Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

Autoři: Chris Wallace aff001
Působiště autorů: Cambridge Institute for Therapeutic Immunology & Infectious Disease, and MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom aff001
Vyšlo v časopise: Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet 16(4): e32767. doi:10.1371/journal.pgen.1008720
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008720


Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal variant per trait. This assumption can be relaxed by stepwise conditioning, but this requires external software and an LD matrix aligned to study alleles. We have now implemented conditioning within coloc, and propose a new alternative method, masking, that does not require LD and approximates conditioning when causal variants are independent. Importantly, masking can be used in combination with conditioning where allelically aligned LD estimates are available for only a single trait. We have implemented these developments in a new version of coloc which we hope will enable more informed choice of priors and overcome the restriction of the single causal variant assumptions in coloc analysis.

Klíčová slova:

Bayesian method – Gene expression – Genetic epidemiology – Genetics of disease – Genome-wide association studies – Genomic signal processing – Molecular genetics – Quantitative traits


1. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–295. doi: 10.1038/ng.3211 25642630

2. Ni G, Moser G, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Wray NR, Lee SH. Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. Am J Hum Genet. 2018;102(6):1185–1194. doi: 10.1016/j.ajhg.2018.03.021 29754766

3. Gray R, Wheatley K. How to avoid bias when comparing bone marrow transplantation with chemotherapy. Bone Marrow Transplant. 1991;7 Suppl 3:9–12. 1855097

4. Chen L, Smith GD, Harbord RM, Lewis SJ. Alcohol intake and blood pressure: a systematic review implementing a Mendelian randomization approach. PLoS Med. 2008;5(3):e52. doi: 10.1371/journal.pmed.0050052 18318597

5. Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey Smith G. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr. 2016;103:965–978. doi: 10.3945/ajcn.115.118216 26961927

6. Hemani G, Zhengn J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. doi: 10.7554/eLife.34408 29846171

7. Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017;169(7):1177–1186. doi: 10.1016/j.cell.2017.05.038 28622505

8. Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22. doi: 10.1093/ije/dyg070 12689998

9. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–487. doi: 10.1038/ng.3538 27019110

10. 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. PLoS Genet. 2014;10(5):e1004383. doi: 10.1371/journal.pgen.1004383 24830394

11. Fortune MD, Guo H, Burren O, Schofield E, Walker NM, Ban M, et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat Genet. 2015; p. 839–849. doi: 10.1038/ng.3330 26053495

12. Giambartolomei C, Liu JZ, Zhang W, Hauberg M, Shi H, Boocock J, et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics. 2018;34(15):2538–2545. doi: 10.1093/bioinformatics/bty147 29579179

13. Foley CN, Staley JR, Breen PG, Sun BB, Kirk PDW, Burgess S, et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits; 2019. Available from: https://www.biorxiv.org/content/10.1101/592238v1.

14. Wakefield J. Bayes factors for genome-wide association studies: comparison with P -values. Genet Epidemiol. 2009;33(1):79–86. doi: 10.1002/gepi.20359 18642345

15. Wellcome Trust Case Control Consortium, Maller JB, McVean G, Byrnes J, Vukcevic D, Palin K, et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet. 2012;44(12):1294–1301. doi: 10.1038/ng.2435 23104008

16. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245–252. doi: 10.1038/ng.3506 26854917

17. Hormozdiari F, van de Bunt M, Segre AV, Li X, Joo JWJ, Bilow M, et al. Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016;99(6):1245–1260. doi: 10.1016/j.ajhg.2016.10.003 27866706

18. Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 2008;4(8):e1000167. doi: 10.1371/journal.pgen.1000167 18769715

19. Yang J, Ferreira T, Morris AP, Medland SE, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44(4):369–75, S1–3. doi: 10.1038/ng.2213 22426310

20. Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9:1825. doi: 10.1038/s41467-018-03621-1 29739930

21. Bhalala OG, Nath AP, Inouye M, Sibley CR, Consortium UKBE. Identification of expression quantitative trait loci associated with schizophrenia and affective disorders in normal brain tissue. PLoS Genet. 2018;14(8):e1007607. doi: 10.1371/journal.pgen.1007607 30142156

22. Bryois J, Garrett ME, Song L, Safi A, Giusti-Rodriguez P, Johnson GD, et al. Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat Commun. 2018;9:3121. doi: 10.1038/s41467-018-05379-y 30087329

23. Endo C, Johnson TA, Morino R, Nakazono K, Kamitsuji S, Akita M, et al. Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations. Sci Rep. 2018;8:8974. doi: 10.1038/s41598-018-27145-2 29895819

24. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50(4):538–548. doi: 10.1038/s41588-018-0092-1 29632383

25. Hannon E, Schendel D, Ladd-Acosta C, Grove J, Hansen CS, Andrews SV, et al. Elevated polygenic burden for autism is associated with differential DNA methylation at birth. Genome Med. 2018;10. doi: 10.1186/s13073-018-0527-4 29587883

26. Hirata T, Koga K, Johnson TA, Morino R, Nakazono K, Kamitsuji S, et al. Japanese GWAS identifies variants for bust-size, dysmenorrhea, and menstrual fever that are eQTLs for relevant protein-coding or long non-coding RNAs. Sci Rep. 2018;8:8502. doi: 10.1038/s41598-018-25065-9 29855537

27. James T, Linden M, Morikawa H, Fernandes SJ, Ruhrmann S, Huss M, et al. Impact of genetic risk loci for multiple sclerosis on expression of proximal genes in patients. Hum Mol Genet. 2018;27(5):912–928. doi: 10.1093/hmg/ddy001 29325110

28. Knowlest DA, Burrowet CK, Blischak JD, Patterson KM, Serie DJ, Norton N, et al. Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes. Elife. 2018;7:e33480. doi: 10.7554/eLife.33480

29. Lamontagne M, Berube JC, Obeidat M, Cho MH, Hobbs BD, Sakornsakolpat P, et al. Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations. Hum Mol Genet. 2018;27(10):1819–1829. doi: 10.1093/hmg/ddy091 29547942

30. Li J, Loebel A, Meltzer HY. Identifying the genetic risk factors for treatment response to lurasidone by genome-wide association study: A meta-analysis of samples from three independent clinical trials. Schizophr Res. 2018;199:203–213. doi: 10.1016/j.schres.2018.04.006 29730043

31. Mo A, Marigorta UM, Arafat D, Chan LHK, Ponder L, Jang SR, et al. Disease-specific regulation of gene expression in a comparative analysis of juvenile idiopathic arthritis and inflammatory bowel disease. Genome Med. 2018;10:48. doi: 10.1186/s13073-018-0558-x 29950172

32. Morrow JD, Glass K, Cho MH, Hersh CP, Pinto-Plata V, Celli B, et al. Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci. Am J Respir Crit Care Med. 2018;197(10):1275–1284. doi: 10.1164/rccm.201707-1434OC 29313708

33. Mullin BH, Zhu K, Xu J, Brown SJ, Mullin S, Tickner J, et al. Expression Quantitative Trait Locus Study of Bone Mineral Density GWAS Variants in Human Osteoclasts. J Bone Miner Res. 2018;33(6):1044–1051. doi: 10.1002/jbmr.3412 29473973

34. Richard AC, Peters JE, Savinykh N, Lee JC, Hawley ET, Meylan F, et al. Reduced monocyte and macrophage TNFSF15/TL1A expression is associated with susceptibility to inflammatory bowel disease. PLoS Genet. 2018;14(9):e1007458. doi: 10.1371/journal.pgen.1007458 30199539

35. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–79. doi: 10.1038/s41586-018-0175-2 29875488

36. Theriault S, Gaudreault N, Lamontagne M, Rosa M, Boulanger MC, Messika-Zeitoun D, et al. A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis. Nat Commun. 2018;9:988. doi: 10.1038/s41467-018-03260-6 29511167

37. Wang L, Pittman KJ, Barker JR, Salinas RE, Stanaway IB, Williams GD, et al. An Atlas of Genetic Variation Linking Pathogen-Induced Cellular Traits to Human Disease. Cell Host Microbe. 2018;24(2):308–323. doi: 10.1016/j.chom.2018.07.007 30092202

38. Wyss AB, Sofer T, Lee MK, Terzikhan N, Nguyen JN, Lahousse L, et al. Multiethnic meta-analysis identifies ancestry-specific and cross-ancestry loci for pulmonary function. Nat Commun. 2018;9:2976. doi: 10.1038/s41467-018-05369-0 30061609

39. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9:2941. doi: 10.1038/s41467-018-04951-w 30054458

40. Venkateswaran S, Prince J, Cutler DJ, Marigorta UM, Okou DT, Prahalad S, et al. Enhanced Contribution of HLA in Pediatric Onset Ulcerative Colitis. Inflamm Bowel Dis. 2018;24(4):829–838. doi: 10.1093/ibd/izx084 29562276

41. Dobbyn A, Huckins LM, Boocock J, Sloofman LG, Glicksberg BS, Giambartolomei C, et al. Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS. Am J Hum Genet. 2018;102(6):1169–1184. doi: 10.1016/j.ajhg.2018.04.011 29805045

42. Yao C, Chen G, Song C, Keefe J, Mendelson M, Huan T, et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat Commun. 2018;9:3268. doi: 10.1038/s41467-018-05512-x 30111768

43. Alasoo K, Rodrigues J, Mukhopadhyay S, Knights AJ, Mann AL, Kundu K, et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat Genet. 2018;50(3):424–431. doi: 10.1038/s41588-018-0046-7 29379200

44. Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018;9:804. doi: 10.1038/s41467-018-03209-9 29476079

45. Aguet F, Brown AA, Castel SE, Davis JR, He Y, Jo B, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–213. doi: 10.1038/nature24277

46. 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(D1):D1005–D1012. doi: 10.1093/nar/gky1120 30445434

47. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856

48. Graur D. An Upper Limit on the Functional Fraction of the Human Genome. Genome Biol Evol. 2017;9(7):1880–1885. doi: 10.1093/gbe/evx121 28854598

49. Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A, Marinov GK, et al. Defining functional DNA elements in the human genome. Proc Natl Acad Sci U S A. 2014;111(17):6131–6138. doi: 10.1073/pnas.1318948111 24753594

50. Pickrell JK, Berisa T, Liu JZ, Segurel L, Tung JY, Hinds DA. Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet. 2016;48(7):709–717. doi: 10.1038/ng.3570 27182965

51. Johnson SR, Tomlinson GA, Hawker GA, Granton JT, Feldman BM. Methods to elicit beliefs for Bayesian priors: a systematic review. J Clin Epidemiol. 2010;63(4):355–369. doi: 10.1016/j.jclinepi.2009.06.003 19716263

52. Guo H, Fortune MD, Burren OS, Schofield E, Todd JA, Wallace C. Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases. Hum Mol Genet. 2015;24(12):3305–3313. doi: 10.1093/hmg/ddv077 25743184

53. Bossini-Castillo L, Glinos DA, Kunowska N, Golda G, Lamikanra A, Spitzer M, et al. Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets; 2019. Available from: https://www.biorxiv.org/content/10.1101/654632v1.

54. Trynka G, Westra HJ, Slowikowski K, Hu X, Xu H, Stranger BE, et al. Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci. Am J Hum Genet. 2015;97(1):139–152. doi: 10.1016/j.ajhg.2015.05.016 26140449

55. Iotchkova V, Ritchie GRS, Geihs M, Morganella S, Min JL, Walter K, et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet. 2019;51(2):343–353. doi: 10.1038/s41588-018-0322-6 30692680

56. Wray NR, Lee SH, Mehta D, Vinkhuyzen AAE, Dudbridge F, Middeldorp CM. Research review: Polygenic methods and their application to psychiatric traits. J Child Psychol Psychiatry. 2014;55(10):1068–1087. doi: 10.1111/jcpp.12295 25132410

57. Berisa T, Pickrell JK. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics. 2016;32(2):283–285. doi: 10.1093/bioinformatics/btv546 26395773

58. Benner C, Havulinna AS, Järvelin MR, Salomaa V, Ripatti S, Pirinen M. Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies. Am J Hum Genet. 2017;101(4):539–551. doi: 10.1016/j.ajhg.2017.08.012 28942963

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PLOS Genetics

2020 Číslo 4
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