Our group focuses on genetic variants that disrupt molecular functions that cause human disease. In this renewal R01 application, we propose to begin the process of realizing our long-term goals, and to expand our original scope of research to include the challenge of understanding genetic disease mutations and polymorphisms that affect gene expression regulation in noncoding regions. Additionally, we have formed collaborations with genetic data managers and will apply these methods to aid in their research and identify new testable hypotheses. We will do this in three aims. First, we will integrate predictions of protein-disease associations with mutation predictions to develop a new quantitative model of genotype and phenotype. Second, we will integrate each of these together to develop a systems level, molecular function genome annotator with functionality to import into the genome databases. Finally, we will continue to build new methods for characterization of mutations using sequence, function and structure and begin testing our published hypotheses. Each of these aims will include collaboration with the maintainers of genetic datasets to better understand their underlying molecular effects.

Public Health Relevance

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Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01LM009722-09
Application #
8722025
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
Project End
Budget Start
Budget End
Support Year
9
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Buck Institute for Age Research
Department
Type
DUNS #
City
Novato
State
CA
Country
United States
Zip Code
94945
Ioannidis, Nilah M; Rothstein, Joseph H; Pejaver, Vikas et al. (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99:877-885
Jiang, Yuxiang; Oron, Tal Ronnen; Clark, Wyatt T et al. (2016) An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biol 17:184
Katzman, Shana M; Strotmeyer, Elsa S; Nalls, Michael A et al. (2015) Mitochondrial DNA Sequence Variation Associated With Peripheral Nerve Function in the Elderly. J Gerontol A Biol Sci Med Sci 70:1400-8
Mooney, Sean D (2015) Progress towards the integration of pharmacogenomics in practice. Hum Genet 134:459-65
Friedberg, Iddo; Wass, Mark N; Mooney, Sean D et al. (2015) Ten simple rules for a community computational challenge. PLoS Comput Biol 11:e1004150
Mort, Matthew; Sterne-Weiler, Timothy; Li, Biao et al. (2014) MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing. Genome Biol 15:R19
Li, Zhiyu; Dilger, Jonathan M; Pejaver, Vikas et al. (2014) Intrinsic Size Parameters for Palmitoylated and Carboxyamidomethylated Peptides. Int J Mass Spectrom 368:6-14
Zykovich, Artem; Hubbard, Alan; Flynn, James M et al. (2014) Genome-wide DNA methylation changes with age in disease-free human skeletal muscle. Aging Cell 13:360-6
Kolker, Eugene; Özdemir, Vural; Martens, Lennart et al. (2014) Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 18:10-4
Wass, Mark N; Mooney, Sean D; Linial, Michal et al. (2014) The automated function prediction SIG looks back at 2013 and prepares for 2014. Bioinformatics 30:2091-2

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