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Human Genetics

, Volume 136, Issue 7, pp 911–919 | Cite as

Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants

  • Christina A. Markunas
  • Eric O. Johnson
  • Dana B. Hancock
Original Investigation

Abstract

Genome-wide association study (GWAS)-identified variants are enriched for functional elements. However, we have limited knowledge of how functional enrichment may differ by disease/trait and tissue type. We tested a broad set of eight functional elements for enrichment among GWAS-identified SNPs (p < 5×10−8) from the NHGRI-EBI Catalog across seven disease/trait categories: cancer, cardiovascular disease, diabetes, autoimmune disease, psychiatric disease, neurological disease, and anthropometric traits. SNPs were annotated using HaploReg for the eight functional elements across any tissue: DNase sites, expression quantitative trait loci (eQTL), sequence conservation, enhancers, promoters, missense variants, sequence motifs, and protein binding sites. In addition, tissue-specific annotations were considered for brain vs. blood. Disease/trait SNPs were compared to a control set of 4809 SNPs matched to the GWAS SNPs (N = 1639) on allele frequency, gene density, distance to nearest gene, and linkage disequilibrium at ~3:1 ratio. Enrichment analyses were conducted using logistic regression, with Bonferroni correction. Overall, a significant enrichment was observed for all functional elements, except sequence motifs. Missense SNPs showed the strongest magnitude of enrichment. eQTLs were the only functional element significantly enriched across all diseases/traits. Magnitudes of enrichment were generally similar across diseases/traits, where enrichment was statistically significant. Blood vs. brain tissue effects on enrichment were dependent on disease/trait and functional element (e.g., cardiovascular disease: eQTLs P TissueDifference = 1.28 × 10−6 vs. enhancers P TissueDifference = 0.94). Identifying disease/trait-relevant functional elements and tissue types could provide new insight into the underlying biology, by guiding a priori GWAS analyses (e.g., brain enhancer elements for psychiatric disease) or facilitating post hoc interpretation.

Notes

Acknowledgements

This work was supported by Grants from NIDA [R01 DA035825 (PI: Hancock); R01 DA036583 (PI: Bierut, Co-Is: Johnson and Hancock)]. We would like to thank Drs. Nathan Gaddis and Yuelong Guo for their critical review of our manuscript.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

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Supplementary material 1 (DOCX 34 kb)
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Supplementary material 2 (DOCX 219 kb)
439_2017_1815_MOESM3_ESM.docx (448 kb)
Supplementary material 3 (DOCX 448 kb)

References

  1. Bullaughey K, Chavarria CI, Coop G, Gilad Y (2009) Expression quantitative trait loci detected in cell lines are often present in primary tissues. Hum Mol Genet 18:4296–4303. doi: 10.1093/hmg/ddp382 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Chung D, Yang C, Li C, Gelernter J, Zhao H (2014) GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet 10:e1004787. doi: 10.1371/journal.pgen.1004787 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Cowley MJ et al (2009) Intra- and inter-individual genetic differences in gene expression. Mamm Genome Off J Int Mamm Genome Soc 20:281–295. doi: 10.1007/s00335-009-9181-x CrossRefGoogle Scholar
  4. Darnell G, Duong D, Han B, Eskin E (2012) Incorporating prior information into association studies. Bioinformatics 28:i147–153. doi: 10.1093/bioinformatics/bts235 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Dimas AS et al (2009) Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325:1246–1250. doi: 10.1126/science.1174148 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Emilsson V et al (2008) Genetics of gene expression and its effect on disease. Nature 452:423–428. doi: 10.1038/nature06758 CrossRefPubMedGoogle Scholar
  7. Ernst J et al (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473:43–49. doi: 10.1038/nature09906 CrossRefPubMedPubMedCentralGoogle Scholar
  8. ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. doi: 10.1038/nature11247 CrossRefGoogle Scholar
  9. Faul F, Erdfelder E, Lang AG, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39:175–191CrossRefPubMedGoogle Scholar
  10. Gamazon ER et al (2013) Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiatry 18:340–346. doi: 10.1038/mp.2011.174 CrossRefPubMedGoogle Scholar
  11. Gibbs JR et al (2010) Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet 6:e1000952. doi: 10.1371/journal.pgen.1000952 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Gui J, Tosteson TD, Borsuk M (2012) Weighted multiple testing procedures for genomic studies. BioData Min 5:4. doi: 10.1186/1756-0381-5-4 CrossRefPubMedPubMedCentralGoogle Scholar
  13. GTEx Consortium (2015) Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:648–660. doi: 10.1126/science.1262110 CrossRefPubMedCentralGoogle Scholar
  14. Hancock DB et al (2015) Cis-expression quantitative trait loci mapping reveals replicable associations with heroin addiction in OPRM1. Biol Psychiatry 78:474–484. doi: 10.1016/j.biopsych.2015.01.003 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106:9362–9367. doi: 10.1073/pnas.0903103106 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hnisz D et al (2013) Super-enhancers in the control of cell identity and disease. Cell 155:934–947. doi: 10.1016/j.cell.2013.09.053 CrossRefPubMedGoogle Scholar
  17. Ho YY, Baechler EC, Ortmann W, Behrens TW, Graham RR, Bhangale TR, Pan W (2014) Using gene expression to improve the power of genome-wide association analysis. Hum Hered 78:94–103. doi: 10.1159/000362837 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Kindt AS, Navarro P, Semple CA, Haley CS (2013) The genomic signature of trait-associated variants. BMC Genom 14:108. doi: 10.1186/1471-2164-14-108 CrossRefGoogle Scholar
  19. Knight J, Barnes MR, Breen G, Weale ME (2011) Using functional annotation for the empirical determination of Bayes Factors for genome-wide association study analysis. PLoS One 6:e14808. doi: 10.1371/journal.pone.0014808 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Lango Allen H et al (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467:832–838. doi: 10.1038/nature09410 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Li L et al (2013) Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma. Front Genet 4:103. doi: 10.3389/fgene.2013.00103 PubMedPubMedCentralGoogle Scholar
  22. Lizio M et al (2015) Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 16:22. doi: 10.1186/s13059-014-0560-6 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Maurano MT et al (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337:1190–1195. doi: 10.1126/science.1222794 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Nica AC, Montgomery SB, Dimas AS, Stranger BE, Beazley C, Barroso I, Dermitzakis ET (2010) Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet 6:e1000895. doi: 10.1371/journal.pgen.1000895 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 6:e1000888. doi: 10.1371/journal.pgen.1000888 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Pasquali L et al (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46:136–143. doi: 10.1038/ng.2870 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Pers TH, Timshel P, Hirschhorn JN (2015) SNPsnap: a web-based tool for identification and annotation of matched SNPs. Bioinformatics 31:418–420. doi: 10.1093/bioinformatics/btu655 CrossRefPubMedGoogle Scholar
  28. Pickrell JK (2014) Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am J Hum Genet 94:559–573. doi: 10.1016/j.ajhg.2014.03.004 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Ramasamy A et al (2014) Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 17:1418–1428. doi: 10.1038/nn.3801 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Richards AL et al (2012) Schizophrenia susceptibility alleles are enriched for alleles that affect gene expression in adult human brain. Mol Psychiatry 17:193–201. doi: 10.1038/mp.2011.11 CrossRefPubMedGoogle Scholar
  31. Roadmap Epigenomics Consortium et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518:317–330. doi: 10.1038/nature14248 CrossRefPubMedCentralGoogle Scholar
  32. Roeder K, Devlin B, Wasserman L (2007) Improving power in genome-wide association studies: weights tip the scale. Genet Epidemiol 31:741–747. doi: 10.1002/gepi.20237 CrossRefPubMedGoogle Scholar
  33. Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, Raychaudhuri S (2013) Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 45:124–130. doi: 10.1038/ng.2504 CrossRefPubMedGoogle Scholar
  34. Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40:D930–934. doi: 10.1093/nar/gkr917 CrossRefPubMedGoogle Scholar
  35. Welter D et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–1006. doi: 10.1093/nar/gkt1229 CrossRefPubMedGoogle Scholar
  36. Westra HJ et al (2013) Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 45:1238–1243. doi: 10.1038/ng.2756 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS (2012) Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res 22:386–397. doi: 10.1101/gr.124370.111 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Christina A. Markunas
    • 1
  • Eric O. Johnson
    • 1
    • 2
  • Dana B. Hancock
    • 1
  1. 1.Behavioral Health and Criminal Justice DivisionRTI InternationalResearch Triangle ParkUSA
  2. 2.Fellow ProgramRTI InternationalResearch Triangle ParkUSA

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