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Related Experiment Video

Updated: Aug 16, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Published on: August 24, 2013

IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined

Stuart Aitken¹, Helen V Firth^2,3, Caroline F Wright⁴

¹MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.

|December 23, 2022

Summary

This summary is machine-generated.

Integrating multiple data sources improves rare developmental disorder diagnosis. The IMPROVE-DD framework enhances variant evaluation by combining Human Phenotype Ontology (HPO) terms with clinical data, leading to more accurate diagnoses.

Keywords:

developmental disease developmental milestones genetic diagnosis genotype growth human phenotype ontology phenotype

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Last Updated: Aug 16, 2025

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Area of Science:

Genomics
Clinical Genetics
Bioinformatics

Background:

Diagnosing rare developmental disorders often relies on analyzing genome-wide sequencing data and reviewing candidate variants.
Current methods frequently use ontologies of categorical clinical terms for variant evaluation.

Purpose of the Study:

To develop and validate a framework, Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD), for enhanced variant evaluation in rare developmental disorders.
To quantify the added value of integrating sex, growth, and development data alongside Human Phenotype Ontology (HPO) terms.

Main Methods:

Implemented a Bayesian framework incorporating likelihood ratios for nominal and quantitative data.
Developed a classifier for HPO terms within this framework.
Utilized data from the Deciphering Developmental Disorders study, focusing on 77 genes with pathogenic variants in at least 10 individuals.

Main Results:

Classifiers integrating multiple phenotypic data sources outperformed those using individual sources, yielding fewer false positives.
Human Phenotype Ontology (HPO) terms were the primary predictor for most genes, but other phenotypic data types were superior for a subset.
IMPROVE-DD models demonstrated good predictive performance on cross-validation, even with limited individual data.

Conclusions:

The IMPROVE-DD framework offers a more robust diagnostic approach for rare developmental disorders by integrating diverse clinical data.
Systematic clinical data collection is valuable for diagnostic programs and candidate gene prioritization.