Colloquium - Scott Hebbring

Scott Hebbring, Research Scientist in the Center for Precision Medicine Research at Marshfield Clinic

The Clinical Utility of Predicted Family Histories for Mendelian and Genetically Complex Forms of Disease

Abstract: One of the best predictors of disease risk is a family history (FamHx).  Unfortunately, collecting a FamHx can be labor intensive, can quickly become outdated, and is limited by a patient’s understanding, memory, and willingness to share.  We developed an algorithm that can reliably predict family relationships in an electronic health record (EHR) resulting in over 170,000 pedigrees consisting of over 700,000 patients. The predicted pedigree data was then cross-referenced with longitudinal health data from the EHR to generate predicted-FamHx (p-FamHx) for a wide variety of common and rare diseases. 

To evaluate the clinical utility of p-FamHx data, we associated p-FamHx with polygenetic risk scores (PRSs) for 239 common traits in 18,000 unselected patients.  Of these, 61 PRSs were associated with p-FamHx [e.g., gout (P=1.2E-13), obesity (P=5.7E-9), and breast cancer (P=2.7E-6)].  We also recruited 2,000 independent patients based on p-FamHx data for two dominantly inherited monogenetic traits (i.e., cancer and familial-hypercholesterolemia).  DNA from these patients were sequenced to quantify pathogenic alleles in known disease causing genes. Preliminary results indicate that patients with a p-FamHx for cancer, regardless of affected status, had a 3-15 fold risk of carrying dominantly inherited cancer causing variants compared to those with no p-FamHx for cancer.  A similar trend is coalescing for those with a predicted family history of hypercholesterolemia.  The totality of these results provides compelling evidence that p-FamHx could be used to predict and possibly prevent many diseases in real-time.

Sunday, November 24, 2019 at 1:05 p.m.

Fisher Hall, 327B
1400 Townsend Drive, Houghton, MI 49931

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Department of Mathematical Sciences

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Ying Sha

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