This year’s Presidential Address will highlight the vital role of community in advancing human genetics and genomics research. It will underscore how collaboration and inclusion are essential to making scientific discoveries that benefit all populations. By embracing global genomic variation, we gain critical insights into human evolutionary history, biological variation, and the genetic underpinnings of disease.

The event will also feature special recognition of Francis Collins, MD, PhD, former Director of the National Institutes of Health, for his enduring contributions to the field.

Learning Objectives

*    Examine the ASHG vision and how it translates into society, policy and knowledge
*    Examine the importance of variation at all levels to promote new discoveries in the field of Human Genetics and Genomics

In the opening plenary session of the 2025 ASHG Annual Meeting, participants heard cutting-edge science using a variety of approaches including massively parallel reporter assays, Mendelian Randomization, multi-omics, quantitative trait locus colocalization with genome wide association study results, and large language models. These studies advance our understanding of functional effects of non-coding variations, GLP1 agnosim, spaceflight-induced physiological changes, genetic architecture of central obesity, and the power of LLMs to aid in clinical genetics workflows.

Learning Objectives

*    Examine obesity mechanisms, genetics of sex-based central fat patterns & potential GLP1R agonist biomarkers
*    Evaluate the functional effects of 5' UTR variants and spaceflight-induced molecular signatures
*    Compare performance of large language models to clinical geneticists when diagnosing genetic conditions using case-based scenarios

Genome-wide association studies (GWAS) have identified numerous variants linked to disease, but their focus on single-nucleotide polymorphisms (SNPs) often overlooks structural variants (SVs) that may be causal. Long-read sequencing (LRS) enables superior detection of SVs, including insertions, deletions, and repeat expansions, which are difficult to resolve with short-read sequencing. This session will be an overview of the benefits of long-read sequencing to identify complex variants and explore the integration of multi-omics data to uncover how these variants are having an effect on gene regulation and disease. We will highlight advances in SV-QTL mapping, disease risk prediction, and regulatory element analysis. Speakers will present findings from large-scale cohorts, including neurodegenerative disease studies and population-scale sequencing efforts, demonstrating how LRS enhances variant interpretation. The session will also discuss analytical challenges, emerging technologies, and the role of LRS in expanding genomic discovery across diverse populations.

Learning Objectives

*    Evaluate how long-read sequencing improves structural variant detection and its impact on molecular trait associations in neurodegenerative diseases.
*    Examine the role of tandem repeat expansions in human genetic variation and how long-read sequencing enhances their characterization.
*    Assess the benefits of long-read sequencing in large-scale population studies for identifying medically relevant structural variants and complex loci.

This session covers recent advances linking transcriptional states to clinical phenotypes to enhance our understanding of pathogenesis in Alzheimer's disease through epigenetic, single cell, and spatial -omics methods. Talks include large-scale studies of gene expression, RNA editing and somatic mutations in AD tissues.

Learning Objectives

*    Identify potential molecular mechanisms driving the progression from primary age-related tauopathy to Alzheimer's disease
*    Evaluate the functional roles of somatic mutations and RNA editing in Alzheimer's Disease

This session will highlight latest advances in applying single-cell multiomic techniques to uncover the genetic mechanisms of cell-type specific gene regulation within human tissues. The speakers will showcase how these insights are transforming our understanding of complex disease traits.

Learning Objectives

*    Apply single-cell multiomic data to differentiate gene regulatory programs across cell types and tissues
*    Examine how cell type-specific gene regulation contributes to complex disease traits

ASHG recognizes two of its annual professional award recipients, each of whom will speak on their accomplishments: Harry (Hal) Dietz, Lifetime Achievement Award recipient and Carole Ober, Mentorship Award recipient.

A fundamental goal of human population genetics is to understand the evolutionary forces impacting the origins and global distribution of disease. Novel technologies and sophisticated methods provide unprecedented insights into how our evolutionary history continues to influence human disease. When modern humans first migrated out of Africa, they encountered and interacted with archaic human populations such as Neanderthals and Denisovans. Genomic studies of modern humans reveal that some segments of DNA inherited from Neanderthals and Denisovans are associated with an altered risk for common diseases. Moreover, the establishment of large global genomic databases containing detailed electronic health records allows us to identify the genetic factors influencing complex traits across globally diverse populations. Looking to the future, climate change is predicted to dramatically impact all aspects of human life. Research on how genetic differences interact with our changing environment helps pave the way for improved disease diagnosis and treatment in the future.

Learning Objectives

*    Summarize how ancient DNA has shed light on the origins of genetic variation that contributes to present-day human biology and health.
*    Examine how ancient DNA can be used to study adaptation at high genetic resolution and the evolution of the architecture of complex genetic traits.
*    Identify current challenges and future opportunities for future multi-ancestry studies.

The capabilities of Artificial Intelligence and Machine Learning tools have rapidly progressed, facilitating adoption in discovery and analysis applications in human genomics. This session covers a broad range areas of such application, from disease state prediction and imaging analysis to literature mining and causal variant prediction.

Learning Objectives

*    Summarize the set of machine learning approaches used across a range of genomics applications
*    Define experimental design techniques that appropriately utilize machine learning methods in scientific inference problems
*    Evaluate the capabilities and challenges in applying machine learning approaches in context of human genomics problems

This session will showcase studies investigating the biology of neurodegenerative disorders through different analytical designs. Specifically, the talks will describe the integration of multiple molecular layers and data types to dissect the mechanisms that predispose to brain degenerative diseases.

Learning Objectives

*    Examine multiple molecular layers to dissect the pathogenesis of neurodegenerative disorders
*    Discuss differences and similarities in the challenges to study brain diseases

This session highlights how human genetics is accelerating therapeutic discovery and advancing precision health. Talks will feature cutting-edge approaches--from large-scale exome sequencing to multimodal genetic analyses--that are driving target identification, drug development, and patient stratification. Both academic and industry-led efforts will be showcased, illustrating the growing impact of human genetics across the therapeutic pipeline.

Learning Objectives

*    Evaluate how human genetic variation informs therapeutic target identification and drug development strategies
*    Analyze the role of rare and common variants in predicting patient-specific responses to treatment

ASHG recognizes two of its annual professional award recipients, each of whom will speak on their accomplishments: Eric Green, Leadership Award recipient and Elizabeth Bhoj, Early Career Award recipient. Winners of the AJHG Award for Outstanding Trainee Publication and HGGA Award for Outstanding Early Career Publication are also recognized. 

This plenary session will highlight the breadth and depth of human genetic research, covering topics related to basic, clinical, and translational research. Specifically, the five talks will highlight i) how cellular models and high-throughput technologies can dissect regulatory mechanisms across biological systems, ii) the associations of complex genomic regions with human traits and diseases, and iii) understand the dynamics related to insurance coverage for germline genetic testing for hereditary cancer.

Learning Objectives

*    Evaluate functional impact of genome regulation at scale
*    Explain insurance coverage for germline genetic testing for hereditary cancer
*    Assess the effect of complex genomic regions on human health and disease

Non-coding genetic variants are important drivers of trait variation in humans and other eukaryotic species. As a result, functionally characterizing non-coding variants is a major goal of modern genetics, with important implications for our understanding of the molecular basis of disease and evolution. Deep learning models are a powerful approach for predicting regulatory activity from genomic sequence and interpreting the cis-regulatory code. In this session, four leading scientists will present their work on leveraging such models to interpret the effects of regulatory variation. These talks will highlight recent advances in deep learning for variant effect interpretation, limitations in existing models, and strategies for improvement. Particular advancements that will be discussed will include improvements in model architecture and training, novel interpretation and variant scoring techniques, and emerging strategies to scale model training across populations and tissue types.

Learning Objectives

 *   Highlight challenges of deep learning models for non-coding variant interpretation.
 *   Design deep learning architectures trained on functional human genetic variation.
 *   Construct deep learning models capturing the syntax of cell type-specific gene expression.

ASHG recognizes three of its annual professional award recipients, each of whom will speak on their accomplishments: Anthony Wynshaw-Boris, Advocacy Award recipient; Audrey Hendricks, Education Award recipient; and Mike Talkowski, Scientific Achievement Award recipient. Winners of the ASHG Trainee Research Excellence Awards are also announced. 

This plenary session will explore the complexity and diversity of the human genome through innovative research on haplotypes, structural variation, and population-specific genetic architecture. Speakers will present findings from large-scale studies on chromatin organization, the evolving human pangenome, and ancestry-specific risk factors for diseases such as breast cancer and dyslipidemia. The session will conclude with insights into how rare deleterious mutations influence human lifespan across diverse populations.

Learning Objectives

*    Examine advances in genome representation and structure
*    Recognize how genetic architecture varies across populations and contexts
*    Explain the pangenome and potential downstream applications

Artificial intelligence (AI) is revolutionizing the field of genomics, unlocking breakthroughs in both research and healthcare that were once unimaginable. By accelerating the analysis of large genomic datasets, AI uncovers critical patterns that illuminate gene networks, highlight risks for common diseases, and help pinpoint the causes of rare conditions. AI also plays a key role in advancing precision medicine by developing novel treatments, customizing therapies to individual genetic profiles, and predicting patient responses to therapies, all of which enhance diagnostic accuracy and treatment outcomes. However, the growing integration of AI in genomics raises important ethical considerations, including concerns about data privacy, informed consent, and the potential for biased algorithms to perpetuate existing inequalities. As AI continues to shape genomics, it is essential to balance innovation with ethical responsibility to ensure equitable access and outcomes for all patients. This session aims to stimulate thoughtful discussion on the transformative potential of AI in genomics while addressing its challenges and ethical implications. Attendees will leave with actionable insights on how to navigate the evolving landscape of AI in genomics research and healthcare and how they can contribute to shaping its future in a responsible and inclusive way.

Learning Objectives

*    Examine modern artificial intelligence compute architectures and how they are being leveraged to impact the practice of medicine
*    Identify the societal, ethical and economic challenges of the tools available for genomic health prediction, and how policymakers can address them
*    Examine the capabilities and limitations of AI in genomic medicine