Over the past decades, large-scale human genetic studies have identified numerous risk genes and variants associated with complex diseases and traits. However, the biological mechanisms underlying these associations remain largely unclear: we do not know in which cell types these genes/variants function or through which pathways they influence disease phenotypes. Recent advances in single-cell omics technologies have provided unprecedented high-resolution views of biological systems, revealing cellular heterogeneity, rare cell types, and dynamic regulatory processes previously obscured in bulk analyses.

A key challenge is effectively integrating these data domains: linking population-level association signals with single-cell molecular mechanisms. This requires novel methods for multi-dimensional heterogeneous omics integration and analytical strategies combining genetic variation with gene signals at single-cell resolution. Breaking through this bottleneck will enable precise identification of disease-relevant cell types, dissection of regulatory networks, and advancement of precision medicine and drug discovery.

This Research Topic aims to bring together cutting-edge computational methodology and innovative application studies at the intersection of single-cell omics and genetics. With the decreasing cost of single-cell sequencing, the rapid growth in sample sizes, and the accumulation of functional genomics and genetic perturbation data, we are at a critical juncture for transitioning from correlation analysis to mechanistic dissection. Recent methodological advances, such as single-cell-level quantitative trait loci (sc-eQTL) mapping, multi-omics data integration frameworks, and causal inference methods based on single-cell data, have demonstrated the feasibility of dissecting genetic effects at single-cell resolution.

This Research Topic will focus on: (1) developing scalable computational methods to integrate single-cell omics and genetic data; (2) establishing analytical pipelines from genetic association signals to pathogenic cell types and molecular mechanisms; (3) elucidating the genetic architecture and heterogeneity of complex diseases through a single-cell lens. We expect this Research Topic to foster interdisciplinary collaboration, promote the field's transition from descriptive to predictive and mechanistic research, and provide new perspectives for understanding disease etiology and developing precision therapeutic strategies.

This Research Topic focuses on computational integration of single-cell omics and genetics in human complex disease research. We welcome innovative studies that advance this interdisciplinary field, including the development of novel computational methods, improvement and evaluation of existing approaches, and innovative applications in disease research. There are no restrictions on disease types studied.

We welcome manuscripts of all types. Whether focusing on algorithmic innovation, data integration strategies, causal inference methods, or mechanistic dissection of specific diseases, we expect high-quality research that showcases the latest advances and future directions in this field. We strongly encourage authors to openly share data and code to promote research reproducibility, and particularly welcome interdisciplinary research that integrates multidisciplinary approaches such as machine learning, statistical genetics, and systems biology.

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Computational Integration of Single-Cell Omics and Genetics for Disease Mechanisms