Northwestern Events Calendar


Statistical methods for gene-environment interaction and modeling cancer screening through risk prediction

When: Monday, November 29, 2021
3:00 PM - 4:00 PM Central

Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students

Contact: Putri Kusumo   (312) 908-1718

Group: Department of Preventive Medicine

Category: Academic


Summer S Han, PhD

My research program focuses on developing and applying novel statistical methods for understanding the genetic and environmental etiology of complex diseases and for enhancing effective screening strategies based on etiological understanding. Understanding the interplay between genes and environmental risk factors is important, as it is well established that genes do not operate in isolation but rather in complex networks and pathways influenced by environmental factors. In the first part of this talk, I will present our recent methods for evaluating interactions between genetic and environmental factors for complex diseases using genome-wide association studies (GWAS) data. This method is under an additive risk model based on the trend effect of genotype using a constrained likelihood approach. In the second part of this talk, I will present our recent work for developing a prediction model for second primary lung cancer (SPLC) among lung cancer survivors based on various demographic, clinical, environmental factors utilizing large population-based cohort data that include more than 15,000 lung cancer cases collected through the International Lung Cancer Consortium (ILCCO). We assess the predictive accuracy of this prediction model, SPLC risk assessment tool (SPLC-RAT), and evaluate the performance of risk-based screening criteria for SPLC through SPLC-RAT versus the NCCN guidelines using real-world patients-outcome data from Mayo Clinic. Our study shows that risk-based screening criteria for SPLC using a comprehensive risk prediction model provides increased efficiency in detecting SPLC cases versus the existing clinical guidelines when using large, real-world patient-outcome data.

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