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Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits

Nature Genetics volume 54, pages 161–169 (2022)Cite this article

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Abstract

While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant–trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer’s disease.

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Fig. 1: Workflow for multi-ancestry eQTL meta-analysis.
Fig. 2: Biologically motivated simulations demonstrate performance of mmQTL workflow: high-correlation scenario.
Fig. 3: Evaluation of mmQTL workflow on real data.
Fig. 4: Properties of brain eQTL meta-analysis.
Fig. 5: Heritability enrichment of variants in the 95% causal set for 22 complex traits.
Fig. 6: Summary of joint fine-mapping colocalization with brain-related traits.
Fig. 7: GWAS-eQTL colocalization by joint fine-mapping.

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Data availability

Brain eQTL meta-analysis resource: http://icahn.mssm.edu/brema

Code availability

mmQTL: https://github.com/jxzb1988/mmQTL and Zenodo79 (https://doi.org/10.5281/zenodo.5560014).

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Acknowledgements

This project was supported by the National Institute of Mental Health (NIH grants nos. R01-MH109677, U01-MH116442, R01-MH125246 and R01-MH109897), the National Institute on Aging (NIH grants nos. R01-AG050986, R01-AG067025 and R01-AG065582) and the Veterans Affairs Merit (no. BX004189) to P.R. G.E.H. was supported in part by NARSAD Young Investigator Grant no. 26313 from the Brain & Behavior Research Foundation. J.B. was supported in part by NARSAD Young Investigator Grant no. 27209 from the Brain & Behavior Research Foundation. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award nos. S10OD018522 and S10OD026880. The content herein is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Biao Zeng, Jaroslav Bendl, Roman Kosoy, John F. Fullard, Gabriel E. Hoffman & Panos Roussos

  2. Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Biao Zeng, Jaroslav Bendl, Roman Kosoy, John F. Fullard, Gabriel E. Hoffman & Panos Roussos

  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Biao Zeng, Jaroslav Bendl, Roman Kosoy, John F. Fullard, Gabriel E. Hoffman & Panos Roussos

  4. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Biao Zeng, Jaroslav Bendl, Roman Kosoy, John F. Fullard, Gabriel E. Hoffman & Panos Roussos

  5. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Biao Zeng, Jaroslav Bendl, Roman Kosoy, John F. Fullard, Gabriel E. Hoffman & Panos Roussos

  6. Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, USA

    Panos Roussos

  7. Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA

    Panos Roussos

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Contributions

B.Z., G.E.H. and P.R. conceived and designed the study. B.Z. designed and implemented the statistical method. B.Z. and G.E.H. performed analyses. J.F.F. generated cell-type-specific expression and chromatin accessibility data. J.B. and R.K. preprocessed and analyzed cell-type-specific expression and chromatin accessibility data. J.F.F. and P.R. supervised data generation. G.E.H. and P.R. supervised data analyses. G.E.H., B.Z. and P.R. wrote the manuscript with the help of all authors.

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Correspondence to Gabriel E. Hoffman or Panos Roussos.

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Extended data

Extended Data Fig. 1 Biologically motivated simulations demonstrate performance of mmQTL workflow: low correlation scenario.

a) QQ plot of results from null simulation shows that the linear mixed model (LMM) with fixed or random effect meta-analysis accurately controls the false positive rate for, while linear regression with 5 genotype principal components did not. The Sidak method was very conservative in both cases. λGC indicates the genomic control inflation factor. Gray band indicates 95% confidence interval under the null. b) Power from LMM followed by 3 types of meta-analysis versus the number of tissues sharing an eQTL. c) Size of the 95% credible sets from fixed- (y-axis) and random- (x-axis) effects meta-analysis from simulations in Fig. 2c.

Extended Data Fig. 2 Lead eQTL SNP sign concordance.

For the lead eQTL SNP of each gene in the meta-analysis, the sign of the mean estimated effect size is compared to the estimated effect sign from neuron and microglia eQTL analyses. The concordance rate increases with the strictness of the p-value cutoff, so a smaller p-value indicates a higher concordance rate. Error bars indicate 95% confidence interval for a binomial proportion. Analysis included 11,709 variants for neuron, and 10,865 variants for microglia.

Extended Data Fig. 3 Impact of effect size heterogeneity.

The test statistic from the random effect meta-analysis used here (Han and Eskin, 2011) is the sum of statistics testing the mean (Smean) and variance (Svariance) of the estimated effect sizes. a) The percent of total signal contributed by the fixed effect (that is Smean / (Smean + Svariance)) is shown for the lead eQTL SNP for multiple orders of conditional analysis. Box plot indicates median, interquartile range (IQR) and 1.5*IQR. b) The relationship between the test statistics is visualized by plotting sqrt Svariance against sqrt Smean from the lead eQTL SNP from the primary eQTL analysis. c) The estimated effect sizes from the lead eQTL SNP for genes with high and low levels of effect size heterogeneity is shown. Box plot indicates median, interquartile range (IQR) and 1.5*IQR.

Extended Data Fig. 4 Properties of conditional eQTLs.

a) The distribution of the distance to the transcription start site is shown for the lead variant for eQTL analysis of increasing degree. P-values indicate significance of one-sided Mann–Whitney U test between adjacent groups. Box plot indicates median, interquartile range (IQR) and 1.5*IQR. b) Cell type specificity metric tau plotted against the number of independent eQTLs discovered for each gene. Gray band indicates 95% confidence interval. c) Bar plot shows that the fraction of genes with high evolutionary constraint (pLI > 0.9) decreases with eQTL degree for the current study, PsychENCODE15, and whole blood78. Error bars indicate standard error based on asymptotic estimate of binomial proportion. Analysis included 10769 genes with eQTLs.

Extended Data Fig. 5 Estimated effect size and minor allele frequencies from conditional eQTL analysis.

The estimated effect size (a) and MAF (b) are shown for the lead eQTL SNP of significant genes for increasing order to conditional eQTL analysis. a) The distribution of estimated effect size is similar for all conditional analyses. b) The MAF shows a marked decrease with increasing order of conditional analysis. Box plot indicates median, interquartile range (IQR) and 1.5*IQR.

Extended Data Fig. 6 Comparison of estimated effect size for bulk and cell-type specific data.

(a-c) Estimated allelic effect size for eQTL lead in (a) neurons (Jaffe, et al. 2020), (b) microglia from Kosoy, et al. (in preparation) and (c) microglia from Young, et al. (2021) compared to effect size estimates from meta-analysis of bulk data from the current study. (d–g) Estimated allelic effect size for eQTL lead SNP in four immune cell types including (d) B cells, (e) CD14, (f) monocytes, (g) NK cells from Ota, et al. (2021) compared to estimates from bulk samples (Ishigaki, et al. 2017).

Extended Data Fig. 7 Number of genes colocalizing for each MeSH category with CLPP > 0.01.

The phenotype with the highest number of colocalized genes for each MeSH category is indicated.

Extended Data Fig. 8 Expression of FURIN and risk for multiple complex traits share rs4702 as a candidate causal variant.

Starting from the top, the plot shows -log10 p-values from eQTL analysis, poster probabilities from statistical fine-mapping of eQTL results, poster probabilities from statistical fine-mapping of GWAS results, and colocalization posterior probabilities (CLPP) for combining eQTL and GWAS fine-mapping. Traits are shown in the box on the right in decreasing order to CLPP value.

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Zeng, B., Bendl, J., Kosoy, R. et al. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat Genet 54, 161–169 (2022). https://doi.org/10.1038/s41588-021-00987-9

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