Genome-wide association study combined with biological context can reveal more disease-related SNPs altering microRNA target seed sites

BMC Genomics

Menglong Li¹  Zhining Wen¹  Jiwei Xue¹  Lifang Zhang¹  Gang Yang¹  Di Wu¹ 

[1] College of Chemistry, Sichuan University, Chengdu 610064, P.R. China

关键词: Human diseases and cancers;    Single nucleotide polymorphisms;    Genome-wide association study;    microRNA;   

Others  :  1216280
DOI  :  10.1186/1471-2164-15-669

 received in 2014-03-06, accepted in 2014-08-01,  发布年份 2014

【 摘 要 】

Background

Emerging studies demonstrate that single nucleotide polymorphisms (SNPs) resided in the microRNA recognition element seed sites (MRESSs) in 3′UTR of mRNAs are putative biomarkers for human diseases and cancers. However, exhaustively experimental validation for the causality of MRESS SNPs is impractical. Therefore bioinformatics have been introduced to predict causal MRESS SNPs. Genome-wide association study (GWAS) provides a way to detect susceptibility of millions of SNPs simultaneously by taking linkage disequilibrium (LD) into account, but the multiple-testing corrections implemented to suppress false positive rate always sacrificed the sensitivity. In our study, we proposed a method to identify candidate causal MRESS SNPs from 12 GWAS datasets without performing multiple-testing corrections. Alternatively, we used biological context to ensure credibility of the selected SNPs.

Results

In 11 out of the 12 GWAS datasets, MRESS SNPs were over-represented in SNPs with p-value ≤ 0.05 (odds ratio (OR) ranged from 1.1 to 2.4). Moreover, host genes of susceptible MRESS SNPs in each of the 11 GWAS dataset shared biological context with reported causal genes. There were 286 MRESS SNPs identified by our method, while only 13 SNPs were identified by multiple-testing corrections with a given threshold of 1 × 10⁻⁵, which is a common cutoff used in GWAS. 27 out of the 286 candidate SNPs have been reported to be deleterious while only 2 out of 13 multiple-testing corrected SNPs were documented in PubMed. MicroRNA-mRNA interactions affected by the 286 candidate SNPs were likely to present negatively correlated expression. These SNPs introduced greater alternation of binding free energy than other MRESS SNPs, especially when grouping by haplotypes (4210 vs. 4105 cal/mol by mean, 9781 vs. 8521 cal/mol by mean, respectively).

Conclusions

MRESS SNPs are promising disease biomarkers in multiple GWAS datasets. The method of integrating GWAS p-value and biological context is stable and effective for selecting candidate causal MRESS SNPs, it reduces the loss of sensitivity compared to multiple-testing corrections. The 286 candidate causal MRESS SNPs provide researchers a credible source to initialize their design of experimental validations in the future.

【 授权许可】

   
2014 Wu et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Brennecke J, Hipfner DR, Stark A, Russell RB, Cohen SM: Bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 2003, 113(1):25-36.
• [2]Wilfred BR, Wang WX, Nelson PT: Energizing miRNA research: a review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways. Mol Genet Metab 2007, 91(3):209-217.
• [3]Liang D, Shen N: MicroRNA involvement in lupus: the beginning of a new tale. Curr Opin Rheumatol 2012, 24(5):489-498.
• [4]Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR: MicroRNA expression profiles classify human cancers. Nature 2005, 435(7043):834-838.
• [5]Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 2005, 433(7027):769-773.
• [6]Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N: Combinatorial microRNA target predictions. Nat Genet 2005, 37(5):495-500.
• [7]Rajewsky N: microRNA target predictions in animals. Nat Genet 2006, 38 Suppl:S8-13.
• [8]Nicoloso MS, Sun H, Spizzo R, Kim H, Wickramasinghe P, Shimizu M, Wojcik SE, Ferdin J, Kunej T, Xiao L, Manoukian S, Secreto G, Ravagnani F, Wang X, Radice P, Croce CM, Davuluri RV, Calin GA: Single-nucleotide polymorphisms inside microRNA target sites influence tumor susceptibility. Cancer Res 2010, 70(7):2789-2798.
• [9]Wang G, van der Walt JM, Mayhew G, Li YJ, Zuchner S, Scott WK, Martin ER, Vance JM: Variation in the miRNA-433 binding site of FGF20 confers risk for Parkinson disease by overexpression of alpha-synuclein. Am J Hum Genet 2008, 82(2):283-289.
• [10]Friedman RC, Farh KK, Burge CB, Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 2009, 19(1):92-105.
• [11]Richardson K, Lai CQ, Parnell LD, Lee YC, Ordovas JM: A genome-wide survey for SNPs altering microRNA seed sites identifies functional candidates in GWAS. BMC Genomics 2011, 12:504. BioMed Central Full Text
• [12]Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 2009, 106(23):9362-9367.
• [13]Thomas LF, Saito T, Saetrom P: Inferring causative variants in microRNA target sites. Nucleic Acids Res 2011, 39(16):e109.
• [14]Liu C, Zhang F, Li T, Lu M, Wang L, Yue W, Zhang D: MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics 2012, 13:661. BioMed Central Full Text
• [15]Visscher PM, Hill WG, Wray NR: Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet 2008, 9(4):255-266.
• [16]Cirulli ET, Goldstein DB: Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 2010, 11(6):415-425.
• [17]Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T: miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 2009, 37(Database issue):D105-110.
• [18]Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu MC, Huang CY, Tsou AP, Huang HD: miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 2011, 39(Database issue):D163-169.
• [19]Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 2007, 27(1):91-105.
• [20]Lu M, Zhang Q, Deng M, Miao J, Guo Y, Gao W, Cui Q: An analysis of human microRNA and disease associations. PLoS ONE 2008, 3(10):e3420.
• [21]Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O’Donnell CJ, de Bakker PI: SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008, 24(24):2938-2939.
• [22]Wellcome Trust Case Control C: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007, 447(7145):661-678.
• [23]Davis AP, Murphy CG, Johnson R, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, Rosenstein MC, Wiegers TC, Mattingly CJ: The comparative toxicogenomics database: update 2013. Nucleic Acids Res 2013, 41(Database issue):D1104-1114.
• [24]Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012, 40(Database issue):D109-114.
• [25]Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, Nativ N, Bahir I, Doniger T, Krug H, Sirota-Madi A, Olender T, Golan Y, Stelzer G, Harel A, Lancet D: GeneCards version 3: the human gene integrator. Database 2010, 2010:baq020.
• [26]Ustek D, Ekmekci C, Oku B, Cosan F, Cakiris A, Abaci N, Celik S, Kamali S, Hatemi G, Kasapcopur O, Ozdogan H, Gul A: MEFV gene 3′-UTR Alu repeat polymorphisms in patients with familial Mediterranean fever. Clin Exp Rheumatol 2008, 26(4 Suppl 50):S72-76.
• [27]Notarnicola C, Didelot MN, Kone-Paut I, Seguret F, Demaille J, Touitou I: Reduced MEFV messenger RNA expression in patients with familial Mediterranean fever. Arthritis Rheum 2002, 46(10):2785-2793.
• [28]Shinar Y, Kosach E, Langevitz P, Zandman-Goddard G, Pauzner R, Rabinovich E, Livneh A, Lidar M: Familial Mediterranean FeVer gene (MEFV) mutations as a modifier of systemic lupus erythematosus. Lupus 2012, 21(9):993-998.
• [29]Pistilli EE, Devaney JM, Gordish-Dressman H, Bradbury MK, Seip RL, Thompson PD, Angelopoulos TJ, Clarkson PM, Moyna NM, Pescatello LS, Visich PS, Zoeller RF, Gordon PM, Hoffman EP: Interleukin-15 and interleukin-15R alpha SNPs and associations with muscle, bone, and predictors of the metabolic syndrome. Cytokine 2008, 43(1):45-53.
• [30]Carbo N, Lopez-Soriano J, Costelli P, Busquets S, Alvarez B, Baccino FM, Quinn LS, Lopez-Soriano FJ, Argiles JM: Interleukin-15 antagonizes muscle protein waste in tumour-bearing rats. Br J Cancer 2000, 83(4):526-531.
• [31]Pistilli EE, Siu PM, Alway SE: Interleukin-15 responses to aging and unloading-induced skeletal muscle atrophy. Am J Physiol Cell Physiol 2007, 292(4):C1298-1304.
• [32]Liu HF, An XJ, Yang Y, Yang L, Li Y, Huang CZ, Tao J, Tu YT: Association of rs10954213 polymorphisms and haplotype diversity in interferon regulatory factor 5 with systemic lupus erythematosus: a meta-analysis. J Huazhong Univ Sci Technolog Med Sci 2013, 33(1):15-21.
• [33]Hu W, Ren H: A meta-analysis of the association of IRF5 polymorphism with systemic lupus erythematosus. Int J Immunogenet 2011, 38(5):411-417.
• [34]Alonso-Perez E, Suarez-Gestal M, Calaza M, Kwan T, Majewski J, Gomez-Reino JJ, Gonzalez A: Cis-regulation of IRF5 expression is unable to fully account for systemic lupus erythematosus association: analysis of multiple experiments with lymphoblastoid cell lines. Arthritis Res Ther 2011, 13(3):R80. BioMed Central Full Text
• [35]Lofgren SE, Yin H, Delgado-Vega AM, Sanchez E, Lewen S, Pons-Estel BA, Witte T, D’Alfonso S, Ortego-Centeno N, Martin J, Alarcon-Riquelme ME, Kozyrev SV: Promoter insertion/deletion in the IRF5 gene is highly associated with susceptibility to systemic lupus erythematosus in distinct populations, but exerts a modest effect on gene expression in peripheral blood mononuclear cells. J Rheumatol 2010, 37(3):574-578.
• [36]Lashine YA, Seoudi AM, Salah S, Abdelaziz AI: Expression signature of microRNA-181-a reveals its crucial role in the pathogenesis of paediatric systemic lupus erythematosus. Clin Exp Rheumatol 2011, 29(2):351-357.
• [37]Guimaraes JM, Guimaraes IC, Duarte ME, Vieira T, Vianna VF, Fernandes MB, Vieira AR, Casado PL: Polymorphisms in BMP4 and FGFR1 genes are associated with fracture non-union. J Orthop Res 2013, 31(12):1971-1979.
• [38]Jun JK, Kim SM: Association study of fibroblast growth factor 2 and fibroblast growth factor receptors gene polymorphism in korean ossification of the posterior longitudinal ligament patients. J Korean Neurosurg Soc 2012, 52(1):7-13.
• [39]Wang Q, Green RP, Zhao G, Ornitz DM: Differential regulation of endochondral bone growth and joint development by FGFR1 and FGFR3 tyrosine kinase domains. Development 2001, 128(19):3867-3876.
• [40]Roig B, Virgos C, Franco N, Martorell L, Valero J, Costas J, Carracedo A, Labad A, Vilella E: The discoidin domain receptor 1 as a novel susceptibility gene for schizophrenia. Mol Psychiatry 2007, 12(9):833-841.
• [41]Bennett R, Hughes GR, Bywaters EG, Holt PJ: Neuropsychiatric problems in systemic lupus erythematosus. Br Med J 1972, 4(5836):342-345.
• [42]Lee SH, Lee J, Cha R, Park MH, Ha JW, Kim S, Kim YS: Genetic variations in soluble epoxide hydrolase and graft function in kidney transplantation. Transplant Proc 2008, 40(5):1353-1356.
• [43]Hama N, Yanagisawa Y, Dono K, Kobayashi S, Marubashi S, Nagano H, Umeshita K, Watanabe S, Uchiyama Y, Monden M: Gene expression profiling of acute cellular rejection in rat liver transplantation using DNA microarrays. Liver Transpl 2009, 15(5):509-521.
• [44]Dumousseau M, Rodriguez N, Juty N, Le Novere N: MELTING, a flexible platform to predict the melting temperatures of nucleic acids. BMC Bioinformatics 2012, 13:101. BioMed Central Full Text
• [45]Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011, 39(Database issue):D152-157.
• [46]Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu YT, Roskin KM, Schwartz M, Sugnet CW, Thomas DJ, Weber RJ, Haussler D, Kent WJ, University of California Santa Cruz: The UCSC genome browser database. Nucleic Acids Res 2003, 31(1):51-54.