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Unraveling phenotypic variance in metabolic syndrome through multi-omics

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Abstract

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics’ roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

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Data availability and software

The genotype and phenotype data of the UK Biobank used in this study are publicly accessible through procedures described on its webpage (http://www.ukbiobank.ac.uk/using-the-resource). We confirm that the data supporting the findings of this study are available within the article and its supplementary files. The source code for MTG version 2.22 is publicly available in https://sites.google.com/view/s-hong-lee-homepage/mtg2.

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Acknowledgements

L.D.A acknowledges the Australian Government Research Training Program (RTP) and University of South Australia for funding his PhD scholarship. We obtained data from UK Biobank under the reference number 14575. We would like to thank all participants and staff of the UK Biobank for their valuable contributions. We would like to thank the Statistical Genetics Research Group for their advice during the conception and planning stages of the study. The analyses were performed using computational resources provided by the Australian Government through Gadi  under the National Computational Merit Allocation Scheme(NCMAS), and HPCs (Statgen servers) managed by UniSA IT. The data used to impute gene expression levels were retrieved from GTEx V8 through the GTEx portal (www.gtexportal.org) and dbGaP accession number is phs000424.v8.p2.

Funding

This research is supported by the Australian Research Council (DP190100766).

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Authors and Affiliations

  1. Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia

    Lamessa Dube Amente, Elina Hyppönen & S. Hong Lee

  2. UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia

    Lamessa Dube Amente & S. Hong Lee

  3. South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia

    Lamessa Dube Amente, Elina Hyppönen & S. Hong Lee

  4. Discipline of Psychiatry, University of Adelaide, Adelaide, SA, 5000, Australia

    Natalie T Mills

  5. UniSA STEM, University of South Australia, Mawson Lakes, SA, 5095, Australia

    Thuc Duy Le

  6. UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia

    Elina Hyppönen

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  1. Lamessa Dube Amente
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Contributions

SHL and LDA conceived the idea, directed the study, performed the data management, and wrote the manuscript; LDA conducted the analyses; SHL supervised the study. SHL and LDA wrote the first draft of the manuscript. All the authors provided critical feedback and suggestions. All the authors critically reviewed the manuscript and approved the final manuscript.

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Correspondence to Lamessa Dube Amente or S. Hong Lee.

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Amente, L.D., Mills, N.T., Le, T.D. et al. Unraveling phenotypic variance in metabolic syndrome through multi-omics. Hum. Genet. 143, 35–47 (2024). https://doi.org/10.1007/s00439-023-02619-0

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