PQG Seminar Series

The goal of the PQG Seminar Series is to encourage the exchanging of ideas and promote interaction, collaboration, and research in quantitative genomics.  It seeks to further the development and application of quantitative methods, especially for high dimensional data, as well as focus on the training of quantitative genomic scientists.

2023/2024 Seminar Organizers: Luca Pinello and Wei Zhou 

Please direct any logistical questions to Amanda King

Note: Harvard Chan School seeks to bring in speakers with a wide range of experiences and perspectives. They’re here to share their own insights; they do not speak for the school or the university.

All PQG seminar meetings for the semester will be held in person unless otherwise noted.

Upcoming Seminar


Tuesday, April 14, 2024 
1:00-2:00 PM
Biostats Conference  Room 2-426

Xiuwei Zhang

 Assistant Professor, School of Computational Science and Engineering
Georgia Institute of Technology

Cell dynamics across various time scales and spatial coordinates

In biological processes like development and disease progression, cells differentiate into various cell types. At the time scale of cell divisions, lineage tracing technologies make it possible to reconstruct the cell division history, and learn how cell types are formed through generations of cell divisions for one individual. At a larger time scale, scRNA-seq data of a number of individuals under different conditions or developmental time points are obtained, which allows for systematic analysis of gene expression changes across conditions and the prediction of data under unseen conditions. Cell temporal processes are closely coupled with spatial organization of cells, and thus the two aspects are best considered in the same picture. After cell division, cells can migrate to different locations in the tissue. We leverage temporal information in tackling current challenges in spatial data analysis, including cell type deconvolution and mapping between scRNA-seq and spatial data. Finally, we have developed a series of de novo simulation tools which help with evaluating computational methods for the problems above and beyond. I will introduce these simulation tools which can be useful for a wide range of computational tasks for single cell omics.

 

2022-2023 Dates


September 19, 2023 - Luke O'Connor, Broad Institute

Luke O’Connor

Associate Member
Broad Institute

Graphical models of linkage disequilibrium and applications
Linkage disequilibrium graphical models (LDGMs) are an efficient new approach to model LD, with promising applications in GWAS. They give rise to a very sparse precision matrix whose inverse closely approximates the LD correlation matrix. My talk will cover what LDGMs are, how they are inferred, and why they are useful. Then I will discuss two applications, to polygenic risk prediction and heritability partitioning with GWAS summary statistics.

 

November 7, 2023 - Jinghui Zhang, St. Jude Children's Research Hospital

Jinghui Zhang

Endowed Chair in Bioinformatics
St. Jude Children’s Research Hospital

Therapy-related clonal evolution driven by mutations and structural alterations of pediatric cancers and long-term survivors

 

December 5, 2023 - Shamil Sunyaev, HMS and the Brigham

Shamil Sunyaev

Professor of Biomedical Informatics, Harvard Medical School
Professor of Medicine, Brigham and Women’s Hospital

Function and Population Genetics of Variants Involved in Complex Traits

Feb 6, 2024 - Xiang Zhou, University of Michigan

Xiang Zhou

Professor of Biostatistics
University of Michigan

Statistical methods for fine-mapping analysis in genome-wide association studies

Genome-wide association studies (GWAS) have identified many SNPs associated with common diseases and disease-relevant complex traits. However, the precise underlying causal signals and molecular mechanisms underlying these associations remain largely unknown. Here, I will discuss a few statistical methods that our group has recently developed for fine-mapping putatively causal SNPs and genes in GWAS. Specifically, I will first talk about MESuSiE, a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. I will talk about PMR, a probabilistic Mendelian randomization (MR) likelihood-based framework that unifies many existing TWAS (transcriptome-wide association study) and MR methods, accommodates multiple correlated instruments, while testing the causal effect of gene on trait in the presence of horizontal pleiotropy. I will also talk about GIFT, a frequentist method that performs conditional TWAS analysis by controlling for other genes residing in a local region to fine-map putatively causal genes, while explicitly accounting for gene expression correlation, cis-SNP linkage disequilibrium, as well as the uncertainty associated with gene expression predictions.

 

March 26, 2024 - Bo Wang, University of Toronto

Bo Wang

Lead Scientist of the Artificial Intelligence Team for Peter Munk Cardiac Centre at University Health Network
Assistant Professor, Departments of Computer Science and Laboratory Medicine & Pathobiology  University of Toronto

Building Foundation Models for Single-cell Omics and Imaging

This talk delves into the innovative utilization of generative AI in propelling biomedical research forward. By harnessing single-cell sequencing data, we developed scGPT, a foundational model that extracts biological insights from an extensive dataset of over 33 million cells. Analogous to how words form text, genes define cells, effectively bridging the technological and biological realms. The strategic application of scGPT via transfer learning significantly boosts its efficacy in diverse applications such as cell-type annotation, multi-batch integration, and gene network inference.Additionally, the talk will spotlight MedSAM, a state-of-the-art segmentation foundational model. Designed for universal application, MedSAM excels across various medical imaging tasks and modalities. It showcased unprecedented advancements in 30 segmentation tasks, outperforming existing models considerably. Notably, MedSAM possesses the unique ability for zero-shot and few-shot segmentation, enabling it to identify previously unseen tumor types and swiftly adapt to novel imaging modalities.Collectively, these breakthroughs emphasize the importance of developing versatile and efficient foundational models. These models are poised to address the expanding needs of imaging and omics data, thus driving continuous innovation in biomedical analysis.

April 9, 2024 - Ruben Dries, Boston University

Ruben Dries

Assistant Professor, Medicine
Boston University

Towards Solutions for Large-Scale Multi-Modal Spatial Data Analysis

In the burgeoning field of spatial biology, the integration of multi-modal spatial omics technologies presents both a formidable challenge and a tremendous opportunity for advancing clinical research and diagnostics. I will discuss the concerted efforts of our laboratory to address the complexities inherent in large-scale multi-modal spatial data analysis, with a specific focus on making spatial biology more accessible for clinical projects. Our approach is threefold: firstly, we focus on implementing the latest spatial omics technologies with the goal to integrate their functional outputs and as such harness the full potential of spatially resolved molecular data.  Secondly, we develop robust data structures tailored for the efficient storage, retrieval, and manipulation of large volumes of multi-modal spatial data, ensuring that our solutions are scalable and adaptable to the ever-evolving landscape of spatial biology. Finally, we prioritize the usability of our analytical tools and strategies, offering a user-friendly interface that empowers clinicians and researchers with minimal computational background to engage in sophisticated spatial data analysis. By addressing these key areas, our laboratory not only aims to advance the methodological framework for spatial data analysis but also to foster the integration of spatial omics data into routine clinical practice, thereby opening new avenues for personalized medicine and biomarker discovery. Through this integrated approach, we contribute to the establishment of a more accessible, efficient, and comprehensive ecosystem for the analysis of spatial biology data, ultimately facilitating the translation of complex spatial omics data into actionable clinical insights.

April 23, 2024 - Seunggeun (Shawn) Lee, University of Michigan

Seunggeun ‘Shawn’ Lee

Adjunct Professor, Biostatistics
University of Michigan

Rare variant association analysis

Rare variants significantly impact complex diseases. This presentation will first introduce SAIGE-GENE and SAIGE-GENE+, methodologies extending SAIGE to gene/region-based rare variant tests. These methods efficiently utilize mixed effects models to adjust for sample relatedness and saddlepoint approximations to account for case-control imbalance. SAIGE-GENE+ additionally incorporates functional annotations and collapsing of ultra-rare variants that can help to improve type I error control and power. In the second part of the talk, I will introduce our recent work to estimate effect sizes of rare variants. The method, RareEffect, uses an empirical Bayesian approach that estimates gene/region-level heritability and then an effect size of each variant. We also show the effect sizes obtained from our model can be leveraged to improve the performance of polygenic scores.

May 14, 2024 - Xiuwei Zhang, Georgia Institute of Technology

Xiuwei Zhang

 Assistant Professor, School of Computational Science and Engineering
Georgia Institute of Technology

Cell dynamics across various time scales and spatial coordinates

In biological processes like development and disease progression, cells differentiate into various cell types. At the time scale of cell divisions, lineage tracing technologies make it possible to reconstruct the cell division history, and learn how cell types are formed through generations of cell divisions for one individual. At a larger time scale, scRNA-seq data of a number of individuals under different conditions or developmental time points are obtained, which allows for systematic analysis of gene expression changes across conditions and the prediction of data under unseen conditions. Cell temporal processes are closely coupled with spatial organization of cells, and thus the two aspects are best considered in the same picture. After cell division, cells can migrate to different locations in the tissue. We leverage temporal information in tackling current challenges in spatial data analysis, including cell type deconvolution and mapping between scRNA-seq and spatial data. Finally, we have developed a series of de novo simulation tools which help with evaluating computational methods for the problems above and beyond. I will introduce these simulation tools which can be useful for a wide range of computational tasks for single cell omics.

 


Seminar Archive