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Wendy K. Chung

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Review Open access Jul 2026

Rationale and Methods for the Prospective Genetic Risk Evaluation and Assessment (PROGRESS) in Autism Center at Columbia University.

BACKGROUND Autism is most often diagnosed after the age of 3, despite evidence that neurodevelopmental differences emerge within the first 2 years of life and that genetic and familial risk can be identified at birth. METHODS Established in September 2022 (anticipated duration of 5-7 years), the Prospective Genetic Risk Evaluation and Assessment (PROGRESS) in Autism Center is an ongoing longitudinal cohort study designed to characterize early developmental trajectories associated with autism and evaluate the impact of providing genetic information to families. RESULTS Infants who undergo genomic newborn screening and enroll in PROGRESS are followed from 3 to 24 months of age and categorized into three groups: identified genetic probability (IGP), familial likelihood without identified genetic probability (Baby Siblings), and no identified genetic probability (NGP). Assessments include electroencephalography, electrocardiography, auditory, eye tracking, developmental testing, caregiver-infant interaction, and caregiver-reported measures. Autism screening is conducted at 18 months, with comprehensive diagnostic evaluation at 24 months. Caregiver psychosocial experiences of receiving early genetic information are assessed through surveys and interviews. CONCLUSION By integrating genomic probability with early neurobehavioral development and family experiences, PROGRESS provides a framework to inform ethical genomic screening, developmental monitoring, and timely access to early intervention supported by a family navigator. IMPACT This study presents the rationale and methods of the ongoing Prospective Genetic Risk Evaluation and Assessment (PROGRESS) in Autism Center at Columbia University, which began in September 2022 (anticipated duration of 5-7 years). PROGRESS is a prospective longitudinal cohort assessing infants with identified genetic probability, familial likelihood, or no identified genetic probability for autism from 3 to 24 months of age. By linking early genetic probability with brain-behavioral trajectories, PROGRESS advances understanding of autism-related differences before clinical diagnosis and provides an empirically grounded foundation for ethical genomic newborn screening and optimized early developmental monitoring and intervention.

Nicolò Pini, Lauren C. Shuffrey, Kally C. O'Reilly Sparks et al. · 0 citations
Review Open access Jul 2026

Using a translational data platform to create clinical-grade genome-informed risk assessments

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

Jennifer Morse, M. He, Hana Bangash et al. · 0 citations
Review Open access Jul 2026

RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.

A machine learning algorithm called RankVar is developed to prioritize causative variants for rare diseases, based on clinical notes and genome/exome sequencing profiles, and may provide a useful framework for prioritizing variants in monogenic or oligogenic diseases.

Yuan Zhang, Mian Umair Ahsan, Peng Wang et al. · 1 citation