ReviewScience China. Life sciences2024
An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.
Review in Science China. Life sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Integrated proteomic, transcriptomic, and epigenomic profiling identifies SRA1 as a novel therapeutic target for postpartum depression.Npj mental health research · 2026Article
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- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- An integrated multiomics-based risk model for predicting the prognosis and immune microenvironment status in patients with papillary thyroid carcinoma.Gland surgery · 2026Article
- Identification and Characterization of sQTLs and eQTLs Associated with Growth Traits in Yellow-Feathered Broilers.Poultry science · 2026Article
- Novel Susceptibility Genes for Sepsis Revealed by a Cross-Tissue Transcriptome-Wide Association Study.Shock (Augusta, Ga.) · 2026Article
- Study on the Potential Molecular Mechanism of Keloid Disease Associated With Single Cell Combined Mendelian Randomization.Journal of cosmetic dermatology · 2026Article
- Identification of circulatory neuro-related proteins in Parkinson's disease: an integrated genetic-proteomic-clinical study.EBioMedicine · 2026Article
- Exploring immune modulation in osteoarthritis: identifying key biomarkers and the role of PTPRC in regulating immune microenvironment for therapeutic intervention.Frontiers in immunology · 2026Article
- Evaluating the Impact of Folate on Male Infertility Using Mendelian Randomization: A Comprehensive Analysis.The world journal of men's health · 2026Article
- Article
- Integrative Multi-Omics Analysis Unveils Candidate Genes and Functional Variants for Growth and Reproductive Traits in Duroc Pigs.Animals : an open access journal from MDPI · 2025Article
- Decades' progress and prospects on maize functional genomics and molecular breeding.Science China. Life sciences · 2025Review
- Identification and external validation of a prognostic signature based on MAPK-related genes to evaluate survival prognosis and treatment efficacy in lung adenocarcinoma.Clinical and experimental medicine · 2025Article
- Leveraging GWAS-Identified Markers in Combination with Bayesian and Machine Learning Models to Improve Genomic Selection in Soybean.International journal of molecular sciences · 2025Article
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- Integrative eQTL and Mendelian randomization analysis reveals key genetic markers in mesothelioma.Respiratory research · 2025Article
- Article
- Mendelian Randomization Combined with Single-Cell Transcriptome Analysis Reveals the Role of the Key Gene PCLAF in the Pathogenesis of Atopic Dermatitis.Clinical, cosmetic and investigational dermatology · 2025Article
- SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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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