External Resources:

Kelly A. Frazer, Ph.D.

Director, Institute for Genomic Medicine
Professor of Pediatrics

Website: http://frazer.ucsd.edu/
Email: kafrazer@ucsd.edu

Pathways/processes:
Teams/collaborations: Bing Ren
Approaches:
Diseases:

Association to Function

Genome Wide Association (GWA) studies have identified statistical associations between hundreds of SNPs and many different complex traits, including many common diseases. However, it is challenging to move from these statistical associations to knowledge of the biological reason for why a genomic interval tracks with a complex trait. This is largely because GWA studies identify a subset of common SNPs tagging linkage disequilibrium bins that are statistically associated with a trait, but the precise variants in each bin contributing to variation in the trait are not known. Many associations are localized to intervals that do not encode genes and gene desert, and the best way to identify the causative variants and functional elements is unclear.

In the laboratory, we are using a combination of approaches to functionally characterize gene deserts including cross-species sequence comparisons to identify evolutionarily conserved elements, chromatin profiling to identify marks indicative of regulatory elements, and in vivo and in silico identification of transcription factor binding sites.

Cancer genomics

Most Cancers have a strong genetic component. Genetic association studies have demonstrated that some patients can carry susceptibility loci that increase risk of specific cancers. The tumor itself is believed to develop from bening to malignant as a consequence of an accumulation of mutation events, in oncogenes or tumor suppressor genes, which lead to an uncontrollable cell proliferation.

By analyzing patient and tumor DNA or tumor RNA we are interested in identifying biomarkers (DNA variant, Gene Expression Modules, Novel Transcripts or splicing isoforms) for cancer susceptibility, cancer progression and DNA variants responsible for tumor growth, distinguishing driver from passenger mutations. By establishing a refined genetic landscape in cancer, we can improve diagnosis and prognosis, classify tumors, open new pathways for drug discovery and help physicians select the optimal therapeutic option for each individual patient.