ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing
Autoři:
Jiaxin Fan aff001; Jian Hu aff001; Chenyi Xue aff002; Hanrui Zhang aff002; Katalin Susztak aff003; Muredach P. Reilly aff002; Rui Xiao aff001; Mingyao Li aff001
Působiště autorů:
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
aff001; Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York City, New York, United States of America
aff002; Departments of Medicine and Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
aff003; The Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York City, New York, United States of America
aff004
Vyšlo v časopise:
ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing. PLoS Genet 16(5): e32767. doi:10.1371/journal.pgen.1008786
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008786
Souhrn
Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.
Klíčová slova:
Gene expression – Genome-wide association studies – Haplotypes – Kidneys – Macrophages – Molecular genetics – Research errors – RNA sequencing
Zdroje
1. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856
2. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90(1):7–24. doi: 10.1016/j.ajhg.2011.11.029 22243964
3. Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nature Reviews Genetics. 2019.
4. Consortium G, Aguet F, Brown AA, Castel SE, Davis JR, He Y, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550:204. doi: 10.1038/nature24277 29022597
5. Nica AC, Dermitzakis ET. Expression quantitative trait loci: present and future. Philosophical transactions of the Royal Society of London.Series B, Biological sciences. 2013;368(1620):20120362. doi: 10.1098/rstb.2012.0362 23650636
6. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS. PLOS Genetics. 2010;6(4):e1000888. doi: 10.1371/journal.pgen.1000888 20369019
7. Gilad Y, Rifkin SA, Pritchard JK. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends in Genetics. 2008;24(8):408–15. doi: 10.1016/j.tig.2008.06.001 18597885
8. Dixon AL, Liang L, Moffatt MF, Chen W, Heath S, Wong KCC, et al. A genome-wide association study of global gene expression. Nat Genet. 2007;39:1202. doi: 10.1038/ng2109 17873877
9. Schliekelman P. Statistical power of expression quantitative trait loci for mapping of complex trait loci in natural populations. Genetics. 2008;178(4):2201–16. doi: 10.1534/genetics.107.076687 18245851
10. Sun W, Hu Y. eQTL Mapping Using RNA-seq Data. Statistics in biosciences. 2013;5(1):198–219. doi: 10.1007/s12561-012-9068-3 23667399
11. Almlöf JC, Lundmark P, Lundmark A, Ge B, Maouche S, Göring H,H.H., et al. Powerful identification of cis-regulatory SNPs in human primary monocytes using allele-specific gene expression. PloS one. 2012;7(12):e52260. doi: 10.1371/journal.pone.0052260 23300628
12. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews.Genetics. 2009;10(1):57–63. doi: 10.1038/nrg2484 19015660
13. Harvey CT, Moyerbrailean GA, Davis GO, Wen X, Luca F, Pique-Regi R. QuASAR: quantitative allele-specific analysis of reads. Bioinformatics. 2014;31(8):1235–42. doi: 10.1093/bioinformatics/btu802 25480375
14. Mayba O, Gilbert HN, Liu J, Haverty PM, Jhunjhunwala S, Jiang Z, et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 2014;15(8):405. doi: 10.1186/s13059-014-0405-3 25315065
15. Edsgärd D, Iglesias MJ, Reilly S, Hamsten A, Tornvall P, Odeberg J, et al. GeneiASE: Detection of condition-dependent and static allele-specific expression from RNA-seq data without haplotype information. Scientific Reports. 2016;6:21134. doi: 10.1038/srep21134 26887787
16. Qiu C, Huang S, Park J, Park Y, Ko Y, Seasock MJ, et al. Renal compartment–specific genetic variation analyses identify new pathways in chronic kidney disease. Nat Med. 2018;24(11):1721–31. doi: 10.1038/s41591-018-0194-4 30275566
17. van dG, McVicker G, Gilad Y, Pritchard JK. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nature Methods. 2015;12:1061. doi: 10.1038/nmeth.3582 26366987
18. Ko Y, Yi H, Qiu C, Huang S, Park J, Ledo N, et al. Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease. The American Journal of Human Genetics. 2017;100(6):940–53. doi: 10.1016/j.ajhg.2017.05.004 28575649
19. Tan X, Wang Y, Han Y, Chang W, Su T, Hou J, et al. Genetic variation in the GSTM3 promoter confer risk and prognosis of renal cell carcinoma by reducing gene expression. Br J Cancer. 2013 Dec 10,;109(12):3105–15. doi: 10.1038/bjc.2013.669 24157827
20. Ooi EMM, Chan DT, Watts GF, Chan DC, Ng TWK, Dogra GK, et al. Plasma apolipoprotein C-III metabolism in patients with chronic kidney disease. J Lipid Res. 2011;52(4):794–800. doi: 10.1194/jlr.M011163 21297177
21. Howard M, Murakami Y, Pagnamenta A, Daumer-Haas C, Fischer B, Hecht J, et al. Mutations in PGAP3 Impair GPI-Anchor Maturation, Causing a Subtype of Hyperphosphatasia with Mental Retardation. The American Journal of Human Genetics. 2014 Feb 6,;94(2):278–87. doi: 10.1016/j.ajhg.2013.12.012 24439110
22. Wang Y, Murakami Y, Yasui T, Wakana S, Kikutani H, Kinoshita T, et al. Significance of GPI-anchored protein enrichment in lipid rafts for the control of autoimmunity. J Biol Chem. 2013 -07-17:jbc.M113.492611.
23. Tan RJ, Zhou D, Xiao L, Zhou L, Li Y, Bastacky SI, et al. Extracellular Superoxide Dismutase Protects against Proteinuric Kidney Disease. Journal of the American Society of Nephrology: JASN. 2015;26(10):2447–59. doi: 10.1681/ASN.2014060613 25644107
24. Liu S, Nheu T, Luwor R, Nicholson SE, Zhu H. SPSB1, a Novel Negative Regulator of the Transforming Growth Factor-β Signaling Pathway Targeting the Type II Receptor. J Biol Chem. 2015 Jul 17,;290(29):17894–908. doi: 10.1074/jbc.M114.607184 26032413
25. Sureshbabu A, Muhsin SA, Choi ME. TGF-β signaling in the kidney: profibrotic and protective effects. Am J Physiol Renal Physiol. 2016 04 01,;310(7):F596–606. doi: 10.1152/ajprenal.00365.2015 26739888
26. Petkovich M, Jones G. CYP24A1 and kidney disease. Current Opinion in Nephrology and Hypertension. 2011 July;20(4):337–344. doi: 10.1097/MNH.0b013e3283477a7b 21610497
27. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Current Protocols in Bioinformatics. 2016;54(1):1.30.1,1.30.33.
28. Tian W, Zhang Z, Cohen DM. MAPK signaling and the kidney. Am J Physiol Renal Physiol. 2000 Oct;279(4):593.
29. Hsu CC, Kao WHL, Coresh J, Pankow JS, Marsh-Manzi J, Boerwinkle E, et al. Apolipoprotein E and Progression of Chronic Kidney Disease. JAMA. 2005;293(23):2892–9. doi: 10.1001/jama.293.23.2892 15956634
30. Wynn TA, Vannella KM. Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity. 2016;44(3):450–62. doi: 10.1016/j.immuni.2016.02.015 26982353
31. Russell DG, Huang L, VanderVen BC. Immunometabolism at the interface between macrophages and pathogens. Nature Reviews Immunology. 2019;19(5):291–304. doi: 10.1038/s41577-019-0124-9 30679807
32. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886
33. Kim-Hellmuth S, Bechheim M, Pütz B, Mohammadi P, Nédélec Y, Giangreco N, et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nature Communications. 2017 -08-16;8(1):1–10. doi: 10.1038/s41467-016-0009-6 28232747
34. Krausgruber T, Blazek K, Smallie T, Alzabin S, Lockstone H, Sahgal N, et al. IRF5 promotes inflammatory macrophage polarization and TH1-TH17 responses. Nat Immunol. 2011;12(3):231–8. doi: 10.1038/ni.1990 21240265
35. Praefcke GJK. Regulation of innate immune functions by guanylate-binding proteins. International Journal of Medical Microbiology. 2018;308(1):237–45. doi: 10.1016/j.ijmm.2017.10.013 29174633
36. Puck A, Aigner R, Modak M, Cejka P, Blaas D, Stöckl J. Expression and regulation of Schlafen (SLFN) family members in primary human monocytes, monocyte-derived dendritic cells and T cells. Results in immunology. 2015;5:23–32. doi: 10.1016/j.rinim.2015.10.001 26623250
37. Lehrke M, Millington SC, Lefterova M, Cumaranatunge RG, Szapary P, Wilensky R, et al. CXCL16 Is a Marker of Inflammation, Atherosclerosis, and Acute Coronary Syndromes in Humans. J Am Coll Cardiol. 2007;49(4):442–9. doi: 10.1016/j.jacc.2006.09.034 17258089
38. Baba T, Mukaida N. Role of macrophage inflammatory protein (MIP)-1α/CCL3 in leukemogenesis. Molecular & cellular oncology. 2014;1(1):e29899.
39. Cuda CM, Misharin AV, Khare S, Saber R, Tsai F, Archer AM, et al. Conditional deletion of caspase-8 in macrophages alters macrophage activation in a RIPK-dependent manner. Arthritis Research & Therapy. 2015;17(1):291.
40. Arend WP, Malyak M, Guthridge CJ, Gabay C. INTERLEUKIN-1 RECEPTOR ANTAGONIST: Role in Biology. Annu Rev Immunol. 1998;16(1):27–55.
41. Kamat SS, Camara K, Parsons WH, Chen D, Dix MM, Bird TD, et al. Immunomodulatory lysophosphatidylserines are regulated by ABHD16A and ABHD12 interplay. Nature chemical biology. 2015;11(2):164–71. doi: 10.1038/nchembio.1721 25580854
42. Kim C, Sano Y, Todorova K, Carlson BA, Arpa L, Celada A, et al. The kinase p38 alpha serves cell type-specific inflammatory functions in skin injury and coordinates pro- and anti-inflammatory gene expression. Nat Immunol. 2008;9(9):1019–27. doi: 10.1038/ni.1640 18677317
43. Yang Y, Kim SC, Yu T, Yi Y, Rhee MH, Sung G, et al. Functional Roles of p38 Mitogen-Activated Protein Kinase in Macrophage-Mediated Inflammatory Responses. Mediators Inflamm. 2014;2014:352371. doi: 10.1155/2014/352371 24771982
44. Ma Z, Moore R, Xu X, Barber GN. DDX24 Negatively Regulates Cytosolic RNA-Mediated Innate Immune Signaling. PLOS Pathogens. 2013;9(10):e1003721. doi: 10.1371/journal.ppat.1003721 24204270
45. Oji Y, Tatsumi N, Fukuda M, Nakatsuka S, Aoyagi S, Hirata E, et al. The translation elongation factor eEF2 is a novel tumor-associated antigen overexpressed in various types of cancers. Int J Oncol. 2014;44(5):1461–9. doi: 10.3892/ijo.2014.2318 24589652
46. Aitkin M. A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models. Biometrics. 1999;55(1):117–28. doi: 10.1111/j.0006-341x.1999.00117.x 11318145
47. npmlreg: Nonparametric Maximum Likelihood Estimation for Random Effect Models [Internet].; 2018 []. Available from: https://CRAN.R-project.org/package=npmlreg.
48. Bates D., Mächler M., Bolker B., Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1).
49. Zhang H, Xue C, Shah R, Bermingham K, Hinkle CC, Li W, et al. Functional analysis and transcriptomic profiling of iPSC-derived macrophages and their application in modeling Mendelian disease. Circ Res. 2015;117(1):17–28. doi: 10.1161/CIRCRESAHA.117.305860 25904599
50. Zhang H, Shi J, Hachet MA, Xue C, Bauer RC, Jiang H, et al. CRISPR/Cas9-Mediated Gene Editing in Human iPSC-Derived Macrophage Reveals Lysosomal Acid Lipase Function in Human Macrophages-Brief Report. Arterioscler Thromb Vasc Biol. 2017;37(11):2156–60. doi: 10.1161/ATVBAHA.117.310023 28882870
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 5
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Raději si zajděte na oční! Jak souvisí citlivost zraku s rozvojem demence?
- Co způsobuje pooperační infekce? Na vině může být i naše vlastní mikrobiota
- Čeká nás průlom v diagnostice karcinomu pankreatu?
- Polibek, který mi „vzal nohy“ aneb vzácný výskyt EBV u 70leté ženy – kazuistika
Nejčtenější v tomto čísle
- The domesticated transposase ALP2 mediates formation of a novel Polycomb protein complex by direct interaction with MSI1, a core subunit of Polycomb Repressive Complex 2 (PRC2)
- Polyploidy breaks speciation barriers in Australian burrowing frogs Neobatrachus
- Congenital hearing impairment associated with peripheral cochlear nerve dysmyelination in glycosylation-deficient muscular dystrophy
- A new neuropeptide insect parathyroid hormone iPTH in the red flour beetle Tribolium castaneum