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Journal article
Network-Based Functional Prediction Augments Genetic Association To Predict Candidate Genes for Histamine Hypersensitivity in Mice
G3 : genes - genomes - genetics, v 9(12), pp 4223-4233
01 Dec 2019
PMID: 31645420
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Genetic mapping is a primary tool of genetics in model organisms; however, many quantitative trait loci (QTL) contain tens or hundreds of positional candidate genes. Prioritizing these genes for validation is often ad hoc and biased by previous findings. Here we present a technique for prioritizing positional candidates based on computationally inferred gene function. Our method uses machine learning with functional genomic networks, whose links encode functional associations among genes, to identify network-based signatures of functional association to a trait of interest. We demonstrate the method by functionally ranking positional candidates in a large locus on mouse Chr 6 (45.9 Mb to 127.8 Mb) associated with histamine hypersensitivity (Histh). Histh is characterized by systemic vascular leakage and edema in response to histamine challenge, which can lead to multiple organ failure and death. Although Histh risk is strongly influenced by genetics, little is known about its underlying molecular or genetic causes, due to genetic and physiological complexity of the trait. To dissect this complexity, we ranked genes in the Histh locus by predicting functional association with multiple Histh-related processes. We integrated these predictions with new single nucleotide polymorphism (SNP) association data derived from a survey of 23 inbred mouse strains and congenic mapping data. The top-ranked genes included Cxcl12, Ret, Cacna1c, and Cntn3, all of which had strong functional associations and were proximal to SNPs segregating with Histh. These results demonstrate the power of network-based computational methods to nominate highly plausible quantitative trait genes even in challenging cases involving large QTL and extreme trait complexity.
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Details
- Title
- Network-Based Functional Prediction Augments Genetic Association To Predict Candidate Genes for Histamine Hypersensitivity in Mice
- Creators
- Anna L. Tyler - Jackson LaboratoryAbbas Raza - Department of Medicine,Dimitry N. Krementsov - Department of Biomedical and Health Sciences,Laure K. Case - Jackson LaboratoryRui Huang - University of Chinese Academy of SciencesRunlin Z. Ma - University of Chinese Academy of SciencesElizabeth P. Blankenhorn - Drexel UniversityCory Teuscher - University of VermontJ. Matthew Mahoney - Department of Computer Science
- Publication Details
- G3 : genes - genomes - genetics, v 9(12), pp 4223-4233
- Publisher
- Oxford Univ Press
- Number of pages
- 11
- Grant note
- R21LM012615 / NATIONAL LIBRARY OF MEDICINE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM) RR-1602-07780 / NMSS; National Multiple Sclerosis Society National Multiple Sclerosis Society (NMSS); National Multiple Sclerosis Society R21 AI145306 / National Institute of Allergy and Infectious Disease; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID) R21AI145306 / NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID) NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA R01 NS097596 / NIH grants from the National Institute of Neurological Disease and Stroke R21 LM012615 / National Library of Medicine of the United States National Institutes of Health (NIH) R01NS097596 / NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Neurological Disorders & Stroke (NINDS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Microbiology and Immunology
- Web of Science ID
- WOS:000511967400029
- Scopus ID
- 2-s2.0-85076197687
- Other Identifier
- 991019168648504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Genetics & Heredity