Webinars

Upcoming webinars

Predictive biomarker approaches for precision medicine
Speaker: Dr. Gina D'Angelo (AstraZeneca)
Date/Time: Friday, March 21, 2025, 1:00 - 2:00 PM (Eastern)
Registration (free but required): zoom.us/webinar/register/WN_sgxyP9aUTamhp2CQbiQNJA

Abstract: Precision medicine is an evolving area in the medical field and rely on biomarkers to make patient enrichment decisions, thereby providing drug development direction. We will discuss various approaches that are used for Phase 1-Phase 3 trials to identify such biomarkers. A traditional statistical approach is to find the cut-off that leads to the minimum p-value of the interaction between the biomarker dichotomized at that cut-off and treatment. Such an approach does not incorporate clinical significance and the biomarker is not evaluated on a continuous scale. We will spend some time on our proposed approach to evaluate a biomarker in a continuous manner from a predicted risk standpoint, based on the model that includes the interaction between the biomarker and treatment. The predicted risk can be graphically displayed to explain the relationship between the outcome and biomarker, whereby suggesting a cut-off for biomarker positive/negative groups. Some features include covariate adjustment, biomarker comparisons, and flexibility in the type of outcome and covariates considered. Examples will be demonstrated.

Bio: Dr. Gina D'Angelo has over 20 years of academic and industry experience on biomarker-related statistical methods, biomarker discovery, and biomarker-driven clinical trials. She received a PhD in Biostatistics from University of Pittsburgh. Currently, Gina is a Director at AstraZeneca Oncology Statistical Innovation and offers her guidance and expertise on many of the early-late phase trials including a program devoted to a novel AI biomarker in Oncology. She has designed numerous trials, consulted on statistical analyses and approaches, and has many publications. She is currently co-editing a book on Statistical and design consideration for biomarkers in clinical trials. Her experience spans from discovery to late phase trials, with small to high-dimensional data, across various therapeutic areas.

Gene-environment Interaction Analysis: A High-dimensional Analysis Perspective
Speaker: Dr. Shuangge (Steven) Ma (Yale)
Date/Time: Monday, April 21, 2025, 2:00 - 3:00 PM (Eastern)
Registration (free but required): https://zoom.us/webinar/register/WN_xzMff3_CS-irTGuCzaygtA

Abstract: Beyond the main genetic and environmental effects, gene-environment (G-E) interactions have been demonstrated to contribute significantly to the development and progression of complex diseases. This presentation will focus on various selective G-E interaction analysis methods, particularly those employing innovative high-dimensional analysis techniques. The three main categories of methods include hypothesis testing, variable selection, and dimension reduction, leading to three overarching frameworks: testing-based, estimation-based, and prediction-based. Linear- and nonlinear-effects analysis, fixed- and random-effects analysis, marginal and joint analysis, and Bayesian and frequentist analysis will be reviewed. Furthermore, we will discuss the statistical properties, computational aspects, applications, and future directions of these techniques.

Bio: Dr. Shuangge (Steven) Ma received his PhD in Statistics from the University of Wisconsin in 2004. From 2004 to 2006, he was a Senior Fellow at the University of Washington. Since 2006, he has been a faculty member in the Department of Biostatistics at the Yale School of Public Health. His research interests include genetic epidemiology, cancer biostatistics, analysis of electronic medical record data, linguistic analysis, and deep learning. He is a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS), an Elected Member of the International Statistical Institute (ISI), and an Elected Member of the Connecticut Academy of Science and Engineering (CASE). Additionally, he serves as the Editor-in-Chief of Briefings in Bioinformatics and as an Associate Editor for several statistics and informatics journals.

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Previous webinars

The recordings for previous webinars can be accessed through the members-only site. To access, please log in and travel to the past webinars tab. If you are not a section member yet, you may join us through Join – Section on Statistics in Genomics and Genetics (amstat.org).

January 2025: The Value of Postdoctoral Training in Statistical Genomics and Genetics (Drs. Andrew Stiemke, Qingyu Chen, Soumik Purkayastha, and Jean Morrison)
November 2024: Leaders Create the Container: Setting the Foundation for How Work is Done (Michelle M. Lamere)
October 2024: Leveraging the Power of Large Language Models (LLMs) by Statisticians: An Example AI Application (Dr. Junshui Ma)
September 2024: Statistical methods for spatial transcriptomics (Dr. Xiang Zhou)
June 2024: Panel Discussion on Collaboration in Statistical Genetics and Genomics (Dr. Mengjie Chen, Dr. Mitchell Machiela, Dr. Arvind Shah, and Dr. Qi Liu)
April 2024: Data thinning to overcome double dipping (Dr. Anna Neufeld)
March 2024: The importance of genetic evidence to improve productivity in drug discovery & development (Dr. Matthew R. Nelson)
January 2024: Translating polygenic risk scores in the clinic: promises and challenges (Dr. Bogdan Pasaniuc)
November 2024: Rigor, Reproducibility, and Mindful Programming (Dr. Claudia Solís-Lemus & Dr. Stephanie Hicks)
October 2023: Time order structure and feature learning for trajectory modeling (Dr. Benedict Anchang)
September 2023: Panel Discussion on Academic Careers and Job Search (Dr. Alison A. Motsinger-Reif, Dr. Nianjun Liu, Dr. Brooke Fridley, Dr. Shilin Li, Dr. Jack O’Brien, and Dr. Kesley Grinde)
July 2023: Tips for (Your First) JSM! (Dr. Nancy Zhang and Dr. Michael Wu)
May 2023: Panel Discussion on Industry Careers and Internships (John Palcza, Dr. Audrey Y. Chu, Dr. Olukayode Sosina, and Dr. Jingchunzi Shi)
March 2023: Bayesian Methods for Spatially Resolved Transcriptomics Data Analysis (Dr. Qiwei Li)
February 2023: Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research (Alyna Khan and Dr. Sarah C. Nelson)
January 2023: Panel Discussion on NIH Grant Funding and Grant Review (Dr. Katerina Kechris, Dr. Jingyi Jessica Li, Dr. Li-Xuan Qin, and Dr. Grzegorz A. Rempala)
November 2022: Manuscript Writing Tips and Guidelines (Dr. Mingyao Li and Dr. Sanjay Shete)
October 2022: Multivariate Integration of Multi-Omics Data (Dr. Kim-Anh Lê Cao)
September 2022: Pharmacogenomics (PGx) at Merck: Strategy, Projects and Research (Dr. Judong Shen)
June 2022: Career Panel on Grant Application Panel Discussion (Dr. Saonli Basu, Dr. Mingyao Li, Dr. Victoriya Volkova, Dr. Judy Huixia Wang)
April 2022: Using Genomic Data Repositories for Secondary Analysis: Promises and Challenges (Dr. Saonli Basu)
March 2022: Multi-Omics Integration: Problems, Potential and Promise (Dr. Katerina Kechris)
February 2022: Leveraging Mentorship (Dr. Ruth Gotian)
November 2021: Network-based methods for analysis of microbiome and metabolomic data (Dr. Jing Ma)
October 2021: Advanced statistical methods for genetic association studies (Dr. Zhengzheng Tang)
September 2021: Statistical Methods for Analysis of Heterogeneous Tumor Samples (Dr. Wenyi Wang)
June 2021: Career Panel on Time Management, Research Strategy and Healthy Habits for Graduate Students (Dr. Kelsey Grinde, Dr. Rui Duan, and Dr. Danielle Braun)
June 2021: Statistical Genomics at Procter and Gamble (Dr. Dionne Swift and Dr. Kellen Kresswell)
May 2021: Computational Analyses of Multi-Modal Single-Cell Data (Dr. John Marioni)
April 2021: Genetics of Within-Subject Variability and Diabetes Complications (Dr. Jin Zhou)
March 2021: Interrogating the Gut Microbiome: Estimation of Bacterial Growth Rate and Prediction of Biosynthetic Gene Clusters (Dr. Hongzhe Li)
February 2021: Addressing bias in genetic epidemiology for admixed populations (Dr. Genevieve Wojcik)