Daniel Pomp, PhD

Research Interests

Key words: genetic architecture of complex traits, obesity and body weight regulation, polygenic mouse models

The Genetics of Growth, Obesity and Body Weight Regulation in Animal Models

Most traits displaying continuous variation are exceptionally complex, with varying contributions of genetic susceptibility and interacting environmental factors. Genetic predisposition to a phenotypic range for complex traits such as body weight and body fat results from combinations of relatively small effects of DNA variations within a large number of unidentified polygenes, known as quantitative trait loci (QTL). While scientists have made great progress in understanding the proteins, pathways, and networks that functionally control a complex phenotype such as obesity, we know very little about the underlying genetic variation that an individual is born with to predispose it to a particular phenotypic range. This large gap between our extensive knowledge of the physiological mechanisms and our embryonic understanding of how genetic predisposition is manifested, impairs gene-based discovery and development of diagnostic and therapeutic tools. The paradigm of “quantitative genomics” is based on large-scale endo-phenotyping at the transcriptional, proteomic, and/or metabolomic levels that is performed within the context of a QTL mapping population. This can be a powerful force in dissecting the genetic architecture of complex traits by synergistically integrating the powers of recombination and functional analyses. Using polygenic mouse models and high-throughput approaches integrating genomics and physiology, the Pomp lab identifies genes underlying predisposition to obesity, and studies how these genes interact with each other and with nutritional interventions. They have also begun to apply their research populations and methods to investigate polygenic and dietary obesity risk factors for mammary and colon cancer susceptibility.

Publications

Pubmed Link

Daniel Pomp in UNC Genetics News