Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice

English version České info

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni -⁠ or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study.

Vyšlo v časopise: Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice. PLoS Genet 10(1): e32767. doi:10.1371/journal.pgen.1004022
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004022

Souhrn

Zdroje

1. GerkeJ, LorenzK, RamnarineS, CohenB (2010) Gene-environment interactions at nu -⁠ cleotide resolution. PLoS Genet 6: e1001144.

2. MureaM, MaL, FreedmanBI (2012) Genetic and environmental factors associated with type 2 diabetes and diabetic vascular complications. Rev Diabet Stud 9 : 6–22.

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

4. TalmudPJ (2007) Gene-environment interaction and its impact on coronary heart disease risk. Nutr Metab Cardiovasc Dis 17 : 148–52.

5. ForsythJK, EllmanLM, TanskanenA, MustonenU, HuttunenMO, et al. (2012) Genetic risk for schizophrenia, obstetric complications, and adolescent school outcome: Evidence for gene-environment interaction. Schizophr Bull 39 : 1067–76.

6. OrozcoLD, BennettBJ, FarberCR, GhazalpourA, PanC, et al. (2012) Unraveling inammatory responses using systems genetics and gene-environment interactions in macrophages. Cell 151 : 658–70.

7. DaiX, WuC, HeY, GuiL, ZhouL, et al. (2013) A genome-wide association study for serum bilirubin levels and gene-environment interaction in a chinese population. Genet Epidemiol 37 : 293–300.

8. PatelCJ, ChenR, KodamaK, IoannidisJPA, ButteAJ (2013) Systematic identification of interaction effects between genome -⁠ and environment-wide associations in type 2 diabetes mellitus. Hum Genet 132 : 495–508.

9. WuC, KraftP, ZhaiK, ChangJ, WangZ, et al. (2012) Genome-wide association analyses of esophageal squamous cell carcinoma in chinese identify multiple susceptibility loci and gene-environment interactions. Nat Genet 44 : 1090–7.

10. MaJ, XiaoF, XiongM, AndrewAS, BrennerH, et al. (2012) Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer. Hum Hered 73 : 185–94.

11. GaoJ, NallsMA, ShiM, JoubertBR, HernandezDG, et al. (2012) An exploratory analysis on gene-environment interactions for parkinson disease. Neurobiol Aging 33 : 2528.e1–6.

12. WeiS, WangLEE, McHughMK, HanY, XiongM, et al. (2012) Genome-wide gene -⁠ environment interaction analysis for asbestos exposure in lung cancer susceptibility. Carcinogenesis 33 : 1531–7.

13. PriceAL, PattersonNJ, PlengeRM, WeinblattME, ShadickNA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38 : 904.

14. DevlinB, RoederK, WassermanL (2001) Genomic control, a new approach to genetic -⁠ based association studies. Theor Popul Biol 60 : 155–66.

15. FurlotteNA, KangEY, Van NasA, FarberCR, LusisAJ, et al. (2012) Increasing asso -⁠ ciation mapping power and resolution in mouse genetic studies through the use of meta -⁠ analysis for structured populations. Genetics 191 : 959–67.

16. HanB, EskinE (2012) Interpreting meta-analyses of genome-wide association studies. PLoS Genet 8: e1002555.

17. FlintJ, EskinE (2012) Genome-wide association studies in mice. Nature Reviews Genetics 13 : 807.

18. BennettBJ, FarberCR, OrozcoL, KangHM, GhazalpourA, et al. (2010) A high -⁠ resolution association mapping panel for the dissection of complex traits in mice. Genome Res 20 : 281–90.

19. GhazalpourA, RauCD, FarberCR, BennettBJ, OrozcoLD, et al. (2012) Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits. Mamm Genome 23 : 680–92.

20. ValdarW, SolbergLC, GauguierD, BurnettS, KlenermanP, et al. (2006) Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 38 : 879–87.

21. YalcinB, NicodJ, BhomraA, DavidsonS, CleakJ, et al. (2010) Commercially available outbred mice for genome-wide association studies. PLoS Genet 6: e1001085.

22. AylorDL, ValdarW, Foulds-MathesW, BuusRJ, VerdugoRA, et al. (2011) Genetic analysis of complex traits in the emerging collaborative cross. Genome Res 21 : 1213–22.

23. WardenCH, HedrickCC, QiaoJH, CastellaniLW, LusisAJ (1993) Atherosclerosis in transgenic mice overexpressing apolipoprotein a-ii. Science 261 : 469–72.

24. DerSimonianR, LairdN (1986) Meta-analysis in clinical trials. Control Clin Trials 7 : 177–88.

25. Cochran WG (2009) The Combination of Estimates from Different Experiments. doi: 10.2307/3001666.

26. HigginsJPT, ThompsonSG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21 : 1539–58.

27. NiswenderCM, WillisBS, WallenA, SweetIR, JettonTL, et al. (2005) Cre recombinase -⁠ dependent expression of a constitutively active mutant allele of the catalytic subunit of protein kinase a. Genesis 43 : 109–19.

28. ManningAK, LaValleyM, LiuCTT, RiceK, AnP, et al. (2011) Meta-analysis of gene -⁠ environment interaction: joint estimation of snp and snp×environment regression coeffi -⁠ cients. Genetic Epidemiology 35 : 11.

29. KirbyA, KangHM, WadeCM, CotsapasC, KostemE, et al. (2010) Fine mapping in 94 inbred mouse strains using a high-density haplotype resource. Genetics 185 : 1081–95.

30. ChurchillGA, AireyDC, AllayeeH, AngelJM, AttieAD, et al. (2004) The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet 36 : 1133–7.

31. IoannidisJPA, PatsopoulosNA, EvangelouE (2007) Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2: e841.

32. IoannidisJPA, PatsopoulosNA, EvangelouE (2007) Uncertainty in heterogeneity esti -⁠ mates in meta-analyses. BMJ 335 : 914–6.

33. HanB, EskinE (2011) Random-effects model aimed at discovering associations in meta -⁠ analysis of genome-wide association studies. Am J Hum Genet 88 : 586–98.

34. HardyRJ, ThompsonSG (1996) A likelihood approach to meta-analysis with random effects. Stat Med 15 : 619–29.

35. LinDYY, SullivanPF (2009) Meta-analysis of genome-wide association studies with over -⁠ lapping subjects. Am J Hum Genet 85 : 862–72.

36. DevlinB, RoederK, BacanuSA (2001) Unbiased methods for population-based association studies. Genet Epidemiol 21 : 273–84.

37. VoightBF, PritchardJK (2005) Confounding from cryptic relatedness in case-control association studies. PLoS Genet 1: e32.

38. LangeK (2002) Mathematical and statistical methods for genetic analysis. Springer Verlag

39. YuJ, PressoirG, BriggsWH, Vroh BiI, YamasakiM, et al. (2006) A unified mixed -⁠ model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38 : 203–8.

40. KangHM, ZaitlenNA, WadeCM, KirbyA, HeckermanD, et al. (2008) Efficient control of population structure in model organism association mapping. Genetics 178 : 1709.

41. LippertC, QuonG, KangEY, KadieCM, ListgartenJ, et al. (2013) The benefits of selecting phenotype-specific variants for applications of mixed models in genomics. Scientific Reports 3 : 1815.

42. ListgartenJ, LippertC, KangEY, XiangJ, KadieCM, et al. (2013) A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics 29 : 1526–1533.

43. StephensM, BaldingDJ (2009) Bayesian statistical methods for genetic association stud -⁠ ies. Nat Rev Genet 10 : 681–90.

44. ParksBW, NamE, OrgE, KostemE, NorheimF, et al. (2013) Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab 17 : 141–52.

45. DavisRC, van NasA, CastellaniLW, ZhaoY, ZhouZ, et al. (2012) Systems genetics of susceptibility to obesity-induced diabetes in mice. Physiol Genomics 44 : 1–13.

46. WangS, YehyaN, SchadtE, WangH, DrakeT, et al. (2006) Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity. PLoS genetics 2: e15.

47. van NasA, Ingram-DrakeL, SinsheimerJS, WangSS, SchadtEE, et al. (2010) Expression quantitative trait loci: replication, tissue -⁠ and sex-specificity in mice. Genetics 185 : 1059–68.

48. van den MaagdenbergAM, HofkerMH, KrimpenfortPJ, de BruijnI, van VlijmenB, et al. (1993) Transgenic mice carrying the apolipoprotein e3-leiden gene exhibit hyperlipopro -⁠ teinemia. J Biol Chem 268 : 10540–5.

49. JiangXC, AgellonLB, WalshA, BreslowJL, TallA (1992) Dietary cholesterol increases transcription of the human cholesteryl ester transfer protein gene in transgenic mice. dependence on natural anking sequences. J Clin Invest 90 : 1290–5.

50. TakasugaS, HorieY, SasakiJ, Sun-WadaGHH, KawamuraN, et al. (2013) Critical roles of type iii phosphatidylinositol phosphate kinase in murine embryonic visceral endoderm and adult intestine. Proc Natl Acad Sci U S A 110 : 1726–31.

51. HeC, BassikMC, MoresiV, SunK, WeiY, et al. (2012) Exercise-induced bcl2-regulated autophagy is required for muscle glucose homeostasis. Nature 481 : 511–5.

52. PlumpAS, AzrolanN, OdakaH, WuL, JiangX, et al. (1997) Apoa-i knockout mice: characterization of hdl metabolism in homozygotes and identification of a post-rna mechanism of apoa-i up-regulation in heterozygotes. J Lipid Res 38 : 1033–47.

53. LiegelR, ChangB, DubielzigR, SidjaninDJ (2011) Blind sterile 2 (bs2), a hypomorphic mutation in agps, results in cataracts and male sterility in mice. Mol Genet Metab 103 : 51–9.

54. HofmannJJ, ZoveinAC, KohH, RadtkeF, WeinmasterG, et al. (2010) Jagged1 in the portal vein mesenchyme regulates intrahepatic bile duct development: insights into alagille syndrome. Development 137 : 4061–72.

55. FareseRV, SajanMP, YangH, LiP, MastoridesS, et al. (2007) Muscle-specific knockout of pkc-lambda impairs glucose transport and induces metabolic and diabetic syndromes. J Clin Invest 117 : 2289–301.

56. QiaoJH, TripathiJ, MishraNK, CaiY, TripathiS, et al. (1997) Role of macrophage colony-stimulating factor in atherosclerosis: studies of osteopetrotic mice. Am J Pathol 150 : 1687–99.

57. StanfordKI, WangL, CastagnolaJ, SongD, BishopJR, et al. (2010) Heparan sulfate 2-o-sulfotransferase is required for triglyceride-rich lipoprotein clearance. J Biol Chem 285 : 286–94.

58. MorganH, BeckT, BlakeA, GatesH, AdamsN, et al. (2010) Europhenome: a repository for high-throughput mouse phenotyping data. Nucleic Acids Res 38: D577–85.

59. LeshanRL, Greenwald-YarnellM, PattersonCM, GonzalezIE, MyersMG (2012) Leptin action through hypothalamic nitric oxide synthase-1-expressing neurons controls energy balance. Nat Med 18 : 820–3.

60. NakataS, TsutsuiM, ShimokawaH, SudaO, MorishitaT, et al. (2008) Spontaneous myocardial infarction in mice lacking all nitric oxide synthase isoforms. Circulation 117 : 2211–23.

61. LiLO, EllisJM, PaichHA, WangS, GongN, et al. (2009) Liver-specific loss of long chain acyl-coa synthetase-1 decreases triacylglycerol synthesis and beta-oxidation and alters phospholipid fatty acid composition. J Biol Chem 284 : 27816–26.

62. NishinaPM, NaggertJK, VerstuyftJ, PaigenB (1994) Atherosclerosis in genetically obese mice: the mutants obese, diabetes, fat, tubby, and lethal yellow. Metabolism 43 : 554–8.

63. CiraoloE, IezziM, MaroneR, MarengoS, CurcioC, et al. (2008) Phosphoinositide 3 -⁠ kinase p110beta activity: key role in metabolism and mammary gland cancer but not development. Sci Signal 1: ra3.

64. GuptaS, PabloAM, JiangXc, WangN, TallAR, et al. (1997) Ifn-gamma potentiates atherosclerosis in apoe knock-out mice. J Clin Invest 99 : 2752–61.

65. KimI, AhnSHH, InagakiT, ChoiM, ItoS, et al. (2007) Differential regulation of bile acid homeostasis by the farnesoid x receptor in liver and intestine. J Lipid Res 48 : 2664–72.

66. WiedmerT, ZhaoJ, LiL, ZhouQ, HevenerA, et al. (2004) Adiposity, dyslipidemia, and insulin resistance in mice with targeted deletion of phospholipid scramblase 3 (plscr3). Proc Natl Acad Sci U S A 101 : 13296–301.

67. FanCY, PanJ, ChuR, LeeD, KluckmanKD, et al. (1996) Hepatocellular and hepatic peroxisomal alterations in mice with a disrupted peroxisomal fatty acyl-coenzyme a oxidase gene. J Biol Chem 271 : 24698–710.

68. SainsburyA, BaldockPA, SchwarzerC, UenoN, EnriquezRF, et al. (2003) Synergistic effects of y2 and y4 receptors on adiposity and bone mass revealed in double knockout mice. Mol Cell Biol 23 : 5225–33.

69. EdmondsonAC, BraundPS, StylianouIM, KheraAV, NelsonCP, et al. (2011) Dense genotyping of candidate gene loci identifies variants associated with high-density lipoprotein cholesterol. Circ Cardiovasc Genet 4 : 145–55.

70. ForestiO, RuggianoA, Hannibal-BachHK, EjsingCS, CarvalhoP (2013) Sterol home -⁠ ostasis requires regulated degradation of squalene monooxygenase by the ubiquitin ligase doa10/teb4. eLife 2: e00953.

71. LiuSPP, LiYSS, ChenYJJ, ChiangEPP, LiAFY, et al. (2007) Glycine n -⁠ methyltransferase-/ -⁠ mice develop chronic hepatitis and glycogen storage disease in the liver. Hepatology 46 : 1413–25.

72. BourreJM, Cl_ementM, G_erardD, Chaudi_ereJ (1989) Alterations of cholesterol synthe -⁠ sis precursors (7-dehydrocholesterol, 7-dehydrodesmosterol, desmosterol) in dysmyelinat -⁠ ing neurological mutant mouse (quaking, shiverer and trembler) in the pns and the cns. Biochim Biophys Acta 1004 : 387–90.

73. FujinoT, AsabaH, KangMJJ, IkedaY, SoneH, et al. (2003) Low-density lipoprotein receptor-related protein 5 (lrp5) is essential for normal cholesterol metabolism and glucose -⁠ induced insulin secretion. Proc Natl Acad Sci U S A 100 : 229–34.

74. KawaharaY, GrimbergA, TeegardenS, MombereauC, LiuS, et al. (2008) Dysregulated editing of serotonin 2c receptor mrnas results in energy dissipation and loss of fat mass. J Neurosci 28 : 12834–44.