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Education Workshop:
Navigating Biobank Data: Advanced Strategies for Genetic Epidemiology Research

Agenda
(subject to modification)

1:00 – 1:10 PM | Welcome and Opening Remarks

• Overview of the workshop goals
• Speaker introductions

1:10 – 2:10 PM | Session 1: Biobank Design and Data Access – From Data to Insight

This session provides foundational knowledge on the construction and content of modern biobanks, with a focus on linked EHR and -omics data curation.

Topics:

• Principles of biobank design: Sampling strategies and diversity
• Phenotype data collection via EHRs, surveys, and clinical measurements
• Omics generation pipelines and quality control
• Harmonization and integration of multi-modal data
• Challenges in standardization and ethical considerations
• Example case for accessing dbGaP data and NIH-funded All of Us from a university in Europe

Speakers:  Dr. Natalia Rivera, Karolinska Institutet and Dr. Callie Zaborenko, Indiana University School of Medicine

2:10 – 3:40 PM | Session 2: Methodological Advances for Biobank-based Genetic Epidemiology

This session explores state-of-the-art statistical and computational approaches and tools to analyze large-scale biobank data.

Topics:

• Overview of analysis platforms: Key functionalities and limitations of common biobank analysis platforms
• Efficient computational workflows for biobank-scale analysis
• Addressing data access, security, and reproducibility: Ensuring robust and transparent research practices
• Challenges of biobank data analysis: analysis of time to event data, causal inference, target trial emulation

Speakers: Dr. Reedik Mägi, University of Tartu and Dr. Krista Fischer, University of Tartu

3:40 – 4:00 PM | Coffee Break

4:00 – 4:45 PM | Session 3: Scalable Computational Approaches for Cross-Biobank Analysis

This session highlights scalable and reproducible methods for combining datasets across the biobanks.

Topics:

• Overview of cross-biobank analysis: benefits and challenges
• Handling population stratification, relatedness and heterogeneity: Methods to ensure robust analyses
• Tools and platforms for privacy-preserving analysis: Enabling collaborative research without individual-level data sharing
• Harmonization of summary statistics from multiple biobanks and consortiums
• Scalable computational approaches and pipelines for cross-biobank analysis 

Speakers: Dr. Xihao Li, University of North Carolina at Chapel Hill and Dr. Jacob Williams, National Cancer Institute

4:45 – 5:30 PM | Session 4: Practical Tutorial – A Walkthrough of Cross-Biobank Analysis

A hands-on lecture-style tutorial to guide participants through a cross-biobank analysis workflow, including PRS construction across biobanks and meta-analysis of sequencing data using rare variant summary statistics.

Speakers: Dr. Jacob Williams, National Cancer Institute and Dr. Xihao Li, University of North Carolina at Chapel Hill

5:30 – 6:00 PM | Panel Discussion + Q&A: Future Directions in Biobank-Based Research

A closing discussion with all speakers on challenges, innovations, and training needs in the era of biobank-driven discovery.

Topics:

• Preparing trainees for data-intensive research
• Cross-country collaboration and harmonization
• The evolving role of AI and machine learning in biobank studies
• Open Q&A

Workshop Speakers

Dr. Natalia V. Rivera
Associate Professor
Department of Medicine Solna
Center for Molecular Medicine (CMM)
Karolinska Institutet, Sweden

 Dr. Rivera is an Associate Professor of Medical Genetics and a genetic epidemiologist. She leads a team at the Center for Molecular Medicine, focusing her research on the genetics and epigenetics of immune-mediated diseases, particularly sarcoidosis and rheumatoid arthritis. Dr. Rivera is the founder and lead principal investigator of the Multi-Ethnic Sarcoidosis Genomics (MESARGEN) Consortium and collaborates with several large genomic-focused consortia, including the Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium, the Global Biobank Meta-Analysis Initiative (GBMI) Consortium, and the Biobank Rare Variant Analysis (BRaVa) Consortium. Her recent work has focused on applications of polygenic risk scores, genetic association studies, genetic correlation, and Mendelian Randomization in autoimmune diseases. Dr. Rivera also serves on the Scientific Program Committee at ASHG and the Education Committee at IGES.

Dr. Callie Zaborenko
Postdoctoral Fellow
Department of Medical and Molecular Genetics
Indiana University School of Medicine, USA 

Dr. Zaborenko is a postdoctoral researcher in the department of Medical and Molecular Genetics at Indiana University School of Medicine in the Schwantes-An Lab. Her work explores how social environments interact with genetic risk to shape health across the life course. Her methodological interests include longitudinal data analysis, polygenic risk scores, and causal inference. She is committed to interdisciplinary approaches to understanding the complex pathways linking biology and society across time and diverse populations, with the goal of advancing understanding of health disparities and intergenerational risk transmission.

Dr. Reedik Mägi
Professor of Bioinformatics
Institute of Genomics
University of Tartu, Estonia

Dr Mägi is a Professor of Bioinformatics at the University of Tartu, Institute of Genomics, and Head of the Centre of Excellence of Personalised Medicine in Estonia. His research focuses on population and statistical genetics, with a strong emphasis on developing computational methods for genetic association studies. He has significantly contributed to meta-analysis, rare variant analysis, and sequencing data interpretation, particularly in relation to type 2 diabetes and anthropometric traits. With postdoctoral experience at the University of Oxford and leadership in international collaborations, Reedik also actively supervises PhD research in genetics, bioinformatics, and personalized medicine.

Dr. Krista Fischer,
Professor
Institute of Mathematics and Statistics
University of Tartu, Estonia

Dr. Fischer is an Estonian biostatistician (PhD in mathematical statistics, University of Tartu, Estonia, 1999), member of the Estonian Academy of Sciences since 2021. Currently (since 2018) she is affiliated as Professor of Mathematical Statistics at the Institute of Mathematics and Statistics, University of Tartu and also as Associate Professor of Biostatistics at the Institute of Genomics, University of Tartu. She has been member of the Estonian biobank research team since 2010.

In the past, she has worked at University of Ghent, Belgium (1999-2001, postdoctoral researcher), Faculty of Medicine, University of Tartu (Associate Professor 2001-2007), MRC Biostatistics Unit, Cambridge, UK (Investigator/Scientist 2007-2010). In 2015-2023 she was a member of the Executive Board of the International Biometric Society (IBS) and she was the president of the IBS Nordic-Baltic Region in 2013-2016. In 2020-2021 Krista Fischer was the member of the Estonian Covid-19 Scientific Advisory board providing advice to the Estonian Government.

Her main research interests include causal inference in genetic epidemiology, as well as statistical modeling of -omics data (polygenic risk scores, models involving metabolomics data, etc), with main focus on methodology for risk prediction.  She has authored or co-authored more than 100 research articles.

Dr. Xihao Li
Assistant Professor
Departments of Biostatistics and Genetics
University of North Carolina at Chapel Hill, USA

Dr. Li is an Assistant Professor in the Department of Biostatistics and Genetics at the University of North Carolina at Chapel Hill. His research focuses on developing novel statistical and machine learning methods for scalable and integrative analysis of biobank-scale whole-genome/exome sequencing and multi-omics data, cross-biobank meta-analysis, multi-trait analysis, polygenic risk prediction, and the prioritization of functional genomic variants, aiming to better understand the relationships among genomic variation, genome function, and phenotypes. Dr. Li actively collaborates with several large genomic research consortia, including the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium and the NHGRI Impact of Genomic Variation on Function (IGVF) Consortium

Dr. Jacob Williams
Postdoctoral Fellow
Division of Cancer Epidemiology and Genetics
National Cancer Institute, USA

Dr. Williams is currently a postdoctoral fellow in the Division of Cancer Epidemiology and Genetics at the National Cancer Institute. Previously, Jacob obtained his PhD in statistics at Virginia Tech where he developed novel Bayesian model selection methods with applications to GWAS. At the National Cancer Institute, his research is centered around the development of novel polygenic risk score methods for biobank-scale sequencing data. Dr. Williams is actively involved in the Confluence Project, a large international project to study breast cancer genetic susceptibility in women and men of multiple ancestries.

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