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

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.

My research at Duke has focused on understanding the dynamic between host and pathogen so as to discover and develop host-response markers that can diagnose and predict health and disease.  This new and evolving approach to diagnosing illness has the potential to significantly impact individual as well as public health considering the rise of antibiotic resistance.

With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine at the time of presentation what the underlying cause of illness is.  For example, acute respiratory illness is among the most frequent reasons for patients to seek care. These symptoms, such as cough, sore throat, and fever may be due to a bacterial infection, viral infection, both, or a non-infectious condition such as asthma or allergies.  Given the difficulties in making the diagnosis, most patients are inappropriately given antibacterials.  However, each of these etiologies (bacteria, virus, or something else entirely) leaves a fingerprint embedded in the host’s response. We are very interested in finding those fingerprints and exploiting them to generate new approaches to understand, diagnose, and manage disease.

These principles also apply to sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Just as with acute respiratory illness, it is often difficult to identify whether infection is responsible for a patient’s critical illness.  We have embarked on a number of research programs that aim to better identify sepsis; define sepsis subtypes that can be used to guide future clinical research; and to better predict sepsis outcomes.  These efforts have focused on many systems biology modalities including transcriptomics, miRNA, metabolomics, and proteomics.  Consequently, our Data Science team has utilized these highly complex data to develop new statistical methods, furthering both the clinical and statistical research communities.

These examples are just a small sampling of the breadth of research Dr. Tsalik and his colleagues have conducted.  

In April 2022, Dr. Tsalik has joined Danaher Diagnostics as the VP and Chief Scientific Officer for Infectious Disease, where he is applying this experience in biomarkers and diagnostics to shape the future of diagnostics in ID. 

Clinical and translational research, COVID-19 therapeutics, clinical biomarkers for infectious disease.

Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.

Dr. Shen’s research interests lie at precision medicine and systems biology. His lab integrates engineering, computational and biological techniques to study cancer, stem cells, microbiota and the nervous system in the gut. This multidisciplinary work has been instrumental in initiating several translational clinical trials in precision therapy. He is the director of the Woo Center for Big Data and Precision Health (DAP) and a core member of the Center for Genomics and Computational Biology (GCB).

1. Emerging Infections
2. Global Health
3. Epidemiology of infectious diseases
4. Clinical microbiology and diagnostics
5. Bioterrorism Preparedness
6. Surveillance for communicable diseases
7. Antimicrobial resistance

The incorporation of personalized medicine to all areas of human health represents a turning point for human genetics studies, a point at which the discoveries made have real implications for clinical medicine.  It is important for students to gain experience in how human genetics studies are conducted and how results of those studies may be used.  As a statistical geneticist and biostatistician my research interests are focused on developing and applying statistical methods to search for genes causing common human diseases.  My research programs combine development and application of statistical methods for genetic studies, with a particular emphasis on understanding the joint effects of genes and environment. 

These studies I work on cover diverse areas in biomedicine but are always collaborative, with the goal of bringing robust data science and statistical methods to the project.  Collaborative studies include genetic and ‘omics studies of cardiovascular disease, health effects of air pollution, genetic analysis of adherence to an exercise program, genetic analysis in evaluating colon cancer risk, genetic analysis of suicide, and systems biology analysis of Gulf War Illness.

Keywords: human genetics, genetic association, gene mapping, genetic epidemiology, statistical genetics, biostatistics, cardiovascular disease, computational biology, diabetes, aging, colon cancer, colon polyps, kidney disease, Gulf War Illness, exercise behavior, suicide

Using Pathogens to Decipher Genetic Variation Connecting Cell Biology and Disease Susceptibility
Despite improvements in public health, advancements in vaccines, and the development of many classes of antibiotics, infectious disease is still responsible for over a quarter of all deaths worldwide. However, even for the most devastating of pandemics, individuals demonstrate a large variability in the severity of infection. The long-term goal of the lab is to understand the genetic basis for differences in susceptibility to infection and related inflammatory disorders. We approach this question through a combination of experimental and computational approaches that combine high-throughput cell biology with quantitative human genetics. The identified genetic differences serve as the starting point for exploring new cell biology and human disease susceptibility genes.