| Title: | Genome-wide association study in 404,302 individuals identifies 7 significant loci for reaction time variability |
| Journal: | Molecular Psychiatry |
| Published: | 1 Sep 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37864076/ |
| DOI: | https://doi.org/10.1038/s41380-023-02292-9 |
| URL: | https://www.nature.com/articles/s41380-023-02292-9.pdf |
Publication 9658
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Abstract
Reaction time variability (RTV), reflecting fluctuations in response time on cognitive tasks, has been proposed as an endophenotype for many neuropsychiatric disorders. There have been no large-scale genome-wide association studies (GWAS) of RTV and little is known about its genetic underpinnings. Here, we used data from the UK Biobank to conduct a GWAS of RTV in participants of white British ancestry (n = 404,302) as well as a trans-ancestry GWAS meta-analysis (n = 44,873) to assess replication. We found 161 genome-wide significant single nucleotide polymorphisms (SNPs) distributed across 7 genomic loci in our discovery GWAS. Functional annotation of the variants implicated genes involved in synaptic function and neural development. The SNP-based heritability (h2SNP) estimate for RTV was 3%. We investigated genetic correlations between RTV and selected neuropsychological traits using linkage disequilibrium score regression, and found significant correlations with several traits, including a positive correlation with mean reaction time and schizophrenia. Despite the high genetic correlation between RTV and mean reaction time, we demonstrate distinctions in the genetic underpinnings of these traits. Lastly, we assessed the predictive ability of a polygenic score (PGS) for RTV, calculated using PRSice and PRS-CS, and found that the RTV-PGS significantly predicted RTV in independent cohorts, but that the generalisability to other ancestry groups was poor. These results identify genetic underpinnings of RTV, and support the use of RTV as an endophenotype for neurological and psychiatric disorders.</p>
9 Authors
- Olivia Wootton
- Alexey A. Shadrin
- Christine Mohn
- Ezra Susser
- Raj Ramesar
- Ruben C. Gur
- Ole A. Andreassen
- Dan J. Stein
- Shareefa Dalvie
1 Application
| Application ID | Title |
|---|---|
| 27412 | Boosting the power of GWAS using novel statistical tools |
Enabling scientific discoveries that improve human health