3 research outputs found
Modelling rock–water interactions in flooded underground coal mines, Northern Appalachian Basin
No full text
Inverse geochemical modelling was used to investigate rock–water interactions in flooded underground coal mines in northern Appalachia, USA. In early flooding, Pittsburgh seam mine waters are usually acidic (
c.
pH 3), with dissolved metals Fe and Al ranging from 10 to >100 mg l
−1
. Within a few decades, however, waters in fully flooded mines usually have pH of about 7 S.U., and alkalinity >300 mg l
−1
CaCO
3
Eq. Eh shifts from oxidizing (
c
. 500 to 700 mv) to reduced (−100 to −200 mv) conditions. Sodium concentrations may increase an order of magnitude; sulphate and iron concentrations may also increase. Water samples were collected from several mine-pools in West Virginia and Pennsylvania. A conceptual model was developed based on quantitative hydrology, mine-pool chemistry, mining conditions and mineralogy. The model was tested with the geochemical code PHREEQC. Simulations included mixing recharge and acid mine waters, precipitation–dissolution reactions involving carbonates, sulphates, oxy-hydroxides and sulphides, and ion adsorption and exchange. Na exchange was a dominant process in all models. Carbonates are orders of magnitude undersaturated in the juvenile mine-pool, but near saturation in the mature mine-pool, suggesting they are a primary source of acid neutralization and alkalinity. The mature mine-pool is simultaneously near equilibrium with iron sulphide, iron carbonate and iron oxy-hydroxide mineral phases. The rapid change in mine-pool water quality has substantial implications for management of these systems. Corresponding author [email protected]
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EMR-linked GWAS study: Investigation of variation landscape of loci for Body Mass Index in children
No full textCommon variation 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 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 Body Mass Index (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 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=1.43x10E-07 (p (rec)=7.34E-08) for the single nucleotide polymorphism (SNP) rs8050136 at the first intron of FTO gene (z=5.26) and with no heterogeneity between cohorts (p=0.77). Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with
Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts
No full textObjective: We report the first pediatric specific Phenome-Wide Association Study (PheWAS) using electronic medical records (EMRs). Given the early success of PheWAS in adult populations, we investigated the feasibility of this approach in pediatric cohorts.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. After standard quality controls, removing missing data and outliers based on principal components (PC) analyses, 4268 samples were used for the PheWAS study. We scanned for associations between 2476 single-nucleotide polymorphisms (SNP) with available genotyping data from previously published GWAS studies and 539 EMR-derived phenotypes. The false discovery rate was calculated and, for any new PheWAS findings, a permutation approach was implemented.Results: This PheWAS replicated a variety of common variants (MAF>10%) with prior GWAS associations in our pediatric cohorts including Juvenile Rheumatoid Arthritis (JIA), Asthma, Autism and Pervasive Developmental Disorder (PDD) and Type 1 Diabetes with a false discovery rate < 0.05 and power of study above 80%. In addition, several new PheWAS findings included a cluster of association near the NDFIP1 gene for mental retardation (best SNP rs10057309, p=4.33x10-7, OR=1.70, 95%CI=1.38-2.09), association at vicinity of (PLCL1, PRIP-1) gene for developmental delays and speech disorder (best SNP rs1595825, p=1.13x10-8, OR=0.65(0.57-0.76)), a cluster of SNP associations in the IL5-IL13 region, previously implicated in Asthma, Allergy, and Eosinophilia, with Eosinophilic Esophagitis (EE) (best SNP rs12653750, p=3.03x10-9, OR=1.73 95%CI=(1.44-2.07)) and association of variants in GCKR and JAZF1, responsible for metabolic disease and diabetes in adults with allergic rhinitis in our pediatric cohorts (best SNP rs780093, p=2.18x10-5, OR=1.39, 95%CI=(1.19-1.61)).Conclusion: By using the PheWAS approach an
