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1.  The emerging landscape of dynamic DNA methylation in early childhood 
BMC Genomics  2017;18:25.
Background
DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood.
Results
By analysing 538 paired DNA blood samples from children at birth and at 4–5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenome-wide significance of p < 1.14 × 10−7. Genes with an increase in age-differential methylation were enriched in pathways related to ‘development’, and were more often located in bivalent transcription start site (TSS) regions, which can silence or activate expression of developmental genes. Genes with a decrease in age-differential methylation were involved in cell signalling, and enriched on H3K27ac, which can predict developmental state. Maternal smoking tended to decrease methylation levels at the identified da-DMSs. We also found 101 a-DMSs (0.71%) that were regulated by genetic variants using cis-differential Methylation Quantitative Trait Locus (cis-dMeQTL) mapping. Moreover, a-DMS-associated genes during early development were significantly more likely to be linked with disease.
Conclusion
Our study provides new insights into the dynamic epigenetic landscape of the first 8 years of life.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-016-3452-1) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-016-3452-1
PMCID: PMC5217260  PMID: 28056824
DNA methylation; Aging; Methylation quantitative trait loci; Maternal smoking
2.  Epigenome-Wide Meta-Analysis of Methylation in Children Related to Prenatal NO2 Air Pollution Exposure 
Environmental Health Perspectives  2016;125(1):104-110.
Background:
Prenatal exposure to air pollution is considered to be associated with adverse effects on child health. This may partly be mediated by mechanisms related to DNA methylation.
Objectives:
We investigated associations between exposure to air pollution, using nitrogen dioxide (NO2) as marker, and epigenome-wide cord blood DNA methylation.
Methods:
We meta-analyzed the associations between NO2 exposure at residential addresses during pregnancy and cord blood DNA methylation (Illumina 450K) in four European and North American studies (n = 1,508) with subsequent look-up analyses in children ages 4 (n = 733) and 8 (n = 786) years. Additionally, we applied a literature-based candidate approach for antioxidant and anti-inflammatory genes. To assess influence of exposure at the transcriptomics level, we related mRNA expression in blood cells to NO2 exposure in 4- (n = 111) and 16-year-olds (n = 239).
Results:
We found epigenome-wide significant associations [false discovery rate (FDR) p < 0.05] between maternal NO2 exposure during pregnancy and DNA methylation in newborns for 3 CpG sites in mitochondria-related genes: cg12283362 (LONP1), cg24172570 (3.8 kbp upstream of HIBADH), and cg08973675 (SLC25A28). The associations with cg08973675 methylation were also significant in the older children. Further analysis of antioxidant and anti-inflammatory genes revealed differentially methylated CpGs in CAT and TPO in newborns (FDR p < 0.05). NO2 exposure at the time of biosampling in childhood had a significant impact on CAT and TPO expression.
Conclusions:
NO2 exposure during pregnancy was associated with differential offspring DNA methylation in mitochondria-related genes. Exposure to NO2 was also linked to differential methylation as well as expression of genes involved in antioxidant defense pathways.
Citation:
Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Antó JM, Auffray C, Ballereau S, Bellander T, Bousquet J, Bustamante M, Charles MA, de Kluizenaar Y, den Dekker HT, Duijts L, Felix JF, Gehring U, Guxens M, Jaddoe VV, Jankipersadsing SA, Merid SK, Kere J, Kumar A, Lemonnier N, Lepeule J, Nystad W, Page CM, Panasevich S, Postma D, Slama R, Sunyer J, Söderhäll C, Yao J, London SJ, Pershagen G, Koppelman GH, Melén E. 2017. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect 125:104–110; http://dx.doi.org/10.1289/EHP36
doi:10.1289/EHP36
PMCID: PMC5226705  PMID: 27448387
3.  Multi-ethnic genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis 
Paternoster, Lavinia | Standl, Marie | Waage, Johannes | Baurecht, Hansjörg | Hotze, Melanie | Strachan, David P | Curtin, John A | Bønnelykke, Klaus | Tian, Chao | Takahashi, Atsushi | Esparza-Gordillo, Jorge | Alves, Alexessander Couto | Thyssen, Jacob P | den Dekker, Herman T | Ferreira, Manuel A | Altmaier, Elisabeth | Sleiman, Patrick MA | Xiao, Feng Li | Gonzalez, Juan R | Marenholz, Ingo | Kalb, Birgit | Yanes, Maria Pino | Xu, Cheng-Jian | Carstensen, Lisbeth | Groen-Blokhuis, Maria M | Venturini, Cristina | Pennell, Craig E | Barton, Sheila J | Levin, Albert M | Curjuric, Ivan | Bustamante, Mariona | Kreiner-Møller, Eskil | Lockett, Gabrielle A | Bacelis, Jonas | Bunyavanich, Supinda | Myers, Rachel A | Matanovic, Anja | Kumar, Ashish | Tung, Joyce Y | Hirota, Tomomitsu | Kubo, Michiaki | McArdle, Wendy L | Henderson, A J | Kemp, John P | Zheng, Jie | Smith, George Davey | Rüschendorf, Franz | Bauerfeind, Anja | Lee-Kirsch, Min Ae | Arnold, Andreas | Homuth, Georg | Schmidt, Carsten O | Mangold, Elisabeth | Cichon, Sven | Keil, Thomas | Rodríguez, Elke | Peters, Annette | Franke, Andre | Lieb, Wolfgang | Novak, Natalija | Fölster-Holst, Regina | Horikoshi, Momoko | Pekkanen, Juha | Sebert, Sylvain | Husemoen, Lise L | Grarup, Niels | de Jongste, Johan C | Rivadeneira, Fernando | Hofman, Albert | Jaddoe, Vincent WV | Pasmans, Suzanne GMA | Elbert, Niels J | Uitterlinden, André G | Marks, Guy B | Thompson, Philip J | Matheson, Melanie C | Robertson, Colin F | Ried, Janina S | Li, Jin | Zuo, Xian Bo | Zheng, Xiao Dong | Yin, Xian Yong | Sun, Liang Dan | McAleer, Maeve A | O'Regan, Grainne M | Fahy, Caoimhe MR | Campbell, Linda E | Macek, Milan | Kurek, Michael | Hu, Donglei | Eng, Celeste | Postma, Dirkje S | Feenstra, Bjarke | Geller, Frank | Hottenga, Jouke Jan | Middeldorp, Christel M | Hysi, Pirro | Bataille, Veronique | Spector, Tim | Tiesler, Carla MT | Thiering, Elisabeth | Pahukasahasram, Badri | Yang, James J | Imboden, Medea | Huntsman, Scott | Vilor-Tejedor, Natàlia | Relton, Caroline L | Myhre, Ronny | Nystad, Wenche | Custovic, Adnan | Weiss, Scott T | Meyers, Deborah A | Söderhäll, Cilla | Melén, Erik | Ober, Carole | Raby, Benjamin A | Simpson, Angela | Jacobsson, Bo | Holloway, John W | Bisgaard, Hans | Sunyer, Jordi | Hensch, Nicole M Probst | Williams, L Keoki | Godfrey, Keith M | Wang, Carol A | Boomsma, Dorret I | Melbye, Mads | Koppelman, Gerard H | Jarvis, Deborah | McLean, WH Irwin | Irvine, Alan D | Zhang, Xue Jun | Hakonarson, Hakon | Gieger, Christian | Burchard, Esteban G | Martin, Nicholas G | Duijts, Liesbeth | Linneberg, Allan | Jarvelin, Marjo-Riitta | Noethen, Markus M | Lau, Susanne | Hübner, Norbert | Lee, Young-Ae | Tamari, Mayumi | Hinds, David A | Glass, Daniel | Brown, Sara J | Heinrich, Joachim | Evans, David M | Weidinger, Stephan
Nature genetics  2015;47(12):1449-1456.
Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified 10 novel risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with novel secondary signals at 4 of these). Notably, the new loci include candidate genes with roles in regulation of innate host defenses and T-cell function, underscoring the important contribution of (auto-)immune mechanisms to atopic dermatitis pathogenesis.
doi:10.1038/ng.3424
PMCID: PMC4753676  PMID: 26482879
4.  To aggregate or not to aggregate high-dimensional classifiers 
BMC Bioinformatics  2011;12:153.
Background
High-throughput functional genomics technologies generate large amount of data with hundreds or thousands of measurements per sample. The number of sample is usually much smaller in the order of ten or hundred. This poses statistical challenges and calls for appropriate solutions for the analysis of this kind of data.
Results
Principal component discriminant analysis (PCDA), an adaptation of classical linear discriminant analysis (LDA) for high-dimensional data, has been selected as an example of a base learner. The multiple versions of PCDA models from repeated double cross-validation were aggregated, and the final classification was performed by majority voting. The performance of this approach was evaluated by simulation, genomics, proteomics and metabolomics data sets.
Conclusions
The aggregating PCDA learner can improve the prediction performance, provide more stable result, and help to know the variability of the models. The disadvantage and limitations of aggregating were also discussed.
doi:10.1186/1471-2105-12-153
PMCID: PMC3113942  PMID: 21569498

Results 1-4 (4)