Curtis Ross E, Yin Junming, Kinnaird Peter, Xing Eric P
Joint Carnegie Mellon-University of Pittsburgh PhD Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Pac Symp Biocomput. 2012:327-38.
Despite the success of genome-wide association studies in detecting novel disease variants, we are still far from a complete understanding of the mechanisms through which variants cause disease. Most of previous studies have considered only genome-phenome associations. However, the integration of transcriptome data may help further elucidate the mechanisms through which genetic mutations lead to disease and uncover potential pathways to target for treatment. We present a novel structured association mapping strategy for finding genome-transcriptome-phenome associations when SNP, gene-expression, and phenotype data are available for the same cohort. We do so via a two-step procedure where genome-transcriptome associations are identified by GFlasso, a sparse regression technique presented previously. Transcriptome-phenome associations are then found by a novel proposed method called gGFlasso, which leverages structure inherent in the genes and phenotypic traits. Due to the complex nature of three-way association results, visualization tools can aid in the discovery of causal SNPs and regulatory mechanisms affecting diseases. Using wellgrounded visualization techniques, we have designed new visualizations that filter through large three-way association results to detect interesting SNPs and associated genes and traits. The two-step GFlasso-gGFlasso algorithmic approach and new visualizations are integrated into GenAMap, a visual analytics system for structured association mapping. Results on simulated datasets show that our approach has the potential to increase the sensitivity and specificity of association studies, compared to existing procedures that do not exploit the full structural information of the data. We report results from an analysis on a publically available mouse dataset, showing that identified SNP-gene-trait associations are compatible with known biology.
尽管全基因组关联研究在检测新的疾病变异方面取得了成功，但我们距离全面了解变异导致疾病的机制仍有很大差距。以前的大多数研究仅考虑了基因组与表型的关联。然而，转录组数据的整合可能有助于进一步阐明基因突变导致疾病的机制，并揭示潜在的治疗靶点途径。当同一队列中可获得单核苷酸多态性（SNP）、基因表达和表型数据时，我们提出了一种新颖的结构化关联映射策略，用于寻找基因组-转录组-表型关联。我们通过两步程序来实现这一点，第一步通过GFlasso（一种先前提出的稀疏回归技术）识别基因组-转录组关联。然后通过一种新提出的称为gGFlasso的方法找到转录组-表型关联，该方法利用了基因和表型特征中固有的结构。由于三方关联结果的复杂性，可视化工具有助于发现影响疾病的因果SNP和调控机制。利用有充分依据的可视化技术，我们设计了新的可视化方法，通过筛选大量的三方关联结果来检测有趣的SNP以及相关基因和特征。GFlasso-gGFlasso两步算法方法和新的可视化方法被集成到GenAMap中，这是一个用于结构化关联映射的可视化分析系统。模拟数据集的结果表明，与未充分利用数据完整结构信息的现有程序相比，我们的方法有可能提高关联研究的敏感性和特异性。我们报告了对一个公开可用的小鼠数据集的分析结果，表明所识别的SNP-基因-特征关联与已知生物学相符。
