Two-Dimensional Enrichment Analysis for Mining High-Level Imaging Genetic Associations

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• 关键词：Imaging genetics ; Enrichment analysis ; Genome wide association study ; Quantitative trait
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9250
• 期：1
• 页码：115-124
• 全文大小：1,195 KB
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• 作者单位：Xiaohui Yao (10) (9)
  Jingwen Yan (10) (9)
  Sungeun Kim (9)
  Kwangsik Nho (9)
  Shannon L. Risacher (9)
  Mark Inlow (11)
  Jason H. Moore (12)
  Andrew J. Saykin (9)
  Li Shen (10) (9)
  [Authorinst]for the Alzheimer’s Disease Neuroimaging Initiative
  
  10. School of Informatics and Computing, Indiana University Indianapolis, Indianapolis, IN, USA
  9. Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
  11. Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA
  12. Biomedical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  
• 丛书名：Brain Informatics and Health
• ISBN：978-3-319-23344-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Enrichment analysis has been widely applied in the genome-wide association studies (GWAS), where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer’s Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 12 significant high level two dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.