Data quality control, analysis strategy and visualization of epigenomic-wide association studies_Chinese Journal of Evidence-Based Medicine

Authors：

FAN Juanjuan ¹ , JI Xinyu ¹ , SHEN Sipeng ^1,2 , ZHANG Ruyang ^1,2 ,  WEI Yongyue ^1,2 , CHEN Feng ^1,2

1. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, P.R.China;
2. Center of Global Health, Nanjing Medical University, Nanjing 211166, P.R.China;

Corresponding author：

WEI Yongyue, Email: ywei@njmu.edu.cn

Keywords：

Epigenome-wide association study; Data quality control; Statistical analysis strategy; Visualization

DOI：

10.7507/1672-2531.202012149

Video：

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Epigenetics refers to the modification effect of external and internal environmental factors on genes under the premise of the unaltered genetic sequence, leading to changes in gene expression level or function, and thereby affecting various phenotypes or disease outcomes. In recent years, epigenetics has attracted increasing attention. Among them, DNA methylation has been shown to be closely related to human development and the development of disease. However, the high-dimensional omics data generated by genome-wide methylation detection can comprehensively reflect the overall and local epigenetic modifications at the genome level, which has become one of the main research contents in this field. Based on genome-wide methylation chip data, this paper summarized the quality control process of this omics data, common epigenetic omics correlation statistical analysis methods and ideas, and visualization realization of main results based on SAS JMP Genomics 10 software, so as to provide reference for similar studies.

Citation： FAN Juanjuan, JI Xinyu, SHEN Sipeng, ZHANG Ruyang, WEI Yongyue, CHEN Feng. Data quality control, analysis strategy and visualization of epigenomic-wide association studies. Chinese Journal of Evidence-Based Medicine, 2021, 21(6): 721-728. doi: 10.7507/1672-2531.202012149 Copy

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31.	Wei Y, Liang J, Zhang R, et al. Epigenetic modifications in KDM lysine demethylases associate with survival of early-stage NSCLC. Clin Epigenetics, 2018, 10: 41.
32.	Zhang R, Chen C, Dong X, et al. Independent validation of early-stage non-small cell lung cancer prognostic scores incorporating epigenetic and transcriptional biomarkers with gene-gene interactions and main effects. Chest, 2020, 158(2): 808-819.
33.	Ng SW, Mitchell A, Kennedy JA, et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature, 2016, 540(7633): 433-437.
34.	Zhang HH, Ahn J, Lin X, et al. Gene selection using support vector machines with non-convex penalty. Bioinformatics, 2006, 22(1): 88-95.
35.	Cutler DR, Edwards TC, Beard KH, et al. Random forests for classification in ecology. Ecology, 2007, 88(11): 2783-2792.
36.	Zhang Z. Naïve Bayes classification in R. Ann Transl Med, 2016, 4(12): 241.
37.	Rahman MS, Ambler G, Choodari-Oskooei B, et al. Review and evaluation of performance measures for survival prediction models in external validation settings. BMC Med Res Methodol, 2017, 17(1): 60.
38.	Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol, 2013, 13: 33.
39.	陈峰, 柏建岭, 赵杨, 等. 全基因组关联研究中的统计分析方法. 中华流行病学杂志, 2011, 32(4): 400-404.

1. Rakyan VK, Down TA, Balding DJ, et al. Epigenome-wide association studies for common human diseases. Nat Rev Genet, 2011, 12(8): 529-541.
2. Kulis M, Queirós AC, Beekman R, et al. Intragenic DNA methylation in transcriptional regulation, normal differentiation and cancer. Biochim Biophys Acta, 2013, 1829(11): 1161-1174.
3. Bird A. DNA methylation patterns and epigenetic memory. Genes Dev, 2002, 16(1): 6-21.
4. Baylin SB. DNA methylation and gene silencing in cancer. Nat Clin Pract Oncol, 2005, 2 Suppl 1: S4-11.
5. Gomez JL. Epigenetics in asthma. Curr Allergy Asthma Rep, 2019, 19(12): 56.
6. Meissner A, Gnirke A, Bell GW, et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res, 2005, 33(18): 5868-5877.
7. Bibikova M, Lin Z, Zhou L, et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res, 2006, 16(3): 383-393.
8. Saadati M, Benner A. Statistical challenges of high-dimensional methylation data. Stat Med, 2014, 33(30): 5347-5357.
9. Chen YA, Lemire M, Choufani S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics, 2013, 8(2): 203-209.
10. Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics, 2014, 30(10): 1363-1369.
11. Teschendorff AE, Marabita F, Lechner M, et al. A beta-mixture quantile normalization method for correcting probe design bias in illumina infinium 450 k DNA methylation data. Bioinformatics, 2013, 29(2): 189-196.
12. Price EM, Robinson WP. Adjusting for batch effects in DNA methylation microarray data, a lesson learned. Front Genet, 2018, 9: 83.
13. Bose M, Wu C, Pankow JS, et al. Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study. BMC Bioinformatics, 2014, 15(1): 312.
14. Chen J, Just AC, Schwartz J, et al. CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies. Bioinformatics, 2016, 32(3): 469-471.
15. Estivill XJ. Human molecular genetics (3rd edition). Garland Sci, 2005, 117(5): 509.
16. Wang S. Method to detect differentially methylated loci with case-control designs using Illumina arrays. Genet Epidemiol, 2011, 35(7): 686-694.
17. Yip WK, Fier H, DeMeo DL, et al. A novel method for detecting association between DNA methylation and diseases using spatial information. Genet Epidemiol, 2014, 38(8): 714-721.
18. Teng M, Wang Y, Kim S, et al. Empirical bayes model comparisons for differential methylation analysis. Comp Funct Genomics, 2012, 2012: 376706.
19. Feng H, Conneely KN, Wu H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res, 2014, 42(8): e69.
20. 张秋伊. 表观遗传关联研究中基于 CpG 集的五种统计分析方法比较. 南京: 南京医科大学, 2016.
21. Wu MC, Lee S, Cai T, et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet, 2011, 89(1): 82-93.
22. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 2013, 14: 7.
23. Barbie DA, Tamayo P, Boehm JS, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 2009, 462(7269): 108-112.
24. Sun H, Wang S. Penalized logistic regression for high-dimensional DNA methylation data with case-control studies. Bioinformatics, 2012, 28(10): 1368-1375.
25. Li C, Li H. Variable selection and regression analysis for graph-structured covariates with an application to genomics. Ann Appl Stat, 2010, 4(3): 1498-1516.
26. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Statist Soc B, 2005, 67(2): 301-320.
27. Chen X, Wang L, Smith JD, et al. Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes. Bioinformatics, 2008, 24(21): 2474-2481.
28. Gao Q, He Y, Yuan Z, et al. Gene- or region-based association study via kernel principal component analysis. BMC Genet, 2011, 12: 75.
29. Breiman L. Random forests. Mach Learn, 2001, 45(1): 5-32.
30. 张秋伊, 赵杨, 魏永越, 等. 高维 DNA 甲基化数据的随机森林降维分析. 中华疾病控制杂志, 2016, 20(6): 630-633.
31. Wei Y, Liang J, Zhang R, et al. Epigenetic modifications in KDM lysine demethylases associate with survival of early-stage NSCLC. Clin Epigenetics, 2018, 10: 41.
32. Zhang R, Chen C, Dong X, et al. Independent validation of early-stage non-small cell lung cancer prognostic scores incorporating epigenetic and transcriptional biomarkers with gene-gene interactions and main effects. Chest, 2020, 158(2): 808-819.
33. Ng SW, Mitchell A, Kennedy JA, et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature, 2016, 540(7633): 433-437.
34. Zhang HH, Ahn J, Lin X, et al. Gene selection using support vector machines with non-convex penalty. Bioinformatics, 2006, 22(1): 88-95.
35. Cutler DR, Edwards TC, Beard KH, et al. Random forests for classification in ecology. Ecology, 2007, 88(11): 2783-2792.
36. Zhang Z. Naïve Bayes classification in R. Ann Transl Med, 2016, 4(12): 241.
37. Rahman MS, Ambler G, Choodari-Oskooei B, et al. Review and evaluation of performance measures for survival prediction models in external validation settings. BMC Med Res Methodol, 2017, 17(1): 60.
38. Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol, 2013, 13: 33.
39. 陈峰, 柏建岭, 赵杨, 等. 全基因组关联研究中的统计分析方法. 中华流行病学杂志, 2011, 32(4): 400-404.

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