EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children

Common variations at the loci harboring the fat mass and obesity gene (FTO), MC4R, and TMEM18 are consistently reported as being associated with obesity and body mass index (BMI) especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated. Method: Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts. For all available samples, gender, age, height, and weight were collected and BMI was calculated. To account for age and sex differences in BMI, BMI z-scores were generated using 2000 Centers of Disease Control and Prevention (CDC) growth charts. A Genome-wide association study (GWAS) was performed with BMI z-score. After removing missing data and outliers based on principal components (PC) analyses, 2860 samples were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and BMI was tested using linear regression adjusting for age, gender, and PC by cohort. The effects of SNPs were modeled assuming additive, recessive, and dominant effects of the minor allele. Meta-analysis was conducted using a weighted z-score approach. Results: The mean age of subjects was 9.8 years (range 2–19). The proportion of male subjects was 56%. In these cohorts, 14% of samples had a BMI ≥95 and 28 ≥ 85%. Meta analyses produced a signal at 16q12 genomic region with the best result of p = 1.43 × 10-7 [p(rec) = 7.34 × 10-8) for the SNP rs8050136 at the first intron of FTO gene (z = 5.26) and with no heterogeneity between cohorts (p = 0.77). Under a recessive model, another published SNP at this locus, rs1421085, generates the best result [z = 5.782, p(rec) = 8.21 × 10-9]. Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with p < 10-6, all at the first intron of FTO locus. When hetero-geneity was permitted between cohorts, signals were also obtained in other previously identified loci, including MC4R (rs12964056, p = 6.87 × 10-7, z = -4.98), cholecystokinin CCK (rs8192472, p = 1.33 × 10-6, z = -4.85), Interleukin 15 (rs2099884, p = 1.27 × 10-5, z = 4.34), low density lipoprotein receptor-related protein 1B [LRP1B (rs7583748, p = 0.00013, z = -3.81)] and near transmembrane protein 18 (TMEM18) (rs7561317, p = 0.001, z = -3.17). We also detected a novel locus at chromosome 3 at COL6A5 [best SNP = rs1542829, minor allele frequency (MAF) of 5% p = 4.35 × 10-9, z = 5.89]. Conclusion: An EMR linked cohort study demonstrates that the BMI-Z measurements can be successfully extracted and linked to genomic data with meaningful confirmatory results. We verified the high prevalence of childhood rate of overweight and obesity in our cohort (28%). In addition, our data indicate that genetic variants in the first intron of FTO, a known adult genetic risk factor for BMI, are also robustly associated with BMI in pediatric population

Multiple interactions between the alpha2C- and beta1-adrenergic receptors influence heart failure survival

<p>Abstract</p> <p>Background</p> <p>Persistent stimulation of cardiac β₁-adrenergic receptors by endogenous norepinephrine promotes heart failure progression. Polymorphisms of this gene are known to alter receptor function or expression, as are polymorphisms of the α_2C-adrenergic receptor, which regulates norepinephrine release from cardiac presynaptic nerves. The purpose of this study was to investigate possible synergistic effects of polymorphisms of these two intronless genes (<it>ADRB1 </it>and <it>ADRA2C</it>, respectively) on the risk of death/transplant in heart failure patients.</p> <p>Methods</p> <p>Sixteen sequence variations in <it>ADRA2C </it>and 17 sequence variations in <it>ADRB1 </it>were genotyped in a longitudinal study of 655 white heart failure patients. Eleven sequence variations in each gene were polymorphic in the heart failure cohort. Cox proportional hazards modeling was used to identify polymorphisms and potential intra- or intergenic interactions that influenced risk of death or cardiac transplant. A leave-one-out cross-validation method was utilized for internal validation.</p> <p>Results</p> <p>Three polymorphisms in <it>ADRA2C </it>and five polymorphisms in <it>ADRB1 </it>were involved in eight cross-validated epistatic interactions identifying several two-locus genotype classes with significant relative risks ranging from 3.02 to 9.23. There was no evidence of intragenic epistasis. Combining high risk genotype classes across epistatic pairs to take into account linkage disequilibrium, the relative risk of death or transplant was 3.35 (1.82, 6.18) relative to all other genotype classes.</p> <p>Conclusion</p> <p>Multiple polymorphisms act synergistically between the <it>ADRA2C </it>and <it>ADRB1 </it>genes to increase risk of death or cardiac transplant in heart failure patients.</p

Correlations of microarray and mRNA-Seq and their DE analytic methods.

<p>(A) Correlation of signal intensity of saline treated 9V/null tissues in microarray platform with mRNA-Seq platforms. The panels show the (Log₂) mRNA-Seq read counts for each gene plotted on the X-axis compared with the (Log₂) intensities from the microarray data on the Y<i>-</i>axis. To avoid log of 0, 1 was added to each of the average counts prior to taking logs. The Pearson's coefficients (at the top of each panel) for each tissue show high correlation between the microarray and mRNA-Seq data. (B) Correlations of three DE analytic methods. edgeR and DESeq for mRNA-Seq and Mixed Model ANOVA for microarray were employed to pick a common subsets of genes from mRNA-Seq and microarray platforms. The genes that met the cut-off criteria (FDR  = 0.05, and a FC ≥ ±1.5) by all three DE methods were interrogated.</p

Top functional categories of the core DEGs in spleen (a), liver (b) and lung (c) under different treatment conditions in 9V/null vs. WT.

<p>1. Functional categories and DEGs were analyzed with Ingenuity Pathway Analysis (IPA). The table shows the number of genes, percentage of the genes and the associated P value in each functional category. Fisher's exact test was used to calculate a P value.</p><p>2. DEGs, Deferentially expressed genes.</p

DEGs in splenic networks.

<p>The networks were generated using IPA software and were from the direct comparisons of imig- vs. vela-treated data sets without normalization to WT. The pathway included DEGs with decreased expression (imig/vela, green symbols) and the DEGs with expression level-increased (imig/vela, red symbols). The gene symbols and their interactions are as indicated. (A) The cell division/proliferation network is composed from total 42 DEGs determined by microarray (12 genes, red star) and mRNA-Seq (37 genes, blue star) which includs 7 common genes (blue and red stars) (see gene list in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074912#pone.0074912.s018" target="_blank">Table S14a</a>). A general decrease in DEG expression levels was found in cell division/proliferation network from imig-treated vs. vela-treated spleen. (B) Hematopoietic system network was composed of total 54 DEGs determined by microarray (16 genes, red star) and mRNA-Seq (49 genes, blue star). Among them, 11 were common genes (red and blue stars) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074912#pone.0074912.s018" target="_blank">Table S14b</a>). (C) Inflammatory response/macrophage network was composed of total 41 DEGs determined by microarray (5 genes, red star) and mRNA-Seq (40 genes blue star), of those 4 were common genes (red and blue stars) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074912#pone.0074912.s018" target="_blank">Table S14c</a>).</p

Functional classifications of the DEGs in spleen.

<p>(A) Functional relationship of spleen core DEGs associated with each treatment. An abstracted view shows the interaction of the biological functions by the core DEGs in 9V/null spleen compared with WT under different treatment conditions. The biological functions associated with the core DEGs from saline (pink node), vela (blue node) and imig (green node) treated 9V/null mouse spleens. Merged nodes indicate the shared functions between treatments. (B) 3-way Venn diagram presents the distribution of the biological functions by the core DEGs in spleen with different treatments. Each color represents a treatment as labeled. The GO were identified with DAVID. There were 16 functions common for 3 treatments. The unique functions for saline were 10, imig were 4, and vela were 56. The top biological functions are listed against each treatment.</p