Bioinformatics and Statistical Genetics Analyses for Omics-Based Dissection of Complex Traits

The genetic architecture of complex traits is shaped by a combination of polygenic effects, gene-environment interactions, and multi-layered regulatory mechanisms. With the growing availability of large-scale biobank datasets and multi-omics resources, statistical genetics has entered a transformative phase. Novel methods are being developed to integrate genomic, transcriptomic, proteomic, and epigenetic layers with phenotypic and clinical data. Despite substantial progress, important challenges remain, such as correcting for confounding, modeling high-dimensional interactions, and dealing with heterogeneous data modalities. This Research Topic focuses on statistical and computational innovations that aim to address these challenges and drive new biological and clinical insights.

This article collection invites contributions at the intersection of statistical genetics, omics integration, and complex trait epidemiology. We aim to highlight advanced modeling strategies for dissecting genetic risk, capturing pleiotropy, and improving phenotype prediction. We welcome both theoretical and applied manuscripts focusing on polygenic risk score (PRS) construction, transcriptome-wide and proteome-wide association studies (TWAS, PWAS), Mendelian Randomization, and fine-mapping techniques. Particular emphasis is placed on studies that leverage large-scale omics data—such as genomics, transcriptomics, epigenomics, proteomics, or metabolomics—to unravel regulatory networks and improve the resolution and interpretability of complex trait biology.

We invite contributions that introduce novel pipelines and workflow tools, or that apply existing methods to large-scale datasets for the processing and analysis of high-dimensional data in statistical genetics.
Topics of particular interest include methods for data harmonization, visualization, and interpretation, as well as benchmarking studies and frameworks that enable causal inference and the integration of regulatory annotation data. Submissions may include R or Python packages, interactive portals, or tools that facilitate transparency, reproducibility, and usability in genomic research. The goal is to foster methodological rigor and translational relevance in the study of complex trait genetics.

We encourage submissions that include, but are not limited to:

● Statistical models for polygenic prediction and risk stratification
● Gene regulation and transcriptome/proteome integration
● Multi-omics data harmonization and dimensionality reduction
● Bayesian and penalized models for high-dimensional inference
● Causal inference using Mendelian Randomization and other approaches
● Visualization tools, R/Python packages, and interactive portals for genomic data
● Scalable, reproducible bioinformatics workflows and pipelines
● Integration of functional annotations and regulatory elements
● Benchmarking of statistical methods and prediction models
● Applications in epidemiological cohorts and biobanks