Resource

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

Received:
Accepted:
Published online:

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.

  • Subscribe to Nature Neuroscience for full access:

    $59

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

  2. 2.

    , , , & Annotating non-coding regions of the genome. Nat. Rev. Genet. 11, 559–571 (2010).

  3. 3.

    Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).

  4. 4.

    et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

  5. 5.

    et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

  6. 6.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  7. 7.

    et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 6, e1000952 (2010).

  8. 8.

    et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat. Neurosci. 19, 48–54 (2016).

  9. 9.

    et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747–749 (2013).

  10. 10.

    et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

  11. 11.

    et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

  12. 12.

    , , & A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214–220 (2016).

  13. 13.

    et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

  14. 14.

    , , & Overview and findings from the religious orders study. Curr. Alzheimer Res. 9, 628–645 (2012).

  15. 15.

    et al. Overview and findings from the Rush Memory and Aging Project. Curr. Alzheimer Res. 9, 646–663 (2012).

  16. 16.

    et al. A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol. Aging 33, 1017 (2012).

  17. 17.

    et al. Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat. Neurosci. 17, 1156–1163 (2014).

  18. 18.

    & A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84, 210–223 (2009).

  19. 19.

    et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  20. 20.

    , , & A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLOS Comput. Biol. 6, e1000770 (2010).

  21. 21.

    et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife 2, e00523 (2013).

  22. 22.

    et al. Mechanisms and disease associations of haplotype-dependent allele-specific DNA methylation. Am. J. Hum. Genet. 98, 934–955 (2016).

  23. 23.

    et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

  24. 24.

    et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

  25. 25.

    & Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

  26. 26.

    et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

  27. 27.

    et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

  28. 28.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  29. 29.

    , & Inflammasomes in the CNS. Nat. Rev. Neurosci. 15, 84–97 (2014).

  30. 30.

    , , , & NALP1/NLRP1 genetic variants are associated with Alzheimer disease. Alzheimer Dis. Assoc. Disord. 26, 277–281 (2012).

  31. 31.

    & ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

  32. 32.

    et al. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol. 13, R7 (2012).

  33. 33.

    et al. Polymorphisms affecting gene transcription and mRNA processing in pharmacogenetic candidate genes: detection through allelic expression imbalance in human target tissues. Pharmacogenet. Genomics 18, 781–791 (2008).

  34. 34.

    et al. Histone H3 acetylated at lysine 9 in promoter is associated with low nucleosome density in the vicinity of transcription start site in human cell. Chromosome Res. 14, 203–211 (2006).

  35. 35.

    , , & Disentangling molecular relationships with a causal inference test. BMC Genet. 10, 23 (2009).

  36. 36.

    et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

  37. 37.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  38. 38.

    et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

  39. 39.

    et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

  40. 40.

    , , & Alzheimer's disease risk polymorphisms regulate gene expression in the ZCWPW1 and the CELF1 loci. PLoS One 11, e0148717 (2016).

  41. 41.

    & Down-regulation of DENN/MADD, a TNF receptor binding protein, correlates with neuronal cell death in Alzheimer's disease brain and hippocampal neurons. Proc. Natl. Acad. Sci. USA 101, 4210–4215 (2004).

  42. 42.

    et al. Functional screening of Alzheimer risk loci identifies PTK2B as an in vivo modulator and early marker of Tau pathology. Mol. Psychiatry 22, 874–883 (2017).

  43. 43.

    et al. Cell specific eQTL analysis without sorting cells. PLoS Genet. 11, e1005223 (2015).

  44. 44.

    , & Improving power in genome-wide association studies: weights tip the scale. Genet. Epidemiol. 31, 741–747 (2007).

  45. 45.

    et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  46. 46.

    et al. Common variants on 8p12 and 1q24.2 confer risk of schizophrenia. Nat. Genet. 43, 1224–1227 (2011).

  47. 47.

    , , , & Further evidence for the association between the LSM1 gene and schizophrenia. Schizophr. Res. 150, 588–589 (2013).

  48. 48.

    et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

  49. 49.

    & The RNA binding protein CPEB regulates dendrite morphogenesis and neuronal circuit assembly in vivo. Proc. Natl. Acad. Sci. USA 105, 20494–20499 (2008).

  50. 50.

    et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol. Psychiatry 19, 1017–1024 (2014).

  51. 51.

    et al. Human-specific histone methylation signatures at transcription start sites in prefrontal neurons. PLoS Biol. 10, e1001427 (2012).

  52. 52.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  53. 53.

    et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  54. 54.

    et al. Diurnal and seasonal molecular rhythms in human neocortex and their relation to Alzheimer's disease. Nat. Commun. 8, 14931 (2017).

  55. 55.

    et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7, 709–715 (2010).

  56. 56.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  57. 57.

    & RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

  58. 58.

    , & Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

  59. 59.

    et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29, 189–196 (2013).

  60. 60.

    , & Supervised normalization of microarrays. Bioinformatics 26, 1308–1315 (2010).

  61. 61.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  62. 62.

    et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  63. 63.

    , & Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).

  64. 64.

    et al. Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge. PLoS One 8, e68141 (2013).

  65. 65.

    et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 10, e1004663 (2014).

  66. 66.

    et al. Histone acetylome-wide association study of autism spectrum disorder. Cell 167, 1385–1397 (2016).

  67. 67.

    et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).

  68. 68.

    et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 884–897 (2010).

  69. 69.

    et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1391–1399 (2016).

  70. 70.

    et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552–555 (2016).

  71. 71.

    Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).

  72. 72.

    CONVERGE Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).

  73. 73.

    et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

  74. 74.

    et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

  75. 75.

    et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

  76. 76.

    et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43, 246–252 (2011).

  77. 77.

    et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

  78. 78.

    et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

Download references

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.

Author information

Author notes

    • Sara Mostafavi
    •  & Philip L De Jager

    These authors contributed equally to this work.

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. Center for Translational & Systems Neuroimmunology, Department of Neurology, 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

Authors

  1. Search for Bernard Ng in:

  2. Search for Charles C White in:

  3. Search for Hans-Ulrich Klein in:

  4. Search for Solveig K Sieberts in:

  5. Search for Cristin McCabe in:

  6. Search for Ellis Patrick in:

  7. Search for Jishu Xu in:

  8. Search for Lei Yu in:

  9. Search for Chris Gaiteri in:

  10. Search for David A Bennett in:

  11. Search for Sara Mostafavi in:

  12. Search for Philip L De Jager in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Sara Mostafavi or Philip L De Jager.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Subject demographics

  2. 2.

    Supplementary Table 2

    Provenance of omic datasets

  3. 3.

    Supplementary Table 3

    Replication based on φ1

  4. 4.

    Supplementary Table 4

    Sharing of xQTL SNPs based on φ1

  5. 5.

    Supplementary Table 5

    Mediation analysis based on causal inference test

  6. 6.

    Supplementary Table 6

    Partitioned heritability (1MB window)

  7. 7.

    Supplementary Table 7

    Partitioned heritability (100KB window)

  8. 8.

    Supplementary Table 8

    Number of SNPs detected with xQTL-weighted GWAS