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Updated: Sep 28, 2025

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
Published on: May 10, 2022
Mapping anorexia nervosa genes to clinical phenotypes.
Jessica S Johnson1,2, Alanna C Cote1,2, Amanda Dobbyn1,2,3
1Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
This study links genetic factors for anorexia nervosa (AN) to clinical outcomes using transcriptomic imputation. Genetically regulated gene expression in AN was associated with autoimmune, metabolic, and gastrointestinal conditions, offering new research avenues.
Area of Science:
- Genetics
- Psychiatry
- Bioinformatics
Background:
- Anorexia nervosa (AN) is a complex psychiatric disorder with significant genetic contributions.
- Genome-wide association studies (GWAS) identify genetic variants linked to AN.
- Transcriptomic imputation (TI) translates genetic findings into regulatory mechanisms and functional outcomes.
Purpose of the Study:
- To translate AN GWAS findings into genetically regulated gene expression (AN-GReX) using TI.
- To assess the clinical consequences of AN-GReX in a large biobank population.
- To investigate the impact of body mass index (BMI) and sex on AN-GReX clinical associations.
Main Methods:
- Applied S-PrediXcan to translate AN GWAS findings into AN-GReX.
- Imputed AN-GReX in the Mount Sinai BioMe™ Biobank.
- Conducted phenome-wide association studies (pheWASs) on over 2000 outcomes.
- Performed secondary analyses for BMI and sex moderation.
Main Results:
- Identified 53 genes associated with AN, including a novel association with the major histocompatibility complex.
- AN-GReX showed associations with autoimmune, metabolic, and gastrointestinal diagnoses, cholesterol, medications, substance use, and pain.
- BMI moderated AN-GReX associations with cholesterol and substance use; sex moderated associations with celiac disease.
Conclusions:
- BMI-stratified results offer potential functional mechanisms for AN-related genes.
- This approach links genetic predispositions to diverse clinical phenotypes, advancing AN research.
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