Ramasamy A.,Kings College London |
Ramasamy A.,University College London |
Trabzuni D.,University College London |
Trabzuni D.,King Faisal Specialist Hospital And Research Center |
And 10 more authors.
Multiple Sclerosis and Related Disorders | Year: 2014
Background Multiple sclerosis (MS) is a common disease of the central nervous system and a major cause of disability amongst young adults. Genome-wide association studies have identified many novel susceptibility loci including rs2248359. We hypothesized that genotypes of this locus could increase the risk of MS by regulating expression of neighboring gene, CYP24A1 which encodes the enzyme responsible for initiating degradation of 1,25-dihydroxyvitamin D3. Methods We investigated this hypothesis using paired gene expression and genotyping data from three independent datasets of neurologically healthy adults of European descent. The UK Brain Expression Consortium (UKBEC) consists of post-mortem samples across 10 brain regions originating from 134 individuals (1231 samples total). The North American Brain Expression Consortium (NABEC) consists of cerebellum and frontal cortex samples from 304 individuals (605 samples total). The brain dataset from Heinzen and colleagues consists of prefrontal cortex samples from 93 individuals. Additionally, we used gene network analysis to analyze UKBEC expression data to understand CYP24A1 function in human brain. Findings The risk allele, rs2248359-C, is strongly associated with increased expression of CYP24A1 in frontal cortex (p-value=1. 45×10-13), but not white matter. This association was replicated using data from NABEC (p-value=7.2×10-6) and Heinzen and colleagues (p-value=1.2×10-4). Network analysis shows a significant enrichment of terms related to immune response in eight out of the 10 brain regions. Interpretation The known MS risk allele rs2248359-C increases CYP24A1 expression in human brain providing a genetic link between MS and vitamin D metabolism, and predicting that the physiologically active form of vitamin D3 is protective. Vitamin D3's involvement in MS may relate to its immunomodulatory functions in human brain. Funding Medical Research Council UK; King Faisal Specialist Hospital and Research Centre, Saudi Arabia; Intramural Research Program of the National Institute on Aging, National Institutes of Health, USA. © 2013 Elsevier B.V. All rights reserved. Source
Slatter A.F.,CNR Institute of Population Genetics |
Campbell S.,Sygnature Discovery Ltd |
Angell R.M.,University of Sussex
Journal of Biomolecular Screening | Year: 2013
The Aurora kinases are a group of serine/threonine protein kinases that regulate key steps during mitosis, and deregulation of these proteins (e.g., by gene amplification or overexpression) has been linked to a wide variety of tumor types. Thus, Aurora-A and Aurora-B have been intensely studied as targets for anticancer therapy and are now clinically validated targets. Here we report on the development of a novel fluorescence intensity binding assay for Aurora-A kinase inhibitors using a fluorescently labeled probe compound that shows intramolecular quenching when unbound but exhibits a dramatic increase in fluorescence when bound to Aurora-A. © 2013 Society for Laboratory Automation and Screening. Source
Scott I.C.,Kings College London |
Seegobin S.D.,Kings College London |
Steer S.,Kings College |
Tan R.,Kings College London |
And 12 more authors.
PLoS Genetics | Year: 2013
The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively. © 2013 Scott et al. Source
Bouzigon E.,French Institute of Health and Medical Research |
Bouzigon E.,University Paris Diderot |
Bouzigon E.,Foundation Jean Dausset Center tude du Polymorphisme Humain |
Forabosco P.,Kings College London |
And 24 more authors.
European Journal of Human Genetics | Year: 2010
Asthma is caused by a heterogeneous combination of environmental and genetic factors. In the context of GA2LEN (Global Allergy and Asthma European Network), we carried out meta-analyses of almost all genome-wide linkage screens conducted to date in 20 independent populations from different ethnic origins (≥3024 families with ≥10 027 subjects) for asthma, atopic asthma, bronchial hyper-responsiveness and five atopy-related traits (total immunoglobulin E level, positive skin test response (SPT) to at least one allergen or to House Dust Mite, quantitative score of SPT (SPTQ) and eosinophils (EOS)). We used the genome scan meta-analysis method to assess evidence for linkage within bins of traditionally 30-cM width, and explored the manner in which these results were affected by bin definition. Meta-analyses were conducted in all studies and repeated in families of European ancestry. Genome-wide evidence for linkage was detected for asthma in two regions (2p21-p14 and 6p21) in European families ascertained through two asthmatic sibs. With regard to atopy phenotypes, four regions reached genome-wide significance: 3p25.3-q24 in all families for SPT and three other regions in European families (2q32-q34 for EOS, 5q23-q33 for SPTQ and 17q12-q24 for SPT). Tests of heterogeneity showed consistent evidence of linkage of SPTQ to 3p11-3q21, whereas between-study heterogeneity was detected for asthma in 2p22-p13 and 6p21, and for atopic asthma in 1q23-q25. This large-scale meta-analysis provides an important resource of information that can be used to prioritize further fine-mapping studies and also be integrated with genome-wide association studies to increase power and better interpret the outcomes of these studies. © 2010 Macmillan Publishers Limited All rights reserved. Source
Cabras S.,University of Cagliari |
Castellanos M.E.,Rey Juan Carlos University |
Biino G.,CNR Institute of Molecular Genetics |
Persico I.,CNR Institute of Population Genetics |
And 7 more authors.
BMC Genetics | Year: 2011
Background: Association studies consist in identifying the genetic variants which are related to a specific disease through the use of statistical multiple hypothesis testing or segregation analysis in pedigrees. This type of studies has been very successful in the case of Mendelian monogenic disorders while it has been less successful in identifying genetic variants related to complex diseases where the insurgence depends on the interactions between different genes and the environment. The current technology allows to genotype more than a million of markers and this number has been rapidly increasing in the last years with the imputation based on templates sets and whole genome sequencing. This type of data introduces a great amount of noise in the statistical analysis and usually requires a great number of samples. Current methods seldom take into account gene-gene and gene-environment interactions which are fundamental especially in complex diseases. In this paper we propose to use a non-parametric additive model to detect the genetic variants related to diseases which accounts for interactions of unknown order. Although this is not new to the current literature, we show that in an isolated population, where the most related subjects share also most of their genetic code, the use of additive models may be improved if the available genealogical tree is taken into account. Specifically, we form a sample of cases and controls with the highest inbreeding by means of the Hungarian method, and estimate the set of genes/environmental variables, associated with the disease, by means of Random Forest.Results: We have evidence, from statistical theory, simulations and two applications, that we build a suitable procedure to eliminate stratification between cases and controls and that it also has enough precision in identifying genetic variants responsible for a disease. This procedure has been successfully used for the beta-thalassemia, which is a well known Mendelian disease, and also to the common asthma where we have identified candidate genes that underlie to the susceptibility of the asthma. Some of such candidate genes have been also found related to common asthma in the current literature.Conclusions: The data analysis approach, based on selecting the most related cases and controls along with the Random Forest model, is a powerful tool for detecting genetic variants associated to a disease in isolated populations. Moreover, this method provides also a prediction model that has accuracy in estimating the unknown disease status and that can be generally used to build kit tests for a wide class of Mendelian diseases. © 2011 Cabras et al; licensee BioMed Central Ltd. Source