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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

dataset
posted on 05.03.2022, 01:40 authored by Essi Laajala, Viivi Halla-aho, Toni Grönroos, Ubaid Ullah Kalim, Mari Vähä-Mäkilä, Mirja Nurmio, Henna Kallionpää, Niina Lietzén, Juha Mykkänen, Omid Rasool, Jorma Toppari, Matej Orešič, Mikael Knip, Riikka Lund, Riitta Lahesmaa, Harri Lähdesmäki

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.

Funding

This research was supported by InFLAMES Flagship Programme of the Academy of Finland (decision number: 337530). R.La. received funding from the Academy of Finland (grants 292335, 294337, 319280, 31444, 319280, 329277, 331790), Business Finland, and by grants from the JDRF, the Sigrid Jusélius Foundation (SJF), Jane and Aatos Erkko Foundation, Novo Nordisk Foundation, Finnish Diabetes Foundation and the Finnish Cancer Foundation. R.La., H.L., M.K., M.O., and J.T. were supported by the Academy of Finland, AoF, Centre of Excellence in Molecular Systems Immunology and Physiology Research (2012–2017) grant 250114 and grant 292482. J.T. was funded by EFSD, Pediatric Research Foundation, and Turku University Hospital Special Governmental Grants, JDRF, and the Academy of Finland. E.L. was supported by Turku Doctoral Programme of Molecular Medicine (TuDMM), Finnish Cultural Foundation, and Kyllikki and Uolevi Lehikoinen Foundation. V.H. was supported by the Academy of Finland (292660, 311584, 335436). T.G. was supported by the Academy of Finland (decision number: 340231).

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