Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases
File(s)
Published version
Author(s)
Greene, D
NIHR BioResource
Richardson, S
Turro, E
Type
Journal Article
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.
Date Issued
2016-02-25
Date Acceptance
2016-01-08
Citation
American Journal of Human Genetics, 2016, 98 (3), pp.490-499
ISSN
1537-6605
Publisher
Elsevier
Start Page
490
End Page
499
Journal / Book Title
American Journal of Human Genetics
Volume
98
Issue
3
Copyright Statement
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Subjects
Actinin
Adaptor Proteins, Signal Transducing
Bayes Theorem
Databases, Genetic
Genetic Association Studies
Guanine Nucleotide Exchange Factors
Humans
Logistic Models
Models, Genetic
Phenotype
Rare Diseases
NIHR BioResource
Genetics & Heredity
06 Biological Sciences
11 Medical And Health Sciences
Publication Status
Published