| Title: | Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder |
| Journal: | Nature Genetics |
| Published: | 20 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37985818/ |
| DOI: | https://doi.org/10.1038/s41588-023-01559-9 |
| URL: | https://www.nature.com/articles/s41588-023-01559-9.pdf |
| Citations: | 9 (9 in last 2 years) as of 8 Aug 2024 |
Publication 8283
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Abstract
Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.</p>
13 Authors
- Andrew Dahl
- Michael Thompson
- Ulzee An
- Morten Krebs
- Vivek Appadurai
- Richard Border
- Silviu-Alin Bacanu
- Thomas Werge
- Jonathan Flint
- Andrew J. Schork
- Sriram Sankararaman
- Kenneth S. Kendler
- Na Cai
1 Application
| Application ID | Title |
|---|---|
| 28709 | Clinical heterogeneity in depression |
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