Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood
Summary (2 min read)
ARTICLE
- Current eQTL studies are biased toward the most accessible tissues (e.g., blood), which are often not the most relevant tissues to the traits and diseases of interest.
- The questions are to what extent the cis-genetic effects on gene expression and DNA methylation (DNAm) in blood differ from those in brain and whether the authors can gain power to detect associations of genes (or DNAm sites) with brain-related traits by using the cis-eQTL (or cis-mQTL) effects estimated from a large blood sample as proxies for those in brain.
Results
- Correlation of cis-eQTL effects between brain and blood.
- Note that the rb method is distinct from the Spearman or Pearson correlation approach13 because the latter does not account for errors in the estimated eQTL effects and thereby leads to an underestimation of the correlation of true eQTL effects.
Discussion
- The authors estimated the correlation (r̂b) of genetic effects at the topassociated cis-eQTLs/mQTLs between brain and blood.
- The genetic differences are partly due to cell-type-specific genetic effects regardless whether cell composition covariates have been included in the eQTL analysis or not.
- On the other hand, however, the strong betweentissue correlation in cis-eQTL effects does not contradict the result that many genes showed differential expression between brain and blood because the difference in cis-eQTL effect is almost independent of the mean difference in gene expression level (Fig. 3).
- Trans-eQTLs may also have an important role in regulating gene expression especially for tissue-specific effects14.
- Because the variance explained by individual trans-eQTL/mQTL is small on average9,38, very large sample sizes (e.g., 10,000s) are required to detect trans-eQTLs to be useful for the SMR analysis21.
Methods
- Summary data of cis-eQTL, cis-mQTL, and GWAS.
- The authors accessed the GTEx eQTL summary statistics of ~9.3 million SNPs for ~32,000 genes in 44 tissues (including 10 brain regions) through GTEx portal (URLs).
- To harmonize the units across data sets, the authors rescaled the effect size and standard error (SE) of each eQTL in the CMC, ROSMAP, and Braineac based on the z-statistic, allele frequency and sample size using the method described in Zhu et al.21 so that the eQTL effects in all data sets can be interpreted in standard deviation (SD) units.
- If i= j, then re= 1 and var bið Þ ¼ varðb̂iÞ var eið Þ, where var(bi) is the variance of true cis-eQTL effects across genes in tissue i.
Additional information
- Reprints and permission information is available online at http://npg.nature.com/ reprintsandpermissions/.
- The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.
- 2Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
- 3The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, 325027 Wenzhou, Zhejiang, China.
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Frequently Asked Questions (13)
Q1. What contributions have the authors mentioned in the paper "Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood" ?
In this paper, the authors estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation ( DNAm ) quantitative trait loci between brain and blood.
Q2. What future works have the authors mentioned in the paper "Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood" ?
Despite these caveats, their findings shed light on the genetic architecture underlying the regulation of gene expression across tissues and provide important guidance for studies in the future to identify functional genes for human complex traits.
Q3. How many SNPs were found in the CMC summary statistics?
The CMC eQTL summary statistics (ascertained at FDR < 0.2) of ~1.1 million SNPs for 14,366 genes were derived from individual-level data in dorsolateral prefrontal cortex of 467 subjects, 209 of which were schizophrenia patients.
Q4. What is the reason for the differences in the eQTL results?
The genetic differences are partly due to cell-type-specific genetic effects regardless whether cell composition covariates have been included in the eQTL analysis or not.
Q5. Why did the authors focus their analyses on cis-eQTLs?
the authors focused their analyses only on cis-eQTLs and cis-mQTLs because trans-eQTLs and trans-mQTLs data were not available in most data sets used in their study.
Q6. What is the significance of the rb method?
Because the rb method accounts for estimation errors, r̂b can be interpreted as an estimate of correlation of true cis-eQTL effects between brain and blood, as demonstrated by simulations (Supplementary Fig. 2).
Q7. How many samples are required to detect trans-eQTLs?
Because the variance explained by individual trans-eQTL/mQTL is small on average9,38, very large sample sizes (e.g., 10,000s) are required to detect trans-eQTLs to be useful for the SMR analysis21.
Q8. What were the eqtl summary data available in GTEx, CAGE, and?
The eQTL summary data available in GTEx, CAGE, and eQTLGen were from previous analyses of standardized gene expression levels with mean 0 and variance 1, whereas expression levels in the other data sets (i.e., CMC, ROSMAP, and Braineac) were not standardized.
Q9. What are the guidelines for using the discovery–replication paradigm?
Their results also provide some guidelines about the use of discovery–replication paradigm to compare eQTL effects between tissues (i.e., detecting eQTLs in one tissue at a stringent P-value threshold and replicating the effects in another tissue after correcting for multiple tests)13,29.
Q10. How many SNPs were found in GTEx?
The authors accessed the GTEx eQTL summary statistics of ~9.3 million SNPs for ~32,000 genes in 44 tissues (including 10 brain regions) through GTEx portal (URLs).
Q11. What is the way to avoid the potential ascertainment bias?
To avoid the potential ascertainment bias, the authors selected the top cis-eQTLs in a reference tissue, i.e., GTEx-muscle (n= 361) or CMC (n= 467; independent of GTEx), using a stringent Pvalue threshold that is required for the SMR analysis21 (see below), and estimated rb between brain and blood using these SNPs (Supplementary Fig. 3).
Q12. How many genes were identified using the eQTLGen data?
The authors identified 61 genes associated with the traits using the brain eQTL data, 41 of which (67.2%) were in common with a larger set of genes (97) identified using the eQTLGen blood eQTL data (Fig. 5b).
Q13. How many eQTLs were found in enhancers?
These examples, however, were rare because only 14 of the 308 eQTLs with Pdifference < 0.05/1388 were located in enhancers and only 4 of the 14 enhancers appeared to be tissue specific.