| Title: | Common genetic variation associated with Mendelian disease severity revealed through cryptic phenotype analysis |
| Journal: | Nature Communications |
| Published: | 27 Jun 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35760791/ |
| DOI: | https://doi.org/10.1038/s41467-022-31030-y |
| URL: | https://www.nature.com/articles/s41467-022-31030-y.pdf |
Publication 7099
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Abstract
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.3 Authors
- David R. Blair
- Thomas J. Hoffmann
- Joseph T. Shieh
Enabling scientific discoveries that improve human health