MAY 08, 2019 10:30 AM PDT

Using Networks to Understand Cancer Risk

C.E. Credits: CEU
Speaker
  • Professor of Computational Biology and Bioinformatics, Chair of the Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute
    BIOGRAPHY

Abstract

One of the central tenants of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that these genetic variants work synergistically to help define phenotype and to regulate processes that are responsible for phenotypic state transitions. We will use gene expression and genetic data to explore gene regulatory networks, to study phenotypic state transitions, and to analyze the connections between genotype, gene expression, and phenotyope, and to explore how cancer-risk SNPs exert an influence on the disease risk that extends beyond one locus.

Learning Objectives: 

1. Genes and genetic variants work together in complex networks that are associated with individual phenotypes and change as phenotype evolves and changes.
2. Differences in network structure can help us to better understand the drivers of health and disease.
3. Networks and their structures can help us understand how small-effect genetic variants can work collectively to influence disease risk.
 


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MAY 08, 2019 10:30 AM PDT

Using Networks to Understand Cancer Risk

C.E. Credits: CEU

Specialty

Gene Sequencing

Molecular Biology

Molecular Diagnostics

Cancer Diagnostics

Cancer Research

Genetics

Cancer

Gene Expression

Genomics

Cell

Dna Sequencing

Infectious Disease

Medicine

Bioinformatics

Clinical Diagnostics

Geography

North America44%

Asia44%

Europe11%

Registration Source

Website Visitors100%

Job Title

Student33%

Medical Doctor/Specialist17%

Facility/Department Manager17%

Educator/Faculty17%

Genetic Counselor17%

Organization

Academic Institution33%

Clinical Laboratory22%

Biotech Company22%

Medical School11%

General Laboratory11%


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