TY - JOUR
T1 - IMPROVE-DD: Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders
AU - Aitken, Stuart
AU - Firth, Helen V.
AU - Wright, Caroline F
AU - Hurles, Matthew E
AU - FitzPatrick, David R
AU - Semple, Colin A
N1 - Funding Information:
The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003 ), a parallel funding partnership between Wellcome and the Department of Health, and the Wellcome Sanger Institute (grant number WT098051 ). The views expressed in this publication are those of the authors and not necessarily those of Wellcome or the Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South REC , and GEN/284/12 granted by the Republic of Ireland REC ). The research team acknowledges the support of the National Institute for Health Research , through the Comprehensive Clinical Research Network. This study makes use of DECIPHER ( https://www.deciphergenomics.org/ ), which is funded by the Wellcome. H.V.F. is supported by the Wellcome Trust (award 200990/Z/16/Z) “Designing, developing and delivering integrated foundations for genomic medicine.” The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. D.R.F. is funded as part of the MRC Human Genetics Unit grant to the University of Edinburgh. C.A.S. and S.A. are supported by MRC Core funding to the MRC Human Genetics Unit (MRC grant MC_UU_00007/16). For the purpose of open access, the authors have applied a CC-BY public copyright license to any author-accepted manuscript version arising from this submission.
Funding Information:
The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between Wellcome and the Department of Health, and the Wellcome Sanger Institute (grant number WT098051). The views expressed in this publication are those of the authors and not necessarily those of Wellcome or the Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South REC, and GEN/284/12 granted by the Republic of Ireland REC). The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. This study makes use of DECIPHER (https://www.deciphergenomics.org/), which is funded by the Wellcome. H.V.F. is supported by the Wellcome Trust (award 200990/Z/16/Z) “Designing, developing and delivering integrated foundations for genomic medicine.” The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. D.R.F. is funded as part of the MRC Human Genetics Unit grant to the University of Edinburgh. C.A.S. and S.A. are supported by MRC Core funding to the MRC Human Genetics Unit (MRC grant MC_UU_00007/16). For the purpose of open access, the authors have applied a CC-BY public copyright license to any author-accepted manuscript version arising from this submission. M.E.H. is a co-founder, consultant, and non-executive director of Congenica Ltd.
Publisher Copyright:
© 2022 The Authors
PY - 2023/1/12
Y1 - 2023/1/12
N2 - Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.
AB - Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.
U2 - 10.1016/j.xhgg.2022.100162
DO - 10.1016/j.xhgg.2022.100162
M3 - Article
SN - 2666-2477
VL - 4
JO - Human Genetics and Genomics Advances
JF - Human Genetics and Genomics Advances
IS - 1
M1 - 100162
ER -