DeepSAGE Reveals Genetic Variants Associated with Alternative Polyadenylation and Expression of Coding and Non-coding Transcripts

English version České info

Many disease-associated variants affect gene expression levels (expression quantitative trait loci, eQTLs) and expression profiling using next generation sequencing (NGS) technology is a powerful way to detect these eQTLs. We analyzed 94 total blood samples from healthy volunteers with DeepSAGE to gain specific insight into how genetic variants affect the expression of genes and lengths of 3′-untranslated regions (3′-UTRs). We detected previously unknown cis-eQTL effects for GWAS hits in disease -⁠ and physiology-associated traits. Apart from cis-eQTLs that are typically easily identifiable using microarrays or RNA-sequencing, DeepSAGE also revealed many cis-eQTLs for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. We also identified and confirmed SNPs that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA). We then combined the power of RNA-sequencing with DeepSAGE by performing a meta-analysis of three datasets, leading to the identification of many more cis-eQTLs. Our results indicate that DeepSAGE data is useful for eQTL mapping of known and unknown transcripts, and for identifying SNPs that affect alternative polyadenylation. Because of the inherent differences between DeepSAGE and RNA-sequencing, our complementary, integrative approach leads to greater insight into the molecular consequences of many disease-associated variants.

Vyšlo v časopise: DeepSAGE Reveals Genetic Variants Associated with Alternative Polyadenylation and Expression of Coding and Non-coding Transcripts. PLoS Genet 9(6): e32767. doi:10.1371/journal.pgen.1003594
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003594

Souhrn

Zdroje

1. SchadtEE, MonksSA, DrakeTA, LusiskAJ, ChekN, et al. (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422 : 297–302.

2. CheungVG, SpielmanRS, EwensKG, WeberTM, MorleyM, et al. (2005) Mapping determinants of human gene expression by regional and genome-wide association. Nature 437 : 1365–1369.

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

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

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

6. FehrmannRSN, JansenRC, VeldinkJH, WestraH-J, ArendsD, et al. (2011) Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet 7: e1002197.

7. OzsolakF, MilosPM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12 : 87–98.

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

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

10. WangZ, GersteinM, SnyderM (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10 : 57–63.

11. 't HoenPAC, AriyurekY, ThygesenHH, VreugdenhilE, VossenRHAM, et al. (2008) Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res 36: e141.

12. NielsenKL, HøghAL, EmmersenJ (2006) DeepSAGE-digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples. Nucleic Acids Res 34: e133.

13. SahaS, SparksAB, RagoC, AkmaevV, WangCJ, et al. (2002) Using the transcriptome to annotate the genome. Nat Biotechnol 20 : 508–512.

14. AsmannYW, KleeEW, ThompsonEA, PerezEA, MiddhaS, et al. (2009) 3′ tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer. BMC Genomics 10 : 531.

15. TianB, HuJ, ZhangH, LutzCS (2005) A large-scale analysis of mRNA polyadenylation of human and mouse genes. Nucleic Acids Res 33 : 201–212.

16. DertiA, Garrett-EngeleP, MacisaacKD, StevensRC, SriramS, et al. (2012) A quantitative atlas of polyadenylation in five mammals. Genome Res 22 : 1173–1183.

17. BarreauC, PaillardL, OsborneHB (2005) AU-rich elements and associated factors: are there unifying principles? Nucleic Acids Res 33 : 7138–7150.

18. SandbergR, NeilsonJR, SarmaA, SharpPA, BurgeCB (2008) Proliferating cells express mRNAs with shortened 3′ untranslated regions and fewer microRNA target sites. Science 320 : 1643–1647.

19. YoonOK, HsuTY, ImJH, BremRB (2012) Genetics and regulatory impact of alternative polyadenylation in human B-lymphoblastoid cells. PLoS Genet 8: e1002882.

20. MaugeriN, PowellJE, 't HoenPAC, de GeusEJC, WillemsenG, et al. (2011) LPAR1 and ITGA4 regulate peripheral blood monocyte counts. Hum Mutat 32 : 873–876.

21. LangmeadB, TrapnellC, PopM, SalzbergSL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10 (3) R25.

22. BiswasS, StoreyJD, AkeyJM (2008) Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis. BMC Bioinformatics 9 : 244.

23. LeekJT, StoreyJD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3 : 1724–1735.

24. CordauxR, BatzerMA (2009) The impact of retrotransposons on human genome evolution. Nat Rev Genet 10 : 691–703.

25. RyanMM, LockstoneHE, HuffakerSJ, WaylandMT, WebsterMJ, et al. (2006) Gene expression analysis of bipolar disorder reveals downregulation of the ubiquitin cycle and alterations in synaptic genes. Mol Psychiatry 11 : 965–978.

26. BousmanCA, ChanaG, GlattSJ, ChandlerSD, LuceroGR, et al. (2010) Preliminary evidence of ubiquitin proteasome system dysregulation in schizophrenia and bipolar disorder: convergent pathway analysis findings from two independent samples. Am J Med Genetics Part B, Neuropsychiatric genetics 153B: 494–502.

27. Cunninghame GrahamDS, MankuH, WagnerS, ReidJ, TimmsK, et al. (2007) Association of IRF5 in UK SLE families identifies a variant involved in polyadenylation. Hum Mol Gen 16 : 579–591.

28. HeapGA, TrynkaG, JansenRC, BruinenbergM, SwertzMA, et al. (2009) Complex nature of SNP genotype effects on gene expression in primary human leucocytes. BMC Med Genomics 2 : 1.

29. HuffCD, WitherspoonDJ, ZhangY, GatenbeeC, DensonLA, et al. (2012) Crohn's disease and genetic hitchhiking at IBD5. Mol Biol Evol 29 : 101–111.

30. TrapnellC, PachterL, SalzbergSL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25 : 1105–1111.

31. BelancioVP, HedgesDJ, DeiningerP (2006) LINE-1 RNA splicing and influences on mammalian gene expression. Nucleic Acids Res 34 : 1512–1521.

32. KimD-S, KimT-H, HuhJ-W, KimI-C, KimS-W, et al. (2006) LINE FUSION GENES: a database of LINE expression in human genes. BMC Genomics 7 : 139.

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

34. 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.

35. WillemsenG, De GeusEJC, BartelsM, Van BeijsterveldtCEMT, BrooksAI, et al. (2010) The Netherlands Twin Register biobank: a resource for genetic epidemiological studies. Twin Res Hum Genet 13 : 231–245.

36. PenninxBWJH, BeekmanATF, SmitJH, ZitmanFG, NolenWA, et al. (2008) The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr 17 : 121–140 doi:10.1002/mpr

37. HestandMS, KlingenhoffA, ScherfM, AriyurekY, RamosY, et al. (2010) Tissue-specific transcript annotation and expression profiling with complementary next-generation sequencing technologies. Nucleic Acids Res 38: e165.

38. BreitlingR, LiY, TessonBM, FuJ, WuC, et al. (2008) Genetical genomics: spotlight on QTL hotspots. PLoS Genet 4: e1000232.

39. QuinlanAR, HallIM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26 : 841–842.

40. GrahamRR, KyogokuC, SigurdssonS, VlasovaIA, DaviesLRL, et al. (2007) Three functional variants of IFN regulatory factor 5 (IRF5) define risk and protective haplotypes for human lupus. Proc Nat Acad Sci U S A 104 : 6758–6763.

41. RamakersC, RuijterJM, DeprezRHL, MoormanAF (2003) Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett 339 : 62–66.

42. RuijterJM, RamakersC, HoogaarsWMH, KarlenY, BakkerO, et al. (2009) Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucl. Acids Res 37: e45.

43. PickrellJK, PaiAA, GiladY, PritchardJK (2010) Noisy splicing drives mRNA isoform diversity in human cells. PLoS Genet 6: e1001236.

44. AltshulerDM, GibbsRA, PeltonenL, DermitzakisE, SchaffnerSF, et al. (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467 : 52–58.

45. DurbinRM, BentleyDR, ChakravartiA, ClarkAG, CollinsFS, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467 : 1061–1073.

46. WestraH-J, JansenRC, FehrmannRSN, Te MeermanGJ, Van HeelD, et al. (2011) MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics 27 : 2104–2111.