Common genetic variation associated with Mendelian disease severity revealed through cryptic phenotype analysis

Clinical heterogeneity is common in Mendelian disease, but small sample sizes make it difficult to identify specific contributing factors. However, if a disease represents the severely affected extreme of a spectrum of phenotypic variation, then modifier effects may be apparent within a larger subse...

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Bibliographic Details
Published in:Nature communications 2022-06, Vol.13 (1), p.3675-3675, Article 3675
Main Authors: Blair, David R., Hoffmann, Thomas J., Shieh, Joseph T.
Format: Article
Language:English
Subjects:
45/43
631/208/1516
631/208/2489/144
Biobanks
Disease
Diseases
Genetic disorders
Genetic diversity
Genetic variability
Genome-Wide Association Study
Genomes
Heterogeneity
Humanities and Social Sciences
multidisciplinary
Phenotype
Phenotypes
Phenotypic variations
Polymorphism, Single Nucleotide
Qualitative analysis
Science
Science (multidisciplinary)
Severity of Illness Index
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Summary:Clinical heterogeneity is common in Mendelian disease, but small sample sizes make it difficult to identify specific contributing factors. However, if a disease represents the severely affected extreme of a spectrum of phenotypic variation, then modifier effects may be apparent within a larger subset of the population. Analyses that take advantage of this full spectrum could have substantially increased power. To test this, we developed cryptic phenotype analysis, a model-based approach that infers quantitative traits that capture disease-related phenotypic variability using qualitative symptom data. By applying this approach to 50 Mendelian diseases in two cohorts, we identify traits that reliably quantify disease severity. We then conduct genome-wide association analyses for five of the inferred cryptic phenotypes, uncovering common variation that is predictive of Mendelian disease-related diagnoses and outcomes. Overall, this study highlights the utility of computationally-derived phenotypes and biobank-scale cohorts for investigating the complex genetic architecture of Mendelian diseases. The severity of rare genetic diseases often varies between individuals, but small sample sizes make it difficult to identify contributing factors. Here, the authors use biobank-scale clinical and genetic data to investigate a role for common genetic variation.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-31030-y