Systems Genetics of Human Complex Diseases, Volume II

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Guiyou Liu

Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS)

Qinghua Jiang

Harbin Institute of Technology

Liangcai Zhang

Janssen Research and Development (United States)

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Given the success of the Research Topic Volume I: ‘Systems Genetics of Human Complex Diseases’, we are pleased to launch a second volume for further submissions.

In recent years, genetic studies, especially genome-wide association studies (GWAS) and next-generation sequencing, have identified multiple risk variants and pathways associated with the potential pathogenesis and genetic mechanisms of human complex diseases. Despite these successes, the majority of genetic risk factors remain to be further identified. The identification of the causative variants or mutations remains challenging and the molecular mechanisms are still rarely characterized.

In the post-genome era, the major challenge is to extract novel disease risks from multi-level omics data using system biology methods, which may expand our knowledge of the causes of human complex disease. This Research Topic will focus on the mechanism of genesis and development of human complex diseases, as well as valuable clues for the development of novel therapeutic approaches of human complex diseases. We believe the systems genetics will help us move from disease risk loci to disease etiology. This Research Topic welcomes reviews and original research papers covering recent genetic research on human complex diseases using system biology methods, including, but are not limited to:

- Statistical methods and database for integrating multi-level omics data
- GWAS
- Expression quantitative trait loci analysis (eQTLs)
- Protein quantitative trait locus (pQTLs)
- Methylation quantitative trait locus (mQTLs)
- Meta-analysis using single genetic variants, GWAS, eQTLs, pQTLs, and mQTLs
- Pathway analysis using gene expression data, GWAS, eQTLs, pQTLs, and mQTLs
- Pathway analysis using GWAS data
- Mendelian randomization analysis using GWAS, eQTLs, pQTLs, and mQTLs
- Integrating GWAS data with pathway analysis
- Integrating GWAS data with eQTLs, pQTLs, and mQTLs
- Identification of shared genetic variants, genes and pathways in different diseases

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Editors

Guiyou Liu

Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS)

Qinghua Jiang

Harbin Institute of Technology

Liangcai Zhang

Janssen Research and Development (United States)

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Frontiers in Genetics

Statistical Genetics and Methodology

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