GUARDIAN: Genomics to Enhance Newborn Screening

GUARDIAN (Genomic Uniform screening Against Rare Diseases In All Newborns) is a pilot study using genome sequencing from newborn screening dried blood spots to assess a defined group of treatable genetic conditions affecting young children with high penetrance. Early results demonstrate high uptake from parents, feasibility in generating and interpreting data on an ancestrally diverse cohort in New York City and adding value to traditional newborn screening.

Presenter, Wendy Chung is a medical and molecular geneticist and the PI of GUARDIAN. She has worked closely with the NY state department of health to perform a series of pilot newborn screening studies, one of which led to the adoption of SMA newborn screening.

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Artificial Intelligence Meets Genomics

Millions of children worldwide are born each year with severe genetic disorders, predominantly Mendelian diseases caused by one or a few genetic variants in a single gene. Each individual’s exome typically contains tens of thousands of variants compared to the reference genome, and even after filtering out common and low-quality variants, hundreds remain, making it time-consuming and complex to identify the causative variant(s). With the rapid development of machine learning and artificial intelligence, computational tools that can accurately and efficiently diagnose genetic cases represent the new frontier. These advanced technologies have the potential to revolutionize genetic diagnostics by providing faster, more accurate identifications of causative variants, ultimately improving patient outcomes and reducing the burden on clinical laboratories.

Presenter, Dr. Zhandong Liu, an Associate Professor and Chao Endowed Chair in the Department of Pediatrics at Baylor College of Medicine and an investigator at the Jan and Dan Duncan Neurological Research Institute. He serves as the Chief of Computational Sciences at Texas Children’s Hospital and Co-Director of the Quantitative and Computational Bioscience Graduate Program. Dr Liu will present his group’s recent work on developing an artificial intelligence algorithm capable of identifying genetic variants based on exome/genome sequencing profiles and patient phenotypes.

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Precision Health: Dream or Reality? Reimagining Precision Medicine

Predictions by thought leaders and luminaries at the conclusion of the Human Genome project promised genomic sequences on credit cards that would travel with patients for use to guide care and prevention. However, these promises have not come to pass. Is precision medicine unattainable? More importantly, how are the tools of precision medicine, or as it is being reimagined precision health, improving our ability to diagnose and treat patients and improve outcomes of importance to patients, families, physicians, and society?

For answers to these questions, attendees were invited to join a live, engaging, and thought-provoking webinar featuring Dr. Marc S. Williams, MD, FACMG, professor and director emeritus of the Department of Genomic Health at Geisinger and Immediate Past President of the ACMG. Dr Williams provided his perspective on the status of the field, breakthroughs already impacting patients and families, and a vision for what is needed to fully realize the promise of precision health.

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The VUS: Here Today, Gone Tomorrow? The realization of genomics-first medicine depends on effectively harnessing population-scale genomic data within the clinical setting and integrating it with rich clinical and phenomic information! 

A major challenge in medical genetics is the fact that our ability to read genomes has vastly outpaced our ability to interpret the variation that we find. This is especially apparent for missense variation in genes that are subject to frequent genetic testing where 30% of panel tests identify at least one variant of uncertain significance (VUS). Functional assays have long been used to provide evidence for variant interpretation, but historically have not kept pace with the rate at which VUS are identified which has limited their utility.

Developed over the last decade, multiplexed assays of variant effect (MAVE) have fundamentally changed the way we can experimentally measure the effects of genetic variation. MAVEs can measure the functional impact of thousands of single nucleotide variants in protein coding genes and other sequence elements such as promoters, enhancers and splice sites in a single experiment. These MAVE-generated functional data can then be translated into evidence for variant interpretation and have been demonstrated to reduce the number of VUS in genes like BRCA1 and MSH2 by 50 - 75%, respectively. Scaling this technology to assess variant effects in more clinically relevant genes could reduce the number of VUS over the next decade.

This free webinar featuring Dr. Lea Starita (University of Washington) as she presents her work on multiplexed assays for variant effect to provide functional evidence to reclassify VUS and a vision for a VUS-free (or greatly reduced) future for genome medicine.

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The Big Data Problem in Genomic Medicine: Acquisition, Processing, Interpretation, storage and Retrieval

Speaker

Kyle Retterer, MS

Chief Data Science Officer, Geisinger

Moderator

Marc S. Williams, MD, FACMG

Professor and Director Emeritus, Department of Genomic Health, Geisinger

The realization of genomics-first medicine depends on effectively harnessing population-scale genomic data within the clinical setting and integrating it with rich clinical and phenomic information. This talk explores the critical infrastructure required to support such ambitious initiatives, examining the challenges of building scalable systems for secure storage, efficient annotation, and rapid querying of these massive datasets.

Crucially, we will discuss the role of integrating genomic data with detailed phenomic information to unlock clinical utility, enabling real-time insights, as well as the challenges presented by genomics-first implementation, including potentially under-appreciated variability in the penetrance and phenotypic spectrum of genetic conditions. We will also examine the innovative application of Large Language Models (LLMs) to extract granular phenotypic insights from large-scale observational data, enabling a deeper understanding of genotype-phenotype relationships and paving the way for personalized, data-driven healthcare.

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