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Gene selection by incorporating genetic networks into case-control association studies

European Journal of Human Genetics volume 32, pages 270–277 (2024)Cite this article

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Abstract

Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based regression approach has been developed to incorporate a biological genetic network and to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in a gene to capture gene-level signals. We employ three linear combination approaches: optimally weighted sum (OWS), beta-based weighted sum (BWS), and LD-adjusted polygenic risk score (LD-PRS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods.

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Fig. 1: The true positive rates of the methods based on different gene-level signals for balance case-control studies with DNA sequence data in scenario 1, where there are five rare variants and five common variants in each gene.
Fig. 2: The true positive rates of the methods based on different gene-level signals for balance case-control studies with DNA methylation data in scenario 1.
Fig. 3: Venn diagrams of genes identified by BWS, LD-PRS, OWS, and nPC for DNA methylation data and DNA sequence data in the real data analyses.

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All data analyzed during this study are included in this published article and its supplemental materials.

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Acknowledgements

Part of this research has been conducted using the UK Biobank Resource under application number 41722 and the NHGRI-EBI GWAS Catalog. XC was partially supported by the Michigan Technological University Health Research Institute Fellowship program and the Portage Health Foundation Graduate Assistantship.

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No financial assistance was received in support of the study.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA

    Xuewei Cao, Shuanglin Zhang & Qiuying Sha

  2. Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA

    Xiaoyu Liang

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  1. Xuewei Cao
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  3. Shuanglin Zhang
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  4. Qiuying Sha
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Contributions

Formal analysis: XC; Methodology: XC, SZ, XL, and QS; Data curation: XC and XL; Visualization: XC; Writing original draft: XC, XL, and QS; Writing review and editing: XC, SZ, XL, and QS.

Corresponding author

Correspondence to Qiuying Sha.

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The authors declare no competing interests.

Ethics approval

This study used DNA sequence data from the UK Biobank, which has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval (approval number: 11/NW/0382). No specific ethical approval was required for DNA methylation data in this study, which is downloaded from GEO publicly available database with access number GSE42861.

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Cao, X., Liang, X., Zhang, S. et al. Gene selection by incorporating genetic networks into case-control association studies. Eur J Hum Genet 32, 270–277 (2024). https://doi.org/10.1038/s41431-022-01264-x

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