Integrating DNA Methylation and Gene Expression data in Placenta Tissue to Predict Childhood Obesity

<p>Recent advances in genomic technologies have made it feasible to measure, on the same individual, multiple types of genomic activity such as genotypes, gene expression, DNA copy number, methylation and microRNA expression. However, in order to benefit from the increasing amounts of heterogeneous data and to obtain a more complete view of genomic functions, there is a great need for statistical and computationally efficient methods that allow us to combine this information in an intelligent way. Challenges with prediction models in this setting arise from the high-dimensional non-linear nature of the data, the large number of measurements compared to the few samples for whom they are collected, and the presence of complex interactions between the different types of data. Methods such as sparse regression, hierarchical clustering and principal component analysis can address any one of these challenges, but can not do so simultaneously. Kernel methods, which use matrices measuring the similarity between two individuals, offer a powerful way of simultaneously addressing these challenges without significantly increasing the computational burden. In this work, we investigate the benefits and challenges that arise from using kernel methods in the context of integrating DNA methylation, gene expression and phenotypic data in a sample of mother-child pairs from a prospective birth cohort. The goal of this study is to identify epigenetic marks observed at birth that help predict childhood obesity.</p

Placental lipoprotein lipase DNA methylation alterations are associated with gestational diabetes and body composition at 5 years of age

<p>Gestational diabetes mellitus (GDM) is associated with obesity in childhood. This suggests that consequences of <i>in utero</i> exposure to maternal hyperglycemia extend beyond the fetal development, possibly through epigenetic programming. The aims of this study were to assess whether placental DNA methylation (DNAm) marks were associated with maternal GDM status and to offspring body composition at 5 years old in a prospective birth cohort. DNAm levels were measured in the fetal side of the placenta in 66 samples (24 from GDM mothers) using bisDNA-pyrosequencing. Anthropometric and body composition (bioimpedance) were measured in children at 5 years of age. Mann-Whitney and Spearman tests were used to assess associations between GDM, placental DNAm levels at the <i>lipoprotein lipase</i> (<i>LPL</i>) locus and children's weight, height, body mass index (BMI), body fat, and lean masses at 5 years of age. Weight, height, and BMI z-scores were computed according to the World Health Organization growth chart. Analyses were adjusted for gestational age at birth, child sex, maternal age, and pre-pregnancy BMI. <i>LPL</i> DNAm levels were positively correlated with birth weight z-scores (r = 0.252, <i>P</i> = 0.04), and with mid-childhood weight z-scores (r = 0.314, <i>P</i> = 0.01) and fat mass (r = 0.275, <i>P</i> = 0.04), and negatively correlated with lean mass (r = −0.306, <i>P</i> = 0.02). We found a negative correlation between <i>LPL</i> DNAm and mRNA levels in placenta (r = −0.459; <i>P</i> < 0.001), which highlights the regulation of transcriptional activity by these epivariations. We demonstrated that alterations in fetal placental DNAm levels at the <i>LPL</i> gene locus are associated with the anthropometric profile in children at 5 years of age. These findings support the concept of fetal metabolic programming through epigenetic changes.</p

Additional file 1: Figure S1. of PPARGC1α gene DNA methylation variations in human placenta mediate the link between maternal hyperglycemia and leptin levels in newborns

Loci analysed in E-21. PRDM16 (A), BMP7 (B), CTBP2 (C) and PPARGC1α (D) genes CpGs epigenotyped are shown. The CpGs within BMP7-A. CTBP2-A and PPARGC1α-A locus were significantly well correlated with each other. For Gen3G, when the CpGs identified in E-21 was covered by 450k array probesets, the exact same CpGs were selected (*cg01046951; ≠cg04873098; ¥cg08550435). Since some CpGs were not covered by the 450k array, probesets covering variable CpGs in close vicinity to those identified in E-21 were selected. PRDM16: cg06814194 (1st intron) and cg23738647 (exon 6); BMP7: cg18759209 (proximal promoter); and PPARGC1α: cg11270806 and cg27514608 (both intron 5). (PDF 180 kb

Additional file 3: Table S2. of PPARGC1α gene DNA methylation variations in human placenta mediate the link between maternal hyperglycemia and leptin levels in newborns

Spearman’s correlation coefficients between placental DNA methylation and metabolic variables of newborns in E-21 and Gen3G cohorts. (PDF 268 kb

Additional file 1: Figure S1. of PPARGC1α gene DNA methylation variations in human placenta mediate the link between maternal hyperglycemia and leptin levels in newborns

Loci analysed in E-21. PRDM16 (A), BMP7 (B), CTBP2 (C) and PPARGC1α (D) genes CpGs epigenotyped are shown. The CpGs within BMP7-A. CTBP2-A and PPARGC1α-A locus were significantly well correlated with each other. For Gen3G, when the CpGs identified in E-21 was covered by 450k array probesets, the exact same CpGs were selected (*cg01046951; ≠cg04873098; ¥cg08550435). Since some CpGs were not covered by the 450k array, probesets covering variable CpGs in close vicinity to those identified in E-21 were selected. PRDM16: cg06814194 (1st intron) and cg23738647 (exon 6); BMP7: cg18759209 (proximal promoter); and PPARGC1α: cg11270806 and cg27514608 (both intron 5). (PDF 180 kb

Additional file 2: Table S1. of PPARGC1α gene DNA methylation variations in human placenta mediate the link between maternal hyperglycemia and leptin levels in newborns

Assay design for pyrosequencing analysis of the DNA methylation of the PRDM16, BMP7, CTBP2 and PPARGC1α gene loci. (PDF 180 kb