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Sökning: WFRF:(Walley Andrew J)

  • Resultat 1-7 av 7
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1.
  • Palmer, Nicholette D, et al. (författare)
  • A genome-wide association search for type 2 diabetes genes in African Americans.
  • 2012
  • Ingår i: PloS one. - San Francisco : Public Library of Science (PLoS). - 1932-6203. ; 7:1, s. e29202-
  • Tidskriftsartikel (refereegranskat)abstract
    • African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.
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2.
  • Saxena, Richa, et al. (författare)
  • Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge
  • 2010
  • Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:2, s. 142-148
  • Tidskriftsartikel (refereegranskat)abstract
    • Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958–30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, β (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 × 10−15). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 × 10−17; ratio of insulin to glucose area under the curve, P = 1.3 × 10−16) and diminished incretin effect (n = 804; P = 4.3 × 10−4). We also identified variants at ADCY5 (rs2877716, P = 4.2 × 10−16), VPS13C (rs17271305, P = 4.1 × 10−8), GCKR (rs1260326, P = 7.1 × 10−11) and TCF7L2 (rs7903146, P = 4.2 × 10−10) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09–1.15, P = 4.8 × 10−18).
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3.
  • Clark, Stephen J, et al. (författare)
  • Association of Sirtuin 1 (SIRT1) Gene SNPs and Transcript Expression Levels With Severe Obesity.
  • 2012
  • Ingår i: Obesity. - : Wiley. - 1930-7381. ; 20:1, s. 178-85
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent studies have reported associations of sirtuin 1 (SIRT1) single nucleotide polymorphisms (SNPs) to both obesity and BMI. This study was designed to investigate association between SIRT1 SNPs, SIRT1 gene expression and obesity. Case-control analyses were performed using 1,533 obese subjects (896 adults, BMI >40 kg/m(2) and 637 children, BMI >97th percentile for age and sex) and 1,237 nonobese controls, all French Caucasians. Two SNPs (in high linkage disequilibrium (LD), r(2) = 0.96) were significantly associated with adult obesity, rs33957861 (P value = 0.003, odds ratio (OR) = 0.75, confidence interval (CI) = 0.61-0.92) and rs11599176 (P value: 0.006, OR = 0.74, CI = 0.61-0.90). Expression of SIRT1 mRNA was measured in BMI-discordant siblings from 154 Swedish families. Transcript expression was significantly correlated to BMI in the lean siblings (r(2) = 0.13, P value = 3.36 × 10(-7)) and lower SIRT1 expression was associated with obesity (P value = 1.56 × 10(-35)). There was also an association between four SNPs (rs11599176, rs12413112, rs33957861, and rs35689145) and BMI (P values: 4 × 10(-4), 6 × 10(-4), 4 × 10(-4), and 2 × 10(-3)) with the rare allele associated with a lower BMI. However, no SNP was associated with SIRT1 transcript expression level. In summary, both SNPs and SIRT1 gene expression are associated with severe obesity.
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4.
  • Benzinou, Michael, et al. (författare)
  • Common nonsynonymous variants in PCSK1 confer risk of obesity.
  • 2008
  • Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 40:8, s. 943-5
  • Tidskriftsartikel (refereegranskat)abstract
    • Mutations in PCSK1 cause monogenic obesity. To assess the contribution of PCSK1 to polygenic obesity risk, we genotyped tag SNPs in a total of 13,659 individuals of European ancestry from eight independent case-control or family-based cohorts. The nonsynonymous variants rs6232, encoding N221D, and rs6234-rs6235, encoding the Q665E-S690T pair, were consistently associated with obesity in adults and children (P = 7.27 x 10(-8) and P = 2.31 x 10(-12), respectively). Functional analysis showed a significant impairment of the N221D-mutant PC1/3 protein catalytic activity.
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5.
  • Clayton, T. Andrew, et al. (författare)
  • Pharmaco-metabonomic phenotyping and personalized drug treatment.
  • 2006
  • Ingår i: Nature. - 1476-4687. ; 440:7087, s. 1073-7
  • Tidskriftsartikel (refereegranskat)abstract
    • There is a clear case for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and reduce the number and severity of adverse drug reactions. However, such personalization of drug treatments requires the ability to predict how different individuals will respond to a particular drug/dose combination. After initial optimism, there is increasing recognition of the limitations of the pharmacogenomic approach, which does not take account of important environmental influences on drug absorption, distribution, metabolism and excretion. For instance, a major factor underlying inter-individual variation in drug effects is variation in metabolic phenotype, which is influenced not only by genotype but also by environmental factors such as nutritional status, the gut microbiota, age, disease and the co- or pre-administration of other drugs. Thus, although genetic variation is clearly important, it seems unlikely that personalized drug therapy will be enabled for a wide range of major diseases using genomic knowledge alone. Here we describe an alternative and conceptually new 'pharmaco-metabonomic' approach to personalizing drug treatment, which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the responses of individual subjects. We provide proof-of-principle for this new approach, which is sensitive to both genetic and environmental influences, with a study of paracetamol (acetaminophen) administered to rats. We show pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration.
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6.
  • Eleftherohorinou, Hariklia, et al. (författare)
  • famCNV: copy number variant association for quantitative traits in families.
  • 2011
  • Ingår i: Bioinformatics (Oxford, England). - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803. ; 27:13, s. 1873-5
  • Tidskriftsartikel (refereegranskat)abstract
    • A program package to enable genome-wide association of copy number variants (CNVs) with quantitative phenotypes in families of arbitrary size and complexity. Intensity signals that act as proxies for the number of copies are modeled in a variance component framework and association with traits is assessed through formal likelihood testing. AVAILABILITY AND IMPLEMENTATION: The Java package is made available at www.imperial.ac.uk/medicine/people/m.falchi/. CONTACT: m.falchi@imperial.ac.uk.
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7.
  • Mardinoglu, Adil, 1982, et al. (författare)
  • Integration of clinical data with a genome-scale metabolic model of the human adipocyte
  • 2013
  • Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292 .- 1744-4292. ; 9, s. 649-
  • Tidskriftsartikel (refereegranskat)abstract
    • We evaluated the presence/absence of proteins encoded by 14 077 genes in adipocytes obtained from different tissue samples using immunohistochemistry. By combining this with previously published adipocyte-specific proteome data, we identified proteins associated with 7340 genes in human adipocytes. This information was used to reconstruct a comprehensive and functional genome-scale metabolic model of adipocyte metabolism. The resulting metabolic model, iAdipocytes1809, enables mechanistic insights into adipocyte metabolism on a genome-wide level, and can serve as a scaffold for integration of omics data to understand the genotype-phenotype relationship in obese subjects. By integrating human transcriptome and fluxome data, we found an increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities in obese subjects compared with lean subjects. Our study hereby shows a path to identify new therapeutic targets for treating obesity through combination of high throughput patient data and metabolic modeling.
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