Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics

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Genome-wide association studies (GWAS) have found a large number of genetic regions (“loci”) affecting clinical end-points and phenotypes, many outside coding intervals. One approach to understanding the biological basis of these associations has been to explore whether GWAS signals from intermediate cellular phenotypes, in particular gene expression, are located in the same loci (“colocalise”) and are potentially mediating the disease signals. However, it is not clear how to assess whether the same variants are responsible for the two GWAS signals or whether it is distinct causal variants close to each other. In this paper, we describe a statistical method that can use simply single variant summary statistics to test for colocalisation of GWAS signals. We describe one application of our method to a meta-analysis of blood lipids and liver expression, although any two datasets resulting from association studies can be used. Our method is able to detect the subset of GWAS signals explained by regulatory effects and identify candidate genes affected by the same GWAS variants. As summary GWAS data are increasingly available, applications of colocalisation methods to integrate the findings will be essential for functional follow-up, and will also be particularly useful to identify tissue specific signals in eQTL datasets.

Vyšlo v časopise: Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. PLoS Genet 10(5): e32767. doi:10.1371/journal.pgen.1004383
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004383

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Zdroje

1. FeeroWG, GuttmacherAE, ManolioTA (2010) Genomewide association studies and assessment of the risk of disease. New England Journal of Medicine 363: 166–176.

2. NicaAC, DermitzakisET (2008) Using gene expression to investigate the genetic basis of complex disorders. Human molecular genetics 17: R129–R134.

3. PickrellJK, MarioniJC, PaiAA, DegnerJF, EngelhardtBE, et al. (2010) Understanding mechanisms underlying human gene expression variation with rna sequencing. Nature 464: 768–772.

4. CooksonW, LiangL, AbecasisG, MoffattM, LathropM (2009) Mapping complex disease traits with global gene expression. Nature Reviews Genetics 10: 184–194.

5. NicaAC, MontgomerySB, DimasAS, StrangerBE, BeazleyC, et al. (2010) Candidate causal regulatory effects by integration of expression qtls with complex trait genetic associations. PLoS genetics 6: e1000895.

6. HuntKA, ZhernakovaA, TurnerG, HeapGA, FrankeL, et al. (2008) Newly identified genetic risk variants for celiac disease related to the immune response. Nature genetics 40: 395–402.

7. HeX, FullerCK, SongY, MengQ, ZhangB, et al. (2013) Sherlock: Detecting gene-disease associations by matching patterns of expression qtl and gwas. The American Journal of Human Genetics 92: 667–680.

8. DuboisPCA, TrynkaG, FrankeL, HuntKA, RomanosJ, et al. (2010) Multiple common variants for celiac disease influencing immune gene expression. Nat Genet 42: 295–302.

9. DingJ, GudjonssonJE, LiangL, StuartPE, LiY, et al. (2010) Gene expression in skin and lymphoblastoid cells: Refined statistical method reveals extensive overlap in cis-eqtl signals. The American Journal of Human Genetics 87: 779–789.

10. FlutreT, WenX, PritchardJ, StephensM (2013) A statistical framework for joint eqtl analysis in multiple tissues. PLoS Genet 9: e1003486.

11. CotsapasC, VoightBF, RossinE, LageK, NealeBM, et al. (2011) Pervasive sharing of genetic effects in autoimmune disease. PLoS genetics 7: e1002254.

12. PlagnolV, SmythDJ, ToddJA, ClaytonDG (2009) Statistical independence of the colocalized association signals for type 1 diabetes and rps26 gene expression on chromosome 12q13. Biostatistics 10: 327–334.

13. WallaceC, RotivalM, CooperJD, RiceCM, YangJH, et al. (2012) Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Human molecular genetics 21: 2815–2824.

14. WallaceC (2013) Statistical testing of shared genetic control for potentially related traits. Genet Epidemiol 37: 802–813.

15. TeslovichTM, MusunuruK, SmithAV, EdmondsonAC, StylianouIM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.

16. SchadtEE, WooS, HaoK (2012) Bayesian method to predict individual snp genotypes from gene expression data. Nature genetics 44: 603–608.

17. MarchiniJ, HowieB (2010) Genotype imputation for genome-wide association studies. Nature Reviews Genetics 11: 499–511.

18. HowieB, MarchiniJ, StephensM (2011) Genotype imputation with thousands of genomes. G3: Genes, Genomes, Genetics 1: 457–470.

19. HowieB, FuchsbergerC, StephensM, MarchiniJ, AbecasisGR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44: 955–959.

20. WakefieldJ (2009) Bayes factors for genome-wide association studies: comparison with p-values. Genetic Epidemiology 33: 79–86.

21. BrownCD, MangraviteLM, EngelhardtBE (2013) Integrative modeling of eqtls and cis-regulatory elements suggests mechanisms underlying cell type specificity of eqtls. PLoS Genet 9: e1003649.

22. TrynkaG, HuntKA, BockettNA, RomanosJ, MistryV, et al. (2011) Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nature genetics 43: 1193–1201.

23. YangJ, FerreiraT, MorrisAP, MedlandSE, MaddenPA, et al. (2012) Conditional and joint multiple-snp analysis of gwas summary statistics identifies additional variants influencing complex traits. Nature genetics 44: 369–375.

24. ConsortiumGLG, et al. (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45: 1274–1283.

25. Newton R, Wernisch L (2007) Rwui: A web application to create user friendly web interfaces for r scripts. New Functions for Multivariate Analysis: 32 . Available: http://sysbio.mrc-bsu.cam.ac.uk/Rwui/tutorial/Technical_Report.pdf. Accessed 22 April 2014.

26. TrabzuniD, RytenM, WalkerR, SmithC, ImranS, et al. (2012) Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. Journal of Neurochemistry 120: 473–473.

27. RamasamyA, TrabzuniD, GibbsJR, DillmanA, HernandezDG, et al. (2013) Resolving the polymorphism-in-probe problem is critical for correct interpretation of expression qtl studies. Nucleic Acids Research 41: e88.

28. GuanY, StephensM (2008) Practical issues in imputation-based association mapping. PLoS Genet 4: e1000279.

29. GersteinMB, KundajeA, HariharanM, LandtSG, YanKK, et al. (2012) Architecture of the human regulatory network derived from encode data. Nature 489: 91–100.

30. Wen X, Stephens M (2011) Bayesian methods for genetic association analysis with heterogeneous subgroups: from meta-analyses to gene-environment interactions. arXiv preprint arXiv:1111.1210.

31. StephensM, BaldingDJ (2009) Bayesian statistical methods for genetic association studies. Nature Reviews Genetics 10: 681–690.

32. BurtonPR, ClaytonDG, CardonLR, CraddockN, DeloukasP, et al. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.

33. NicolaeDL, GamazonE, ZhangW, DuanS, DolanME, et al. (2010) Trait-associated snps are more likely to be eqtls: annotation to enhance discovery from gwas. PLoS Genetics 6: e1000888.

34. KnightJ, BarnesMR, BreenG, WealeME (2011) Using functional annotation for the empirical determination of bayes factors for genome-wide association study analysis. PloS ONE 6: e14808.

35. JohanssonM, RobertsA, ChenD, LiY, Delahaye-SourdeixM, et al. (2012) Using prior information from the medical literature in gwas of oral cancer identifies novel susceptibility variant on chromosome 4-the adapt method. PloS ONE 7: e36888.

36. RichardsA, JonesL, MoskvinaV, KirovG, GejmanP, et al. (2011) Schizophrenia susceptibility alleles are enriched for alleles that affect gene expression in adult human brain. Molecular psychiatry 17: 193–201.

37. DimasAS, DeutschS, StrangerBE, MontgomerySB, BorelC, et al. (2009) Common regulatory variation impacts gene expression in a cell type–dependent manner. Science 325: 1246–1250.

38. HernandezDG, NallsMA, MooreM, ChongS, DillmanA, et al. (2012) Integration of gwas snps and tissue specific expression profiling reveal discrete eqtls for human traits in blood and brain. Neurobiol Dis 47: 20–28.

39. Team RDC (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org.

40. PruimRJ, WelchRP, SannaS, TeslovichTM, ChinesPS, et al. (2010) Locuszoom: regional visualization of genome-wide association scan results. Bioinformatics 26: 2336–2337.

41. YilmazY, ErenF, ColakY, SenatesE, CelikelCA, et al. (2012) Hepatic expression and serum levels of syndecan 1 (cd138) in patients with nonalcoholic fatty liver disease. Scandinavian journal of gastroenterology 47: 1488–1493.

42. GarverWS, KrishnanK, GallagosJR, MichikawaM, FrancisGA, et al. (2002) Niemann-pick c1 protein regulates cholesterol transport to the trans-golgi network and plasma membrane caveolae. Journal of lipid research 43: 579–589.

43. JohnsonMP, BrenneckeSP, EastCE, GöringHH, KentJWJr, et al. (2012) Genome-wide association scan identifies a risk locus for preeclampsia on 2q14, near the inhibin, beta b gene. PloS ONE 7: e33666.

44. WangCW, LeeSC (2012) The ubiquitin-like (ubx)-domain-containing protein ubx2/ubxd8 regulates lipid droplet homeostasis. Journal of Cell Science 125: 2930–2939.

45. NasarreL, Juan-BabotO, GastelurrutiaP, Llucia-ValldeperasA, BadimonL, et al. (2012) Low density lipoprotein receptor–related protein 1 is upregulated in epicardial fat from type 2 diabetes mellitus patients and correlates with glucose and triglyceride plasma levels. Acta Diabetol 51: 23–30.

46. SarriaA, PaniniS, EvansR (1992) A functional role for vimentin intermediate filaments in the metabolism of lipoprotein-derived cholesterol in human sw-13 cells. Journal of Biological Chemistry 267: 19455–19463.

47. HafnerM, RezenT, RozmanD (2011) Regulation of hepatic cytochromes p450 by lipids and cholesterol. Current drug metabolism 12: 173–185.

48. SaitoK, AdachiN, KoyamaH, MatsushitaM (2010) Ogfod1, a member of the 2-oxoglutarate and iron dependent dioxygenase family, functions in ischemic signaling. FEBS letters 584: 3340–3347.

49. WassellJ, et al. (1999) Haptoglobin: function and polymorphism. Clinical laboratory 46: 547–552.

50. NielsenMJ, PetersenSV, JacobsenC, OxvigC, ReesD, et al. (2006) Haptoglobin-related protein is a high-affinity hemoglobin-binding plasma protein. Blood 108: 2846–2849.

51. StaelsB, MaesM, ZambonA (2008) Fibrates and future pparα agonists in the treatment of cardiovascular disease. Nature Clinical Practice Cardiovascular Medicine 5: 542–553.