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Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples
Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku Finland; Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland; Department of Computer Science, Aalto University, Espoo, Finland.
Department of Computer Science, Aalto University, Espoo, Finland.
Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku Finland.
Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku Finland.
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2022 (English)In: Epigenetics, ISSN 1559-2294, E-ISSN 1559-2308, Vol. 17, no 12, p. 1608-1627Article in journal (Refereed) Published
Abstract [en]

DNA methylation patterns are largely established in-utero and might mediate the impacts of in-utero conditions on later health outcomes. Associations between perinatal DNA methylation marks and pregnancy-related variables, such as maternal age and gestational weight gain, have been earlier studied with methylation microarrays, which typically cover less than 2% of human CpG sites. To detect such associations outside these regions, we chose the bisulphite sequencing approach. We collected and curated clinical data on 200 newborn infants; whose umbilical cord blood samples were analysed with the reduced representation bisulphite sequencing (RRBS) method. A generalized linear mixed-effects model was fit for each high coverage CpG site, followed by spatial and multiple testing adjustment of P values to identify differentially methylated cytosines (DMCs) and regions (DMRs) associated with clinical variables, such as maternal age, mode of delivery, and birth weight. Type 1 error rate was then evaluated with a permutation analysis. We discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. The inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. Based on empirically estimated significance thresholds, very little differential methylation was associated with any of the studied clinical variables, other than sex. With this analysis workflow, the sex-associated differentially methylated regions were highly reproducible across studies, technologies, and statistical models.

Place, publisher, year, edition, pages
Taylor & Francis, 2022. Vol. 17, no 12, p. 1608-1627
Keywords [en]
DNA methylation, RRBS, analysis workflow, bisulphite sequencing, differential methylation, pregnancy, sex, spatial correlation, type 1 error, umbilical cord blood
National Category
Medical Genetics
Identifiers
URN: urn:nbn:se:oru:diva-97835DOI: 10.1080/15592294.2022.2044127ISI: 000764920200001PubMedID: 35246015Scopus ID: 2-s2.0-85126064394OAI: oai:DiVA.org:oru-97835DiVA, id: diva2:1642598
Funder
Academy of Finland, 340231 292335 294337 319280 31444 329277 331790 292660 311584 335436 250114 292482Novo Nordisk
Note

Funding agencies:

InFLAMES Flagship Programme of the Academy of Finland 337530

Business Finland

Juvenile Diabetes Research Foundation

Sigrid Juselius Foundation

Jane and Aatos Erkko Foundation

Finnish Diabetes Foundation

Finnish Cancer Foundation

EFSD

Pediatric Research Foundation

Turku University Hospital Special Governmental Grants

Turku Doctoral Programme of Molecular Medicine (TuDMM)

Finnish Cultural Foundation

Finnish IT center for science

Kyllikki and Uolevi Lehikoinen Foundation

 

Available from: 2022-03-07 Created: 2022-03-07 Last updated: 2023-12-08Bibliographically approved

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Oresic, Matej

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