ALOHA: Aggregated Local Extrema Splines for High-Throughput Dose-Response Analysis

Details

• Personal Author:
  Auerbach SS ; Davidson SE ; Medvedovic M ; Sivaganesan S ; Wheeler, Matthew W.
• Description:
  Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene's dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in 'co-regulated' gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency. [Description provided by NIOSH]
• Subjects:
  Genetics Occupational Health Safety Toxicology
• Keywords:
  Analytical Models Author Keywords: Bayesian Clustering Bioinformatics Biological Pathways Dose Response Genes Genomic Benchmark Dose Genomics High Throughput Data Pathogenicity
• ISSN:
  2468-1113
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  North Carolina ; Ohio ; OSHA Region 4 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  21
• NIOSHTIC Number:
  nn:20068007
• Citation:
  Comput Toxicol 2022 Feb; 21:100196
• Contact Point Address:
  Sarah E. Davidson, Department of Environmental Health Sciences Division of Biostatistics and Bioinformatics, University of Cincinnati, Cincinnati, OH, United States
• Email:
  davidsss@mail.uc.edu
• Federal Fiscal Year:
  2022
• Peer Reviewed:
  True
• Source Full Name:
  Computational Toxicology
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:4050950dbcd46922d5ee88473f7c4da2dc4ec4c62665c801a641b66dbe0f7b43559fedfa3c1a06882738c1228065bd8cd33b36cb02f2ee87822504a6dbfedf8b
• Download URL:
  https://stacks.cdc.gov/view/cdc/230238/cdc_230238_DS1.pdf
• File Type:
  [PDF - 2.38 MB ]