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An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome

Nature Neuroscience volume 20, pages 1418–1426 (2017)Cite this article

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Abstract

We report a multi-omic resource generated by applying quantitative trait locus (xQTL) analyses to RNA sequence, DNA methylation and histone acetylation data from the dorsolateral prefrontal cortex of 411 older adults who have all three data types. We identify SNPs significantly associated with gene expression, DNA methylation and histone modification levels. Many of these SNPs influence multiple molecular features, and we demonstrate that SNP effects on RNA expression are fully mediated by epigenetic features in 9% of these loci. Further, we illustrate the utility of our new resource, xQTL Serve, by using it to prioritize the cell type(s) most affected by an xQTL. We also reanalyze published genome wide association studies using an xQTL-weighted analysis approach and identify 18 new schizophrenia and 2 new bipolar susceptibility variants, which is more than double the number of loci that can be discovered with a larger blood-based expression eQTL resource.

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Figure 1: Overview of xQTL analysis.
Figure 2: Cross-tissue replication analysis.
Figure 3: Genomic enrichment of xQTLs and their overlap.
Figure 4: Epigenetic mediation of eQTLs.
Figure 5: Application of the xQTL Resource for translational studies.

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Acknowledgements

We thank the participants of ROS and MAP for their essential contributions and gift to these projects. This work has been supported by National Institute of Health (NIH) grants P330AG10161, U01 AG046152, R01AG16042, R01 AG036836, R01 AG015819, R01 AG017917 and R01 AG036547. The U01 AG046152 grant (to P.L.D.J. and D.A.B.) is a component of the AMP-AD Target Discovery and Preclinical Validation Consortium, a program of the National Institute of Aging and the Foundation of the NIH.

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  1. Sara Mostafavi and Philip L De Jager: These authors contributed equally to this work.

Authors and Affiliations

  1. Departments of Statistics and Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada

    Bernard Ng & Sara Mostafavi

  2. Centre for Molecular Medicine and Therapeutics, Vancouver, British Columbia, Canada

    Bernard Ng & Sara Mostafavi

  3. Broad Institute, Cambridge, Massachusetts, USA

    Charles C White, Hans-Ulrich Klein, Cristin McCabe, Ellis Patrick, Jishu Xu & Philip L De Jager

  4. Department of Neurology, Center for Translational & Systems Neuroimmunology, Columbia University Medical Center, New York, New York, USA

    Hans-Ulrich Klein & Philip L De Jager

  5. Sage Bionetworks, Seattle, Washington, USA

    Solveig K Sieberts

  6. Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA

    Lei Yu, Chris Gaiteri & David A Bennett

  7. Canadian Institute for Advanced Research, CIFAR Program in Child and Brain Development, Toronto, Ontario, Canada

    Sara Mostafavi

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Contributions

Study design: S.M., B.N., P.L.D.J. Sample collection: D.A.B. Data generation and quality control analyses: B.N., C.M., H.-U.K., E.P., J.X., S.K.S., S.M., P.L.D.J. Analyses: B.N., C.C.W., C.G., S.M. Interpretation of results and critical review of the manuscript: B.N., C.M., H.-U.K., E.P., J.X., C.G., S.K.S., L.Y., D.A.B., S.M., P.L.D.J.

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Correspondence to Sara Mostafavi or Philip L De Jager.

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Integrated supplementary information

Supplementary Figure 1 Number of aligned reads for RNA-seq data per batch and RIN scores per RNA-seq batch.

Supplementary Figure 2 –log10 P-values of Spearman’s correlation between top expression PCs and 11 technical and biological confounding factors.

Batch refers to the date of RNA preparation. PMI refers to the postmortem interval. Genotype PCs were computed as the top 3 PCs of genotype data. Study index refers to RUSH vs MAP samples.

Supplementary Figure 3 Quality control metrics for H3K9Ac acetylation ChIP-seq dataset.

Supplementary Figure 4 Effect of hidden confound removal.

Number of features (genes, methylation probes, histone peaks) associated with significant xQTLs vs. the number of PCs (hidden confounds) shown. The optimal number of PCs to account for was 10 for all three -omic data types. To avoid overfitting, this analysis was only performed on features that reside on chromosome 18.

Supplementary Figure 5 Sharing between mQTL and eQTL vs. window sizes.

π1 statistics used for assessing xQTL sharing. As the window size for testing mQTL decreases, which by construction reduces the number of mQTL SNPs, the π1 of eQTL p-values associated with the mQTL SNPs is found to increase.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 584 kb)

Life Sciences Reporting Summary (PDF 173 kb)

Supplementary Table 1

Subject demographics (XLSX 12 kb)

Supplementary Table 2

Provenance of omic datasets (XLSX 12 kb)

Supplementary Table 3

Replication based on φ1 (XLSX 16 kb)

Supplementary Table 4

Sharing of xQTL SNPs based on φ1 (XLSX 12 kb)

Supplementary Table 5

Mediation analysis based on causal inference test (XLSX 13 kb)

Supplementary Table 6

Partitioned heritability (1MB window) (XLSX 36 kb)

Supplementary Table 7

Partitioned heritability (100KB window) (XLSX 18 kb)

Supplementary Table 8

Number of SNPs detected with xQTL-weighted GWAS (XLSX 40 kb)

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Ng, B., White, C., Klein, HU. et al. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat Neurosci 20, 1418–1426 (2017). https://doi.org/10.1038/nn.4632

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