Statistics in Genetics and Genomics

This special issue aims to highlight how statistical science, together with emerging machine learning and deep learning techniques, advances data mining and integrative analysis in modern genetics and genomics. Rapid developments in high-throughput sequencing and multi-omics technologies have generated unprecedented volumes of heterogeneous data, creating both opportunities and challenges for statistical modeling and inference. From the perspective of statistical genetics and genomics, there is a critical need for principled methods capable of addressing high dimensionality, complex dependence structures, and diverse sources of bias, while providing interpretable results and rigorous uncertainty quantification.

We invite contributions that develop innovative methodologies, offer theoretical advances, or demonstrate impactful applications to real genetic and genomic data. Of particular interest are studies that integrate multiple data types, leverage underlying biological or structural information such as networks, pathways, or spatial and temporal patterns, or bridge classical statistical modeling with modern machine learning and deep learning approaches. We also welcome work on study design, reproducible analytical pipelines, and software tools that support rigorous, transparent, and scalable analysis in genetics and genomics. Through this special issue, we aim to promote cross-disciplinary dialogue and highlight how statistically principled approaches can advance biological insight, uncover functional patterns, and enhance our understanding of genetic architecture and function.

Topics of interest include, but are not limited to:

· Statistical methods for genomic and genetic data analysis
· High-dimensional inference in genetics and genomics
· Integrative analysis of multi-omics data
· Statistical models for gene regulation and functional genomics
· Network- and pathway-based analytical approaches
· Modeling of spatial and temporal patterns in genomic data
· Machine learning or deep learning techniques for genomic data mining
· Data mining and exploratory analysis of large-scale genomic datasets
· Statistical approaches for single-cell and spatial omics
· Reproducible workflows and software tools for genomic analysis

Guest Editor
Prof. Libo Jiang (School of Life Science and Medicine, Shandong University of Technology)

Deadline
The deadline for manuscript submissions is January 31st, 2027, but we can accommodate extensions on a case-by-case basis. All papers will be published as open-access articles upon acceptance.

Submission Instructions
Please submit the full manuscript to Statistics Innovation via our Submission System. Additionally, please choose a topic of this special issue when submitting and mention it in your cover letter. For further inquiries, please contact Guest Editors and Statistics Innovation Editorial Office(stati@maxapress.com).