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Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Stuart Aitken1, Helen V Firth2, Jeremy McRae3

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Summary
This summary is machine-generated.

This study enhances genetic diagnoses in developmental disorders by integrating phenotype data with whole-exome sequencing. It identifies new causative variants and in silico syndromes, improving diagnostic yield for rare diseases.

Keywords:
developmental diseasegenotypenaive BayesphenotypetSNE

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Area of Science:

  • Genetics and Genomics
  • Developmental Biology
  • Bioinformatics

Background:

  • Trio-based whole-exome sequencing (WES) achieves diagnoses in approximately 40% of individuals with developmental disorders.
  • The Deciphering Developmental Disorders (DDD) study possesses extensive phenotypic information crucial for augmenting genetic diagnoses.
  • Integrating phenotypic data with WES can improve variant discovery and diagnostic rates in undiagnosed probands.

Purpose of the Study:

  • To leverage phenotypic data from the DDD study to enhance genetic diagnoses and discover novel disease-causing variants.
  • To assess the utility of phenotypic similarity metrics and machine learning for identifying causative genetic variants.
  • To explore a 'phenotype first' approach for variant discovery in individuals with developmental disorders.

Main Methods:

  • Calculated median Euclidean distances (mEuD) to measure phenotypic similarity in quantitative data for individuals with de novo mutations (DNM) in specific genes.
  • Employed naive Bayes classification to model growth and developmental data for identifying causative DNM genes.
  • Utilized unsupervised naive Bayes classification on WES and phenotypic data from 6,993 probands to define in silico syndromes (ISSs).

Main Results:

  • Phenotypic similarity was observed in 46.4% of growth/developmental metrics and 35.7% of Human Phenotype Ontology (HPO) terms for 28 developmental disorder genes.
  • Analysis of ANKRD11 identified 5 likely causative inherited variants and 2 unrecognized DNMs, increasing gene-specific diagnosis by 18%.
  • Unsupervised classification defined 23 ISSs and highlighted heterozygous de novo variants in SPTBN2 as causative in three probands.

Conclusions:

  • Integrating detailed phenotypic information with WES data significantly enhances diagnostic yield in developmental disorders.
  • Phenotypic similarity measures and machine learning approaches are effective tools for identifying causative variants and novel genetic syndromes.
  • The 'phenotype first' strategy, combined with stringent variant filtering, aids in discovering causative genotypes for rare developmental disorders.