Mostafavi, Sara
Canadian Institute for Advanced Research · University of Washington
32 papers on CoChatORCID 0000-0003-4698-1177
Most cited papers
- Verified
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science
Genetic Associations and EpidemiologyGenomics and Chromatin Dynamics
Cited by 5,062
- Verified
The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function
Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C. T., et al. (2010) · Nucleic Acids Research
Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies
Cited by 4,835
- Verified
Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression
eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics
Genetic Associations and EpidemiologyTreatment of Major Depression
Cited by 3,005
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The Genetic Landscape of a Cell
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science
Bioinformatics and Genomic NetworksFungal and yeast genetics research
Cited by 2,162
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Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders
Lee, P. H., Anttila, V., Won, H., Feng, Y.-C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019) · Cell
Genetic Associations and EpidemiologySchizophrenia research and treatment
Cited by 1,381
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A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research
De Jager, P. L., Ma, Y., McCabe, C., Xu, J., Vardarajan, B. N., Felsky, D., Klein, H.-U., White, C. C., Peters, M. A., Lodgson, B., et al. (2018) · Scientific Data
Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments
Cited by 598
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Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals
Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., Haudenschild, C. D., Beckman, K. B., Shi, J., Mei, R., et al. (2014) · Genome Research
Genetic Associations and EpidemiologyRNA Research and Splicing
Cited by 547
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A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease
Mostafavi, S., Gaiteri, C., Sullivan, S. E., White, C. C., Tasaki, S., Xu, J., Taga, M., Klein, H.-U., Patrick, E., Komashko, V., et al. (2018) · Nature Neuroscience
Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research
Cited by 509
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Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes
Raj, T., Rothamel, K., Mostafavi, S., Ye, C., Lee, M. N., Replogle, J. M., Feng, T., Lee, M., Asinovski, N., Frohlich, I., et al. (2014) · Science
Genetic Associations and EpidemiologyGenomics and Chromatin Dynamics
Cited by 506
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An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience
Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals
Cited by 452
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Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility
Raj, T., Li, Y. I., Wong, G., Humphrey, J., Wang, M., Ramdhani, S., Wang, Y.-C., Ng, B., Gupta, I., Haroutunian, V., et al. (2018) · Nature Genetics
RNA Research and SplicingAlzheimer's disease research and treatments
Cited by 410
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Parsing the interferon transcriptional network and its disease associations
Mostafavi, S., Yoshida, H., Moodley, D., LeBoité, H., Rothamel, K., Raj, T., Ye, C. J., Chevrier, N., Zhang, S.-Y., Feng, T., et al. (2016) · Cell
interferon and immune responsesCytokine Signaling Pathways and Interactions
Cited by 337
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Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions
Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., Allen, M., Reddy, J. S., Hoffman, G. E., Dang, K. K., Calley, J., Ebert, P. J., Eddy, J., et al. (2020) · Scientific Data
Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals
Cited by 318
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Effect of predicted protein-truncating genetic variants on the human transcriptome
Rivas, M. A., Pirinen, M., Conrad, D. F., Lek, M., Tsang, E. K., Karczewski, K. J., Maller, J. B., Kukurba, K. R., DeLuca, D. S., Fromer, M., et al. (2015) · Science
Genomics and Rare DiseasesRNA Research and Splicing
Cited by 313
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Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports
Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research
Cited by 295
- Verified
Biological embedding of experience: a primer on epigenetics
Aristizabal, M. J., Anreiter, I., Halldorsdottir, T., Odgers, C. L., McDade, T. W., Goldenberg, A., Mostafavi, S., Kobor, M. S., Binder, E. B., Sokolowski, M. B., et al. (2020) · Proceedings of the National Academy of Sciences
Epigenetics and DNA MethylationBirth, Development, and Health
Cited by 268
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The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics
Genetic Associations and EpidemiologyGenomics and Chromatin Dynamics
Cited by 263
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A critical assessment of Mus musculusgene function prediction using integrated genomic evidence
Peña-Castillo, L., Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. K., et al. (2008) · Genome Biology
Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies
Cited by 248
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Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors
Mullins, N., Kang, J., Campos, A. I., Coleman, J. R. I., Edwards, A. C., Galfalvy, H., Levey, D. F., Lori, A., Shabalin, A., Starnawska, A., et al. (2022) · Biological Psychiatry
Suicide and Self-Harm StudiesGenetic Associations and Epidemiology
Cited by 238
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Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing
Mostafavi, S., Battle, A., Zhu, X., Potash, J. B., Weissman, M. M., Shi, J., Beckman, K., Haudenschild, C., McCormick, C., Mei, R., et al. (2014) · Molecular Psychiatry
Tryptophan and brain disordersTreatment of Major Depression
Cited by 187
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Sharing and Specificity of Co-expression Networks across 35 Human Tissues
Pierson, E., the GTEx Consortium, Koller, D., Battle, A., & Mostafavi, S. (2015) · PLOS Computational Biology
Bioinformatics and Genomic NetworksGene Regulatory Network Analysis
Cited by 177
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Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it?
Levinson, D. F., Mostafavi, S., Milaneschi, Y., Rivera, M., Ripke, S., Wray, N. R., & Sullivan, P. F. (2014) · Biological Psychiatry
Genetic Associations and EpidemiologyTreatment of Major Depression
Cited by 159
- Verified
Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns
Czamara, D., Eraslan, G., Page, C. M., Lahti, J., Lahti-Pulkkinen, M., Hämäläinen, E., Kajantie, E., Laivuori, H., Villa, P. M., Reynolds, R. M., et al. (2019) · Nature Communications
Epigenetics and DNA MethylationBirth, Development, and Health
Cited by 143
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Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings
Sasse, A., Ng, B., Spiro, A. E., Tasaki, S., Bennett, D. A., Gaiteri, C., De Jager, P. L., Chikina, M., & Mostafavi, S. (2023) · Nature Genetics
Genetic Associations and EpidemiologyGenomics and Chromatin Dynamics
Cited by 139
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