Genome-Wide Haplotype Analysis of Expression Quantitative Trait Loci in Monocytes

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In order to assess whether gene expression variability could be influenced by several SNPs acting in cis, either through additive or more complex haplotype effects, a systematic genome-wide search for cis haplotype expression quantitative trait loci (eQTL) was conducted in a sample of 758 individuals, part of the Cardiogenics Transcriptomic Study, for which genome-wide monocyte expression and GWAS data were available. 19,805 RNA probes were assessed for cis haplotypic regulation through investigation of ∼2,1×10⁹ haplotypic combinations. 2,650 probes demonstrated haplotypic p-values >10⁴-fold smaller than the best single SNP p-value. Replication of significant haplotype effects were tested for 412 probes for which SNPs (or proxies) that defined the detected haplotypes were available in the Gutenberg Health Study composed of 1,374 individuals. At the Bonferroni correction level of 1.2×10⁻⁴ (∼0.05/412), 193 haplotypic signals replicated. 1000G imputation was then conducted, and 105 haplotypic signals still remained more informative than imputed SNPs. In-depth analysis of these 105 cis eQTL revealed that at 76 loci genetic associations were compatible with additive effects of several SNPs, while for the 29 remaining regions data could be compatible with a more complex haplotypic pattern. As 24 of the 105 cis eQTL have previously been reported to be disease-associated loci, this work highlights the need for conducting haplotype-based and 1000G imputed cis eQTL analysis before commencing functional studies at disease-associated loci.

Vyšlo v časopise: Genome-Wide Haplotype Analysis of Expression Quantitative Trait Loci in Monocytes. PLoS Genet 9(1): e32767. doi:10.1371/journal.pgen.1003240
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003240

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Zdroje

1. CooksonW, LiangL, AbecasisG, MoffattM, LathropM (2009) Mapping complex disease traits with global gene expression. Nat Rev Genet 10: 184–194.

2. LiJ, BurmeisterM (2005) Genetical genomics: combining genetics with gene expression analysis. Hum Mol Genet 14 Spec No. 2: R163–169.

3. MontgomerySB, DermitzakisET (2009) The resolution of the genetics of gene expression. Hum Mol Genet 18: R211–215.

4. GoringHH, CurranJE, JohnsonMP, DyerTD, CharlesworthJ, et al. (2007) Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet 39: 1208–1216.

5. DixonAL, LiangL, MoffattMF, ChenW, HeathS, et al. (2007) A genome-wide association study of global gene expression. Nat Genet 39: 1202–1207.

6. StrangerBE, NicaAC, ForrestMS, DimasA, BirdCP, et al. (2007) Population genomics of human gene expression. Nat Genet 39: 1217–1224.

7. EmilssonV, ThorleifssonG, ZhangB, LeonardsonAS, ZinkF, et al. (2008) Genetics of gene expression and its effect on disease. Nature 452: 423–428.

8. SchadtEE, MolonyC, ChudinE, HaoK, YangX, et al. (2008) Mapping the genetic architecture of gene expression in human liver. PLoS Biol 6: e107 doi:10.1371/journal.pbio.0060107.

9. ZellerT, WildP, SzymczakS, RotivalM, SchillertA, et al. (2010) Genetics and beyond–the transcriptome of human monocytes and disease susceptibility. PLoS ONE 5: e10693 doi:10.1371/journal.pone.0010693.

10. JansenRC, NapJP (2001) Genetical genomics: the added value from segregation. Trends Genet 17: 388–391.

11. MurphyA, ChuJH, XuM, CareyVJ, LazarusR, et al. (2010) Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes. Hum Mol Genet 19: 4745–4757.

12. NicaAC, DermitzakisET (2008) Using gene expression to investigate the genetic basis of complex disorders. Hum Mol Genet 17: R129–134.

13. MoffattMF, KabeschM, LiangL, DixonAL, StrachanD, et al. (2007) Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448: 470–473.

14. HeinigM, PetrettoE, WallaceC, BottoloL, RotivalM, et al. (2010) A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature 467: 460–464.

15. BuilA, TregouetDA, SoutoJC, SautN, GermainM, et al. (2010) C4BPB/C4BPA is a new susceptibility locus for venous thrombosis with unknown protein S-independent mechanism: results from genome-wide association and gene expression analyses followed by case-control studies. Blood 115: 4644–4650.

16. FrereC, TregouetDA, MorangePE, SautN, KouassiD, et al. (2006) Fine mapping of quantitative trait nucleotides underlying thrombin-activatable fibrinolysis inhibitor antigen levels by a transethnic study. Blood 108: 1562–1568.

17. BodinL, VerstuyftC, TregouetDA, RobertA, DubertL, et al. (2005) Cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase (VKORC1) genotypes as determinants of acenocoumarol sensitivity. Blood 106: 135–140.

18. JohnsonAD, HandsakerRE, PulitSL, NizzariMM, O'DonnellCJ, et al. (2008) SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24: 2938–2939.

19. TregouetDA, KonigIR, ErdmannJ, MunteanuA, BraundPS, et al. (2009) Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat Genet 41: 283–285.

20. GagliardiF, JonesB, GreyF, BeginME, HeikkurinenM (2005) Building an infrastructure for scientific Grid computing: status and goals of the EGEE project. Philos Transact A Math Phys Eng Sci 363: 1729–1742.

21. TregouetDA, GarelleV (2007) A new JAVA interface implementation of THESIAS: testing haplotype effects in association studies. Bioinformatics 23: 1038–1039.

22. LiY, WillerCJ, DingJ, ScheetP, AbecasisGR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816–834.

23. HindorffLA, SethupathyP, JunkinsHA, RamosEM, MehtaJP, et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106: 9362–9367.

24. KamataniY, MatsudaK, OkadaY, KuboM, HosonoN, et al. (2010) Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat Genet 42: 210–215.

25. ChoMH, BoutaouiN, KlandermanBJ, SylviaJS, ZinitiJP, et al. (2010) Variants in FAM13A are associated with chronic obstructive pulmonary disease. Nat Genet 42: 200–202.

26. AlbaghaOM, WaniSE, ViscontiMR, AlonsoN, GoodmanK, et al. (2011) Genome-wide association identifies three new susceptibility loci for Paget's disease of bone. Nat Genet 43: 685–689.

27. TaoS, WangZ, FengJ, HsuFC, JinG, et al. (2012) A genome-wide search for loci interacting with known prostate cancer risk-associated genetic variants. Carcinogenesis 33: 598–603.

28. KoonerJS, SaleheenD, SimX, SehmiJ, ZhangW, et al. (2011) Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet 43: 984–989.

29. NicaAC, MontgomerySB, DimasAS, StrangerBE, BeazleyC, et al. (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.

30. NicolaeDL, GamazonE, ZhangW, DuanS, DolanME, et al. (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.

31. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

32. EichlerEE, FlintJ, GibsonG, KongA, LealSM, et al. (2011) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11: 446–450.

33. ShahS, NelsonCP, GauntTR, van der HarstP, BarnesT, et al. (2011) Four Genetic Loci Influencing Electrocardiographic Indices of Left Ventricular Hypertrophy. Circ Cardiovasc Genet

34. RotivalM, ZellerT, WildPS, MaoucheS, SzymczakS, et al. (2011) Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans. PLoS Genet 7: e1002367 doi:10.1371/journal.pgen.1002367.

35. CastagneR, RotivalM, ZellerT, WildPS, TruongV, et al. (2011) The choice of the filtering method in microarrays affects the inference regarding dosage compensation of the active X-chromosome. PLoS ONE 6: e23956 doi:10.1371/journal.pone.0023956.

36. CastagneR, ZellerT, RotivalM, SzymczakS, TruongV, et al. (2011) Influence of sex and genetic variability on expression of X-linked genes in human monocytes. Genomics 98: 320–326.

37. Barbosa-MoraisNL, DunningMJ, SamarajiwaSA, DarotJF, RitchieME, et al. (2010) A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res 38: e17.

38. DuP, KibbeWA, LinSM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24: 1547–1548.

39. LambertJC, Grenier-BoleyB, HaroldD, ZelenikaD, ChourakiV, et al. (2012) Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer's disease. Mol Psychiatry [Epub ahead of print]

40. TregouetDA, RicardS, NicaudV, ArnouldI, SoubigouS, et al. (2004) In-depth haplotype analysis of ABCA1 gene polymorphisms in relation to plasma ApoA1 levels and myocardial infarction. Arterioscler Thromb Vasc Biol 24: 775–781.

41. TregouetDA, EscolanoS, TiretL, MalletA, GolmardJL (2004) A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm. Ann Hum Genet 68: 165–177.

42. MorangePE, SautN, AlessiMC, YudkinJS, MargaglioneM, et al. (2007) Association of plasminogen activator inhibitor (PAI)-1 (SERPINE1) SNPs with myocardial infarction, plasma PAI-1, and metabolic parameters: the HIFMECH study. Arterioscler Thromb Vasc Biol 27: 2250–2257.

43. PriceAL, PattersonNJ, PlengeRM, WeinblattME, ShadickNA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.