A mega-analysis of expression quantitative trait loci in retinal tissue

Autoři: Tobias Strunz aff001;  Christina Kiel aff001;  Felix Grassmann aff001;  Rinki Ratnapriya aff003;  Madeline Kwicklis aff003;  Marcus Karlstetter aff004;  Sascha Fauser aff005;  Nicole Arend aff006;  Anand Swaroop aff003;  Thomas Langmann aff004;  Armin Wolf aff007;  Bernhard H. F. Weber aff001
Působiště autorů: Institute of Human Genetics, University of Regensburg, Regensburg, Germany aff001;  Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom aff002;  Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, Bethesda, United States of America aff003;  Laboratory for Experimental Immunology of the Eye, Department of Ophthalmology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany aff004;  Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland aff005;  Department of Ophthalmology, Ludwig-Maximilians-University, Munich, Germany aff006;  Department of Ophthalmology, University of Ulm, Ulm, Germany aff007;  Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany aff008
Vyšlo v časopise: A mega-analysis of expression quantitative trait loci in retinal tissue. PLoS Genet 16(9): e1008934. doi:10.1371/journal.pgen.1008934
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008934


Significant association signals from genome-wide association studies (GWAS) point to genomic regions of interest. However, for most loci the causative genetic variant remains undefined. Determining expression quantitative trait loci (eQTL) in a disease relevant tissue is an excellent approach to zoom in on disease- or trait-associated association signals and hitherto on relevant disease mechanisms. To this end, we explored regulation of gene expression in healthy retina (n = 311) and generated the largest cis-eQTL data set available to date. Genotype- and RNA-Seq data underwent rigorous quality control protocols before FastQTL was applied to assess the influence of genetic markers on local (cis) gene expression. Our analysis identified 403,151 significant eQTL variants (eVariants) that regulate 3,007 genes (eGenes) (Q-Value < 0.05). A conditional analysis revealed 744 independent secondary eQTL signals for 598 of the 3,007 eGenes. Interestingly, 99,165 (24.71%) of all unique eVariants regulate the expression of more than one eGene. Filtering the dataset for eVariants regulating three or more eGenes revealed 96 potential regulatory clusters. Of these, 31 harbour 130 genes which are partially regulated by the same genetic signal. To correlate eQTL and association signals, GWAS data from twelve complex eye diseases or traits were included and resulted in identification of 80 eGenes with potential association. Remarkably, expression of 10 genes is regulated by eVariants associated with multiple eye diseases or traits. In conclusion, we generated a unique catalogue of gene expression regulation in healthy retinal tissue and applied this resource to identify potentially pleiotropic effects in highly prevalent human eye diseases. Our study provides an excellent basis to further explore mechanisms of various retinal disease etiologies.

Klíčová slova:

Eye diseases – Gene expression – Gene regulation – Genetics of disease – Genome-wide association studies – Genomic signal processing – Genomics – Retina


1. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47: D1005–D1012. doi: 10.1093/nar/gky1120 30445434

2. Cannon ME, Mohlke KL. Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci. Am J Hum Genet. 2018;103: 637–653. doi: 10.1016/j.ajhg.2018.10.001 30388398

3. Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. Cell Press; 2017. pp. 1177–1186. doi: 10.1016/j.cell.2017.05.038

4. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M. Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009;10: 184–194. doi: 10.1038/nrg2537 19223927

5. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27: 3641–3649. doi: 10.1093/hmg/ddy271 30124842

6. Springelkamp H, Iglesias AI, Mishra A, Höhn R, Wojciechowski R, Khawaja AP, et al. New insights into the genetics of primary open-angle glaucoma based on meta-analyses of intraocular pressure and optic disc characteristics. Hum Mol Genet. 2017;26: ddw399. doi: 10.1093/hmg/ddw399

7. Ratnapriya R, Sosina OA, Starostik MR, Kwicklis M, Kapphahn RJ, Fritsche LG, et al. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet. 2019;51: 606–610. doi: 10.1038/s41588-019-0351-9 30742112

8. Chakravarthy U, Evans J, Rosenfeld PJ. Age related macular degeneration. BMJ. 2010;340: c981–c981. doi: 10.1136/bmj.c981 20189972

9. Weinreb RN, Aung T, Medeiros FA. The Pathophysiology and Treatment of Glaucoma. JAMA. 2014;311: 1901. doi: 10.1001/jama.2014.3192 24825645

10. Aguet F, Barbeira AN, Bonazzola R, Brown A, Castel SE, Jo B, et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. bioRxiv. 2019; 787903. doi: 10.1101/787903

11. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. Williams SMeditor. PLoS Genet. 2014;10: e1004383. doi: 10.1371/journal.pgen.1004383 24830394

12. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22: 1790–1797. doi: 10.1101/gr.137323.112 22955989

13. Strunz T, Grassmann F, Gayán J, Nahkuri S, Souza-Costa D, Maugeais C, et al. A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Sci Rep. 2018;8: 5865. doi: 10.1038/s41598-018-24219-z 29650998

14. Crowder M. Meta-analysis and Combining Information in Genetics and Genomics edited by Rudy Guerra, Darlene R. Goldstein. Int Stat Rev. 2011;79: 134–135. doi: 10.1111/j.1751-5823.2011.00134_20.x

15. Kim Y, Xia K, Tao R, Giusti-Rodriguez P, Vladimirov V, van den Oord E, et al. A meta-analysis of gene expression quantitative trait loci in brain. Transl Psychiatry. 2014;4: e459. doi: 10.1038/tp.2014.96 25290266

16. Yates A, Akanni W, Amode MR, Barrell D, Billis K, Carvalho-Silva D, et al. Ensembl 2016. Nucleic Acids Res. 2016;44: D710–D716. doi: 10.1093/nar/gkv1157 26687719

17. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507: 455–461. doi: 10.1038/nature12787 24670763

18. Lai F, Orom UA, Cesaroni M, Beringer M, Taatjes DJ, Blobel GA, et al. Activating RNAs associate with Mediator to enhance chromatin architecture and transcription. Nature. 2013;494: 497–501. doi: 10.1038/nature11884 23417068

19. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011;25: 1915–27. doi: 10.1101/gad.17446611 21890647

20. Khalil AM, Guttman M, Huarte M, Garber M, Raj A, Morales DR, et al. Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc Natl Acad Sci U S A. 2009;106: 11667–11672. doi: 10.1073/pnas.0904715106 19571010

21. Kovalenko TF, Patrushev LI. Pseudogenes as Functionally Significant Elements of the Genome. Biochemistry (Moscow). Pleiades Publishing; 2018. pp. 1332–1349. doi: 10.1134/S0006297918110044

22. Chiang JJ, Sparrer KMJ, van Gent M, Lässig C, Huang T, Osterrieder N, et al. Viral unmasking of cellular 5S rRNA pseudogene transcripts induces RIG-I-mediated immunity. Nat Immunol. 2018;19: 53–62. doi: 10.1038/s41590-017-0005-y 29180807

23. Germain RN, Margulies DH. The Biochemistry and Cell Biology of Antigen Processing and Presentation. Annu Rev Immunol. 1993;11: 403–450. doi: 10.1146/annurev.iy.11.040193.002155 8476568

24. Kikuchi M, Hara N, Hasegawa M, Miyashita A, Kuwano R, Ikeuchi T, et al. Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping. BMC Med Genomics. 2019;12: 128. doi: 10.1186/s12920-019-0574-8 31500627

25. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518: 317–330. doi: 10.1038/nature14248 25693563

26. Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002;295: 1306–11. doi: 10.1126/science.1067799 11847345

27. Strunz T, Lauwen S, Kiel C, Fritsche LG, Igl W, Bailey JNC, et al. A transcriptome-wide association study based on 27 tissues identifies 106 genes potentially relevant for disease pathology in age-related macular degeneration. Sci Rep. 2020;10: 1584. doi: 10.1038/s41598-020-58510-9

28. Giannuzzi G, Siswara P, Malig M, Marques-Bonet T, NISC Comparative Sequencing Program NCS, Mullikin JC, et al. Evolutionary dynamism of the primate LRRC37 gene family. Genome Res. 2013;23: 46–59. doi: 10.1101/gr.138842.112 23064749

29. Raposo G, Marks MS. Melanosomes—Dark organelles enlighten endosomal membrane transport. Nature Reviews Molecular Cell Biology. Nature Publishing Group; 2007. pp. 786–797. doi: 10.1038/nrm2258 17878918

30. Braschi B, Denny P, Gray K, Jones T, Seal R, Tweedie S, et al. Genenames.org: the HGNC and VGNC resources in 2019. Nucleic Acids Res. 2019;47: D786–D792. doi: 10.1093/nar/gky930 30304474

31. Wolf AH, Welge-Lüen UC, Priglinger S, Kook D, Grueterich M, Hartmann K, et al. Optimizing the Deswelling Process of Organ-Cultured Corneas. Cornea. 2009;28: 524–529. doi: 10.1097/ICO.0b013e3181901dde 19421045

32. R Team Core. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Vienna: R Foundation for Statistical Computing; 2017. p. 2017. Available: www.R-project.org/.

33. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012;28: 3326–3328. doi: 10.1093/bioinformatics/bts606 23060615

34. Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A, Clark AG, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491: 56–65. doi: 10.1038/nature11632 23128226

35. Delaneau O, Marchini J, Zagury JF. A linear complexity phasing method for thousands of genomes. Nat Methods. 2012;9: 179–181. doi: 10.1038/nmeth.1785

36. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR, Chakravarti A, et al. A global reference for human genetic variation. Nature. 2015;526: 68–74. doi: 10.1038/nature15393 26432245

37. Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3 Genes, Genomes, Genet. 2011;1: 457–470. doi: 10.1534/g3.111.001198

38. Rosenbloom KR, Armstrong J, Barber GP, Casper J, Clawson H, Diekhans M, et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 2015;43: D670–D681. doi: 10.1093/nar/gku1177 25428374

39. Babraham Bioinformatics—FastQC A Quality Control tool for High Throughput Sequence Data. [cited 11 Nov 2019]. Available: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

40. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32: 3047–3048. doi: 10.1093/bioinformatics/btw354 27312411

41. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

42. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: 15–21. doi: 10.1093/bioinformatics/bts635 23104886

43. Ensembl version 97 homo sapiens. [cited 11 Nov 2019]. Available: ftp://ftp.ensembl.org/pub/release-97/fasta/homo_sapiens/dna/

44. Li B, Dewey CN. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12: 323. doi: 10.1186/1471-2105-12-323 21816040

45. Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26: 139–140. doi: 10.1093/bioinformatics/btp616 19910308

46. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8: 118–127. doi: 10.1093/biostatistics/kxj037 16632515

47. Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics. 2016;32: 1479–1485. doi: 10.1093/bioinformatics/btv722 26708335

48. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100: 9440–9445. doi: 10.1073/pnas.1530509100 12883005

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PLOS Genetics

2020 Číslo 9

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