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

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...

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Published inAmerican journal of human genetics Vol. 111; no. 1; pp. 48 - 69
Main Authors He, Jingni, Antonyan, Lilit, Zhu, Harold, Ardila, Karen, Li, Qing, Enoma, David, Zhang, William, Liu, Andy, Chekouo, Thierry, Cao, Bo, MacDonald, M. Ethan, Arnold, Paul D., Long, Quan
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 04.01.2024
Elsevier
Subjects
Brain Diseases - genetics
cerebellum
Genetic Predisposition to Disease
Genome-Wide Association Study - methods
GWAS
Humans
image-derived phenotypes
image-mediated association study
neuropsychiatric disorders
Phenotype
Polymorphism, Single Nucleotide - genetics
Quantitative Trait Loci - genetics
image-derived phenotypes
neuropsychiatric disorders
image-mediated association study
GWAS
cerebellum
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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. The authors developed a computational tool, the image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. A cerebellar-mediated mechanism was identified to be common to the four disorders.
AbstractList 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. The authors developed a computational tool, the image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. A cerebellar-mediated mechanism was identified to be common to the four disorders.
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.
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.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.
Author He, Jingni
Ardila, Karen
Li, Qing
Antonyan, Lilit
Cao, Bo
Enoma, David
Chekouo, Thierry
Zhu, Harold
Zhang, William
Liu, Andy
MacDonald, M. Ethan
Arnold, Paul D.
Long, Quan
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  fullname: Cao, Bo
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  email: quan.long@ucalgary.ca
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Keywords image-derived phenotypes
neuropsychiatric disorders
image-mediated association study
GWAS
cerebellum
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Snippet Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive...
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StartPage 48
SubjectTerms Brain Diseases - genetics
cerebellum
Genetic Predisposition to Disease
Genome-Wide Association Study - methods
GWAS
Humans
image-derived phenotypes
image-mediated association study
neuropsychiatric disorders
Phenotype
Polymorphism, Single Nucleotide - genetics
Quantitative Trait Loci - genetics
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Title A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders
URI https://dx.doi.org/10.1016/j.ajhg.2023.11.006
https://www.ncbi.nlm.nih.gov/pubmed/38118447
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