Assessing the Combined Impact of 18 Common Genetic Variants of Modest Effect Sizes on Type 2 Diabetes Risk
Hana Lango 1 2 ,
the U.K. Type 2 Diabetes Genetics Consortium ,
Colin N.A. Palmer 3 ,
Andrew ...D. Morris 4 ,
Eleftheria Zeggini 5 ,
Andrew T. Hattersley 1 2 ,
Mark I. McCarthy 5 6 ,
Timothy M. Frayling 1 2 and
Michael N. Weedon 1 2
1 Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, U.K
2 Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, Exeter, U.K
3 Population Pharmacogenetics Group, Biomedical Research Centre, Ninewells Hospital and Medical School, University of Dundee,
Dundee, U.K
4 Diabetes Research Group, Division of Medicine and Therapeutics, Ninewells Hospital and Medical School, University of Dundee,
Dundee, U.K
5 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
6 Oxford Centre for Diabetes, Endocrinology and Medicine, University of Oxford, Churchill Hospital, Oxford, U.K
Corresponding author: Michael Weedon, michael.weedon{at}pms.ac.uk
Abstract
OBJECTIVES— Genome-wide association studies have dramatically increased the number of common genetic variants that are robustly associated
with type 2 diabetes. A possible clinical use of this information is to identify individuals at high risk of developing the
disease, so that preventative measures may be more effectively targeted. Here, we assess the ability of 18 confirmed type
2 diabetes variants to differentiate between type 2 diabetic case and control subjects.
RESEARCH DESIGN AND METHODS— We assessed index single nucleotide polymorphisms (SNPs) for the 18 independent loci in 2,598 control subjects and 2,309 case
subjects from the Genetics of Diabetes Audit and Research Tayside Study. The discriminatory ability of the combined SNP information
was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes
and by calculating the area under the receiver-operator characteristic curve (AUC).
RESULTS— Individuals carrying more risk alleles had a higher risk of type 2 diabetes. For example, 1.2% of individuals with >24 risk
alleles had an odds ratio of 4.2 (95% CI 2.11–8.56) against the 1.8% with 10–12 risk alleles. The AUC (a measure of discriminative
accuracy) for these variants was 0.60. The AUC for age, BMI, and sex was 0.78, and adding the genetic risk variants only marginally
increased this to 0.80.
CONCLUSIONS— Currently, common risk variants for type 2 diabetes do not provide strong predictive value at a population level. However,
the joint effect of risk variants identified subgroups of the population at substantially different risk of disease. Further
studies are needed to assess whether individuals with extreme numbers of risk alleles may benefit from genetic testing.
Footnotes
Published ahead of print at http://diabetes.diabetesjournals.org on 30 June 2008.
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work
is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore
be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Accepted June 24, 2008.
Received April 16, 2008.
DIABETES
Since biology is by and large a 3-dimensional phenomenon, it is hardly surprising that 3D imaging has had a significant impact on many challenges in the life sciences. Imaging mass spectrometry (MS) ...is a spatially resolved label-free analytical technique that recently maturated into a powerful tool for in situ localization of hundreds of molecular species. Serial 3D imaging MS reconstructs 3D molecular images from serial sections imaged with mass spectrometry. As such, it provides a novel 3D imaging modality inheriting the advantages of imaging MS. Serial 3D imaging MS has been steadily developing over the past decade, and many of the technical challenges have been met. Essential tools and protocols were developed, in particular to improve the reproducibility of sample preparation, speed up data acquisition, and enable computationally intensive analysis of the big data generated. As a result, experimental data is starting to emerge that takes advantage of the extra spatial dimension that 3D imaging MS offers. Most studies still focus on method development rather than on exploring specific biological problems. The future success of 3D imaging MS requires it to find its own niche alongside existing 3D imaging modalities through finding applications that benefit from 3D imaging and at the same time utilize the unique chemical sensitivity of imaging mass spectrometry. This perspective critically reviews the challenges encountered during the development of serial-sectioning 3D imaging MS and discusses the steps needed to tip it from being an academic curiosity into a tool of choice for answering biological and medical questions.
Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type ...2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.
SpectralAnalysis: Software for the Masses Race, Alan M.; Palmer, Andrew D.; Dexter, Alex ...
Analytical chemistry (Washington),
10/2016, Letnik:
88, Številka:
19
Journal Article
Recenzirano
Odprti dostop
The amount of data produced by spectral imaging techniques, such as mass spectrometry imaging, is rapidly increasing as technology and instrumentation advances. This, combined with an increasingly ...multimodal approach to analytical science, presents a significant challenge in the handling of large data from multiple sources. Here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, memory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF), and probabilistic latent semantic analysis (PLSA)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. SpectralAnalysis was also developed with extensibility in mind to stimulate development, comparisons, and evaluation of data analysis algorithms.
Uric acid is the end product of purine metabolism in humans and great apes, which have lost hepatic uricase activity, leading to uniquely high serum uric acid concentrations (200-500 μM) compared ...with other mammals (3-120 μM). About 70% of daily urate disposal occurs via the kidneys, and in 5-25% of the human population, impaired renal excretion leads to hyperuricemia. About 10% of people with hyperuricemia develop gout, an inflammatory arthritis that results from deposition of monosodium urate crystals in the joint. We have identified genetic variants within a transporter gene, SLC2A9, that explain 1.7-5.3% of the variance in serum uric acid concentrations, following a genome-wide association scan in a Croatian population sample. SLC2A9 variants were also associated with low fractional excretion of uric acid and/or gout in UK, Croatian and German population samples. SLC2A9 is a known fructose transporter, and we now show that it has strong uric acid transport activity in Xenopus laevis oocytes.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Common Variation in the FTO Gene Alters Diabetes-Related Metabolic Traits to the Extent Expected Given Its Effect on BMI
Rachel M. Freathy 1 ,
Nicholas J. Timpson 2 3 ,
Debbie A. Lawlor 3 4 ,
Anneli ...Pouta 5 ,
Yoav Ben-Shlomo 4 ,
Aimo Ruokonen 5 ,
Shah Ebrahim 6 ,
Beverley Shields 1 ,
Eleftheria Zeggini 2 ,
Michael N. Weedon 1 ,
Cecilia M. Lindgren 2 7 ,
Hana Lango 1 ,
David Melzer 1 ,
Luigi Ferrucci 8 ,
Giuseppe Paolisso 9 ,
Matthew J. Neville 7 ,
Fredrik Karpe 7 ,
Colin N.A. Palmer 10 ,
Andrew D. Morris 10 ,
Paul Elliott 11 ,
Marjo-Riitta Jarvelin 5 11 ,
George Davey Smith 3 4 ,
Mark I. McCarthy 2 7 ,
Andrew T. Hattersley 1 and
Timothy M. Frayling 1
1 Peninsula Medical School, Exeter, U.K
2 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
3 MRC Centre for Causal Analyses in Translational Epidemiology, Bristol University, Bristol, U.K
4 Department of Social Medicine, Bristol University, Bristol, U.K
5 National Public Health Institute and University of Oulu, Oulu, Finland
6 London School of Hygiene and Tropical Medicine, London, U.K
7 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K
8 National Institute on Aging, National Institutes of Health, Bethesda, Maryland
9 II University of Naples, Naples, Italy
10 Ninewells Hospital and Medical School, University of Dundee, Nethergate, Dundee, Scotland, U.K
11 Department of Epidemiology and Public Health, Imperial College, London, U.K
Corresponding author: Prof. Timothy M. Frayling, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen
Rd., Exeter, EX1 2LU, U.K. E-mail: tim.frayling{at}pms.ac.uk
Abstract
OBJECTIVE— Common variation in the FTO gene is associated with BMI and type 2 diabetes. Increased BMI is associated with diabetes risk factors, including raised
insulin, glucose, and triglycerides. We aimed to test whether FTO genotype is associated with variation in these metabolic traits.
RESEARCH DESIGN AND METHODS— We tested the association between FTO genotype and 10 metabolic traits using data from 17,037 white European individuals. We compared the observed effect of FTO genotype on each trait to that expected given the FTO -BMI and BMI-trait associations.
RESULTS— Each copy of the FTO rs9939609 A allele was associated with higher fasting insulin (0.039 SD 95% CI 0.013–0.064; P = 0.003), glucose (0.024 0.001–0.048; P = 0.044), and triglycerides (0.028 0.003–0.052; P = 0.025) and lower HDL cholesterol (0.032 0.008–0.057; P = 0.009). There was no evidence of these associations when adjusting for BMI. Associations with fasting alanine aminotransferase,
γ-glutamyl-transferase, LDL cholesterol, A1C, and systolic and diastolic blood pressure were in the expected direction but
did not reach P < 0.05. For all metabolic traits, effect sizes were consistent with those expected for the per allele change in BMI. FTO genotype was associated with a higher odds of metabolic syndrome (odds ratio 1.17 95% CI 1.10–1.25; P = 3 × 10 −6 ).
CONCLUSIONS— FTO genotype is associated with metabolic traits to an extent entirely consistent with its effect on BMI. Sample sizes of >12,000
individuals were needed to detect associations at P < 0.05. Our findings highlight the importance of using appropriately powered studies to assess the effects of a known diabetes
or obesity variant on secondary traits correlated with these conditions.
ALT, alanine aminotransferase
BWHHS, British Women's Heart and Health Study
EFSOCH, Exeter Family Study of Childhood Health
GGT, γ-glutamyl-transferase
NCEP, National Cholesterol Education Program
NFBC1966, Northern Finland Birth Cohort of 1966
NIA, National Institute on Aging
SNP, single nucleotide polymorphism
UKT2D GCC, U.K. Type 2 Diabetes Genetics Consortium Collection
Footnotes
Published ahead of print at http://diabetes.diabetesjournals.org on 17 March 2008. DOI: 10.2337/db07-1466.
Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-1466 .
R.M.F. and N.J.T. contributed equally to this work.
M.-R.J., G.D.S., M.I.M., A.T.H., and T.M.F. contributed equally to this work.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore
be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Accepted January 27, 2008.
Received October 13, 2007.
DIABETES
We studied genes involved in pancreatic β cell function and survival, identifying associations between SNPs in WFS1 and diabetes risk in UK populations that we replicated in an Ashkenazi population ...and in additional UK studies. In a pooled analysis comprising 9,533 cases and 11,389 controls, SNPs in WFS1 were strongly associated with diabetes risk. Rare mutations in WFS1 cause Wolfram syndrome; using a gene-centric approach, we show that variation in WFS1 also predisposes to common type 2 diabetes.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Highlights • Communication difficulty (CD) was associated with altered social relationships. • CD predicted social isolation, less social participation, & greater loneliness. • CD may affect positive ...more than negative aspects of social relationships. • Older adults with CD may be at higher risk for mental & physical health problems.
GS:SFHS is a family-based genetic epidemiology study with DNA and socio-demographic and clinical data from about 24 000 volunteers across Scotland aged 18-98 years, from February 2006 to March 2011. ...Biological samples and anonymized data form a resource for research on the genetics of health, disease and quantitative traits of current and projected public health importance. Specific and important features of GS:SFHS include the family-based recruitment, with the intent of obtaining family groups; the breadth and depth of phenotype information, including detailed data on cognitive function, personality traits and mental health; consent and mechanisms for linkage of all data to comprehensive routine health-care records; and 'broad' consent from participants to use their data and samples for a wide range of medical research, including commercial research, and for re-contact for the potential collection of other data or samples, or for participation in related studies and the design and review of the protocol in parallel with in-depth sociological research on (potential) participants and users of the research outcomes. These features were designed to maximize the power of the resource to identify, replicate or control for genetic factors associated with a wide spectrum of illnesses and risk factors, both now and in the future.
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