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Am J Hum Genet. 2016 Mar 3;98(3):490-499. doi: 10.1016/j.ajhg.2016.01.008. Epub 2016 Feb 25.

Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases.

Author information

1
Department of Haematology, University of Cambridge, NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK; Medical Research Council Biostatistics Unit, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
2
Medical Research Council Biostatistics Unit, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
3
Department of Haematology, University of Cambridge, NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK; Medical Research Council Biostatistics Unit, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK. Electronic address: et341@cam.ac.uk.

Abstract

Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases.

PMID:
26924528
PMCID:
PMC4827100
DOI:
10.1016/j.ajhg.2016.01.008
[Indexed for MEDLINE]
Free PMC Article

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