Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues

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Gene expression is a fundamental cellular process that contributes to phenotypic diversity. Gene expression can vary between alleles of an individual through differences in genomic imprinting or cis-acting regulatory variation. Distinguishing allelic activity is important for informing the abundance of altered mRNA and protein products. Advances in sequencing technologies allow us to quantify patterns of allele-specific expression (ASE) in different individuals and cell-types. Previous studies have identified patterns of ASE across human populations for single cell-types; however the degree of tissue-specificity of ASE has not been deeply characterized. In this study, we compare patterns of ASE across multiple tissues from a single individual using whole transcriptome sequencing (RNA-Seq) and a targeted, high-resolution assay (mmPCR-Seq). We detect patterns of ASE for rare deleterious and loss-of-function protein-coding variants, informing the frequency at which allelic expression could modify the functional impact of personal deleterious protein-coding across tissues. We demonstrate that these interactions occur for one third of such variants however large direction flips in allelic expression are infrequent.

Vyšlo v časopise: Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues. PLoS Genet 10(5): e32767. doi:10.1371/journal.pgen.1004304
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004304

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1. MacArthurDG, BalasubramanianS, FrankishA, HuangN, MorrisJ, et al. (2012) A systematic survey of loss-of-function variants in human protein-coding genes. Science 335 : 823–828.

2. KeinanA, ClarkAG (2012) Recent explosive human population growth has resulted in an excess of rare genetic variants. Science 336 : 740–743.

3. MacArthurDG, Tyler-SmithC (2010) Loss-of-function variants in the genomes of healthy humans. Human Molecular Genetics 19: R125–130.

4. GeB, PokholokDK, KwanT, GrundbergE, MorcosL, et al. (2009) Global patterns of cis variation in human cells revealed by high-density allelic expression analysis. Nature Genetics 41 : 1216–1222.

5. VerlaanDJ, GeB, GrundbergE, HobermanR, LamKCL, et al. (2009) Targeted screening of cis-regulatory variation in human haplotypes. Genome Research 19 : 118–127.

6. BerlivetS, MoussetteS, OuimetM, VerlaanDJ, KokaV, et al. (2012) Interaction between genetic and epigenetic variation defines gene expression patterns at the asthma-associated locus 17q12-q21 in lymphoblastoid cell lines. Human Genetics 131 : 1161–1171.

7. JentarraGM, RiceSG, OlfersS, RajanC, SaffenDM, et al. (2012) Skewed allele-specific expression of the NF1 gene in normal subjects: a possible mechanism for phenotypic variability in neurofibromatosis type 1. Journal of Child Neurology 27 : 695–702.

8. FinchN, CarrasquilloMM, BakerM, RutherfordNJ, CoppolaG, et al. (2011) TMEM106B regulates progranulin levels and the penetrance of FTLD in GRN mutation carriers. Neurology 76 : 467–474.

9. EmisonES, Garcia-BarceloM, GriceEA, LantieriF, AmielJ, et al. (2010) Differential contributions of rare and common, coding and noncoding Ret mutations to multifactorial Hirschsprung disease liability. American Journal of Human Genetics 87 : 60–74.

10. MaiaA-T, AntoniouAC, O'ReillyM, SamarajiwaS, DunningM, et al. (2012) Effects of BRCA2 cis-regulation in normal breast and cancer risk amongst BRCA2 mutation carriers. Breast Cancer Research 14: R63–R63.

11. ZhangR, RamawamiG, SmithK, GustavoT, MontgomerySB, et al. (2013) Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing. Nature Methods [Available online 24 November 2013].

12. SherryST, WardMH, KholodovM, BakerJ, PhanL, et al. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Research 29 : 308–311.

13. AbecasisGR, AltshulerD, AutonA, BrooksLD, DurbinRM, et al. (2010) A map of human genome variation from population-scale sequencing. Nature 467 : 1061–1073.

14. NHLBI GO Exome Sequencing Project (ESP). Available: https://esp.gs.washington.edu/drupal/. Accessed 8 April 2014.

15. NgPC, HenikoffS (2003) SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research 31 : 3812–3814.

16. AdzhubeiIA, SchmidtS, PeshkinL, RamenskyVE, GerasimovaA, et al. (2010) A method and server for predicting damaging missense mutations. Nature Methods 7 : 248–249.

17. DegnerJF, MarioniJC, PaiAA, PickrellJK, NkadoriE, et al. (2009) Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25 : 3207–3212.

18. JayP, RougeulleC, MassacrierA, MonclaA, MatteiMG, et al. (1997) The human necdin gene, NDN, is maternally imprinted and located in the Prader-Willi syndrome chromosomal region. Nat Genet 17 : 357–361.

19. TuskanRG, TsangS, SunZ, BaerJ, RozenblumE, et al. (2008) Real-time PCR analysis of candidate imprinted genes on mouse chromosome 11 shows balanced expression from the maternal and paternal chromosomes and strain-specific variation in expression levels. Epigenetics 3 : 43–50.

20. PantPV, TaoH, BeilharzEJ, BallingerDG, CoxDR, et al. (2006) Analysis of allelic differential expression in human white blood cells. Genome Res 16 : 331–339.

21. ZhangK, LiJB, GaoY, EgliD, XieB, et al. (2009) Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human. Nature Methods 6 : 613–618.

22. BoyadjievSA, JabsEW (2000) Online Mendelian Inheritance in Man (OMIM) as a knowledgebase for human developmental disorders. Clin Genet 57 : 253–266.

23. MayatepekE, KohlmüllerD (1998) Transient trimethylaminuria in childhood. Acta Paediatrica 87 : 1205–1207.

24. DolphinCT, JanmohamedA, SmithRL, ShephardEA, PhillipsIR (1997) Missense mutation in flavin-containing mono-oxygenase 3 gene, FMO3, underlies fish-odour syndrome. Nature Genetics 17 : 491–494.

25. LappalainenT, MontgomerySB, NicaAC, DermitzakisET (2011) Epistatic Selection between Coding and Regulatory Variation in Human Evolution and Disease. American Journal of Human Genetics 89 : 459–463.

26. GibsonG (2011) Rare and common variants: twenty arguments. Nature Reviews Genetics 13 : 135–145.

27. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25 : 1754–1760.

28. McKennaA, HannaM, BanksE, SivachenkoA, CibulskisK, et al. (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20 : 1297–1303.

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

30. TrapnellC, WilliamsBa, PerteaG, MortazaviA, KwanG, et al. (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology 28 : 511–515.

31. DobinA, DavisCa, SchlesingerF, DrenkowJ, ZaleskiC, et al. (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England) 29 : 15–21.

32. LiH, HandsakerB, WysokerA, FennellT, RuanJ, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 : 2078–2079.