Liley James, Todd John A, Wallace Chris
JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
Nat Genet. 2017 Feb;49(2):310-316. doi: 10.1038/ng.3751. Epub 2016 Dec 26.
Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.
许多常见疾病表现出广泛的表型变异。我们提出了一种统计方法，用于确定疾病病例的表型定义亚组是否代表不同的遗传结构，其中疾病相关变异在两个亚组中具有不同的效应大小。我们的方法用混合高斯模型对全基因组遗传关联统计分布进行建模。我们应用全局检验，无需明确识别疾病相关变异，因此与标准的逐个变异亚组分析相比，能最大限度地提高检验效能。在发现遗传亚组证据的情况下，我们提出了事后识别有贡献的遗传变异的方法。我们在一系列模拟和测试数据集上演示了该方法，这些数据集的预期结果是已知的。我们研究了由自身抗体阳性定义的1型糖尿病（T1D）个体亚组，为甲状腺过氧化物酶特异性抗体阳性的差异遗传结构建立了证据，这通常由已知T1D相关基因组区域的变异驱动。
