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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National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Ye, Jane
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Buck Institute for Age Research
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Cirincione, Ann G; Clark, Kaylyn L; Kann, Maricel G (2018) Pathway networks generated from human disease phenome. BMC Med Genomics 11:75
Peterson, Thomas A; Gauran, Iris Ivy M; Park, Junyong et al. (2017) Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples. PLoS Comput Biol 13:e1005428
Cai, Binghuang; Li, Biao; Kiga, Nikki et al. (2017) Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges. Hum Mutat 38:1266-1276
Pejaver, Vikas; Mooney, Sean D; Radivojac, Predrag (2017) Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges. Hum Mutat 38:1092-1108
Lugo-Martinez, Jose; Pejaver, Vikas; Pagel, Kymberleigh A et al. (2016) The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease. PLoS Comput Biol 12:e1005091
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
Peterson, Thomas A; Mort, Matthew; Cooper, David N et al. (2016) Regulatory Single-Nucleotide Variant Predictor Increases Predictive Performance of Functional Regulatory Variants. Hum Mutat 37:1137-1143
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
Friedberg, Iddo; Wass, Mark N; Mooney, Sean D et al. (2015) Ten simple rules for a community computational challenge. PLoS Comput Biol 11:e1004150

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