Abstract
BackgroundThe 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.ResultsTo 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.ConclusionsResults 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.
Highlights
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
Omicbased association analysis holds great promise for discovering novel disease-associated biomarkers, the discovery process is hampered by the lack of appropriate statistical tools to consolidate and analyze multi-level omic data
(Adj-sequence kernel association test (SKAT)) is modified as: Q = (Y − μ )T K S(Y − μ ), where the elements of K S are defined as K S(Gi, Gj) =
Summary
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. The collection of multi-level omic data from these studies provides us a great opportunity to integrate information from different levels of omic data into association analysis [3,4,5,6]. The diagnostic assessments of human diseases can often be of different types (e.g., binary, ordinal and continuous) and follow known or unknown distributions. This issue is, paid less attention by the existing methods
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