The Laboratory of Translational Genomics (LTG) investigates the biological basis of common inherited genetic variants associated with cancer susceptibility and outcomes. Using large-scale genome-wide association studies and familial linkage studies in high-risk families, investigators continue to identify loci within the human genome that are associated with cancer risk. The laboratory is at the forefront of applications in bioinformatics and advanced genetic analyses with new platforms designed to evaluate the biological effects of dense sets of single nucleotide polymorphisms (SNPs), which are the most common genetic variants in the human genome. Specifically, the laboratory has integrated approaches to identify and validate common SNPs and ancestral haplotypes, which could be used to dissect the genetic basis of disease susceptibility. Together with the NCIs Core Genotyping Facility (CGF), the LTG carries out genome-wide association studies (GWAS), including the project known as CGEMS (see below). Using data from large-scale GWAS that evaluate millions of SNPs, investigators at the LTG are able to identify in great detail new regions in the human genome that are associated with cancer risk and to estimate the magnitude of effect of these risks. GWAS have been instrumental in the discovery of new regions of the genome, which influence the basic etiological risk factors. In addition, these novel findings may, at a later time, also bear important predictive value for disease as well as highlight potential molecular pathways related to both disease etiology and perhaps therapeutic intervention. To understand the biology underlying these associations, investigators are following up with focused validation studies, deep-sequencing, and functional analyses, such as analyses of expression levels and methylation patterns. This research relies on multidisciplinary approaches from population genetics, epidemiology and molecular evolution. Cancer Genetic Markers of Susceptibility (CGEMS) has been a collaborative project employing GWAS technologies to investigate several types of cancer, risk factors, and outcomes such as survival. Recently, the LTG has focused on a series of follow-up studies from CGEMS bladder and pancreas cancer scans. Investigators at the LTG are mapping those common and uncommon genetic variants in order to nominate suitable variants for further functional studies. Investigation of GWAS signals requires extensive bioinformatic follow-up to examine unannotated transcripts, regulatory elements, as well as functional elements for novel transcripts. Regulatory effects are queried with respect to the alteration of gene levels, epigenetics, and long-ranging effects on other genes at a distance. The LTG is also investigating several possible biologic mechanisms including whether variations in these identified regions may affect regulatory elements of neighboring genes, the impact of miRNA polymorphisms acting upon fragile chromosomal sites, and epigenetic effects across multi-susceptibility regions. The LTG has extended such genetic analyses for other malignancies, including melanoma and kidney cancer.We have developed a series of collaborations with leading epidemiologists and biostatisticians in the Division of Cancer Epidemiology and Genetics (DCEG). Data pooling is being used to achieve the statistical power necessary to detect associations between genomic variants and a variety of health outcomes. These efforts would not be possible without NCI's long-term investment in the Cohort Consortium, an international partnership pooling four million people from 41 cohorts, comprised of diverse populations and ethnicities, including but not limited to African-American, Hispanic, Yuruba, European, and Chinese populations. The cohorts provide extensive risk factor data (such as individual-level smoking behavior, body mass index (BMI), alcohol consumption) and biospecimens, including germline DNA. Partnerships between the LTG and numerous NCI grantee research organizations are underway, allowing investigation into carcinogenic pathways and gene-environment interactions.
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