Cis and Trans Effects of Human Genomic Variants on Gene Expression

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Humans differ in their genetic sequences at millions of positions but only a subset of these differences have a functional effect. In order to detect functional genetic differences, we assessed the impact of common genetic variants on gene expression in 869 individuals and discovered that the expression of many genes is affected by common variants in cis or in trans. We show that the effect of some variants on gene expression cannot be detected in other tissues, highlighting the tissue specificity of gene regulation. In addition, we show that variants associated to common diseases are more likely to affect gene expression in cis and in trans. Finally, we show that variants affecting gene expression in cis often affect gene expression in trans, which suggests that the trans effects are due to the cis genes expression. We tested this hypothesis and discovered several cases of genes regulated in trans by a cis regulated gene in a causal manner. This shows that a population-based strategy with a large number of individuals has the potential to detect secondary effects of common variants that can be used to construct short directed regulatory networks.

Vyšlo v časopise: Cis and Trans Effects of Human Genomic Variants on Gene Expression. PLoS Genet 10(7): e32767. doi:10.1371/journal.pgen.1004461
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004461

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Zdroje

1. 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.

2. GrundbergE, SmallKS, HedmanAK, NicaAC, BuilA, et al. (2012) Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 44: 1084–1089.

3. 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.

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

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

6. MontgomerySB, SammethM, Gutierrez-ArcelusM, LachRP, IngleC, et al. (2010) Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464: 773–777.

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

8. StrangerBE, ForrestMS, DunningM, IngleCE, BeazleyC, et al. (2007) Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315: 848–853.

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

10. GreenawaltDM, DobrinR, ChudinE, HatoumIJ, SuverC, et al. (2011) A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort. Genome Res 21: 1008–1016.

11. GaffneyDJ (2013) Global properties and functional complexity of human gene regulatory variation. PLoS Genet 9: e1003501.

12. MontgomerySB, DermitzakisET (2011) From expression QTLs to personalized transcriptomics. Nat Rev Genet 12: 277–282.

13. WoodAR, HernandezDG, NallsMA, YaghootkarH, GibbsJR, et al. (2011) Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association. Hum Mol Genet 20: 4082–4092.

14. PowellJE, HendersAK, McRaeAF, KimJ, HemaniG, et al. (2013) Congruence of additive and non-additive effects on gene expression estimated from pedigree and SNP data. PLoS Genet 9: e1003502.

15. 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. Am J Hum Genet 87: 779–789.

16. StoreyJD, TibshiraniR (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440–9445.

17. LappalainenT, SammethM, FriedlanderMR, t HoenPA, MonlongJ, et al. (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501: 506–511.

18. 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.

19. WestraHJ, PetersMJ, EskoT, YaghootkarH, SchurmannC, et al. (2013) Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 45: 1238–1243.

20. HildnerK, EdelsonBT, PurthaWE, DiamondM, MatsushitaH, et al. (2008) Batf3 deficiency reveals a critical role for CD8alpha+ dendritic cells in cytotoxic T cell immunity. Science 322: 1097–1100.

21. SchramlBU, HildnerK, IseW, LeeWL, SmithWA, et al. (2009) The AP-1 transcription factor Batf controls T(H)17 differentiation. Nature 460: 405–409.

22. WangW, GrimmerJF, Van De WaterTR, LufkinT (2004) Hmx2 and Hmx3 homeobox genes direct development of the murine inner ear and hypothalamus and can be functionally replaced by Drosophila Hmx. Dev Cell 7: 439–453.

23. MillerND, NanceMA, WohlerES, Hoover-FongJE, LisiE, et al. (2009) Molecular (SNP) analyses of overlapping hemizygous deletions of 10q25.3 to 10qter in four patients: evidence for HMX2 and HMX3 as candidate genes in hearing and vestibular function. Am J Med Genet A 149A: 669–680.

24. HuangH, RambaldiI, DanielsE, FeatherstoneM (2003) Expression of the Wdr9 gene and protein products during mouse development. Dev Dyn 227: 608–614.

25. MillsteinJ, ZhangB, ZhuJ, SchadtEE (2009) Disentangling molecular relationships with a causal inference test. BMC Genet 10: 23.

26. WallaceC, RotivalM, CooperJD, RiceCM, YangJH, et al. (2012) Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum Mol Genet 21: 2815–2824.

27. LoughranSJ, KruseEA, HackingDF, de GraafCA, HylandCD, et al. (2008) The transcription factor Erg is essential for definitive hematopoiesis and the function of adult hematopoietic stem cells. Nat Immunol 9: 810–819.

28. KlopockiE, MundlosS (2011) Copy-number variations, noncoding sequences, and human phenotypes. Annu Rev Genomics Hum Genet 12: 53–72.

29. TalukderAH, MengQ, KumarR (2006) CRIPak, a novel endogenous Pak1 inhibitor. Oncogene 25: 1311–1319.

30. InselTR (2010) The challenge of translation in social neuroscience: a review of oxytocin, vasopressin, and affiliative behavior. Neuron 65: 768–779.

31. BoydA, GoldingJ, MacleodJ, LawlorDA, FraserA, et al. (2012) Cohort Profile: The ‘Children of the 90s’–the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 42: 111–147.

32. JonesRW, RingS, TyfieldL, HamvasR, SimmonsH, et al. (2000) A new human genetic resource: a DNA bank established as part of the Avon longitudinal study of pregnancy and childhood (ALSPAC). Eur J Hum Genet 8: 653–660.

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

34. LiY, WillerC, SannaS, AbecasisG (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10: 387–406.

35. ConradDF, PintoD, RedonR, FeukL, GokcumenO, et al. (2010) Origins and functional impact of copy number variation in the human genome. Nature 464: 704–712.

36. Genomes Project C (2012) AbecasisGR, AutonA, BrooksLD, DePristoMA, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65.

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. SchadtEE, LambJ, YangX, ZhuJ, EdwardsS, et al. (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37: 710–717.

39. ZhuJ, ZhangB, SmithEN, DreesB, BremRB, et al. (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40: 854–861.

40. Gutierrez-ArcelusM, LappalainenT, MontgomerySB, BuilA, OngenH, et al. (2013) Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife 2: e00523.

41. ScutariM (2010) Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software 35: 1–22.