Guolian Kang Group

The goal of our independent research is to develop new statistical methods to analyze the diverse forms of mega-genomics and population- and family-based data.

Secondary traits genetic and exposure-secondary outcomes association studies

We propose a novel set-valued (SV) method to assess secondary trait genetic association studies and exposure-secondary outcome association studies using data collected under case-control study design (SV2bc) and extreme phenotype study design (STEPS). Here, secondary traits can be binary or continuous variables and can be associated with gene expression, gene methylation, etc.   

Gene-environment (G-E) interactions

We have assessed efficient study designs of gene-environmental (G-E) interaction studies for a binary environmental variable in a case-control study on the power for assessing G-E interactions or for assessing genetic or environmental effects in the presence of G-E interactions.

Rare variants association studies

We have developed set-valued system identification approaches (SVSI) on rare variant association approaches in next generation sequencing studies for binary outcomes and ordered categorical outcomes.

Genome-wide genetic mediation analysis (GMEPS)

We have proposed a fast and versatile genome-wide mediation analysis approach (GMEPS) to analyze data collected under different study designs such as nonrandom Extreme or random Phenotype Sampling design. Here, the mediator can be binary or continuous variables and can be gene expression or methylation data.

Mendelian randomization analysis (MREPS)

We have proposed a versatile and efficient approach for Mendelian randomization analysis under different study designs. They can be random sampling design, extreme tails of the primary outcome of interest, or extreme tails of the risk factor that is the primary outcome of interest in the original study. Here, the risk factor is a continuous variable and can be gene expression or methylation data.

Sickle cell disease

A major focus for our group is to provide statistical support for the Sickle Cell Research and Intervention Program (SCCRIP). Our work in this area helps investigators assess large datasets from SCCRIP and its coupled sickle cell genomic project using established statistical approaches as well as novel approaches equipped to handle rich sets of phenotypic and genomic data. Much of our clinical collaborative work concentrates on prospective sickle cell disease trials along with retrospective research efforts.

Other non-malignant hematology

We support gene-therapy clinical trials focused on sickle cell disease (SCD) and other non-malignant hematologic diseases by designing, monitoring, reporting clinical trials, and writing manuscripts.

Pre-clinical and translational research

We are active participants in experimental hematology projects and provide statistical support to investigators by designing basic science studies and writing statistical considerations in various grant applications, amongst other activities.     

Through our daily work, we analyze a large amount of phenotype data, such as growth development, bone mineral density, kidney, heart, lung, quality-of-life, neurocognitive data, etc, which we then correlate with genomic data parameters by considering environmental and treatment factors. This correlation allows us to establish relationships among genetics, treatment, environmental, hematologic parameters, and outcomes. Our ultimate hope is to develop new statistical approaches to further assess and establish these types of relationships that help the research community understand the impact on patient outcomes and quality of life.