Genome-wide association studies have identified thousands of genetic loci that are significantly associated with complex traits and diseases status. However, the functions/roles of the majority (~90%) of these associations remain poorly understood. Systematic characterization of their function is challenging because the function of variants of most traits likely act in a tissue or cell-type specific fashion. The recent maturation of single-cell sequencing technologies enables to profile genetic, epigenetic, proteomic, and transcriptomic information at individual cell level. This technology provides an unprecedented opportunity, alongside computational challenges, to systematically investigate the cellular heterogeneity and the function of variants for complex traits with different genetic architectures.
Integrative analysis that jointly incorporates known functional annotation information (e.g., cell-type specific expression levels or tissue-specific SNP annotations) into genome-wide associations can help elucidate the underlying biological mechanisms, prioritize important functional variants or achieve accurate prediction performance, etc.. Although various computational tools have been developed for such analyses, there is a pressing need to develop computationally scalable integrative analysis for large-scale genome-wide association studies, such as UK Biobank, China Kadoorie Biobank and FINNGEN. To address this need, this Research Topic focuses on integrative analysis to highlight the interpretation of genome-wide associations by leveraging the latest advances in single-cell sequencing studies.
This Research Topic covers integrative analysis of genome-wide association studies and single-cell sequencing studies within (but not limited to) the following topics:
• Single-cell multi-omics and integrative analysis
• Polygenic scores (PGS)/polygenic risk scores (PRS)
• Cell-type specific eQTLs
• Large-scale association studies
• Gene-environment interaction
• Tumor cell atlas and tumor microenvironment
Original Research, Reviews, Methods, Technology and Code, Data Reports and, Commentary Articles are all welcome.
Genome-wide association studies have identified thousands of genetic loci that are significantly associated with complex traits and diseases status. However, the functions/roles of the majority (~90%) of these associations remain poorly understood. Systematic characterization of their function is challenging because the function of variants of most traits likely act in a tissue or cell-type specific fashion. The recent maturation of single-cell sequencing technologies enables to profile genetic, epigenetic, proteomic, and transcriptomic information at individual cell level. This technology provides an unprecedented opportunity, alongside computational challenges, to systematically investigate the cellular heterogeneity and the function of variants for complex traits with different genetic architectures.
Integrative analysis that jointly incorporates known functional annotation information (e.g., cell-type specific expression levels or tissue-specific SNP annotations) into genome-wide associations can help elucidate the underlying biological mechanisms, prioritize important functional variants or achieve accurate prediction performance, etc.. Although various computational tools have been developed for such analyses, there is a pressing need to develop computationally scalable integrative analysis for large-scale genome-wide association studies, such as UK Biobank, China Kadoorie Biobank and FINNGEN. To address this need, this Research Topic focuses on integrative analysis to highlight the interpretation of genome-wide associations by leveraging the latest advances in single-cell sequencing studies.
This Research Topic covers integrative analysis of genome-wide association studies and single-cell sequencing studies within (but not limited to) the following topics:
• Single-cell multi-omics and integrative analysis
• Polygenic scores (PGS)/polygenic risk scores (PRS)
• Cell-type specific eQTLs
• Large-scale association studies
• Gene-environment interaction
• Tumor cell atlas and tumor microenvironment
Original Research, Reviews, Methods, Technology and Code, Data Reports and, Commentary Articles are all welcome.
