Bioinformatics and Genome Analysis

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console

The generation of massive omics and phenotypic data has enabled investigators to study the genetic architecture and markers in many complex diseases; however, it poses a significant challenge in efficiently uncovering valuable knowledge. Here, we introduce GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging ChatGPT, we aim to make GENEVIC a biologist’s ‘copilot’. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer’s disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. The implementation of BrainGeneBot is set to transform genomic research for AD and other brain diseases by improving data accessibility, accelerating discovery processes, and refining the precision of genetic insights.

Learning Objectives

• Understand genetic association with complex disease and the methods for association studies. 
• Learn the appropriate deep learning technologies for mining complex and heterogeneous genetic datasets. 
• Assess the computational methods and resources for integrative studies of genetic markers in brain disease.

Speaker

• Zhongming Zhao, PhD (University of Texas Heal Sci Ctr Houston)

Estimating single sample gene program dysregulation using latent factor causal graphs

Gene expression programs that establish and maintain specific cellular states are orchestrated through a regulatory network composed of transcription factors, cofactors, and chromatin regulators. Dysregulation of this network can lead to a broad range of diseases. In this work, we introduce LaGrACE, a novel method designed to estimate the magnitude of dysregulation of gene programs utilizing both omics data and clinical information. LaGrACE first learns gene programs, represented as latent factors, from gene expression data of a set of reference samples. Then, it facilitates grouping of samples exhibiting similar patterns of gene program dysregulation, thereby enhancing the discovery of underlying molecular mechanisms. We rigorously evaluated LaGrACE’s performance using synthetic data, breast cancer and chronic obstructive pulmonary disease (COPD) datasets, and single-cell RNA sequencing (scRNA-seq) datasets. Our findings demonstrate that LaGrACE is exceptionally robust in identifying biologically meaningful and prognostic subtypes. Additionally, it effectively discerns drug-response signals at a single-cell resolution. The COPD analysis revealed a new association between LEF1 and COPD molecular mechanisms and mortality. Collectively, these results underscore the utility of LaGrACE as a valuable tool for elucidating disease mechanisms.

Learning Objectives

• Explain the role of transcription factors, cofactors, and chromatin regulators in orchestrating gene expression programs and how their dysregulation contributes to disease.
• Describe the functionality and workflow of LaGrACE, including how it learns gene programs from gene expression data and identifies dysregulated patterns.

Speaker

• Panayiotis Benos, PhD (University of Florida)

Genotype and phenotype risk score analyses of genetically admixed multiple sclerosis patients in All of Us

Multiple sclerosis (MS) is a demyelinating disease influenced by genetic and environmental risk factors. Current research indicates improved patients’ long-term health outcomes are associated with earlier diagnosis and treatment initiation. We developed a well-performing risk score model for MS based on genetic burden alone, and demonstrate the utility of phenotype-based risk scoring. Combination genotype-phenotype risk models have potential to aid in early screening and diagnosis of MS.

Learning Objectives

• Identify various types of risk scores that can be derived from electronic health records (EHRs) to assess and predict complex diseases.

Speaker

• Mary Davis, PhD (Brigham Young University)

AI-driven model to bridge pathology image and transcriptomics

Computational pathology has emerged as a powerful tool for revolutionizing routine pathology through AI-driven analysis of pathology images. Recent advancements in omics technologies, such as spatial transcriptomics, have further enriched the field by providing detailed transcriptomic information alongside tissue histology. However, existing sequencing platforms lack the ability to effectively harness the synergies between tissue images and genomic data. To address this gap, we develop Thor, an AI-based infrastructure for seamless integration of histological and genomic analysis of tissues. Thor infers single-cell resolution spatial transcriptome through an anti-shrinking Markov diffusion method. Its effectiveness and versatility were validated through simulations, diverse datasets, and compelling case studies involving human carcinoma and heart failure samples. Thor enabled unbiased screening of breast cancer hallmarks and identification of fibrotic regions in myocardial infarction tissue. With an extensible framework for genomic and tissue image analysis accessible through an interactive web platform, Thor empowers researchers to understand biological structures and decipher disease pathogenesis, paving the way for significant advancements in research and clinical applications. Our code is available at: GitHub Repository.

Learning Objectives

• Explain how computational pathology and AI-driven analysis of pathology images enhance routine pathology and facilitate integration with omics data.
• Describe the functionality of Thor, including its use of an anti-shrinking Markov diffusion method to infer single-cell resolution spatial transcriptomes.
• Demonstrate how Thor’s interactive web platform integrates histological and genomic analysis to empower researchers in understanding biological structures and disease pathogenesis.

Speaker

• Guangyu Wang, PhDS (Houston Methodist)

Clinical and Genomic Insights into Immune-Related Adverse Events

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by enhancing the immune system’s ability to target tumor cells, significantly improving survival outcomes in various cancers. However, ICIs are frequently associated with immune-related adverse events (irAEs), including acute kidney injury (ICI-AKI), which complicate patient management. Using data from the OneFlorida+ Clinical Research Network and the All of Us (AoU) cohort, this study identifies clinical and genetic risk factors for these adverse events. In the OneFlorida+ cohort of 6,526 ICI-treated patients, 56.2% developed irAEs, with younger patients, females, and those with comorbidities (e.g., myocardial infarction and renal disease) being at higher risk. Cancer type and treatment regimens also influenced irAE risk, with combined CTLA4+PD(L)1 inhibitors increasing the risk by 35%. Severe irAEs significantly impacted overall survival and the timing of irAE onset. The genetic analysis of 414 ICI-treated patients from the AoU cohort identified the rs16957301 variant in the PCCA gene as a significant risk marker for ICI-AKI in Caucasians. Patients with the risk genotypes (TC/CC) developed AKI significantly earlier (median: 3.6 months) than those with the reference genotype (TT, median: 7.0 months). The variant’s specificity to ICI-treated patients highlights its potential utility in personalized risk assessment. These findings emphasize the importance of integrating clinical and genomic insights to optimize ICI therapy. Identifying high-risk patients through genetic screening and tailored management strategies could mitigate adverse events and improve patient outcomes. Future research should validate these findings in diverse populations and explore underlying biological mechanisms.

Learning Objectives

• Identify clinical and genetic risk factors for irAEs and ICI-AKI, including demographic characteristics, comorbidities, cancer types, treatment regimens, and the rs16957301 variant in the PCCA gene.
• Analyze the influence of severe irAEs on overall survival and the timing of irAE onset in ICI-treated patients, based on data from the OneFlorida+ and All of Us (AoU) cohorts.
• Discuss the implications of integrating clinical and genomic insights to optimize ICI management and propose future research directions to validate findings and explore underlying biological mechanisms.

Speaker

• Qianqian Song, Ph.D. (University of Florida)

Continuing Education Credit

Physicians

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

The American Medical Informatics Association designates this online enduring material for 1.0 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.

ACHIPs^TM

AMIA Health Informatics Certified Professionals^TM (ACHIPs^TM) can earn 1 professional development unit (PDU) per contact hour.

ACHIPs^TM may use CME/CNE certificates or the ACHIPs^TM  Recertification Log to report 2025 Summit sessions attended for ACHIPs^TM Recertification.

Claim credit no later than within three years of the release date or within two years of your purchase date, whichever is sooner. 

FAQs