Two-dimensional enrichment analysis for mining high-level imaging genetic associations

Xiaohui Yao, Jingwen Yan, Sungeun Kim, Kwangsik Nho, Shannon L. Risacher, Mark Inlow, Jason H. Moore, Andrew Saykin, Li Shen, Alzheimer’s Disease Neuroimaging Initiative The Alzheimer’s Disease Neuroimaging Initiative

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Enrichment analysis has been widely applied in the genomewide 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 howgenetic 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 AllenHuman BrainAtlas 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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages115-124
Number of pages10
Volume9250
ISBN (Print)9783319233437
DOIs
StatePublished - 2015
Event8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom
Duration: Aug 30 2015Sep 2 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9250
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th International Conference on Brain Informatics and Health, BIH 2015
CountryUnited Kingdom
CityLondon
Period8/30/159/2/15

Fingerprint

Genetic Association
Mining
Imaging
Imaging techniques
Brain
Genes
Neuroimaging
Gene
Pathway
Neurodegenerative diseases
Hospital beds
Module
Statistical Power
Alzheimer's Disease
Field Study
Networks (circuits)
Gene Expression Data
Gene expression
Phenotype
Testbed

Keywords

  • Enrichment analysis
  • Genome wideassociation study
  • Imaging genetics
  • Quantitative trait

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yao, X., Yan, J., Kim, S., Nho, K., Risacher, S. L., Inlow, M., ... The Alzheimer’s Disease Neuroimaging Initiative, A. D. N. I. (2015). Two-dimensional enrichment analysis for mining high-level imaging genetic associations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9250, pp. 115-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9250). Springer Verlag. https://doi.org/10.1007/978-3-319-23344-4_12

Two-dimensional enrichment analysis for mining high-level imaging genetic associations. / Yao, Xiaohui; Yan, Jingwen; Kim, Sungeun; Nho, Kwangsik; Risacher, Shannon L.; Inlow, Mark; Moore, Jason H.; Saykin, Andrew; Shen, Li; The Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Neuroimaging Initiative.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9250 Springer Verlag, 2015. p. 115-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9250).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yao, X, Yan, J, Kim, S, Nho, K, Risacher, SL, Inlow, M, Moore, JH, Saykin, A, Shen, L & The Alzheimer’s Disease Neuroimaging Initiative, ADNI 2015, Two-dimensional enrichment analysis for mining high-level imaging genetic associations. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9250, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9250, Springer Verlag, pp. 115-124, 8th International Conference on Brain Informatics and Health, BIH 2015, London, United Kingdom, 8/30/15. https://doi.org/10.1007/978-3-319-23344-4_12
Yao X, Yan J, Kim S, Nho K, Risacher SL, Inlow M et al. Two-dimensional enrichment analysis for mining high-level imaging genetic associations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9250. Springer Verlag. 2015. p. 115-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-23344-4_12
Yao, Xiaohui ; Yan, Jingwen ; Kim, Sungeun ; Nho, Kwangsik ; Risacher, Shannon L. ; Inlow, Mark ; Moore, Jason H. ; Saykin, Andrew ; Shen, Li ; The Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Neuroimaging Initiative. / Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9250 Springer Verlag, 2015. pp. 115-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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