Bioinformatic Advancements in GWAS Analysis: Addressing Causality, Multiple Testing, and Regulatory Roles of Non-coding SNPs

Abstract

Genome-wide association studies (GWAS) have transformed genetic research by uncovering thousands of genetic variants linked to complex traits and diseases, offering opportunities for personalized medicine. Yet, three major obstacles remain: (1) statistical associations alone do not confirm causation, (2) expanding datasets exacerbate the multiple testing burden, and (3) most identified variants fall in non-coding regions whose regulatory mechanisms are difficult to decipher. In response, advanced bioinformatic tools, such as Fine-mapping, colocalization, Mendelian Randomization, and transcriptome-wide association studies (TWAS), have emerged to localize probable causal variants, integrate molecular QTL data, and test pathways underlying gene regulation. Two key trends now drive these innovations. First, integrative strategies increasingly combine multi-omic and tissue-specific datasets to reveal how non-coding SNPs influence gene expression and disease pathways. Second, methodological convergence merges complementary techniques in multi-step workflows, boosting causal inference and highlighting the most functionally relevant genetic factors. Despite progress, challenges persist, from the need for higher-resolution tissue data to the computational demands of integrating large-scale datasets. Nevertheless, as reference resources expand and analytical methods mature, these integrative, convergent approaches promise deeper insights into disease mechanisms. Ultimately, such advances stand to accelerate clinically meaningful applications of GWAS, paving the way toward more precise diagnostics, interventions, and truly personalized healthcare.