Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice
| Autor: | Igor Filipov, Vanessa C. Wheeler, Matthew J. Mazzella, Liliana B. Menalled, Melinda C Ruiz, Ana Sanchez, Sylvie Ramboz, Brenda Lager, Marcy E. MacDonald, Ian Russell, Kimberly Cox, Miguel A. Gomez, Afshin Ghavami, Vadim Alexandrov, Seung Kwak, Dani Brunner, Justin Torello, Jeff Aaronson, Mei Kwan, Andrea E. Kudwa, James F. Gusella, Judy Watson-Johnson, Jim Rosinski, David Howland, Emily Sabath |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Biomedical Engineering Bioengineering Genome-wide association study Computational biology Phenome Biology Polymorphism Single Nucleotide Applied Microbiology and Biotechnology Mice 03 medical and health sciences 0302 clinical medicine Huntington's disease Genetic variation Genotype medicine Huntingtin Protein Animals Genetics Genome Behavior Animal Neurodegeneration Chromosome Mapping High-Throughput Nucleotide Sequencing medicine.disease Huntington Disease Phenotype 030104 developmental biology Molecular Medicine Age of onset 030217 neurology & neurosurgery Genome-Wide Association Study Biotechnology |
| Zdroj: | Nature Biotechnology. 34:838-844 |
| ISSN: | 1546-1696 1087-0156 |
| Popis: | Rapid technological advances for the frequent monitoring of health parameters have raised the intriguing possibility that an individual's genotype could be predicted from phenotypic data alone. Here we used a machine learning approach to analyze the phenotypic effects of polymorphic mutations in a mouse model of Huntington's disease that determine disease presentation and age of onset. The resulting model correlated variation across 3,086 behavioral traits with seven different CAG-repeat lengths in the huntingtin gene (Htt). We selected behavioral signatures for age and CAG-repeat length that most robustly distinguished between mouse lines and validated the model by correctly predicting the repeat length of a blinded mouse line. Sufficient discriminatory power to accurately predict genotype required combined analysis of >200 phenotypic features. Our results suggest that autosomal dominant disease-causing mutations could be predicted through the use of subtle behavioral signatures that emerge in large-scale, combinatorial analyses. Our work provides an open data platform that we now share with the research community to aid efforts focused on understanding the pathways that link behavioral consequences to genetic variation in Huntington's disease. |
| Databáze: | OpenAIRE |
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