| Title: | Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS |
| Journal: | Nature Genetics |
| Published: | 8 Mar 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33686288/ |
| DOI: | https://doi.org/10.1038/s41588-021-00787-1 |
| URL: | https://www.nature.com/articles/s41588-021-00787-1.pdf |
| Citations: | 71 (35 in last 2 years) as of 8 Aug 2024 |
Publication 12877
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Abstract
Psychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case-case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case-control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case-case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case-control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data.</p>
17 Keywords
- Alleles
- Axons
- Bipolar Disorder
- Case-Control Studies
- Datasets as Topic
- Depressive Disorder, Major
- Gene Expression
- Gene Frequency
- Genetic Loci
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Kruppel-Like Factor 6
- Kruppel-Like Transcription Factors
- Neuronal Outgrowth
- Polymorphism, Single Nucleotide
- Schizophrenia
2 Authors
- Wouter J. Peyrot
- Alkes L. Price
1 Application
| Application ID | Title |
|---|---|
| 10438 | Application of fast mixed model association and principal component analysis methods |
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