Identification of Shared Genes and Pathways: A Comparative Study of Multiple Sclerosis Susceptibility, Severity and Response to Interferon Beta Treatment

<div><p>Recent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-ß) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes <i>ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A</i> and <i>TUBA4A</i> were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10⁻⁷⁹) with <i>UBC</i> as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from <i>signalling molecules and interaction</i>, and <i>signal transduction</i> categories were found to be highest shared pathways between 3 phenotypes. Finally, <i>FYN</i> was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.</p> </div

Overview of GWA studies investigated in this analysis.

<p>Overview of GWA studies investigated in this analysis.</p

Number of shared first degree interactors between each of the three GWAS phenotype categories.

<p>Number of shared first degree interactors between each of the three GWAS phenotype categories.</p

Primary “drug modulated/modulating” genes (large blue circles) and their extended common interactors (small red circles).

<p>‘+’ on each gene (node) indicates that the gene’s linkages are suppressed and ‘−’ indicates all linkages of the gene have been shown. The different coloured interaction between genes represents various biological processes that identified the interactions. Every line (edge) connecting each gene pair represents an interaction. The colour of the line specifies the experimental method used to identify that interaction. For example, “black” coloured line connecting genes <i>ITGAV</i> and <i>FN</i> represents the <i>transcriptional upregulation</i> method. If an interaction is identified by several different biological methods, then the line will be coloured in segments with corresponding colours for each methods. For example 5 different colours indicated in line connecting genes <i>ITGAV</i> and <i>ITGB1</i> represent 5 biological methods used for identification of this interaction i.e. <i>in vivo, inferred by curator, affinity chromatography, co- immunoprecipitation</i> and <i>pull down</i> method.</p

Number of shared pathways relating to the interactomes of the three GWAS phenotype categories.

<p>Number of shared pathways relating to the interactomes of the three GWAS phenotype categories.</p

Serum 25-Hydroxyvitamin D Status and Longitudinal Changes in Weight and Waist Circumference: Influence of Genetic Predisposition to Adiposity

<div><p>Studies of the relationship between serum 25-hydroxyvitamin D (25(OH)D) and changes in measures of adiposity have shown inconsistent results, and interaction with genetic predisposition to obesity has rarely been examined. We examined whether 25(OH)D was associated with subsequent annual changes in body weight (ΔBW) or waist circumference (ΔWC), and whether the associations were modified by genetic predisposition to a high BMI, WC or waist-hip ratio adjusted for BMI (WHR_BMI). The study was based on 10,898 individuals from the Danish <i>Inter99</i>, the <i>1958 British Birth Cohort</i> and the <i>Northern Finland Birth Cohort 1966</i>. We combined 42 adiposity-associated Single Nucleotide Polymorphisms (SNPs) into four scores indicating genetic predisposition to BMI, WC and WHR_BMI, or all three traits combined. Linear regression was used to examine the association between serum 25(OH)D and ΔBW or ΔWC, SNP-score × 25(OH)D interactions were examined, and results from the individual cohorts were meta-analyzed. In the meta-analyses, we found no evidence of an association between 25(OH)D and ΔBW (-9.4 gram/y per 10 nmol/L higher 25(OH)D [95% CI: -23.0, +4.3; P = 0.18]) or ΔWC (-0.06 mm/y per 10 nmol/L higher 25(OH)D [95% CI: -0.17, +0.06; P = 0.33]). Furthermore, we found no statistically significant interactions between the four SNP-scores and 25(OH)D in relation to ΔBW or ΔWC. Thus, in view of the narrow CIs, our results suggest that an association between 25(OH)D and changes in measures of adiposity is absent or marginal. Similarly, the study provided evidence that there is either no or very limited dependence on genetic predisposition to adiposity.</p></div

Interaction between genetic predisposition scores and 25-hydroxyvitamin D in relation to subsequent change in body weight.

<p>Abbreviations: BMI score, sum of body mass index associated risk-alleles; WC score, sum of waist circumference associated risk-alleles; WHR score, sum of waist-hip ratio adjusted for BMI associated risk-alleles; Composite score, sum of SNP associated to all three phenotypes. Results presented as annual weight change (g/y) effect-modification for each additional risk-allele per 10 nmol/L higher 25-hydroxyvitamin D. The study-specific SNP-score × 25-hydroxyvitamin D interactions were calculated using linear regression and corresponding meta-analysis results were derived using a random effects approach. The results were adjusted for baseline measure of body weight, height, gender, age, smoking status, alcohol consumption, physical activity, education, menopausal status for women and season of blood draw.</p

Interaction between genetic predisposition scores and 25-hydroxyvitamin D in relation to subsequent change in waist circumference.

<p>Abbreviations: BMI score, sum of body mass index associated risk-alleles; WC score, sum of waist circumference associated risk-alleles; WHR score, sum of waist-hip ratio adjusted for BMI associated risk-alleles; Composite score, sum of SNP associated to all three phenotypes. Results presented as annual change in waist circumference (mm/y) effect-modification for each additional risk-allele per 10 nmol/L higher 25-hydroxyvitamin D. The study-specific SNP-score × 25-hydroxyvitamin D interactions were calculated using linear regression and corresponding meta-analysis results were derived using a random effects approach. The results were adjusted for baseline measure of waist circumference, height, gender, age, smoking status, alcohol consumption, physical activity, education, menopausal status for women and season of blood draw.</p

Association between SNP-scores and annual change in waist circumference (mm/y) stratified by baseline vitamin D status.

<p>Association between SNP-scores and annual change in waist circumference (mm/y) stratified by baseline vitamin D status.</p

Information on 25-hydroxyvitamin D, anthropometrics, genetic predisposition scores and covariates in <i>Inter99</i>, <i>1958BC</i> and <i>NFBC1966</i>.

<p>Reported as median (5–95 percentiles) unless otherwise stated.</p