Cross-Population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation
Expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression phenotypes. Mapping eQTLs enables us to study the genetic basis of gene expression variation across individuals. In this study, we introduce a statistical framework for analyzing genotype-expression data collected from multiple population groups. We show that our approach is particularly effective in identifying multiple independent eQTL signals that are consistently presented across populations in the proximity of a gene. In addition, our analysis framework allows effective integration of genomic annotations into eQTL analysis, which is helpful in dissecting the functional basis of eQTLs.
Vyšlo v časopise:
Cross-Population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation. PLoS Genet 11(4): e32767. doi:10.1371/journal.pgen.1005176
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1005176
Souhrn
Expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression phenotypes. Mapping eQTLs enables us to study the genetic basis of gene expression variation across individuals. In this study, we introduce a statistical framework for analyzing genotype-expression data collected from multiple population groups. We show that our approach is particularly effective in identifying multiple independent eQTL signals that are consistently presented across populations in the proximity of a gene. In addition, our analysis framework allows effective integration of genomic annotations into eQTL analysis, which is helpful in dissecting the functional basis of eQTLs.
Zdroje
1. Nica AC, Montgomery SB, Dimas AS, Stranger BE, Beazley C, et al. (2010) Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS genetics 6: e1000895. doi: 10.1371/journal.pgen.1000895 20369022
2. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, et al. (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genetics 6: e1000888. doi: 10.1371/journal.pgen.1000888 20369019
3. Hao K, Bosse Y, Nickle DC, Pare PD, Postma DS, et al. (2012) Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS genetics 8: e1003029. doi: 10.1371/journal.pgen.1003029 23209423
4. GTEx Consortium (2013) The genotype-tissue expression (gtex) project. Nature Genetics 45: 580–585. doi: 10.1038/ng.2653 23715323
5. Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, et al. (2009) Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325: 1246–1250. doi: 10.1126/science.1174148 19644074
6. Maranville JC, Luca F, Richards AL, Wen X, Witonsky DB, et al. (2011) Interactions between glucocorticoid treatment and cis-regulatory polymorphisms contribute to cellular response pheno-types. PLoS genetics 7: e1002162. doi: 10.1371/journal.pgen.1002162 21750684
7. Barreiro LB, Tailleux L, Pai AA, Gicquel B, Marioni JC, et al. (2012) Deciphering the genetic architecture of variation in the immune response to mycobacterium tuberculosis infection. Proceedings of the National Academy of Sciences 109: 1204–1209. doi: 10.1073/pnas.1115761109
8. Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, et al. (2014) Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344: 519–523. doi: 10.1126/science.1249547 24786080
9. Brown CD, Mangravite LM, Engelhardt BE (2013) Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genetics 9: e1003649. doi: 10.1371/journal.pgen.1003649 23935528
10. Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the encode pilot project. Nature 447: 799–816. doi: 10.1038/nature05874 17571346
11. Pique-Regi R, Degner JF, Pai AA, Gaffney DJ, Gilad Y, et al. (2011) Accurate inference of transcription factor binding from dna sequence and chromatin accessibility data. Genome research 21: 447–455. doi: 10.1101/gr.112623.110 21106904
12. Hoffman MM, Ernst J, Wilder SP, Kundaje A, Harris RS, et al. (2012) Integrative annotation of chromatin elements from encode data. Nucleic acids research: gks1284.
13. Liang L, Morar N, Dixon AL, Lathrop GM, Abecasis GR, et al. (2013) A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome research 23: 716–726. doi: 10.1101/gr.142521.112 23345460
14. Flutre T, Wen X, Pritchard J, Stephens M (2013) A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genetics 9: e1003486. doi: 10.1371/journal.pgen.1003486 23671422
15. Wen X (2014) Bayesian model selection in complex linear systems, as illustrated in genetic association studies. Biometrics 70: 73–83. doi: 10.1111/biom.12112 24350677
16. Sul JH, Han B, Ye C, Choi T, Eskin E (2013) Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. PLoS genetics 9: e1003491. doi: 10.1371/journal.pgen.1003491 23785294
17. Gaffney DJ, Veyrieras JB, Degner JF, Pique-Regi R, Pai AA, et al. (2012) Dissecting the regulatory architecture of gene expression QTLs. Genome Biol 13: R7. doi: 10.1186/gb-2012-13-1-r7 22293038
18. Veyrieras JB, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, et al. (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS genetics 4: e1000214. doi: 10.1371/journal.pgen.1000214 18846210
19. Lee SI, Dudley AM, Drubin D, Silver PA, Krogan NJ, et al. (2009) Learning a prior on regulatory potential from eQTL data. PLoS genetics 5: e1000358. doi: 10.1371/journal.pgen.1000358 19180192
20. Lappalainen T, Sammeth M, Friedländer MR, T Hoen PA, Monlong J, et al. (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501: 506–511. doi: 10.1038/nature12531 24037378
21. Wen X, Stephens M (2014) Bayesian methods for genetic association analysis with heterogeneous subgroups: From meta-analyses to gene-environment interactions. The Annals of Applied Statistics 8: 176–203. doi: 10.1214/13-AOAS695
22. Morris AP (2011) Transethnic meta-analysis of genomewide association studies. Genetic epidemiology 35: 809–822. doi: 10.1002/gepi.20630 22125221
23. Marigorta UM, Navarro A (2013) High trans-ethnic replicability of gwas results implies common causal variants. PLoS genetics 9: e1003566. doi: 10.1371/journal.pgen.1003566 23785302
24. Li G, Shabalin AA, Rusyn I, Wright FA, Nobel AB (2013) An empirical bayes approach for multiple tissue eQTL analysis. arXiv preprint arXiv:13112948.
25. Yang J, Ferreira T, Morris AP, Medland SE, Madden PA, et al. (2012) Conditional and joint multiple-snp analysis of gwas summary statistics identifies additional variants influencing complex traits. Nature Genetics 44: 369–375. doi: 10.1038/ng.2213 22426310
26. Coviello AD, Haring R, Wellons M, Vaidya D, Lehtimäki T, et al. (2012) A genome-wide association meta-analysis of circulating sex hormone-binding globulin reveals multiple loci implicated in sex steroid hormone regulation. PLoS genetics 8: e1002805. doi: 10.1371/journal.pgen.1002805 22829776
27. 1000 Genomes Project Consortium, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65. doi: 10.1038/nature11632 23128226
28. Gilad Y, Rifkin SA, Pritchard JK (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends in Genetics 24: 408–415. doi: 10.1016/j.tig.2008.06.001 18597885
29. Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ, et al. (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482: 390–394. doi: 10.1038/nature10808 22307276
30. Moyerbrailean GA, Harvey CT, Kalita CA, Wen X, Luca F, et al. (2014) Are all genetic variants in dnase i sensitivity regions functional? bioRxiv: 007559.
31. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, et al. (2012) Gencode: the reference human genome annotation for the encode project. Genome research 22: 1760–1774. doi: 10.1101/gr.135350.111 22955987
32. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, et al. (2010) Understanding mechanisms underlying human gene expression variation with rna sequencing. Nature 464: 768–772. doi: 10.1038/nature08872 20220758
33. Stegle O, Parts L, Piipari M, Winn J, Durbin R (2012) Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpret ability of gene expression analyses. Nature protocols 7: 500–507. doi: 10.1038/nprot.2011.457 22343431
34. Wen X (2013) Robust bayesian FDR control with bayes factors. arXiv preprint arXiv:13113981.
35. Newton MA, Noueiry A, Sarkar D, Ahlquist P (2004) Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5: 155–76. doi: 10.1093/biostatistics/5.2.155 15054023
36. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, et al. (2014) Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949. doi: 10.1126/science.1246949 24604202
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2015 Číslo 4
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- Lack of GDAP1 Induces Neuronal Calcium and Mitochondrial Defects in a Knockout Mouse Model of Charcot-Marie-Tooth Neuropathy
- Proteolysis of Virulence Regulator ToxR Is Associated with Entry of into a Dormant State
- Frameshift Variant Associated with Novel Hoof Specific Phenotype in Connemara Ponies
- Ataxin-2 Regulates Translation in a New BAC-SCA2 Transgenic Mouse Model