Predicting the impact of non-coding variants on DNA methylation

Nucleic Acids Research

Volumn 45, Issue 11, 2017, Pages

  Haoyang, Zeng ^a   Gifford, David K ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

ACCESS TO INFORMATION; ACCURACY; ALLELE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CPG ISLAND; DATA ANALYSIS SOFTWARE; DISEASE ASSOCIATION; DNA METHYLATION; GENE LINKAGE DISEQUILIBRIUM; GENE MUTATION; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; HUMAN; INTERNET; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE TRAIT LOCUS; SENSITIVITY ANALYSIS; SEQUENCE ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; SUPPORT VECTOR MACHINE;

EID: 85027266662     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkx177     Document Type: Article

References (42)

1. Hindorff,L.A., Sethupathy,P., Junkins,H.A., Ramos,E.M., Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. U.S.A., 106, 9362-9367.
2. Maurano,M.T., Humbert,R., Rynes,E., Thurman,R.E., Haugen,E., Wang,H., Reynolds,A.P., Sandstrom,R., Qu,H., Brody,J. et al. (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science, 337, 1190-1195.
3. Gusev,A., Lee,S.H., Trynka,G., Finucane,H., Vilhjálmsson,B.J., Xu,H., Zang,C., Ripke,S., Bulik-Sullivan,B., Stahl,E. et al. (2014) Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet., 95, 535-552.
4. Kircher,M., Witten,D.M., Jain,P., O'Roak,B.J., Cooper,G.M. and Shendure,J. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet., 46, 310.
5. Ritchie,G.R., Dunham,I., Zeggini,E. and Flicek,P. (2014) Functional annotation of noncoding sequence variants. Nat. Methods, 11, 294-296.
6. Zhou,J. and Troyanskaya,O.G. (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods, 12, 931-934.
7. Kelley,D.R., Snoek,J. and Rinn,J.L. (2016) Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res., 26, 990-999.
8. Lee,D., Gorkin,D.U., Baker,M., Strober,B.J., Asoni,A.L., McCallion,A.S. and Beer,M.A. (2015) A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet., 47, 955-961.
9. Bird,A. (2002) DNA methylation patterns and epigenetic memory. Genes Dev., 16, 6-21.
10. Bock,C. (2012) Analysing and interpreting DNA methylation data. Nat. Rev. Genet., 13, 705-719.
11. Barlow,D.P. (2011) Genomic imprinting: a mammalian epigenetic discovery model. Annu. Rev. Genet., 45, 379-403.
12. Martin,M. and Herceg,Z. (2012) From hepatitis to hepatocellular carcinoma: a proposed model for cross-talk between inflammation and epigenetic mechanisms. Genome Med., 4, 1.
13. Meissner,A. (2010) Epigenetic modifications in pluripotent and differentiated cells. Nat. Biotechnol., 28, 1079-1088.
14. Bestor,T.H. (1998) The host defence function of genomic methylation patterns. Novartis Found. Symp., 214, 187-195.
15. Lee,H.J., Lowdon,R.F.,Maricque,B., Zhang,B., Stevens,M., Li,D., Johnson,S.L. and Wang,T. (2015) Developmental enhancers revealed by extensive DNA methylome maps of zebrafish early embryos. Nat. Commun., 6, 6315.
16. Hwang,W., Oliver,V.F.,Merbs,S.L., Zhu,H. and Qian,J. (2015) Prediction of promoters and enhancers using multiple DNA methylation-associated features. BMC Genomics, 16, 1.
17. Bhasin,M., Zhang,H., Reinherz,E.L. and Reche,P.A. (2005) Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett., 579, 4302-4308.
18. Kim,S., Li,M., Paik,H.-i.H., Nephew,K.P., Shi,H., Kramer,R., Xu,D. and Huang,T.H.-M. (2008) Predicting DNA methylation susceptibility using CpG flanking sequences. Pacific Symp. Biocomput. Citeseer, 13, 315-326.
19. Lu,L., Lin,K., Qian,Z., Li,H., Cai,Y. and Li,Y. (2010) Predicting DNA methylation status using word composition. J. Biomed. Sci. Eng., 3, 672.
20. Zhou,X., Li,Z., Dai,Z. and Zou,X. (2012) Prediction of methylation CpGs and their methylation degrees in human DNA sequences. Comput. Biol. Med., 42, 408-413.
21. Kaplow,I.M., MacIsaac,J.L., Mah,S.M., McEwen,L.M., Kobor,M.S. and Fraser,H.B. (2015) A pooling-based approach to mapping genetic variants associated with DNA methylation. Genome Res., 25, 907-917.
22. Marçais,G. and Kingsford,C. (2011) A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics, 27, 764-770.
23. Breiman,L., Friedman,J., Stone,C.J. and Olshen,R.A. (1984) Classification and Regression Trees, CRC Press.
24. Alipanahi,B., Delong,A., Weirauch,M.T. and Frey,B.J. (2015) Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol., 33, 831-838.
25. Gupta,S., Stamatoyannopoulos,J.A., Bailey,T.L. and Noble,W.S. (2007) Quantifying similarity between motifs. Genome Biol., 8, 1.
26. Weirauch,M.T., Yang,A., Albu,M., Cote,A.G., Montenegro-Montero,A., Drewe,P., Najafabadi,H.S., Lambert,S.A., Mann,I., Cook,K. et al. (2014) Determination and inference of eukaryotic transcription factor sequence specificity. Cell, 158, 1431-1443.
27. Matys,V., Fricke,E., Geffers,R., Gößling,E., Haubrock,M., Hehl,R., Hornischer,K., Karas,D., Kel,A.E., Kel-Margoulis,O.V. et al. (2003) TRANSFAC®: transcriptional regulation, from patterns to profiles. Nucleic Acids Res., 31, 374-378.
28. Zhang,W., Spector,T.D., Deloukas,P., Bell,J.T. and Engelhardt,B.E. (2015) Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. Genome Biol., 16, 14.
29. Fan,S., Huang,K., Ai,R., Wang,M. and Wang,W. (2016) Predicting CpG methylation levels by integrating Infinium HumanMethylation450 BeadChip array data. Genomics, 107, 132-137.
30. Hervouet,E., Vallette,F.M. and Cartron,P.-F. (2009) Dnmt3/transcription factor interactions as crucial players in targeted DNA methylation. Epigenetics, 4, 487-499.
31. Das,R., Dimitrova,N., Xuan,Z., Rollins,R.A., Haghighi,F., Edwards,J.R., Ju,J., Bestor,T.H. and Zhang,M.Q. (2006) Computational prediction of methylation status in human genomic sequences. Proc. Natl. Acad. Sci. U.S.A., 103, 10713-10716.
32. Mtatiro,S.N., Singh,T., Rooks,H., Mgaya,J., Mariki,H., Soka,D., Mmbando,B., Msaki,E., Kolder,I., Thein,S.L. et al. (2014) Genome wide association study of fetal hemoglobin in sickle cell anemia in Tanzania. PLoS One, 9, e111464.
33. Kathiresan,S., Willer,C.J., Peloso,G.M., Demissie,S., Musunuru,K., Schadt,E.E., Kaplan,L., Bennett,D., Li,Y., Tanaka,T. et al. (2009) Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet., 41, 56-65.
34. AMD Gene Consortium (2013) Seven new loci associated with age-related macular degeneration. Nat. Genet., 45, 433-439.
35. Fox,C.S., Liu,Y., White,C.C., Feitosa,M., Smith,A.V., Heard-Costa,N., Lohman,K., Johnson,A.D., Foster,M.C., Greenawalt,D.M. et al. (2012) Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet., 8, e1002695.
36. Bauer,D.E., Kamran,S.C., Lessard,S., Xu,J., Fujiwara,Y., Lin,C., Shao,Z., Canver,M.C., Smith,E.C., Pinello,L. et al. (2013) An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science, 342, 253-257.
37. Musunuru,K., Strong,A., Frank-Kamenetsky,M., Lee,N.E., Ahfeldt,T., Sachs,K.V., Li,X., Li,H., Kuperwasser,N., Ruda,V.M. et al. (2010) From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature, 466, 714-719.
38. Leslie,R., O'Donnell,C.J. and Johnson,A.D. (2014) GRASP: analysis of genotype-phenotype results from 1390 genome-wide association studies and corresponding open access database. Bioinformatics, 30, i185-i194.
39. Welter,D., MacArthur,J., Morales,J., Burdett,T., Hall,P., Junkins,H., Klemm,A., Flicek,P., Manolio,T., Hindorff,L. et al. (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res., 42, D1001-D1006.
40. 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature, 491, 56-65.
41. Fu,Y., Liu,Z., Lou,S., Bedford,J., Mu,X.J., Yip,K.Y., Khurana,E. and Gerstein,M. (2014) FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol., 15, 1.
42. Zeng,H., Edwards,M.D., Liu,G. and Gifford,D.K. (2016) Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics, 32, i121-i127.