Effect of summer daylight exposure and genetic background on growth in growth hormone-deficient children

Abstract

The response to growth hormone in humans is dependent on phenotypic, genetic and environmental factors. The present study in children with growth hormone deficiency (GHD) collected worldwide characterised gene-environment interactions on growth response to recombinant human growth hormone (r-hGH). Growth responses in children are linked to latitude, and we found that a correlate of latitude, summer daylight exposure (SDE), was a key environmental factor related to growth response to r-hGH. In turn growth response was determined by an interaction between both SDE and genes known to affect growth response to r-hGH. In addition, analysis of associated networks of gene expression implicated a role for circadian clock pathways and specifically the developmental transcription factor NANOG. This work provides the first observation of gene-environment interactions in children treated with r-hGH.

Trial registration: ClinicalTrials.gov NCT00699855.

Figures

Figure 1

(a) The absolute latitude at each study site extracted from the GIS. Patients were divided into three groups according to the summer daylight exposure (SDE) at their site: high (>75th percentile: ⩾15.5 h; n=22), intermediate (between the 25th and 75th percentiles: >15.5 to <14.3 h; n=73) and low (<25th percentile: ⩽14.3 h; n=23) SDE. (b) Distribution of the summer daylight exposure groups according to the latitude of the centres: four centres with lower SDE and nearest to the equator (low latitude), 10 sites had highest SDE and were farthest from the equator (high latitude) and 14 sites were in the intermediate latitude group. Number of patients at each site is shown. The three groups are coloured differently.

Figure 2

Correlation between HV (cm per year) and summer daylight. The variables are expressed as natural logarithm (Ln).

Figure 3

The importance of each variable to the prediction of HV (cm per year) was assessed by partial least squares regression (PLSR). Variables were plotted according to their importance in the prediction of HV. A cutoff (dashed line) of 0.8 has been used to identify ‘important‘ variables. High values indicate that the variable has high impact in the prediction of HV. Panel (a) shows the effect of latitude, summer daylight and the main clinical variables; and panel (b) includes the effect of genotypes.

Figure 4

Delta HV (ΔHV, cm per year) between carriers and non-carriers for each SNP by summer daylight group is shown. This relates to the results from the generalised linear model, evaluating a carriage (carriage vs non-carriage of the SNP) and group (high vs intermediate vs low summer daylight exposure (SDE)) effect on 1-year growth velocity (cm per year). P*=significant (a) the ΔHV is greatest in the group with the highest number of SDE hours (for IGFBP-3, TGF-α and TP53). The difference in HV varies among SDE groups, ranging from 2.4 to 1.6 cm per year at higher to 0.0–0.7 cm per year at lower SDE. In (b) the ΔHV is greatest in the group with the lowest number of SDE hours (for GRB10 and CYP19A1). The difference in HV ranged from 2.4 to 1.7 cm per year at lower to 0.2 to 1.8 cm per year at higher SDE.

Figure 5

Overlap of gene expression associated with both HV and SDE and subsequent network analysis of the common genes [n=60 GHD patients]. (a) Venn diagram of overlap (397, P=0.0015 hypergeometric test) between genes correlated with SDE (1868) and HV (4098) (no covariates, rank regression P<0.05). (b) Overlapping gene set correlated with SDE and HV using the same covariates as used for the partial least square regression (gender, GH peak, r-hGH dose, BW SDS, baseline age, BMI and distance to TH SDS) (Supplementary Table S1) was used to generate an interactome model. Clusters of related genes were identified within the interactome model using the Moduland algorithm and a network of the cluster modules was generated (shown) where the different coloured octagons represent clusters and the gene name is the most central gene element within that cluster (Supplementary Table S2). (c) Biological pathways associated with the overlap between the clusters were identified using the Geneontology.org database (hypergeometric test with a Benjamini–Hochberg false discovery rate (FDR) modified P-value).

Figure 6

Causal analysis and mechanistic modelling of the subset of genes common to both HV and SDE. (a) Causal network analysis takes the genes with altered expression (examples numbered 1–5, green (low expression) and red (high expression)) and identifies upstream molecules up to three steps distant. This approach provides insight into information flow within the network using the known literature to identify network edges linking to upstream regulators (a–c) and master regulators (A), for which there is statistical evidence (Fisher's exact test) to support a corresponding causal relationship (within Ingenuity Pathway Analysis software). The most significant causal edges between regulators are then used to construct networks downstream of a ‘master' regulator to indicate possible mechanisms. (b) Regulators of gene expression with matched action in both HV and SDE were identified by causal network analysis and hierarchical clustering of results (Supplementary Table S3). These data were mapped onto the clusters identified within the network model of the overlap of gene expression and implicated NANOG as a prime target of regulation. Grey=opposing correlated expression with HV and SDE, green=negatively correlated with both HV and SDE, red=positively correlated with both HV and SDE, uncoloured=inferred interaction, orange=predicted activated regulator, blue=predicted inhibited regulator.

Figure 7

Correlation of NANOG expression with gene expression from genes with single-nucleotide polymorphisms associated with both height velocity and summer daylight exposure. NANOG gene expression probeset 220184_at correlated against: (a) IGFBP-3, a gene where the rs3110697 SNP has a greater effect on height velocity at higher levels of SDE (Figure 5), using gene expression probeset 210095_s_at and (b) GRB10, a gene where the rs1024531, rs12536500 and rs933360 SNPs have a reduced effect on height velocity at higher levels of SDE (Figure 5), using gene expression probeset 215248_at. Analysis performed with the same covariates as used for the PLSR (gender, GH peak, r-hGH dose, BW SDS, baseline age, BMI and distance to TH SDS). Red (high) to green (low) gradation of colour represents level of expression of NANOG.