Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

BackgroundCommonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the “CHAMBER” algorithm).
Methodology/Principal FindingsThis algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.
ConclusionsThe CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease.

背景：常见疾病的病因往往涉及基因与暴露因素的复杂组合，进而产生病因异质性。本文提出一种用于生物医学与流行病学研究中处理异质性与多维度问题的计算算法——“CHAMBER”算法，该算法采用团簇（clique）识别方法。

研究方法与主要结果：该算法通过构建图谱实现两大目标：（1）识别影响疾病风险的遗传变异；（2）基于个体的遗传基因型预测其疾病发病风险。本研究采用集合覆盖算法以识别最优团簇，并通过布尔函数划分存在病因异质性的个体亚群。我们首先利用包含2基因与4基因模式的模拟病例-对照基因型-疾病关联数据对该方法进行验证，CHAMBER算法成功识别出了这些模拟设定的病因。此外，我们还针对非洲裔美国女性与高加索女性群体，开展了两项基于人群的乳腺癌与子宫内膜癌病例-对照研究，分析了类固醇激素代谢相关的基因型数据。我们在两类癌种中均发现了全新的基因模式，这些基因参与雌激素或儿茶酚雌激素的硫酸化与葡糖醛酸化过程，其关联结果与这些基因已被证实的生物学功能相符。同时，我们还识别出可反映多个候选基因联合效应的团簇，提示在两类种族人群的乳腺癌与子宫内膜癌中，均存在具有生物学合理性的激素代谢基因组合。

结论：CHAMBER算法可用于探索复杂疾病的多因素病因与病因异质性，具备潜在应用价值。