Abstract
Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case–control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.
Highlights
Multi-omics data integration is widely used to understand the genetic architecture of disease
In presence of limited sample size, missing individual-level information on multiple assays poses a great loss of information
Imputation might lead to bias in such a small sample size as the percentage of missing data is large
Summary
Multi-omics data integration is widely used to understand the genetic architecture of disease. We develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case–control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. To understand the genetic architecture of disease, genome-wide association s tudies and several other studies based on single-omic data such as gene expression or DNA methylation have catalogued many disease-associated loci. Integration methods combine multiple omics data from large consortiums of different cohorts15,24 Such methods are prone to spurious prioritisation of associated genes owing to substantial cross-cell-type variation. Such methods are prone to spurious prioritisation of associated genes owing to substantial cross-cell-type variation25 For these reasons and to reduce the stratification bias due to population diversity, increasing attempts are being made to create large scale multiomics datasets recently by combining multiple assays from the same set of s amples. Gene expression and/or methylation assays are rarely repeated for generating the missing data due to various reasons such as the huge cost
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