Xuanjun Fang

Hainan Provincial Key Laboratory of Crop Molecular Breeding, Hainan Institute of Tropical Agricultural Resources (HITAR), Sanya, 572025, Hainan, China
Author    Correspondence author
Plant Gene and Trait, 2026, Vol. 17, No. 3   
Received: 30 Mar., 2026    Accepted: 26 Apr., 2026    Published: 20 May, 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits, yet translating these statistical signals into biological mechanisms remains a major challenge. A key difficulty lies in distinguishing between association, shared genetic architecture, and causal relationships across multiple layers of molecular regulation. In this study, we present a unified analytical framework for causal inference in statistical genetics that integrates GWAS, molecular quantitative trait loci (QTL), transcriptome-wide association studies (TWAS), colocalization analysis, and Mendelian randomization (MR). Within this framework, different methods address distinct inferential targets: GWAS identifies variant–trait associations; molecular QTL and TWAS link genetic variation to intermediate phenotypes; colocalization evaluates the consistency of signals across datasets; and MR estimates the direction and magnitude of potential effects under explicit assumptions. We emphasize that these components should not be interpreted in isolation but as part of a sequential process of evidence refinement. In particular, colocalization is necessary for prioritizing candidate mechanisms but does not establish causality, while MR provides effect estimates that remain sensitive to instrument validity, pleiotropy, and data heterogeneity. We further discuss practical considerations for implementation, including instrument selection, diagnostic evaluation, and cross-population validation, as well as challenges arising from pleiotropy, tissue specificity, and environmental interactions. Finally, we extend this framework to plant systems and emerging multi-omics contexts, highlighting the role of single-cell and epigenomic data in refining causal interpretation. By clarifying the roles and limitations of individual methods within an integrated framework, this study provides a structured approach for moving from genetic associations toward biologically interpretable and experimentally testable hypotheses.

Statistical genetics; Causal inference; Genome-wide association study (GWAS); Molecular QTL (eQTL, sQTL, pQTL); Transcriptome-wide association study (TWAS); Colocalization; Mendelian randomization; Multi-omics integration; Pleiotropy; Complex traits