BACKGROUND The advance of high-throughput technologies has made it cost-effective to collect diverse types of omic data in large-scale clinical and biological studies. While the collection of the vast amounts of multi-level omic data from these studies provides a great opportunity for genetic research, the high dimensionality of omic data and complex relationships among multi-level omic data bring tremendous analytic challenges. RESULTS To address these challenges, we develop an integrative U (IU) method for the design and analysis of multi-level omic data. While non-parametric methods make less model assumptions and are flexible for analyzing different types of phenotypes and omic data, they have been less developed for association analysis of omic data. The IU method is a nonparametric method that can accommodate various types of omic and phenotype data, and consider interactive relationship among different levels of omic data. Through simulations and a real data application, we compare the IU test with commonly used variance component tests. CONCLUSIONS Results show that the proposed test attains more robust type I error performance and higher empirical power than variance component tests under various types of phenotypes and different underlying interaction effects.

中文翻译：

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用于多级Omic数据联合分析的集成U方法。

背景技术高通量技术的进步使得在大规模临床和生物学研究中收集各种类型的眼科数据具有成本效益。尽管从这些研究中收集了大量的多级Omic数据为遗传研究提供了巨大的机会，但Omic数据的高维性和多级Omic数据之间的复杂关系带来了巨大的分析挑战。结果为了解决这些挑战，我们开发了一种用于设计和分析多层Omic数据的集成U（IU）方法。尽管非参数方法的模型假设较少，并且可以灵活地分析不同类型的表型和Omic数据，但为进行Omic数据的关联分析而开发的方法则较少。IU方法是一种非参数方法，可以容纳各种类型的Omic和表型数据，并考虑不同级别的Omic数据之间的交互关系。通过仿真和实际数据应用，我们将IU检验与常用方差成分检验进行了比较。结论结果表明，在各种类型的表型和不同的潜在相互作用效应下，与方差分量检验相比，所提出的检验具有更高的鲁棒性I型错误表现和更高的经验功效。