Graham School Events

MScBMI Seminar Series | Heart Failure Genetics in the Big Data Era: How Much is Enough?

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Join us in downtown Chicago for a discussion with Megan Roy-Puckelwartz, PhD on ways to use big data in healthcare to better understand cardiac diseases.

Heart failure is an increasing medical problem that is disabling and costly, and affects more than 5 million Americans. One of the leading causes of heart failure is cardiomyopathy, a disorder with a high heritable component. Mutations in the two genes encoding the thick filament explain ~75% of inherited hypertrophic cardiomyopathy (HCM), leading to the observation that HCM is a disease of the sarcomere. In contrast, dilated cardiomyopathy (DCM) is far more genetically heterogeneous with mutations in genes encoding cytoskeletal, nucleoskeletal, mitochondrial, and calcium handling proteins. Rare mutations account for most genetic cardiomyopathy with few hotspots or recurring mutations. Current genetic testing using high-throughput next generation sequencing now samples > 100 genes, however this testing has only ~50% sensitivity. Moreover, there is considerable phenotypic variability within families, where all members share the same primary mutation but with differing age of onset and expression. The “missing heritability” for cardiomyopathy may be due to multiple factors, including but not limited to 1) undiscovered primary or “driver” gene mutations and/or 2) an oligogenic genetic mechanism involving the interplay between driver variants and the genomic context in which they are expressed. The genomic context for cardiomyopathy includes genetic modifiers, which are not restricted to coding regions of the genome. Genetic modifiers are defined as genetic variants that alter the phenotypic expression of a primary mutation, and identifying these pathways is useful for clinical prognosis and potentially to identify pathways around which therapy can be developed. Historically, studies of the genetics of cardiomyopathy were limited to a small fraction of the genome with limited information on the larger genomic signature of heart failure. Whole genome sequencing provides a more comprehensive picture of genomic context, including both rare and common variation, that shapes the manifestation of driver variant(s) extending beyond the coding region. Whole genome analysis requires advanced computational tools and computing environments. Interpreting genetic variation in the presence of phenotype data provides additional power to detect genes and pathways that contribute to the variability present in inherited heart failure. Mining and incorporating electronic health data and integrating phenotype information with genomic data is a powerful tool that presents new challenges for the informatics community.

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MSc in Biomedical Informatics