Supplementary Material for: A Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies

Background: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. Methods: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. Results: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. Conclusion: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.

背景：全基因组关联研究（Genome-Wide Association Studies, GWASs）已鉴定出数百个与复杂疾病相关的遗传变异，但此类变异对疾病遗传力的解释能力极为有限。传统的单位点关联分析无法检出效应量较小的变异，也难以捕捉这些变异间的高阶交互作用。多位点关联分析则提供了一种强有力的替代方案：其可联合建模基因或通路上的变异，并减轻全基因组关联研究中多重假设检验的负担。
方法：本研究提出一种高效且灵活的降维方法以建模多位点关联。我们采用贝叶斯分区模型（Bayesian partitioning model），依据关联方向对单核苷酸多态性（Single Nucleotide Polymorphism, SNPs）进行聚类，通过灵活的计分方案建模高阶交互作用，并借助后验边际概率检测SNP集合与疾病间的关联。
结果：我们通过大规模模拟研究验证了所提方法，并将其应用于社区动脉粥样硬化风险（Atherosclerosis Risk in Communities, ARIC）全基因组关联数据集，以检测2型糖尿病相关的多位点交互作用。
结论：研究表明，相较于多种现有方法，本方法在检测多位点交互作用时具备更优的统计效力。当应用于包含9328名受试者的ARIC研究数据集，开展2型糖尿病的基因层面关联分析时，本方法成功识别出了传统单位点关联分析未检出的若干新型遗传变异。