PT  - JOURNAL ARTICLE
AU  - Essi Laajala
AU  - Viivi Halla-aho
AU  - Toni Grönroos
AU  - Ubaid Ullah
AU  - Mari Vähä-Mäkilä
AU  - Mirja Nurmio
AU  - Henna Kallionpää
AU  - Niina Lietzén
AU  - Juha Mykkänen
AU  - Omid Rasool
AU  - Jorma Toppari
AU  - Matej Orešič
AU  - Mikael Knip
AU  - Riikka Lund
AU  - Riitta Lahesmaa
AU  - Harri Lähdesmäki
TI  - Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples
AID  - 10.1101/2021.05.18.444359
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.18.444359
4099  - http://biorxiv.org/content/early/2021/07/03/2021.05.18.444359.short
4100  - http://biorxiv.org/content/early/2021/07/03/2021.05.18.444359.full
AB  - Background 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 bisulfite sequencing approach.Methods We collected and curated all available clinical data on 200 newborn infants; whose umbilical cord blood samples were analyzed with the reduced representation bisulfite 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.Results We discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. 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.Conclusions The inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. With standard significance thresholds, type 1 error rates were high with both these implementations, across alternative parameter settings and analysis strategies. We conclude that comb-p and RADMeth are convenient methods for the detection of differentially methylated regions, but the statistical significance should either be determined empirically or before the spatial adjustment. Our RRBS data analysis workflow is available in https://github.com/EssiLaajala/RRBS_workflow.Competing Interest StatementThe authors have declared no competing interest.CpGA genomic site where cytosine (C) is followed by guanine (G). The p stands for phosphate which connects two adjacent bases in the genome.DMCDifferentially methylated cytosineDMRDifferentially methylated regionFDRFalse discovery rateGLMMGeneralized linear mixed effects modelRRBSReduced representation bisulfite sequencingPC1 and PC2Projections of the sample-specific methylation proportion vectors on the first two orthonormal principal components of the methylation proportion matrix