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.This work was supported by NIHR award RG65966 (D.G. and E.T.) and the Medical Research Council programme grant MC UP 0801/1 (D.G. and S.R.). The NIHR BioResource – Rare Diseases projects were approved by Research Ethics Committees in the UK and appropriate national ethics authorities in non-UK enrolment centres (see Supplemental Note). We are grateful to Dr William J Astle for advice on the statistical model and for providing comments on the manuscript. We are particularly thankful to the BPD project members for granting access to detailed HPO terms of patientsThis is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ajhg.2016.01.00
