计。例如，在多病例家族研究中，根据受影响亲属的数量选择家族。在这种情况下，模型参数的有效推断依赖于对家族内相关性和依赖结果的采样（也称为确定）的正确建模。一种灵活的建模方法是确定校正混合效应模型，但它仅在渐近可识别，因为在小样本中，可用数据没有提供足够的信息来估计截距和遗传方差。为了解决这个问题，我们提出了一种惩罚最大似然估计程序，该程序通过使用基于人群的外部信息，可在小型家族研究中可靠地估计模型参数。
