| Title: | A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders |
| Journal: | American Journal of Human Genetics |
| Published: | 19 Dec 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38118447/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.11.006 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10806749 |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 9144
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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.</p>
7 Keywords
- Brain Diseases
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Phenotype
- Polymorphism, Single Nucleotide
- Quantitative Trait Loci
13 Authors
- Jingni He
- Lilit Antonyan
- Harold Zhu
- Karen Ardila
- Qing Li
- David Enoma
- William Zhang
- Andy Liu
- Thierry Chekouo
- Bo Cao
- M Ethan MacDonald
- Paul D Arnold
- Quan Long
1 Application
| Application ID | Title |
|---|---|
| 100532 | Statistical methods development for genetic basis of complex traits and predictions |
Enabling scientific discoveries that improve human health