Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA.
Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
Brief Bioinform. 2019 Mar 25;20(2):585-597. doi: 10.1093/bib/bby029.
Disease diagnosis using cell-free DNA (cfDNA) has been an active research field recently. Most existing approaches perform diagnosis based on the detection of sequence variants on cfDNA; thus, their applications are limited to diseases associated with high mutation rate such as cancer. Recent developments start to exploit the epigenetic information on cfDNA, which could have substantially wider applications. In this work, we provide thorough reviews and discussions on the statistical method developments and data analysis strategies for using cfDNA epigenetic profiles, in particular DNA methylation, to construct disease diagnostic models. We focus on two important aspects: marker selection and prediction model construction, under different scenarios. We perform simulations and real data analysis to compare different approaches, and provide recommendations for data analysis.
利用游离细胞 DNA (cfDNA) 进行疾病诊断是近年来的一个活跃研究领域。大多数现有的方法基于 cfDNA 上序列变异的检测来进行诊断；因此，它们的应用仅限于与高突变率相关的疾病，如癌症。最近的研究开始利用 cfDNA 上的表观遗传信息，这可能会有更广泛的应用。在这项工作中，我们对利用 cfDNA 表观遗传谱（特别是 DNA 甲基化）构建疾病诊断模型的统计方法发展和数据分析策略进行了全面的回顾和讨论。我们重点关注两个重要方面：在不同场景下的标记选择和预测模型构建。我们通过模拟和真实数据分析来比较不同的方法，并为数据分析提供建议。
