| Title: | Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood |
| Journal: | Nature Communications |
| Published: | 11 Jun 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29891976/ |
| DOI: | https://doi.org/10.1038/s41467-018-04558-1 |
| URL: | https://www.nature.com/articles/s41467-018-04558-1.pdf |
| Citations: | 310 (89 in last 2 years) as of 8 Aug 2024 |
Publication 12039
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Abstract
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (rb). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (r^b=0.70$$\hat r_b = 0.70$$ for cis-eQTLs and r^b=0.78$$\hat r_ b = 0.78$$ for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.</p>
18 Authors
- Ting Qi
- Yang Wu
- Jian Zeng
- Futao Zhang
- Angli Xue
- Longda Jiang
- Zhihong Zhu
- Kathryn Kemper
- Loic Yengo
- Zhili Zheng
- eQTLGen Consortium
- Riccardo E. Marioni
- Grant W. Montgomery
- Ian J. Deary
- Naomi R. Wray
- Peter M. Visscher
- Allan F. McRae
- Jian Yang
1 Application
| Application ID | Title |
|---|---|
| 12514 | The limits of predicting complex traits and diseases from genetic data |
Enabling scientific discoveries that improve human health