Designing and developing statistical approaches to optimize basic science, clinical, translational, population, and family-based studies
A person’s genetic makeup can have a significant impact on the onset, development, and progression of disease. As investigators and clinicians study the genome and its role in pediatric catastrophic disease, a robust statistical approach advances the understanding of genetics, environment, and treatment in relation to disease. Our laboratory provides statistical services and methodology development that support laboratory research, clinical, translational genomics, and precision medicine efforts as we advance cures for pediatric catastrophic diseases.
Our group’s work focuses on the advancement of clinical and laboratory research through the development and application of robust and valid statistical approaches. In this endeavor, we partner with the Department of Hematology to support their research efforts into sickle cell disease and other non-malignant hematologic diseases.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dr. Guolian Kang leads their laboratory in research that supports the development and application of mathematical/statistical methods to detect genetic and non-genetic factors that affect complex diseases and clinical outcomes. Kang received a PhD from the Academy of Mathematics and Systems Sciences in China and is now an Associate Member of Biostatistics at St. Jude. Kang’s extensive research experience allows them to possess an interdisciplinary knowledge of biostatistics, statistical genetics/genomics, genetic epidemiology, experimental design, and expertise in system modeling of complex traits. This interdisciplinary knowledge is reflected in Kang’s laboratory work, which strives to advance research efforts at St. Jude.
A curious team of biostatisticians, driven by St. Jude’s mission, who work to develop novel statistical methodologies that help investigators conduct rigorous and effective research.