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An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs

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Abstract

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

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Acknowledgement

This work was supported by the National Key Research and Development Program of China (2022YFD1201504), the Fundamental Research Funds for the Central Universities (2662022YLYJ010, 2021ZKPY018, 2662021JC008, SZYJY2021003), the Major Project of Hubei Hongshan Laboratory (2022HSZD031), the Major Science and Technology Project of Hubei Province (2021AFB002), and the Yingzi Tech & Huazhong Agricultural University Intelligent Research Institute of Food Health (IRIFH202209).

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  1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China

    Yang Zhang, Mengyao Wang, Zhenguo Li, Xuan Yang, Keqin Li, Ao Xie, Jianbing Yan & Jianxiao Liu

  2. Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan, 430070, China

    Yang Zhang, Mengyao Wang, Zhenguo Li, Xuan Yang, Keqin Li, Ao Xie & Jianxiao Liu

  3. Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, 430070, China

    Yang Zhang, Mengyao Wang, Zhenguo Li, Xuan Yang, Keqin Li, Ao Xie & Jianxiao Liu

  4. College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China

    Yang Zhang, Mengyao Wang, Zhenguo Li, Xuan Yang, Keqin Li, Ao Xie, Shihan Wang & Jianxiao Liu

  5. College of Life Sciences, Nankai University, Tianjin, 300071, China

    Fang Dong

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Zhang, Y., Wang, M., Li, Z. et al. An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs. Sci. China Life Sci. (2024). https://doi.org/10.1007/s11427-023-2522-8

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  • DOI: https://doi.org/10.1007/s11427-023-2522-8

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