Linkage disequilibrium and SNP frequencies across DNMT genes in mothers and infants.

<p>Alleles = Major: Minor, GT % = genotyping success rate (mothers total N = 333, infants total N = 454), MAF = minor allele frequency, HWE p = Hardy Weinberg Equilibrium p-value. Single underscore = putative functional SNPs; strikethrough = excluded from analysis.</p

Baseline characteristics of the study population.

†<p>Mothers' red cell folate and B₁₂ concentrations were measured from routine antenatal blood samples (mean (SD) gestation = 10.6 (4.3) weeks).</p

Associations between methylation and genetic predictors.

†<p>Associations between methylation and SNP genotypes were tested initially under a genotypic model using a non-parametric Kruskal-Wallis Test, unless otherwise stated. Those showing association were tested further under dominant/recessive and additive models using Kruskal-Wallis and Trend tests, respectively.</p>‡<p>Test statistics and p-values from the most appropriate model are presented.</p>Φ<p>SNP <i>GCPII/FOLHI</i> 1561C>T and <i>CβS</i> 644ins were tested under a dominant model (with respect to the minor allele) only due to their low MAF (i.e. 5–15%). *A higher methylation ratio is indicative of less methylated DNA.</p

Univariate and multiple linear regression analysis.

*<p>Dominant models were applied for these SNPs, hence coefficients reflect the difference in methylation level for carriers of the minor allele compared to major allele homozgyotes (reference group).</p>†<p>Females were compared to males (reference group).</p>‡<p>Additive models were applied for these SNPs, hence coefficients reflect the difference in methylation level for each additional copy of the minor allele compared to major allele homozygotes (reference group).</p>Φ<p>Recessive models were applied for these SNPs, hence coefficients reflect the difference in methylation level for minor allele homozygotes compared to carriers of the major allele (reference group).</p>ł<p>Reduced numbers in multiple regression models are due to limited maternal genotype data and removal of outliers, consequently, these reduced numbers may in part account for the lack of significance seen with some predictor variables. Note also that mean methylation levels were utilized for multiple regression modelling despite not always demonstrating the strongest effect size with individual predictors. Standardised beta coefficients are obtained by first standardizing all variables to have a mean of 0 and a standard deviation of 1, they denote the increase in methylation for a standard deviation increase in the predictor variables. Multiple regression analysis was not performed for ZNT5 associations as mean methylation was not considered across this locus.</p

Associations between methylation and non-genetic predictors.

†<p>Non-parametric Kruskal-Wallis test for association was performed between methylation and categorical predictor variables. Spearman's rank correlation was assessed between methylation and continuous predictor variables.</p>*<p>A higher methylation ratio is indicative of less methylated DNA therefore the positive correlation reported shows that a higher maternal serum B₁₂ level is associated with lower genomic DNA methylation.</p

Overview of study design.

<p>Gene expression analysis was conducted on RNA samples collected at age 11–13 years when children in the Preterm Birth Growth Study (PTBGS) attended clinical assessment which included body composition measurement. Genes highlighted as being differentially expressed in relation to high/low BMI in this study group were then analysed in cord blood DNA samples from the Avon Longitudinal Study of Parents and Children (ALSPAC). Methylation levels were then analysed in relation to later body composition assessments carried out at 9 years in this study group.</p

A summary of the relationship between increased DNA methylation in the genes listed (at a single CpG site defined by an Illumina GoldenGate probe), gene expression and indices of body composition.

<p>Brief details of the known gene function and evidence of literature pertinent to body composition and/or DNA methylation for each gene are summarised.</p>¶<p>As defined by GeneCards (<a href="http://www.genecards.org/" target="_blank">http://www.genecards.org/</a>).</p

Distribution of methylation-phenotype associations.

<p>Distribution of −log₁₀<i>p</i>-values for bootstrap analysis of BMI and DNA methylation in cord blood DNA according to mean methylation levels at the 44 CpG sites analysed.</p

Bootstrap analysis of cord blood DNA methylation as a predictor of body composition (BMI, fat mass, lean mass and height) at age 9 years.

<p>The estimate provides the magnitude and direction of effect (%) on phenotype for a 1% increase in DNA methylation at that CpG site.</p