Mean placental weight according to potential confounders and child/adolescent mental health outcomes.

a<p>for heterogeneity, analysis of variance.</p>b<p>assessment of symptoms based on fulfilment of criteria according to the Rutter B2/Strengths and Weaknesses of ADHD symptoms and Normal behavior (SWAN) scale.</p>c<p>assessment based on Rutter item number 16.</p>d<p>assessment based on sum of Rutter items 1 and 3.</p>e<p>SWAN subscale consisting of sum of 9 items.</p>f<p>SWAN subscale consisting of sum of 9 items.</p

Flowchart of study participants.

<p>Flowchart of study participants.</p

Logistic regression results for the association between male placental size (weight, surface area and placental-to-birth-weight ratio) and mental health outcomes.

a<p>adjusted for gestational age, birth weight, socio-demographic factors (maternal age, family structure, education and social class) and medical factors (smoking during pregnancy, parity, pre-pregnancy BMI and gestational weight gain).</p>b<p>adjusted as above, except for birth weight.</p>*<p>p<.05; **p<.01.</p

Logistic regression results for the association between female placental size (weight, surface area and placental-to-birth-weight ratio) and mental health outcomes.

a<p>adjusted for gestational age, birth weight, socio-demographic factors (maternal age, family structure, education and social class) and medical factors (smoking during pregnancy, parity, pre-pregnancy BMI and gestational weight gain).</p>b<p>adjusted as above, except for birth weight.</p

Birth and child/adolescent characteristics presented as means ± SD or n (%).

a<p>assessment of symptoms based on fulfilment of criteria according to the Rutter B2/Strengths and Weaknesses of ADHD symptoms and Normal behavior (SWAN) scale.</p>b<p>assessment based on Rutter item number 16.</p>c<p>assessment based on sum of Rutter items 1 and 3.</p>d<p>SWAN subscale consisting of sum of 9 items.</p>e<p>SWAN subscale consisting of sum of 9 items.</p

Multiple Linear Regression Analysis (GLM) of the association between social adversity and birth size [birth weight (g), length (cm), head circumference (cm) and ponderal index] in the whole NFBC86 Cohort (<i>n</i> = 9135) and stratified by sex.

*<p><i>n</i> in the adjusted model.</p>**<p>Adjusting for gestational age, maternal smoking, maternal alcohol consumption, parity, maternal pre-pregnancy BMI, gestational diabetes and hypertension during pregnancy. <b>^r</b> Additionally adjusting for sex.</p

Mean differences (95% confidence intervals, CI) in birth size as predicted by the additive effects of neighborhood social disparity and at least one risk allele (<i>CCNL1/LEKR1</i>- rs900400).

*<p><i>n</i> in the adjusted model,</p>**<p>controlling for gestational age, maternal smoking, maternal alcohol consumption, parity, maternal pre-pregnancy BMI, sex, gestational diabetes and hypertension during pregnancy.</p

Mean differences (95% confidence intervals, CI) in birth size as predicted by the additive effects of social Adversity and at least one risk allele (<i>CCNL1/LEKR1</i>- rs900400).

*<p><i>n</i> in the adjusted model,</p>**<p>controlling for gestational age, maternal smoking, maternal alcohol consumption, parity, maternal pre-pregnancy BMI, sex, gestational diabetes and hypertension during pregnancy.</p

Multiple Linear Regression Analysis (GLM) of the association between neighborhood social disparity and birth size [birth weight (g), length (cm), head circumference (cm) and ponderal index] in the whole NFBC86 Cohort (<i>n</i> = 9135) and stratified by sex.

*<p><i>n</i> in the adjusted model.</p>**<p>Adjusting for gestational age, maternal smoking, maternal alcohol consumption, parity, maternal pre-pregnancy BMI, gestational diabetes and hypertension during pregnancy. <b>^r</b> dditionally adjusting for sex. Neighborhood social disparity =  living in an environment different to individual SES.</p

Analysis strategy for identifying coordinated behaviour between disease dysregulated pathways.

<p>Disease genes (e.g. FYN, SRC and LCK) that are targeted by anti-inflammatory drugs and associated with biomarkers of disease-relevant biological processes provide insight into the biological function resulting from the coordinated behaviour of both dysregulated pathways identified by integrating GWAS data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074821#pone.0074821-Ramasamy1" target="_blank">[6]</a> and gene expression data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074821#pone.0074821-Benson1" target="_blank">[2]</a> (a) Co-enrichment analysis of Pareto-efficient pathways identify pathways that are involved in the systemic response to pollen sensitisation and involved in the cellular response to pollen allergen challenge; in this study, complement system was the top hit. (b) Coordination between disease dysregulated pathway (CD4+ T cell activation) and the pathway identified in the disease context (Complement system) is studied using inter-pathway interactions network analysis (INPAR-N). (C) Regression and correlation enrichment analysis is applied to test if the INPAR-N is associated with markers of the biological process involved in the disease onset, i.e. Th2 priming. (D) The genes of the INPAR-N are mapped to disease pathophysiology using drug target network analysis.</p