Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Nature Communications

Volumn 9, Issue 1, 2018, Pages

  Qi, Ting ^a   Wu, Yang ^a   Zeng, Jian ^a   Zhang, Futao ^a   Xue, Angli ^a   Jiang, Longda ^a   Zhu, Zhihong ^a   Kemper, Kathryn ^a   Yengo, Loic ^a   Zheng, Zhili ^a,b   Agbessi, Mawussé ^d   Ahsan, Habibul ^e   Alves, Isabel ^d   Andiappan, Anand ^f   Awadalla, Philip ^d   Battle, Alexis ^g   Beutner, Frank ^h   Jan Bonder, Marc ⁱ   Boomsma, Dorret ^j   Christiansen, Mark ^k   Claringbould, Annique ⁱ   Deelen, Patrick ⁱ   Esko, Tõnu ^l   Favé, Marie Julie ^d   Franke, Lude ⁱ   Frayling, Timothy ^m   Gharib, Sina ⁿ   Gibson, Gregory ^o   Hemani, Gibran ^p   Jansen, Rick ^j   Kähönen, Mika ^q,r   Kalnapenkis, Anette ^l   Kasela, Silva ^l   Kettunen, Johannes ^s   Kim, Yungil ^g   Kirsten, Holger ^h   Kovacs, Peter ^h   Krohn, Knut ^t   Kronberg Guzman, Jaanika ^l   Kukushkina, Viktorija ^l   Kutalik, Zoltan ^u   Lee, Bernett ^f   Lehtimäki, Terho ^r   Loeffler, Markus ^h   Marigorta, Urko M ^o   Metspalu, Andres ^l   Milani, Lili ^l   Müller Nurasyid, Martina ^v   Nauck, Matthias ^w   Nivard, Michel ^j   Penninx, Brenda ^j   Perola, Markus ^s   Pervjakova, Natalia ^l   Pierce, Brandon ^e   Powell, Joseph ^a   Prokisch, Holger ^v   Psaty, Bruce ^k   Raitakari, Olli ^x,y   Ring, Susan ^p   Ripatti, Samuli ^s   Rotzschke, Olaf ^f   Ruëger, Sina ^u   Saha, Ashis ^g   Scholz, Markus ^h   Schramm, Katharina ^v   Seppälä, Ilkka ^r   Stumvoll, Michael ^h   Sullivan, Patrick ^z   Teumer, Alexander ^w   Thiery, Joachim ^h   Tong, Lin ^e   Tönjes, Anke ^h   Van Dongen, Jenny ^j   Van Meurs, Joyce ^aa   Verlouw, Joost ^aa   Völker, Uwe ^w   Võsa, Urmo ⁱ   Yaghootkar, Hanieh ^m   Zeng, Biao ^o   Marioni, Riccardo E ^c   Montgomery, Grant W ^a   Deary, Ian J ^c   Wray, Naomi R ^a   Visscher, Peter M ^a   McRae, Allan F ^a   Yang, Jian ^a,b   more..

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b WENZHOU MEDICAL UNIVERSITY   (China)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

^d ONTARIO INSTITUTE FOR CANCER RESEARCH   (Canada)

^e UNIVERSITY OF CHICAGO   (United States)

^f AGENCY FOR SCIENCE   (Singapore)

^g Whiting School of Engineering   (United States)

^h UNIVERSITY OF LEIPZIG   (Germany)

ⁱ UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^j VU UNIVERSITY AMSTERDAM   (Netherlands)

^k UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^l UNIVERSITY OF TARTU   (Estonia)

^m UNIVERSITY OF EXETER MEDICAL SCHOOL   (United Kingdom)

ⁿ UNIVERSITY OF WASHINGTON   (United States)

^o GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^p UNIVERSITY OF BRISTOL   (United Kingdom)

^q TAMPERE UNIVERSITY HOSPITAL   (Finland)

^r TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^s UNIVERSITY OF HELSINKI   (Finland)

^t UNIVERSITY HOSPITAL LEIPZIG   (Germany)

^u LAUSANNE UNIVERSITY HOSPITAL   (Switzerland)

^v INSTITUTE OF EPIDEMIOLOGY   (Germany)

^w UNIVERSITY MEDICINE GREIFSWALD   (Germany)

^x TURKU UNIVERSITY HOSPITAL   (Finland)

^y UNIVERSITY OF TURKU   (Finland)

^z KAROLINSKA INSTITUTE   (Sweden)

^aa ERASMUS MEDICAL CENTER   (Netherlands)

more..

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD; BRAIN; DATA SET; GENE; GENE EXPRESSION; METHYLATION; PHENOTYPE; RNA;

ARTICLE; BRAIN; DNA METHYLATION; EXPRESSION QUANTITATIVE TRAIT LOCUS; MAJOR CLINICAL STUDY; PHENOTYPE; QUANTITATIVE ANALYSIS; SAMPLE SIZE; BLOOD; ENHANCER REGION; GENE EXPRESSION REGULATION; GENETIC MARKER; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; META ANALYSIS; METABOLISM; PROMOTER REGION; QUANTITATIVE TRAIT LOCUS; TISSUE DISTRIBUTION;

DNA; TRANSCRIPTOME;

BRAIN; DNA; DNA METHYLATION; ENHANCER ELEMENTS, GENETIC; GENE EXPRESSION REGULATION; GENETIC MARKERS; GENOME-WIDE ASSOCIATION STUDY; HUMANS; PHENOTYPE; PROMOTER REGIONS, GENETIC; QUANTITATIVE TRAIT LOCI; TISSUE DISTRIBUTION; TRANSCRIPTOME;

EID: 85048415370     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-018-04558-1     Document Type: Article

References (63)

1. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-D1006 (2013).
2. Visscher, P. M., Brown, M. A., McCarthy, M. I. &Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7-24 (2012).
3. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5-22 (2017).
4. Roussos, P. et al. A role for noncoding variation in schizophrenia. Cell Rep. 9, 1417-1429 (2014).
5. Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337-343 (2015).
6. Ward, L. D. &Kellis, M. Interpreting noncoding genetic variation in complex traits and human disease. Nat. Biotechnol. 30, 1095-1106 (2012).
7. Torres, J. M. et al. Cross-tissue and tissue-specific eQTLs: partitioning the heritability of a complex trait. Am. J. Hum. Genet. 95, 521-534 (2014).
8. Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238-1243 (2013).
9. Lloyd-Jones, L. R. et al. The genetic architecture of gene expression in peripheral blood. Am. J. Hum. Genet. 100, 228-237 (2017).
10. Wright, F. A. et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46, 430-437 (2014).
11. GTEx Consortium. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648-660 (2015).
12. Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580-585 (2013).
13. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204-213 (2017).
14. Liu, X. et al. Functional architectures of local and distal regulation of gene expression in multiple human tissues. Am. J. Hum. Genet. 100, 605-616 (2017).
15. Ip, H. et al. Stratified linkage disequilibrium score regression reveals enrichment of eQTL effects on complex traits is not tissue specific. Preprint at https://doi.org/10.1101/107482 (2017).
16. Hannon, E., Weedon, M., Bray, N., O'Donovan, M. &Mill, J. Pleiotropic effects of trait-associated genetic variation on DNA methylation: utility for refining GWAS loci. Am. J. Hum. Genet. 100, 954-959 (2017).
17. Hauberg, M. E. et al. Large-scale identification of common trait and disease variants affecting gene expression. Am. J. Hum. Genet. 100, 885-894 (2017).
18. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442-1453 (2016).
19. Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat. Neurosci. 20, 1418-1426 (2017).
20. Trabzuni, D. et al. Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J. Neurochem. 119, 275-282 (2011).
21. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481-487 (2016).
22. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245-252 (2016).
23. Barbeira, A. et al. MetaXcan: summary statistics based gene-level association method infers accurate PrediXcan results. Preprint at https://doi.org/10.1101/ 045260 (2016).
24. Lin, Y. et al. Housekeeping genes, revisited at the single-cell level. Preprint at https://doi.org/10.1101/229815 (2017).
25. Eisenberg, E. &Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569-574 (2013).
26. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369-375 (2012).
27. Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108 (2009).
28. Backenroth, D. et al. FUN-LDA: A latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: methods and applications. Am. J. Hum. Genet. 102, 920-942 (2018).
29. Dimas, A. S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246-1250 (2009).
30. Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502-510 (2012).
31. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330 (2015).
32. Randall, J. C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013).
33. Yang, J. et al. Genome-wide genetic homogeneity between sexes and populations for human height and body mass index. Hum. Mol. Genet. 24, 7445-7449 (2015).
34. Finucane, H. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621-629 (2018).
35. Boyle, E. A., Li, Y. I. &Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177-1186 (2017).
36. Hannon, E. et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat. Neurosci. 19, 48-54 (2016).
37. Jaffe, A. E. et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat. Neurosci. 19, 40-47 (2016).
38. McRae, A. et al. Identification of 55,000 replicated DNA methylation QTL. Preprint at https://doi.org/10.1101/166710 (2017).
39. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229-237 (2018).
40. Han, B. et al. A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. Hum. Mol. Genet. 25, 1857-1866 (2016).
41. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236-1241 (2015).
42. Shi, H., Kichaev, G. &Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139-153 (2016).
43. Lin, D.-Y. &Sullivan, P. F. Meta-analysis of genome-wide association studies with overlapping subjects. Am. J. Hum. Genet. 85, 862-872 (2009).
44. Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
45. Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807-812 (2011).
46. Pavlides, J. M. W. et al. Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med. 8, 84 (2016).
47. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539-542 (2016).
48. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421-427 (2014).
49. Sonnega, A. et al. Cohort profile: the health and retirement study (HRS). Int. J. Epidemiol. 43, 576-585 (2014).
50. Storey, J. D. &Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440-9445 (2003).
51. Ding, J. et al. Gene expression in skin and lymphoblastoid cells: refined statistical method reveals extensive overlap in cis-eQTL signals. Am. J. Hum. Genet. 87, 779-789 (2010)
52. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65 (2012).
53. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760-1774 (2012).
54. Chen, B. H. et al. DNA methylation-based measures of biological age: metaanalysis predicting time to death. Aging 8, 1844 (2016).
55. Powell, J. E. et al. The Brisbane Systems Genetics Study: genetical genomics meets complex trait genetics. PLoS ONE 7, e35430 (2012).
56. Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at https://doi.org/10.1101/166298 (2017).
57. Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279-1283 (2016).
58. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284-290 (2015).
59. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52-58 (2010).
60. Lloyd-Jones, L. R., Robinson, M. R., Yang, J. &Visscher, P. M. Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics 208, 1397-1408 (2018).
61. Ernst, J. &Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215-216 (2012).
62. Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).
63. Zhu, X. et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am. J. Hum. Genet. 96, 21-36 (2015).