An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility.



While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility.


Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity.


Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at and the software code at .





Published Version (Please cite this version)


Publication Info

Wang, Liuyang, Thomas J Balmat, Alejandro L Antonia, Florica J Constantine, Ricardo Henao, Thomas W Burke, Andy Ingham, Micah T McClain, et al. (2021). An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility. Genome medicine, 13(1). p. 83. 10.1186/s13073-021-00904-z Retrieved from

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Liuyang Wang

Assistant Research Professor of Molecular Genetics and Microbiology

Leveraging bioinformatics and big data to understand the intricacies of human diseases.

My overall research goals are centered on unraveling the molecular mechanism underpinning human disease susceptibility and harnessing these findings to innovative diagnostic and therapeutic strategies. I have adopted a multidisciplinary approach that integrates genomics, transcriptomics, and computational biology. Leveraging high-throughput cellular screening and genome-wide association study (GWAS), we have successfully identified hundreds of genomic loci associated with 8 different pathogens (Wang et al. 2018). Utilizing single-cell RNA-seq, we developed scHi-HOST to rapidly identify host genes associated with the influenza virus (Schott and Wang, et al. 2022). I also have developed several novel statistical tools, CPAG and iCPAGdb, that estimate genetic associations among human diseases and traits (Wang et al. 2015, 2021). Combining experimental and computational approaches, I expect to gain a deeper understanding of the genetic architecture of human susceptibility to infection and inflammatory disorders.


Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics

Thomas Burke

Manager, Systems Project

Micah Thomas McClain

Associate Professor of Medicine

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