Catalina Cabrera-Salcedo1,2, Colin P Hawkes3,4, Leah Tyzinski1, Melissa Andrew1,5, Guillaume Labilloy6, Diego Campos7, Amalia Feld8, Annalisa Deodati5,9, Vivian Hwa1,10, Joel N Hirschhorn8,11, Adda Grimberg3,4, Andrew Dauber12,13,14. 1. Cincinnati Center for Growth Disorders, Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 2. Division of Endocrinology, Department of Pediatrics, University of Louisville, Louisville, Kentucky, USA. 3. Division of Endocrinology and Diabetes, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. 4. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 5. Division of Endocrinology and Center for Genetic Medicine Research, Children's National Hospital, Washington, District of Columbia, USA. 6. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 7. Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. 8. Division of Endocrinology, Boston Children's Hospital, Boston, Massachusetts, USA. 9. Dipartimento Pediatrico Universitario Ospedaliero "Bambino Gesù" Children's Hospital-Tor Vergata University, Rome, Italy. 10. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 11. Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. 12. Cincinnati Center for Growth Disorders, Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA, adauber@childrensnational.org. 13. Division of Endocrinology and Center for Genetic Medicine Research, Children's National Hospital, Washington, District of Columbia, USA, adauber@childrensnational.org. 14. Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA, adauber@childrensnational.org.
Abstract
INTRODUCTION: Short stature is one of the most common reasons for referral to a pediatric endocrinologist and can result from many etiologies. However, many patients with short stature do not receive a definitive diagnosis. OBJECTIVE: To ascertain whether integrating targeted bioinformatics searches of electronic health records (EHRs) combined with genomic studies could identify patients with previously undiagnosed rare genetic etiologies of short stature. We focused on a specific rare phenotypic subgroup: patients with short stature and elevated IGF-I levels. METHODS: We performed a cross-sectional cohort study at three large academic pediatric healthcare networks. Eligible subjects included children with heights below -2 SD, IGF-I levels >90th percentile, and no known etiology for short stature. We performed a search of the EHRs to identify eligible patients. Patients were then recruited for phenotyping followed by exome sequencing and in vitro assays of IGF1R function. RESULTS: A total of 234 patients were identified by the bioinformatics algorithm with 39 deemed eligible after manual review (17%). Of those, 9 were successfully recruited. A genetic etiology was identified in 3 of the 9 patients including 2 novel variants in IGF1R and a de novo variant in CHD2. In vitro studies supported the pathogenicity of the IGF1R variants. CONCLUSIONS: This study provides proof of principle that patients with rare phenotypic subgroups can be identified based on discrete data elements in the EHRs. Although limitations exist to fully automating this approach, these searches may help find patients with previously unidentified rare genetic disorders.
INTRODUCTION:Short stature is one of the most common reasons for referral to a pediatric endocrinologist and can result from many etiologies. However, many patients with short stature do not receive a definitive diagnosis. OBJECTIVE: To ascertain whether integrating targeted bioinformatics searches of electronic health records (EHRs) combined with genomic studies could identify patients with previously undiagnosed rare genetic etiologies of short stature. We focused on a specific rare phenotypic subgroup: patients with short stature and elevated IGF-I levels. METHODS: We performed a cross-sectional cohort study at three large academic pediatric healthcare networks. Eligible subjects included children with heights below -2 SD, IGF-I levels >90th percentile, and no known etiology for short stature. We performed a search of the EHRs to identify eligible patients. Patients were then recruited for phenotyping followed by exome sequencing and in vitro assays of IGF1R function. RESULTS: A total of 234 patients were identified by the bioinformatics algorithm with 39 deemed eligible after manual review (17%). Of those, 9 were successfully recruited. A genetic etiology was identified in 3 of the 9 patients including 2 novel variants in IGF1R and a de novo variant in CHD2. In vitro studies supported the pathogenicity of the IGF1R variants. CONCLUSIONS: This study provides proof of principle that patients with rare phenotypic subgroups can be identified based on discrete data elements in the EHRs. Although limitations exist to fully automating this approach, these searches may help find patients with previously unidentified rare genetic disorders.
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