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Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies.
Lu, Zeyun; Gopalan, Shyamalika; Yuan, Dong; Conti, David V; Pasaniuc, Bogdan; Gusev, Alexander; Mancuso, Nicholas.
  • Lu Z; Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA. Electronic address: zeyunlu@usc.edu.
  • Gopalan S; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Evolutionary Anthropology, Duke University, Durham, NC, USA.
  • Yuan D; Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
  • Conti DV; Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Pasaniuc B; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of M
  • Gusev A; Division of Population Sciences, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA, USA; Division of Genetics, Brigham & Women's Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA, USA.
  • Mancuso N; Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Quantitative and Computational Biology, University
Am J Hum Genet ; 109(8): 1388-1404, 2022 08 04.
Article en En | MEDLINE | ID: mdl-35931050
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Estudio de Asociación del Genoma Completo / Transcriptoma Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article
Texto completo
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Estudio de Asociación del Genoma Completo / Transcriptoma Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article