#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Congruence of Additive and Non-Additive Effects on Gene Expression Estimated from Pedigree and SNP Data


There is increasing evidence that heritable variation in gene expression underlies genetic variation in susceptibility to disease. Therefore, a comprehensive understanding of the similarity between relatives for transcript variation is warranted—in particular, dissection of phenotypic variation into additive and non-additive genetic factors and shared environmental effects. We conducted a gene expression study in blood samples of 862 individuals from 312 nuclear families containing MZ or DZ twin pairs using both pedigree and genotype information. From a pedigree analysis we show that the vast majority of genetic variation across 17,994 probes is additive, although non-additive genetic variation is identified for 960 transcripts. For 180 of the 960 transcripts with non-additive genetic variation, we identify expression quantitative trait loci (eQTL) with dominance effects in a sample of 339 unrelated individuals and replicate 31% of these associations in an independent sample of 139 unrelated individuals. Over-dominance was detected and replicated for a trans association between rs12313805 and ETV6, located 4MB apart on chromosome 12. Surprisingly, only 17 probes exhibit significant levels of common environmental effects, suggesting that environmental and lifestyle factors common to a family do not affect expression variation for most transcripts, at least those measured in blood. Consistent with the genetic architecture of common diseases, gene expression is predominantly additive, but a minority of transcripts display non-additive effects.


Vyšlo v časopise: Congruence of Additive and Non-Additive Effects on Gene Expression Estimated from Pedigree and SNP Data. PLoS Genet 9(5): e32767. doi:10.1371/journal.pgen.1003502
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003502

Souhrn

There is increasing evidence that heritable variation in gene expression underlies genetic variation in susceptibility to disease. Therefore, a comprehensive understanding of the similarity between relatives for transcript variation is warranted—in particular, dissection of phenotypic variation into additive and non-additive genetic factors and shared environmental effects. We conducted a gene expression study in blood samples of 862 individuals from 312 nuclear families containing MZ or DZ twin pairs using both pedigree and genotype information. From a pedigree analysis we show that the vast majority of genetic variation across 17,994 probes is additive, although non-additive genetic variation is identified for 960 transcripts. For 180 of the 960 transcripts with non-additive genetic variation, we identify expression quantitative trait loci (eQTL) with dominance effects in a sample of 339 unrelated individuals and replicate 31% of these associations in an independent sample of 139 unrelated individuals. Over-dominance was detected and replicated for a trans association between rs12313805 and ETV6, located 4MB apart on chromosome 12. Surprisingly, only 17 probes exhibit significant levels of common environmental effects, suggesting that environmental and lifestyle factors common to a family do not affect expression variation for most transcripts, at least those measured in blood. Consistent with the genetic architecture of common diseases, gene expression is predominantly additive, but a minority of transcripts display non-additive effects.


Zdroje

1. FisherRA (1918) The Correlation Between Relatives on the Supposition of Mendelian Inheritance. Transactions of the Royal Society of Edinburgh 52: 399–433.

2. Falconer DS, Mackay TFC (1996) Introduction to Quantitative Genetics.

3. Lynch M, Walsh B (1997) Genetics and Analysis of Quantitative Traits: Sinauer Assoc.

4. HillWG, GoddardME, VisscherPM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4: e1000008 doi:10.1371/journal.pgen.1000008.

5. ZukO, HechterE, SunyaevSR, LanderES (2012) The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A 109: 1193–1198.

6. PritchardJK (2001) Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 69: 124–137.

7. YangJ, BenyaminB, McEvoyBP, GordonS, HendersAK, et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565–569.

8. SchadtEE (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461: 218–223.

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

10. MauranoMT, HumbertR, RynesE, ThurmanRE, HaugenE, et al. (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337: 1190–1195.

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

12. ZhangX, CalAJ, BorevitzJO (2011) Genetic architecture of regulatory variation in Arabidopsis thaliana. Genome Res 21: 725–733.

13. ZhangX, ByrnesJK, GalTS, LiWH, BorevitzJO (2008) Whole genome transcriptome polymorphisms in Arabidopsis thaliana. Genome Biol 9: R165.

14. StuparRM, HermansonPJ, SpringerNM (2007) Nonadditive expression and parent-of-origin effects identified by microarray and allele-specific expression profiling of maize endosperm. Plant Physiol 145: 411–425.

15. CuiX, AffourtitJ, ShockleyKR, WooY, ChurchillGA (2006) Inheritance patterns of transcript levels in F1 hybrid mice. Genetics 174: 627–637.

16. PowellJE, HendersAK, McRaeAF, CaracellaA, SmithS, et al. (2012) The Brisbane Systems Genetics Study: genetical genomics meets complex trait genetics. PLoS ONE 7: e35430 doi:10.1371/journal.pone.0035430.

17. QinSP, KimJ, ArafatD, GibsonG (2012) Effect of Normalization on Statistical and Biological Interpretation of Gene Expression Profiles. Front Genet 3.

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

19. PriceAL, HelgasonA, ThorleifssonG, McCarrollSA, KongA, et al. (2011) Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genet 7: e1001317 doi:10.1371/journal.pgen.1001317.

20. AshburnerM, BallCA, BlakeJA, BotsteinD, ButlerH, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29.

21. ZhengQ, WangXJ (2008) GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis. Nucleic Acids Res 36: W358–363.

22. LandiMT, DrachevaT, RotunnoM, FigueroaJD, LiuH, et al. (2008) Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS ONE 3: e1651 doi:10.1371/journal.pone.0001651.

23. ViguerieN, MontastierE, MaoretJJ, RousselB, CombesM, et al. (2012) Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation. PLoS Genet 8: e1002959 doi:10.1371/journal.pgen.1002959.

24. WeirBS (2008) Linkage disequilibrium and association mapping. Annu Rev Genomics Hum Genet 9: 129–142.

25. PowellJE, KranisA, FloydJ, DekkersJC, KnottS, et al. (2012) Optimal use of regression models in genome-wide association studies. Anim Genet 43: 133–143.

26. DempsterAP (1972) Covariance Selection. Biometrics 28: 157–175.

27. KiiveriHT (2011) Multivariate analysis of microarray data: differential expression and differential connection. BMC Bioinformatics 12: 42.

28. GjuvslandAB, HayesBJ, OmholtSW, CarlborgO (2007) Statistical epistasis is a generic feature of gene regulatory networks. Genetics 175: 411–420.

29. WangJ, TianL, LeeHS, WeiNE, JiangH, et al. (2006) Genomewide nonadditive gene regulation in Arabidopsis allotetraploids. Genetics 172: 507–517.

30. WayneML, Telonis-ScottM, BonoLM, HarshmanL, KoppA, et al. (2007) Simpler mode of inheritance of transcriptional variation in male Drosophila melanogaster. Proc Natl Acad Sci U S A 104: 18577–18582.

31. LemosB, AraripeLO, FontanillasP, HartlDL (2008) Dominance and the evolutionary accumulation of cis- and trans-effects on gene expression. Proc Natl Acad Sci U S A 105: 14471–14476.

32. GibsonG, Riley-BergerR, HarshmanL, KoppA, VachaS, et al. (2004) Extensive sex-specific nonadditivity of gene expression in Drosophila melanogaster. Genetics 167: 1791–1799.

33. BirchlerJA, YaoH, ChudalayandiS (2006) Unraveling the genetic basis of hybrid vigor. Proc Natl Acad Sci U S A 103: 12957–12958.

34. HochholdingerF, HoeckerN (2007) Towards the molecular basis of heterosis. Trends Plant Sci 12: 427–432.

35. PowellJE, HendersAK, McRaeAF, WrightMJ, MartinNG, et al. (2012) Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent. Genome Res 22: 456–466.

36. LeeSH, DeCandiaTR, RipkeS, YangJ, SullivanPF, et al. (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44: 247–250.

37. MedlandSE, NyholtDR, PainterJN, McEvoyBP, McRaeAF, et al. (2009) Common variants in the trichohyalin gene are associated with straight hair in Europeans. Am J Hum Genet 85: 750–755.

38. AlbertsR, TerpstraP, LiY, BreitlingR, NapJP, et al. (2007) Sequence polymorphisms cause many false cis eQTLs. PLoS ONE 2: e622 doi:10.1371/journal.pone.0000622.

39. YangJ, LeeSH, GoddardME, VisscherPM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88: 76–82.

40. PowellJE, VisscherPM, GoddardME (2010) Reconciling the analysis of IBD and IBS in complex trait studies. Nat Rev Genet 11: 800–805.

41. GilmourAR, ThompsonJR, CullisBR (1995) Average Information REML: An efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51: 1440–1450.

42. Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson JR (2009) ASReml User Guide Release 3.0: VSN International Ltd, Hemel Hempstead.

43. PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.

44. BenjaminiY, HochbergY (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B 57: 289–300.

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2013 Číslo 5
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#