Konigorski Stefan, Glicksberg Benjamin S
Digital Health & Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany.
Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA.
Methods Mol Biol. 2021;2212:225-243. doi: 10.1007/978-1-0716-0947-7_14.
Unraveling the complex biological mechanisms underlying human health and disease is a great challenge. With genomic data, many aspects can be investigated in great detail, such as interactions between different genetic variants as well as their effects on one or multiple traits. Modeling epistasis and pleiotropy jointly necessitates appropriate statistical methods. A suitable tool for this is C-JAMP, which is a recently proposed method based on copula functions. In this chapter, we outline C-JAMP and how it can be applied to investigate epistatic effects on multiple traits to advance our understanding of biological processes. We further discuss important aspects of this area of research, such as polygenic risk scores and ancestry-specific modeling, which we propose to include in future extensions of the software.
揭示人类健康与疾病背后复杂的生物学机制是一项巨大挑战。借助基因组数据,可以对许多方面进行详细研究,例如不同基因变异之间的相互作用及其对一个或多个性状的影响。联合建模上位性和多效性需要合适的统计方法。适用于此的一个工具是C-JAMP,它是最近基于copula函数提出的一种方法。在本章中,我们概述了C-JAMP及其如何应用于研究对多个性状的上位性效应,以增进我们对生物过程的理解。我们还进一步讨论了该研究领域的重要方面,如多基因风险评分和特定血统建模,我们建议将其纳入该软件未来的扩展版本中。