Abanish Singh, PhD

With a unique skill set resulting from outstanding training, my sole aim was to help improve human health through cutting-edge translational research. Specifically, I have been interested in illuminating the mechanisms responsible for the causes and progression of the leading public health conditions, which may help with the development and enhancement of precision medicine.  As part of this endeavor, I also became interested in studying the measurement of biobehavioral risk factors and environmental stressors and their interactions with genes that may influence cardiovascular disease (CVD) risk factors and endophenotypes, adversely affecting the CVD pathways.

I joined medical research with my early research training on computational biology, high-throughput genomics, next-gen DNA sequencing, genome-wide studies, and big data analytics, which resulted in some of prominent findings on human genome (PMID: 18048317, PMID: 20223737, PMID: 20598109, PMID: 21703177). These findings included a significant contribution to the scientific community’s understanding that I made during my postdoctoral fellowship with Dr. David Goldstein at Duke Center for Human Genome Variation that how well RNA-Seq can identify human coding variants just using a small fraction of genome (transcriptome) as compared to whole genome (PMID: 20598109). This work was important not only scientifically, but also in pragmatic terms, given the high cost of sequencing.

In relatively recent work I discovered a novel CVD risk gene EBF1, where  a common genetic variant contributed to inter-individual differences in human central obesity, fasting blood glucose, diabetes, and CVD risk factors in the presence of chronic psychosocial stress (PMID: 25271088). This work demonstrated the genetic variant-specific significant path from chronic psychosocial stress to common carotid intimal–media thickness (CCIMT), a surrogate marker for atherosclerosis, via central obesity and fasting glucose. I also developed an algorithm to create a synthetic measure of stress using the proxy indicators of its components (PMID: 26202568).  Other more recent work has elucidated the race, sex, and age related differences in the EBF1 gene-by-stress interaction (Singh et al. ASHG 2017), which suggests the need for careful evaluation of environmental measures in different ethnicities in cross-ethnic gene-by-stress interaction studies.