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Integration of Multiple-Omics Data to Analyze the Population-Specific Differences for Coronary Artery Disease.

ABSTRACT: Significant differences may exist among different descents, but the current studies are mainly based on European populations. In the present study, we analyzed the population-specific differences of coronary artery disease (CAD) between European and East Asian descents. In stage 1, we identified CAD susceptibility genes by gene-based tests in European and East Asian populations. We identified two novel susceptibility genes for CAD, namely, CUX2 and OAS3. In stage 2, we carried out meta-analyses for the population-specific variants. rs599839 (PSRC1) represented a protective variant for CAD in East Asian populations (OR_ASN = 0.72. 95% CI: 0.63-0.81) but a risk factor in European populations (OR_EUR = 1.13, 95% CI: 0.93-1.36). In stage 3, we enriched the risk genes and explored the population-specific differences in Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), regulatory element, tissues, and cell types. In stage 4, in order to predict genes that showed pleiotropic/potentially causal association with CAD, we integrated summary-level data from independent genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) by using summary data-based Mendelian randomization (SMR). The results showed that NBEAL1 and FGD6 were population-specific pleiotropic/causal genes. Although some potential mutations and risk genes of CAD are shared, it is still of great significance to elucidate the genetic differences among different populations. Our analysis provides a better understanding of the pathogenic mechanisms and potential therapeutic targets for CAD.

SUBMITTER: Hu Y

PROVIDER: S-EPMC8384508 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.ResultsA simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.ConclusionsSimulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

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Integration of Multiple-Omics Data to Analyze the Population-Specific Differences for Coronary Artery Disease.

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