Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease

Bastarache, Lisa; Hughey, Jacob J.; Goldstein, Jeffrey A.; Bastraache, Julie A; Das, Satya; Zaki, Neil; Zeng, Chenjie; Tang, Leigh Anne; Roden, Dan M.; Denny, Joshua C.

doi:10.1093/jamia/ocz179

Cited by 40 publications

(49 citation statements)

References 23 publications

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“…Enabling large scale integration of biomedical knowledge with clinical patient data requires robust and accurate mappings between standardized clinical terminology concepts and ontologies, like the HPO. Existing work has demonstrated the power of the HPO to enrich clinical data including craniofacial and oral phenotypes ( 57 ), rare and Mendelian disease ( 58 , 59 ), and infectious disease ( 60 ). There have also been more generalized mapping efforts aimed at aligning different clinical terminologies to the HPO including free-text narratives ( 61 ) and structured data like diagnosis codes ( 62 , 63 ).…”

Section: Ehr Integrationmentioning

confidence: 99%

The Human Phenotype Ontology in 2021

Köhler

Gargano

Matentzoglu

et al. 2020

Nucleic Acids Research

881

688

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The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

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Section: Ehr Integrationmentioning

confidence: 99%

The Human Phenotype Ontology in 2021

Köhler

Gargano

Matentzoglu

et al. 2020

Nucleic Acids Research

881

688

View full text Add to dashboard Cite

show abstract

“…Rapid and accurate variant interpretation remains a challenge in clinical genetics. We have previously showed that PheRS could augment variant interpretation 9,10 . Using PheRS including new phenotypes identified from the PheWAS serves as a test of the potential utility of these new phenotypes.…”

Section: Discussionmentioning

confidence: 99%

“…However, our ability to predict the pathogenicity of rare genetic variants remains poor, and these actionable genes still contain many variants of uncertain significance (VUS). We have previously demonstrated that aggregating related EHR phenotypes for Mendelian diseases could aid in variant interpretation 9,10 .…”

mentioning

confidence: 99%

“…We next identified new associations and replicated them in an independent cohort of patients undergoing clinical genetic testing. We then tested the utility of EHR phenotypes in assessing the pathogenicity of rare variants through the application of phenotype risk scores (PheRS) 9,10 to aid in future variant interpretation in clinical genetic testing. Figure 1 provides an overview of the study design.…”

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confidence: 99%

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A scalable EHR-based approach for phenotype discovery and variant interpretation for hereditary cancer genes

Zeng

Bastarache

Tao

et al. 2021

Preprint

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Knowledge of the clinical spectrum of rare genetic disorders helps in disease management and variant pathogenicity interpretation. Leveraging electronic health record (EHR)-linked genetic testing data from the eMERGE network, we determined the associations between a set of 23 hereditary cancer genes and 3017 phenotypes in 23544 individuals. This phenome-wide association study replicated 45% (184/406) of known gene-phenotype associations (P = 5.1 ×10-125). Meta-analysis with an independent EHR-derived cohort of 3242 patients confirmed 14 novel associations with phenotypes in the neoplastic, genitourinary, digestive, congenital, metabolic, mental and neurologic categories. Phenotype risk scores (PheRS) based on weighted aggregations of EHR phenotypes accurately predicted variant pathogenicity for at least 50% of pathogenic variants for 8/23 genes. We generated a catalog of PheRS for 7800 variants, including 5217 variants of uncertain significance, to provide empirical evidence of potential pathogenicity. This study highlights the potential of EHR data in genomic medicine.

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“…Other risk score models based only on clinical symptoms, or integrating these with laboratory data have recently been proposed 14,15 . As we did not have access to these models, including the script, model parameters, HPO terms, laboratory variables and ICD-codes, we were unable to replicate and to test them on the family study of the HEIRS cohort.…”

Section: Discussionmentioning

confidence: 99%

An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis

Conde

Sauter

Nguyen

2020

Sci Rep

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Hereditary haemochromatosis (HH) is an autosomal recessive disease, where HFE C282Y homozygosity accounts for 80–85% of clinical cases among the Caucasian population. HH is characterised by the accumulation of iron, which, if untreated, can lead to the development of liver cirrhosis and liver cancer. Since iron overload is preventable and treatable if diagnosed early, high-risk individuals can be identified through effective screening employing artificial intelligence-based approaches. However, such tools expose novel challenges associated with the handling and integration of large heterogeneous datasets. We have developed an efficient computational model to screen individuals for HH using the family study data of the Hemochromatosis and Iron Overload Screening (HEIRS) cohort. This dataset, consisting of 254 cases and 701 controls, contains variables extracted from questionnaires and laboratory blood tests. The final model was trained on an extreme gradient boosting classifier using the most relevant risk factors: HFE C282Y homozygosity, age, mean corpuscular volume, iron level, serum ferritin level, transferrin saturation, and unsaturated iron-binding capacity. Hyperparameter optimisation was carried out with multiple runs, resulting in 0.94 ± 0.02 area under the receiving operating characteristic curve (AUCROC) for tenfold stratified cross-validation, demonstrating its outperformance when compared to the iron overload screening (IRON) tool.

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Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease

Cited by 40 publications

References 23 publications

The Human Phenotype Ontology in 2021

The Human Phenotype Ontology in 2021

A scalable EHR-based approach for phenotype discovery and variant interpretation for hereditary cancer genes

An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis

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