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Automated quantitative trait locus analysis (AutoQTL).
Freda, Philip J; Ghosh, Attri; Zhang, Elizabeth; Luo, Tianhao; Chitre, Apurva S; Polesskaya, Oksana; St Pierre, Celine L; Gao, Jianjun; Martin, Connor D; Chen, Hao; Garcia-Martinez, Angel G; Wang, Tengfei; Han, Wenyan; Ishiwari, Keita; Meyer, Paul; Lamparelli, Alexander; King, Christopher P; Palmer, Abraham A; Li, Ruowang; Moore, Jason H.
Afiliación
  • Freda PJ; Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
  • Ghosh A; Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
  • Zhang E; Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
  • Luo T; Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
  • Chitre AS; Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
  • Polesskaya O; Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
  • St Pierre CL; Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
  • Gao J; Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
  • Martin CD; Department of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 955 Main Street, Suite 3102, Buffalo, NY, 14203, USA.
  • Chen H; Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
  • Garcia-Martinez AG; Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
  • Wang T; Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
  • Han W; Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA.
  • Ishiwari K; Department of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 955 Main Street, Suite 3102, Buffalo, NY, 14203, USA.
  • Meyer P; Clinical and Research Institute on Addictions, University at Buffalo, 1021 Main Street, Buffalo, NY, 14203-1016, USA.
  • Lamparelli A; Department of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA.
  • King CP; Department of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA.
  • Palmer AA; Department of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA.
  • Li R; Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
  • Moore JH; Institute for Genomic Medicine, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA.
BioData Min ; 16(1): 14, 2023 Apr 10.
Article en En | MEDLINE | ID: mdl-37038201
BACKGROUND: Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data. RESULTS: Using a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. CONCLUSIONS: This proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioData Min Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioData Min Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido