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Impact of Natural Genetic Variation on Gene Expression Dynamics


DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.


Vyšlo v časopise: Impact of Natural Genetic Variation on Gene Expression Dynamics. PLoS Genet 9(6): e32767. doi:10.1371/journal.pgen.1003514
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003514

Souhrn

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.


Zdroje

1. DermitzakisET (2008) From gene expression to disease risk. Nature Genetics 40: 492–493.

2. AltshulerD, DalyMJ, LanderES (2008) Genetic mapping in human disease. Science 322: 881–888.

3. ZhongH, BeaulaurierJ, LumPY, MolonyC, YangX, et al. (2010) Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes. PLoS Genetics 6: e1000932.

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

5. NicaAC, PartsL, GlassD, NisbetJ, BarrettA, et al. (2011) The architecture of gene regulatory variation across multiple human tissues: The MuTHER study. PLoS Genetics 7: e1002003.

6. LohmuellerKE, PearceCL, PikeM, LanderES, HirschhornJN (2003) Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nature Genetics 33: 177–182.

7. CalifanoA, ButteAJ, FriendS, IdekerT, SchadtE (2012) Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nature Genetics 44: 841–847.

8. SmithEN, KruglyakL (2008) Gene-environment interaction in yeast gene expression. PLoS Biol 6: e83.

9. GerritsA, DykstraB, OttenM, BystrykhL, HaanG (2008) Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny. Immunogenetics 60: 411–422.

10. GerritsA, LiY, TessonBM, BystrykhLV, WeersingE, et al. (2009) Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genetics 5: e1000692.

11. LiY, lvarezOA, GuttelingEW, TijstermanM, FuJ, et al. (2006) Mapping determinants of gene expression plasticity by genetical genomics in c. elegans. PLoS Genetics 2: e222.

12. Breiman L (2001) Random forests. In: Machine Learning, volume 45. pp. 5–32.

13. AmaratungaD, CabreraJ, LeeYS (2008) Enriched random forests. Bioinformatics (Oxford, England) 24: 2010–2014.

14. BriggsFBS, GoldsteinBA, McCauleyJL, ZuvichRL, De JagerPL, et al. (2010) Variation within DNA repair pathway genes and risk of multiple sclerosis. American Journal of Epidemiology 172: 217–224.

15. BureauA, DupuisJ, HaywardB, FallsK, Van EerdeweghP (2003) Mapping complex traits using random forests. BMC Genetics 4 Suppl 1: S64.

16. BureauA, DupuisJ, FallsK, LunettaKL, HaywardB, et al. (2005) Identifying SNPs predictive of phenotype using random forests. Genetic Epidemiology 28: 171–182.

17. GoldsteinBA, HubbardAE, CutlerA, BarcellosLF (2010) An application of random forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genetics 11: 49.

18. Gonzlez-RecioO, ForniS (2011) Genome-wide prediction of discrete traits using bayesian regressions and machine learning. Genetics, Selection, Evolution: GSE 43: 7.

19. LeeSSF, SunL, KustraR, BullSB (2008) EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis. Bioinformatics (Oxford, England) 24: 1603–1610.

20. LiuC, AckermanHH, CarulliJP (2011) A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility. Human Genetics 129: 473–485.

21. RodinAS, LitvinenkoA, KlosK, MorrisonAC, WoodageT, et al. (2009) Use of wrapper algorithms coupled with a random forests classifier for variable selection in large-scale genomic association studies. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology 16: 1705–1718.

22. WangM, ChenX, ZhangM, ZhuW, ChoK, et al. (2009) Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests. BMC Proceedings 3 Suppl 7: S69.

23. YangWW, GuCC (2009) Selection of important variables by statistical learning in genome-wide association analysis. BMC Proceedings 3 Suppl 7: S70.

24. AckermannM, Clément-ZizaM, MichaelsonJJ, BeyerA (2012) Teamwork: Improved eQTL mapping using combinations of machine learning methods. PLoS ONE 7: e40916.

25. MichaelsonJJ, AlbertsR, SchughartK, BeyerA (2010) Data-driven assessment of eQTL mapping methods. BMC genomics 11: 502.

26. LunettaKL, HaywardLB, SegalJ, Van EerdeweghP (2004) Screening large-scale association study data: exploiting interactions using random forests. BMC Genetics 5: 32.

27. Motsinger-ReifAA, ReifDM, FanelliTJ, RitchieMD (2008) A comparison of analytical methods for genetic association studies. Genetic Epidemiology 32: 767–778.

28. RoshanU, ChikkagoudarS, WeiZ, WangK, HakonarsonH (2011) Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest. Nucleic Acids Research 39: e62.

29. ShivdasaniRA, OrkinSH (1996) The transcriptional control of hematopoiesis. Blood 87: 4025–4039.

30. OrkinSH, ZonLI (2008) Hematopoiesis: An evolving paradigm for stem cell biology. Cell 132: 631–644.

31. IwasakiH, AkashiK (2007) Myeloid lineage commitment from the hematopoietic stem cell. Immunity 26: 726–740.

32. SwiersG, PatientR, LooseM (2006) Genetic regulatory networks programming hematopoietic stem cells and erythroid lineage specification. Developmental Biology 294: 525–540.

33. Müller-SieburgCE, ChoRH, SieburgHB, KupriyanovS, RibletR (2000) Genetic control of hematopoietic stem cell frequency in mice is mostly cell autonomous. Blood 95: 2446–2448.

34. Van ZantG, EldridgePW, BehringerRR, DeweyMJ (1983) Genetic control of hematopoietic kinetics revealed by analyses of allophenic mice and stem cell suicide. Cell 35: 639–645.

35. PetrettoE, MangionJ, DickensNJ, CookSA, KumaranMK, et al. (2006) Heritability and tissue specificity of expression quantitative trait loci. PLoS Genetics 2: e172.

36. LoguercioS, OverallRW, MichaelsonJJ, WiltshireT, PletcherMT, et al. (2010) Integrative analysis of low- and high-resolution eQTL. PLoS ONE 5: e13920.

37. SieburthDS, SunQ, HanM (1998) SUR-8, a conserved ras-binding protein with leucine-rich repeats, positively regulates ras-mediated signaling in c. elegans. Cell 94: 119–130.

38. ReuterCWM, MorganMA, BergmannL (2000) Targeting the ras signaling pathway: A rational, mechanism-based treatment for hematologic malignancies? Blood 96: 1655–1669.

39. KianiA, KuithanH, KuithanF, KyttäläS, HabermannI, et al. (2007) Expression analysis of nuclear factor of activated t cells (NFAT) during myeloid differentiation of CD34+ cells: regulation of fas ligand gene expression in megakaryocytes. Experimental hematology 35: 757–770.

40. SzklarczykD, FranceschiniA, KuhnM, SimonovicM, RothA, et al. (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Research 39: D561–568.

41. BakshS, WidlundHR, Frazer-AbelAA, DuJ, FosmireS, et al. (2002) NFATc2-Mediated repression of cyclin-dependent kinase 4 expression. Molecular Cell 10: 1071–1081.

42. KianiA (2004) Expression and regulation of NFAT (nuclear factors of activated t cells) in human CD34+ cells: down-regulation upon myeloid differentiation. Journal of Leukocyte Biology 76: 1057–1065.

43. LeeTH, KimSU, YuSL, KimSH, ParkDS, et al. (2003) Peroxiredoxin II is essential for sustaining life span of erythrocytes in mice. Blood 101: 5033–5038.

44. JohnsonRM, HoYS, YuDY, KuypersFA, RavindranathY, et al. (2010) The effects of disruption of genes for peroxiredoxin-2, glutathione peroxidase-1, and catalase on erythrocyte oxidative metabolism. Free Radical Biology & Medicine 48: 519–525.

45. GhaffariS (2008) Oxidative stress in the regulation of normal and neoplastic hematopoiesis. Antioxidants & Redox Signaling 10: 1923–1940.

46. DanL, KlimenkovaO, KlimiankouM, KlusmanJH, van den Heuvel-EibrinkMM, et al. (2011) The role of sirtuin 2 activation by nicotinamide phosphoribosyltransferase in the aberrant proliferation and survival of myeloid leukemia cells. Haematologica 97: 551–559.

47. JacobsenSE, VeibyOP, SmelandEB (1993) Cytotoxic lymphocyte maturation factor (interleukin 12) is a synergistic growth factor for hematopoietic stem cells. The Journal of Experimental Medicine 178: 413–418.

48. DybedalI, LarsenS, JacobsenSE (1995) IL-12 directly enhances in vitro murine erythropoiesis in combination with IL-4 and stem cell factor. The Journal of Immunology 154: 4950–4955.

49. ChungHK, YiYW, JungNC, KimD, SuhJM, et al. (2003) CR6-interacting factor 1 interacts with gadd45 family proteins and modulates the cell cycle. The Journal of Biological Chemistry 278: 28079–28088.

50. AbdollahiA, LordKA, Hoffman-LiebermannB, LiebermannDA (1991) Sequence and expression of a cDNA encoding MyD118: a novel myeloid differentiation primary response gene induced by multiple cytokines. Oncogene 6: 165–167.

51. YenA, AlbrightKL (1984) Evidence for cell cycle phase-specific initiation of a program of HL-60 cell myeloid differentiation mediated by inducer uptake. Cancer Research 44: 2511–2515.

52. AshburnerM, BallCA, BlakeJA, BotsteinD, ButlerH, et al. (2000) Gene ontology: tool for the unification of biology. the gene ontology consortium. Nature Genetics 25: 25–29.

53. RockmanMV, KruglyakL (2006) Genetics of global gene expression. Nature Reviews Genetics 7: 862–872.

54. Alexa A, Rahnenführer J (2010). topGO: enrichment analysis for gene ontology.

55. TakakuraN, WatanabeT, SuenobuS, YamadaY, NodaT, et al. (2000) A role for hematopoietic stem cells in promoting angiogenesis. Cell 102: 199–209.

56. GeestCR, CofferPJ (2009) MAPK signaling pathways in the regulation of hematopoiesis. Journal of Leukocyte Biology 86: 237–250.

57. Alberts B (2002) Molecular biology of the cell. New York: Garland Science.

58. KangHP, MorganAA, ChenR, SchadtEE, ButteAJ (2012) Coanalysis of GWAS with eQTLs reveals disease-tissue associations. AMIA Summits on Translational Science proceedings 2012: 35–41.

59. 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.

60. WangJ, WilliamsRW, ManlyKF (2003) WebQTL: web-based complex trait analysis. Neuroinformatics 1: 299–308.

61. LiFX, ZhuJW, HoganCJ, DeGregoriJ (2003) Defective gene expression, s phase progression, and maturation during hematopoiesis in E2F1/E2F2 mutant mice. Molecular and Cellular Biology 23: 3607–3622.

62. FisherCL, PineaultN, BrookesC, HelgasonCD, OhtaH, et al. (2010) Loss-of-function additional sex combs like 1 mutations disrupt hematopoiesis but do not cause severe myelodysplasia or leukemia. Blood 115: 38–46.

63. IdekerT, KroganNJ (2012) Differential network biology. Molecular Systems Biology 8: 565.

64. DaviesMN, LawnS, WhatleyS, FernandesC, WilliamsRW, et al. (2009) To what extent is blood a reasonable surrogate for brain in gene expression studies: Estimation from mouse hippocampus and spleen. Frontiers in neuroscience 3: 54.

65. R Development Core Team (2011). R: A language and environment for statistical computing. Available: http://www.R-project.org.

66. DurinckS, SpellmanPT, BirneyE, HuberW (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols 4: 1184–1191.

67. Agresti A (2002) Categorical data analysis. 2nd edition. New York: Wiley-Interscience.

68. ShafferJP (1995) Multiple hypothesis testing. Annual Review of Psychology 46: 561–584.

69. YoshidaM, KoikeA (2011) SNPInterForest: a new method for detecting epistatic interactions. BMC Bioinformatics 12: 469.

70. DutkowskiJ, IdekerT (2011) Protein networks as logic functions in development and cancer. PLoS Computational Biology 7: e1002180.

71. SakoparnigT, KockmannT, ParoR, BeiselC, BeerenwinkelN (2012) Binding profiles of chromatinmodifying proteins are predictive for transcriptional activity and promoter-proximal pausing. Journal of Computational Biology 19: 126–138.

72. BenjaminiY, HochbergY (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc 57: 289–300.

73. AlexaA, RahnenführerJ, LengauerT (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22: 1600–1607.

74. FuJ, WolfsMGM, DeelenP, WestraHJ, FehrmannRSN, et al. (2012) Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genetics 8: e1002431.

75. PowellJE, HendersAK, McRaeAF, WrightMJ, MartinNG, et al. (2011) Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent. Genome Research 22: 456–466.

76. AlbertsR, ChenH, PommerenkeC, SmitAB, SpijkerS, et al. (2011) Expression QTL mapping in regulatory and helper T cells from the BXD family of strains reveals novel cell-specific genes, gene-gene interactions and candidate genes for auto-immune disease. BMC genomics 12: 610.

77. PriceAL, HelgasonA, ThorleifssonG, McCarrollSA, KongA, et al. (2011) Single-tissue and crosstissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genetics 7: e1001317.

78. 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.

79. BullaugheyK, ChavarriaCI, CoopG, GiladY (2009) Expression quantitative trait loci detected in cell lines are often present in primary tissues. Human Molecular Genetics 18: 4296–4303.

80. HeinzenEL, GeD, CroninKD, MaiaJM, ShiannaKV, et al. (2008) Tissue-specific genetic control of splicing: Implications for the study of complex traits. PLoS Biology 6: e1.

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

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Genetika Reprodukčná medicína

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PLOS Genetics


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