Jiang Yunxuan, Conneely Karen N, Epstein Michael P
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA.
Department of Human Genetics, Emory University, Atlanta, GA.
Stat Biosci. 2018 Dec;10(3):491-505. doi: 10.1007/s12561-017-9197-9. Epub 2017 Jun 5.
Next generation sequencing technology has propelled the development of statistical methods to identify rare polygenetic variation associated with complex traits. The majority of these statistical methods are designed for case-control or population-based studies, with few methods that are applicable to family-based studies. Moreover, existing methods for family-based studies mainly focus on trios or nuclear families; there are far fewer existing methods available for analyzing larger pedigrees of arbitrary size and structure. To fill this gap, we propose a method for rare-variant analysis in large pedigree studies that can utilize information from all available relatives. Our approach is based on a kernel-machine regression (KMR) framework, which has the advantages of high power, as well as fast and easy calculation of p-values using the asymptotic distribution. Our method is also robust to population stratification due to integration of a QTDT framework (Abecasis, et al. 2000b) with the KMR framework. In our method, we first calculate the expected genotype (between-family component) of a non-founder using all founders' information and then calculate the deviates (within-family component) of observed genotype from the expectation, where the deviates are robust to population stratification by design. The test statistic, which is constructed using within-family component, is thus robust to population stratification. We illustrate and evaluate our method using simulated data and sequence data from Genetic Analysis Workshop 18 (GAW18).
下一代测序技术推动了用于识别与复杂性状相关的罕见多基因变异的统计方法的发展。这些统计方法大多是为病例对照研究或基于人群的研究设计的，适用于基于家系研究的方法很少。此外，现有的基于家系研究的方法主要集中在三联体或核心家庭；可用于分析任意大小和结构的更大谱系的现有方法要少得多。为了填补这一空白，我们提出了一种在大型谱系研究中进行罕见变异分析的方法，该方法可以利用所有可用亲属的信息。我们的方法基于核机器回归（KMR）框架，该框架具有强大的功效，并且使用渐近分布可以快速简便地计算p值。由于将QTDT框架（Abecasis等人，2000b）与KMR框架相结合，我们的方法对群体分层也具有鲁棒性。在我们的方法中，我们首先使用所有创始者的信息计算非创始者的预期基因型（家系间成分），然后计算观察到的基因型与预期值的偏差（家系内成分），其中该偏差在设计上对群体分层具有鲁棒性。因此，使用家系内成分构建的检验统计量对群体分层具有鲁棒性。我们使用来自遗传分析研讨会18（GAW18）的模拟数据和序列数据来说明和评估我们的方法。
