Maximizing ecological and evolutionary insight in bisulfite sequencing data sets

Abstract

Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-model systems. Here, we highlight how these considerations influence a study’s power to link methylation variation with a predictor variable of interest. Relative to current practice, we argue that sample sizes will need to increase to provide robust insights. We also provide recommendations for overcoming common challenges and an R Shiny app to aid in study design.

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Figure 1: Overview of reduced representation bisulfite sequencing and whole-genome bisulfite sequencing.
Figure 2: Estimates of effect sizes and their impact on the power of differential methylation analysis.
Figure 3: Properties of CpG methylation levels vary across data sets and influence power.

References

  1. 1.

    Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2011).

    Article  Google Scholar 

  2. 2.

    Jones, P. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492 (2012).

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Seymour, D. K. & Becker, C. The causes and consequences of DNA methylome variation in plants. Curr. Opin. Plant Biol. 36, 56–63 (2017).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Verhoeven, K. J. F., Jansen, J. J., van Dijk, P. J. & Biere, A. Stress-induced DNA methylation changes and their heritability in asexual dandelions. New Phytol. 185, 1108–1118 (2010).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Zhao, Y. et al. Adaptive methylation regulation of p53 pathway in sympatric speciation of blind mole rats, Spalax. Proc. Natl Acad. Sci. USA 113, 2146–2151 (2016).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Durand, S., Bouché, N., Perez Strand, E., Loudet, O. & Camilleri, C. Rapid establishment of genetic incompatibility through natural epigenetic variation. Curr. Biol. 22, 326–331 (2012).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Hernando-Herraez, I. et al. Dynamics of DNA methylation in recent human and great ape evolution. PLoS Genet. 9, e1003763 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Hernando-Herraez, I., Garcia-Perez, R., Sharp, A. J. & Marques-Bonet, T. DNA methylation: insights into human evolution. PLoS Genet. 11, e1005661 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Snell-Rood, E. The importance of epigenetics for behavioral ecologists (and vice versa). Behav. Ecol. 19, 2012 (2012).

    Google Scholar 

  11. 11.

    Ledon-Rettig, C. C., Richards, C. L. & Martin, L. B. Epigenetics for behavioral ecologists. Behav. Ecol. 24, 311–324 (2012).

    Article  Google Scholar 

  12. 12.

    Glastad, K. M., Hunt, B. G. & Goodisman, M. A. Evolutionary insights into DNA methylation in insects. Curr. Opin. Insect Sci. 1, 25–30 (2014).

    Article  Google Scholar 

  13. 13.

    Feng, S. et al. Conservation and divergence of methylation patterning in plants and animals. Proc. Natl Acad. Sci. USA 107, 8689–8694 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Schmitz, R. J. et al. Patterns of population epigenomic diversity. Nature 495, 193–198 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Schmitz, R. J. et al. Transgenerational epigenetic instability is a source of novel methylation variants. Science 334, 369–373 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Cortijo, S. et al. Mapping the epigenetic basis of complex traits. Science 343, 1145–1148 (2014).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Lister, R., Pelizzola, M., Dowen, R. & Hawkins, R. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Cokus, S. J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215–219 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Dolzhenko, E. & Smith, A. D. Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinform. 15, 215 (2014).

    Article  Google Scholar 

  21. 21.

    Sun, D. et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 15, R38 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Feng, H., Conneely, K. N. & Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42, 1–11 (2014).

    Article  Google Scholar 

  23. 23.

    Hansen, K., Langmead, B. & Irizarry, R. BSmooth : from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Tsai, P. C. & Bell, J. T. Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation. Int. J. Epidemiol. 44, 1429–1441 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Ziller, M. J., Hansen, K. D., Meissner, A. & Aryee, M. J. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat. Methods 12, 2–5 (2014).

    Google Scholar 

  26. 26.

    Rakyan, V. K., Down, Ta, Balding, D. J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–41 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Harris, R. A. et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat. Biotechnol. 28, 1097–1105 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Hansen, K. D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Pacis, A. et al. Bacterial infection remodels the DNA methylation landscape of human dendritic cells. Genome Res. 25, 1801–1811 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Zemach, A., McDaniel, I. E., Silva, P. & Zilberman, D. Genome-wide evolutionary analysis of eukaryotic DNA methylation. Science 328, 916–919 (2010).

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Takuno, S., Ran, J.-H. & Gaut, B. S. Evolutionary patterns of genic DNA methylation vary across land plants. Nat. Plants 2, 15222 (2016).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Klughammer, J. et al. Differential DNA methylation analysis without a reference genome. Cell Rep. 13, 2621–2633 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Verhoeven, K. J. F., VonHoldt, B. M. & Sork, V. L. Epigenetics in ecology and evolution: what we know and what we need to know. Mol. Ecol. 25, 1631–1638 (2016).

    Article  PubMed  Google Scholar 

  34. 34.

    Becker, C. et al. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480, 245–249 (2011).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Lea, A., Tung, J. & Zhou, X. A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data. PLoS Genet. 11, e1005650 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Lea, A. J., Altmann, J., Alberts, S. C. & Tung, J. Resource base influences genome-wide DNA methylation levels in wild baboons (Papio cynocephalus). Mol. Ecol. 25, 1681–1696 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Tung, J. et al. Social environment is associated with gene regulatory variation in the rhesus macaque immune system. Proc. Natl Acad. Sci. USA 109, 6490–6495 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Banovich, N. E. et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 10, e1004663 (2014).

    Article  Google Scholar 

  39. 39.

    Zhang, X. et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell 126, 1189–1201 (2006).

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Libbrecht, R., Oxley, P. R., Keller, L. & Kronauer, D. J. C. Robust DNA methylation in the clonal raider ant brain. Curr. Biol. 26, 391–395 (2016).

    Article  Google Scholar 

  41. 41.

    Boyle, P., Clement, K., Gu, H. & Smith, Z. Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol. 13, R92 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Krueger, F. Trim Galore! v. 0.4.1 (2015).

  43. 43.

    Murgatroyd, C. et al. Dynamic DNA methylation programs persistent adverse effects of early-life stress. Nat. Neurosci. 12, 1559–1566 (2009).

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Elliott, E., Ezra-Nevo, G., Regev, L., Neufeld-Cohen, A. & Chen, A. Resilience to social stress coincides with functional DNA methylation of the CRF gene in adult mice. Nat. Neurosci. 13, 1351–1353 (2010).

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Tobi, E. W. et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat. Commun. 5, 5592 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Dubin, M. J. et al. DNA methylation variation in Arabidopsis has a genetic basis and appears to be involved in local adaptation. eLife 4, e05255 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Hernando-Herraez, I. et al. The interplay between DNA methylation and sequence divergence in recent human evolution. Nucleic Acids Res. 43, 8204–8214 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Janowitz Koch, I. et al. The concerted impact of domestication and transposon insertions on methylation patterns between dogs and grey wolves. Mol. Ecol. 25, 1838–1855 (2016).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Taudt, A., Colomé-Tatché, M. & Johannes, F. Genetic sources of population epigenomic variation. Nat. Rev. Genet. 17, 319–332 (2016).

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Gugger, P. F., Fitz-Gibbon, S., Pellegrini, M. & Sork, V. L. Species-wide patterns of DNA methylation variation in Quercus lobata and its association with climate gradients. Mol. Ecol. 25, 1665–1680 (2016).

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Kang, H. M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–8 (2006).

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011).

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Liu, Y., Siegmund, K. D., Laird, P. W. & Berman, B. P. Bis-SNP: Combined DNA methylation and SNP calling for bisulfite-seq data. Genome Biol. 13, R61 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Gao, S. et al. BS-SNPer: SNP calling in bisulfite-seq data. Bioinformatics 31, 4006–4008 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Jablonka, E. & Raz, G. Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q. Rev. Biol. 84, 131–176 (2009).

    Article  PubMed  Google Scholar 

  58. 58.

    Heard, E. & Martienssen, R. A. Transgenerational epigenetic inheritance: myths and mechanisms. Cell 157, 95–109 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Bewick, A. J., Vogel, K. J., Moore, A. J. & Schmitz, R. J. Evolution of DNA methylation across insects. Mol. Biol. Evol. 34, msw264 (2016).

    Article  Google Scholar 

  60. 60.

    Bonasio, R. et al. Genome-wide and caste-specific DNA methylomes of the ants Camponotus floridanus and Harpegnathos saltator. Curr. Biol. 22, 1755–1764 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Lyko, F. et al. The honey bee epigenomes: differential methylation of brain DNA in queens and workers. PLoS Biol. 8, e1000506 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Wang, J. & Fan, C. A neutrality test for detecting selection on DNA methylation using single methylation polymorphism frequency spectrum. Genome Biol. Evol. 7, 154–171 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Vidalis, A. et al. Methylome evolution in plants. Genome Biol. 17, 264 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Shah, S. et al. Genetic and environmental exposures constrain epigenetic drift over the human life course. Genome Res. 24, 1725–1733 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    McRae, A. F. et al. Contribution of genetic variation to transgenerational inheritance of DNA methylation. Genome Biol. 15, R73 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Weigel, D. & Colot, V. Epialleles in plant evolution. Genome Biol. 13, 249 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Hansen, K. D. et al. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Charlesworth, B. & Jain, K. Purifying selection, drift, and reversible mutation with arbitrarily high mutation rates. Genetics 198, 1587–1602 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Jaffe, A. E. & Irizarry, R. A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 15, R31 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Beldomenico, P. M. et al. Poor condition and infection: a vicious circle in natural populations. Proc. R. Soc. B 275, 1753–1759 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Charruau, P. et al. Pervasive effects of aging on gene expression in wild wolves. Mol. Biol. Evol. 33, 1967–1978 (2016).

    CAS  Article  PubMed  Google Scholar 

  72. 72.

    Merino, S., Moreno, J., Sanz, J. J. & Arriero, E. Are avian blood parasites pathogenic in the wild? A medication experiment in blue tits (Parus caeruleus). Proc. R. Soc. B 267, 2507–2510 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Ots, I., Murumägi, A. & Hõrak, P. Haematological health state indices of reproducing great tits: methodology and sources of natural variation. Funct. Ecol. 12, 700–707 (1998).

    Article  Google Scholar 

  74. 74.

    Watkins, N. A. et al. A HaemAtlas: characterizing gene expression in differentiated human blood cells. Blood 113, e1–e9 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Kawakatsu, T. et al. Unique cell-type-specific patterns of DNA methylation in the root meristem. Nat. Plants 2, 16058 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 13, 86 (2012).

    Article  Google Scholar 

  77. 77.

    Hattab, M. W. et al. Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies. Genome Biol. 18, 24 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Zheng, S. C. et al. Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses. Nat. Methods 14, 216–217 (2017).

    CAS  Article  PubMed  Google Scholar 

  79. 79.

    Zou, J., Lippert, C., Heckerman, D., Aryee, M. & Listgarten, J. Epigenome-wide association studies without the need for cell-type composition. Nat. Methods 11, 309–11 (2014).

    CAS  Article  PubMed  Google Scholar 

  80. 80.

    Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2009).

    Article  Google Scholar 

  81. 81.

    Houseman, E. A., Molitor, J. & Marsit, C. J. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Eckhardt, F. et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 38, 1378–1385 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Bell, J. T. et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Klein, H. U. & Hebestreit, K. An evaluation of methods to test predefined genomic regions for differential methylation in bisulfite sequencing data. Brief. Bioinform. 17, 796–807 (2016).

    Article  PubMed  Google Scholar 

  85. 85.

    Akalin, A. & Kormaksson, M. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Jaffe, A. E. et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int. J. Epidemiol. 41, 200–209 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).

    Google Scholar 

  88. 88.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Jühling, F. et al. Metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 26, 256–262 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Li, S. et al. An optimized algorithm for detecting and annotating regional differential methylation. BMC Bioinform. 14(suppl. 5), S10 (2013).

    Article  Google Scholar 

  91. 91.

    Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Hebestreit, K., Dugas, M. & Klein, H. U. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29, 1647–1653 (2013).

    CAS  Article  PubMed  Google Scholar 

  93. 93.

    Virdi, K. S. et al. Arabidopsis MSH1 mutation alters the epigenome and produces heritable changes in plant growth. Nat. Commun. 6, 6386 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Rockman, M. V. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012).

    Article  PubMed  Google Scholar 

  95. 95.

    Klug, M. & Rehli, M. Functional analysis of promoter CpG methylation using a CpG-free luciferase reporter vector. Epigenetics 1, 127–130 (2006).

    Article  PubMed  Google Scholar 

  96. 96.

    Vojta, A. et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 44, 5615–5628 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Wu, C., DeWan, A., Hoh, J. & Wang, Z. A comparison of association methods correcting for population stratification in case-control studies. Ann. Hum. Genet. 75, 418–27 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Perry, G. et al. Comparative RNA sequencing reveals substantial genetic variation in endangered primates. Genome Res. 22, 602–610 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Piskol, R., Ramaswami, G. & Li, J. B. Reliable identification of genomic variants from RNA-seq data. Am. J. Hum. Genet. 93, 641–651 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Horton, M. W. et al. Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nat. Genet. 44, 212–216 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

We thank K. Hansen and I. Hernando-Herraez for providing processed file formats from their previously published work. We also thank N. Snyder-Mackler, L. Barreiro and X. Zhou for helpful comments and suggestions, M. Cetinkaya-Rundel for coding suggestions on the R Shiny app, M. Gavery for beta-testing it, the Baylor College of Medicine Human Genome Sequencing Center for access to the current version of the baboon genome assembly (Panu 2.0). This work was supported by NIH R21-AG049936 and 1R01GM102562 to J.T., NSF BCS-1455808 to J.T. and A.J.L.; P.A.P.D. is supported by NIH K12GM000678 from the Training, Workforce Development and Diversity division of the National Institute of General Medical Sciences.

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A.J.L. and J.T. conceived the study; A.J.L., T.P.V. and P.A.P.D. analysed previously published and simulated data; T.P.V. wrote the R Shiny app; and A.J.L. and J.T. wrote the manuscript, with input from all co-authors. All authors gave final approval for publication.

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Correspondence to Amanda J. Lea or Jenny Tung.

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Supplementary Methods, Supplementary Tables 1–2, Supplementary Figures 1–9, Supplementary References

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Lea, A.J., Vilgalys, T.P., Durst, P.A.P. et al. Maximizing ecological and evolutionary insight in bisulfite sequencing data sets. Nat Ecol Evol 1, 1074–1083 (2017). https://doi.org/10.1038/s41559-017-0229-0

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