Current BioinformaticsCurrent BioinformaticsCurrent Bioinformatics1574-89362212-392XBentham Science64375310.2174/0115748936276861240109045208Life SciencesResearch ArticleGenotype and Phenotype Association Analysis Based on Multi-omics Statistical DataGuoXinpenginfo@benthamscience.netSongYafeiinfo@benthamscience.netXuDongyaninfo@benthamscience.netJinXuepinginfo@benthamscience.netShangXuequninfo@benthamscience.netSchool of Air and Missile Defense, Air Force Engineering UniversityDepartment of Basic Sciences, Air Force Engineering UniversitySchool of Computer Science and Engineering, Northwestern Polytechnical University01102024191093394207012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://journals.eco-vector.com/1574-8936/article/view/643753

Background:When using clinical data for multi-omics analysis, there are issues such as the insufficient number of omics data types and relatively small sample size due to the protection of patients' privacy, the requirements of data management by various institutions, and the relatively large number of features of each omics data. This paper describes the analysis of multi-omics pathway relationships using statistical data in the absence of clinical data.

Methods:We proposed a novel approach to exploit easily accessible statistics in public databases. This approach introduces phenotypic associations that are not included in the clinical data and uses these data to build a three-layer heterogeneous network. To simplify the analysis, we decomposed the three-layer network into double two-layer networks to predict the weights of the inter-layer associations. By adding a hyperparameter β, the weights of the two layers of the network were merged, and then k-fold cross-validation was used to evaluate the accuracy of this method. In calculating the weights of the two-layer networks, the RWR with fixed restart probability was combined with PBMDA and CIPHER to generate the PCRWR with biased weights and improved accuracy.

Results:The area under the receiver operating characteristic curve was increased by approximately 7% in the case of the RWR with initial weights.

Conclusion:Multi-omics statistical data were used to establish genotype and phenotype correlation networks for analysis, which was similar to the effect of clinical multi-omics analysis.

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