Our Projects

We are a group of highly motivated and creative people, discovering and innovating together to make precision medicine a reality through the catalytic power of big genomic data, statistical genetics, and artificial intelligence.


New disease risk gene and novel biomarker discovery

We have extensive experiences, solid genetic knowledge, and professional data analyzing skills on disease risk gene and novel biomarker discoveries (Zhang#, Wang#* et al. 2018, PNAS; Wang et al. 2019, J Am Soc Nephrol; Chun*, Wang* et al. 2020, KI Reports; Khetarpal*, Wang* et al. 2019,N. Engl. J. Med.) . We will analyze large-scale biomedical data to profile the associations between genetics and phenotypes, which including but are not limited to the associations from, large-scale genotyping data, sequencing data, in-depth phenotyping data by clinical images, transcriptomics data, proteomics data, biomarkers, and electronic health records data from biobanks, to build a comprehensive “geno-pheo” association map. From this map, we will fine map the causal variants and genes for disease etiology and will use Mendelian randomization to distinguish causations from associations.


Disease risk prediction

Building on our previous works on disease risk prediction (Wang et al. 2020 J Am Coll Cardiol.; Emdin*, Bhatnagar*, Wang* et al. 2020, and Dron*, Wang* et al. 2021, Circ Genomic Precis Med.; Patel*, Wang* et al. 2020, JAMA network open; Fahed*, Wang*, homburger*, et al. 2020,Nat. Commun), we will develop novel statistical genetics and deep-learning based risk prediction models to quantify the risk information jointly from disease risk factors, genetic mutations, biomarkers, latent features derived from raw clinical imaging data, correlations from different diseases and genomic annotations. The models will have the advantages of integrating inherited innate genetic risks and late age responses to developmental and environmental stimuli. As a result, the prediction will be more powerful and precise. Furthermore, taking advantage of the large-scale data from the Chinese people, we will increase model transferability for East Asian populations that are tailored, specific, and precise to the Chinese people.