Evidence map›Paper›PMID 38118447›Full record

ArticleAmerican journal of human genetics2024

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.

Jingni He, Lilit Antonyan, Harold Zhu, Karen Ardila, Qing Li, David Enoma, William Zhang, Andy Liu, Thierry Chekouo, Bo Cao and 3 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
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

8 citing papers in PubMed, 11 citations in OpenAlex.

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  8. Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies.IEEE transactions on computational biology and bioinformatics
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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

13 authors at 4 institutions in 2 countries.

Jingni HeDepartment of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Lilit AntonyanDepartment of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Harold ZhuDepartment of Biological Sciences, Faculty of Science, University of Calgary, Calgary, AB, Canada.
Karen ArdilaDepartment of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.
Qing LiDepartment of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
David EnomaDepartment of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
William ZhangThe Harker School, San Jose, CA, USA.
Andy LiuSir Winston Churchill High School, Calgary, AB, Canada; College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA.
Thierry ChekouoDepartment of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Bo CaoDepartment of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada.
M Ethan MacDonaldThe Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Paul D ArnoldDepartment of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. Electronic address: paul.arnold@ucalgary.ca.
Quan LongDepartment of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada. Electronic address: quan.long@ucalgary.ca.
University of Calgary · CAUniversity of Alberta · CAUniversity of California, Los Angeles · USUniversity of Minnesota · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

Indexed as

Brain DiseasesGenome-Wide Association StudyGenetic Predisposition to DiseaseHumansPhenotypePolymorphism, Single NucleotideQuantitative Trait LocicerebellumGWASimage-derived phenotypesimage-mediated association studyneuropsychiatric disorders

Identifiers

PMID38118447
PMCPMC10806749
OpenAlexW4389955870

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.