npj Genomic Medicine · Published 2026-07-03 · DOI 10.1038/s41525-026-00592-x
Cathy Shyr, Rory J. Tinker, Rebekah F. Brown, Adam Wright, Josh F. Peterson, John A. Phillips, S. Trent Rosenbloom, Lisa Bastarache
Abstract Growth modeling is central to human genetics, as deviations from typical growth can signal an underlying disorder. In this cohort study, we developed a generalizable framework for generating growth charts across genetic conditions using electronic health records (EHR). Leveraging 22 years of longitudinal EHR data from 452,470 patients across 15 genetic conditions and unaffected individuals, we generated sex- and condition-specific growth charts using Generalized Additive Models for Location, Scale, and Shape, and quantified differences in size, timing, and intensity using SuperImposition by Translation and Rotation (SITAR). SITAR-derived growth parameters showed strong concordance with established annotations in OMIM and Orphanet, and identified previously unreported growth patterns. We stratified cystic fibrosis by CFTR functional class and observed greater growth impairment in individuals with homozygous minimal-function variants compared to those with residual function. This framework provides a generalizable approach for leveraging EHR data to refine genotype-phenotype relationships and enable continuous updating of growth charts across genetic conditions.
Abstract from DOAJ. Public domain (CC0 1.0).
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Shyr, C., Tinker, R., Brown, R., et al. (2026). An EHR-based framework for modeling growth curves and constructing growth centile charts for genetic disorders. npj Genomic Medicine. https://doi.org/10.1038/s41525-026-00592-x