Samantha Lent1, Andres Cardenas2, Sheryl L Rifas-Shiman3, Patrice Perron4,5, Luigi Bouchard5,6,7, Ching-Ti Liu1, Marie-France Hivert3,4,8, Josée Dupuis1. 1. Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA. 2. Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, 94704, USA. 3. Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Health Care Institute, Boston, MA, 02215, USA. 4. Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, J1H 5N4, Canada. 5. Centre de Recherche, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, J1H 5N4, Canada. 6. Department of Biochemistry & Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, J1H 5N4, Canada. 7. Department of Medical Biology, CIUSSS Saguenay-Lac-Saint-Jean, Hôpital de Chicoutimi, Saguenay, QC, G7H 5H6, Canada. 8. Diabetes Unit, Massachusetts General Hospital, Boston, MA, 02114, USA.
Abstract
Aim: We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff. Materials & methods: We used a simulation study and real data analysis to evaluate performance. Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood. Results: Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1-2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods. Conclusion: This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.
Aim: We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff. Materials & methods: We used a simulation study and real data analysis to evaluate performance. Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood. Results: Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1-2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods. Conclusion: This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.
Authors: Matthew E Ritchie; Belinda Phipson; Di Wu; Yifang Hu; Charity W Law; Wei Shi; Gordon K Smyth Journal: Nucleic Acids Res Date: 2015-01-20 Impact factor: 16.971
Authors: Andrew E Teschendorff; Francesco Marabita; Matthias Lechner; Thomas Bartlett; Jesper Tegner; David Gomez-Cabrero; Stephan Beck Journal: Bioinformatics Date: 2012-11-21 Impact factor: 6.937
Authors: Eugene Andres Houseman; William P Accomando; Devin C Koestler; Brock C Christensen; Carmen J Marsit; Heather H Nelson; John K Wiencke; Karl T Kelsey Journal: BMC Bioinformatics Date: 2012-05-08 Impact factor: 3.169
Authors: Stephanie H Witt; Josef Frank; Maria Gilles; Maren Lang; Jens Treutlein; Fabian Streit; Isabell A C Wolf; Verena Peus; Barbara Scharnholz; Tabea S Send; Stefanie Heilmann-Heimbach; Sugirthan Sivalingam; Helene Dukal; Jana Strohmaier; Marc Sütterlin; Janine Arloth; Manfred Laucht; Markus M Nöthen; Michael Deuschle; Marcella Rietschel Journal: BMC Genomics Date: 2018-04-25 Impact factor: 3.969
Authors: Anne K Bozack; Elena Colicino; Allan C Just; Robert O Wright; Andrea A Baccarelli; Rosalind J Wright; Alison G Lee Journal: Epigenetics Date: 2021-10-22 Impact factor: 4.861
Authors: Yunsung Lee; Espen Riskedal; Karl Trygve Kalleberg; Mette Istre; Andreas Lind; Fridtjof Lund-Johansen; Olaug Reiakvam; Arne V L Søraas; Jennifer R Harris; John Arne Dahl; Cathrine L Hadley; Astanand Jugessur Journal: Sci Rep Date: 2022-07-07 Impact factor: 4.996
Authors: Tamara J Hagoel; Eduardo Cortes Gomez; Ajay Gupta; Clare J Twist; Rafal Kozielski; Jeffrey C Martin; Lingui Gao; Joseph Kuechle; Prashant K Singh; Miranda Lynch; Lei Wei; Song Liu; Jianmin Wang; Joyce E Ohm Journal: Cold Spring Harb Mol Case Stud Date: 2022-01-10
Authors: Anne K Bozack; Sheryl L Rifas-Shiman; Brent A Coull; Andrea A Baccarelli; Robert O Wright; Chitra Amarasiriwardena; Diane R Gold; Emily Oken; Marie-France Hivert; Andres Cardenas Journal: Clin Epigenetics Date: 2021-11-19 Impact factor: 6.551