Evidence map›Paper›PMID 31742256›Full record

Article... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics2019

Mining Regional Imaging Genetic Associations via Voxel-wise Enrichment Analysis.

Xiaohui Yao, Shan Cong, Jingwen Yan, Shannon L Risacher, Andrew J Saykin, Jason H Moore, Li Shen

Abstract read
In one paragraph

Article in ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020
    Article
  2. A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xiaohui YaoBiostatistics, Epidemiology and Informatics University of Pennsylvania, Philadelphia, PA.
Shan CongElectrical and Computer Engineering Purdue University, West Lafayette, IN.
Jingwen YanInformatics and Computing Indiana University, Indianapolis, IN.
Shannon L RisacherRadiology and Imaging Sciences Indiana University School of Medicine, Indianapolis, IN.
Andrew J SaykinRadiology and Imaging Sciences Indiana University School of Medicine, Indianapolis, IN.
Jason H MooreBiostatistics, Epidemiology and Informatics University of Pennsylvania, Philadelphia, PA.
Li ShenBiostatistics, Epidemiology and Informatics University of Pennsylvania, Philadelphia, PA.

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI RICHARD Justin PERRIN · 2016 to 2026
$226.7M
Research Education ComponentP30AG010133 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 1991 to 2020
$37.3M
Memory Circuitry in MCI and Early Alzheimer’s Disease Prodrome: Molecular DriversR01AG019771 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 2001 to 2021
$6.9M
Integrative Bioinformatics Approaches to Human Brain Genomics and ConnectomicsR01EB022574 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI SHEN, LI · 2016 to 2019
$1.9M
Bioinformatics Strategies for Multidimensional Brain Imaging GeneticsR01LM011360 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI MOORE, JASON H., SAYKIN, ANDREW J · 2012 to 2015
$1.4M
NIA NIH HHS P30 AG010133NIA NIH HHS R01 AG019771NIA NIH HHS U19 AG024904NIBIB NIH HHS R01 EB022574NLM NIH HHS R01 LM011360
6 · The paper itself

Abstract

Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful annotation and modest dimensionality compared with voxel-wise approaches. Typical ROI-level measures used in these studies are summary statistics from voxel-wise measures in the region, without making full use of individual voxel signals. In this paper, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxel-wise enrichment analysis, which embraces the collective effect of weak voxel-level signals within an ROI. We demonstrate our method on an imaging genetic analysis using data from the Alzheimers Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxel-wise FDGPET measures between 116 ROIs and 19 AD candidate SNPs. Compared with traditional ROI-wise and voxel-wise approaches, our method identified 102 additional significant associations, some of which were further supported by evidences in brain tissue-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.

Indexed as

enrichment analysisgenetic association analysisimaging geneticsvoxel-wise analysis

Identifiers

PMID31742256
PMCPMC6860973

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.