A)Prostheses with Sensory Feedback

Freiburg researchers have developed electrodes that help amputees to grasp at objects
An artificial limb that enables amputees to grasp at an object and feel it as though they were using their real hand: Thanks to Freiburg microsystems engineer Prof. Dr. Thomas Stieglitz and the international research group participating in the project LifeHand2, this has now become a reality. The scientists present the findings of their project in the journal Science Translational Medicine.
Surgeons implanted two ultra-thin electrodes each directly into the ulnar and median nerves in the upper arm of Dennis Aabo Sørensen, a patient with an amputated lower arm. The electrodes send sensory data by means of electrical impulses from the patient’s artificial hand directly to his brain over the peripheral nervous system. They give him information about the shape and consistency of the objects he grasps at – even when he cannot see them.
The patient learned to control his artificial hand with only little prior training and more quickly than the scientists had thought possible. He managed to sense objects like a plastic cup, a mandarin orange, and a heavy block of wood while being blindfolded and to take hold of them with a precise grip and the right amount of force. The combination of technology and the patient’s biological system worked almost intuitively.
The electrodes were developed in Thomas Stieglitz’ laboratory, professor of Biomedical Microtechnology at the Department of Microsystems Engineering of the University of Freiburg. “Our research helps patients who have lost a limb to move their prostheses in a completely natural way. It is always a very special moment for me as an engineer to see technological developments be implemented successfully on a patient after many years in the lab,” said the researcher. As this was only an initial test, the electrodes will have to be removed after 30 days as per the European directive on medical devices. The team plans to conduct further studies on patients in Rome, Italy; Lausanne, Switzerland; and Aalborg, Denmark.
Six research institutions in Italy, Switzerland, and Germany are participating in the project LifeHand 2. Launched in 2008, the project originated from the European Union-funded project TIME and the Italian project NEMESIS. The clinical director of the study is Prof. Dr. Paolo Maria Rossini, and the operation was performed by Prof. Dr. Eduardo Marcos Fernandez. Both are from the Agostino Gemelli University Polyclinic in Rome. The project director is Prof. Dr. Silvestro Micera from the Swiss Federal Institute of Technology in Lausanne.


  • The patient Dennis Aabo Sorensen grasps at a mandarin orange with his artificial hand. Source: LifeHand

    Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses

  • Abstract

    Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly “natural” sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and “life-like” quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.

    • Copyright © 2014, American Association for the Advancement of Science
    1. Stanisa Raspopovic1,2,
    2. Marco Capogrosso1,2,*,
    3. Francesco Maria Petrini3,4,*,
    4. Marco Bonizzato2,*,
    5. Jacopo Rigosa1,
    6. Giovanni Di Pino3,5,
    7. Jacopo Carpaneto1,
    8. Marco Controzzi1,
    9. Tim Boretius6,
    10. Eduardo Fernandez7,
    11. Giuseppe Granata4,
    12. Calogero Maria Oddo1,
    13. Luca Citi8,
    14. Anna Lisa Ciancio3,
    15. Christian Cipriani1,
    16. Maria Chiara Carrozza1,
    17. Winnie Jensen9,
    18. Eugenio Guglielmelli3,
    19. Thomas Stieglitz6,
    20. Paolo Maria Rossini4,7,*, and
    21. Silvestro Micera1,2,*,


B)Potential Epigenetic Dysregulation of Genes Associated with Mody and Type 2 diabetes in Humans Exposed to A Diabetic Intrauterine Environment: An Analysis of Genome-Wide DNA Methylation

Experimental animal studies are excellent for exploring the role of in utero environment. However, sooner or later, experimental findings need to be verified in humans. In a population of Pima Indians, del Rosario and coauthors analyzed whether DNA methylation is associated with diabetes in humans exposed to a diabetic intrauterine environment. This population is unusual in that they only develop type 2 diabetes, regardless of age, and prior work has shown that their risk of diabetes may be mediated by in utero exposure as well as genetics.

For this study, the authors selected nondiabetic subjects whose mothers did or did not have diabetes during pregnancy and who were a part of longitudinal community study of diabetes. Subjects were at least 50% American Indian heritage. The authors analyzed DNA from peripheral blood leukocytes and noted a tendency for global DNA hypomethylation in the offspring of diabetic mothers relative to offspring from nondiabetic mothers; 14.4% of gene regions were differentially methylated between the two groups. The researchers identified 13 pathways from the Kyoto Encyclopedia of Genes and Genomics that were enriched with differentially methylated regions. The majority of these were associated with maturity-onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling. Interestingly, methylation of genes associated with obesity was not statistically different between the groups.

The authors acknowledge that analysis of leukocytes may not represent methylation patterns for all types of cells. In addition, further investigations, especially longitudinal ones, are needed to establish the exact role of epigenetic modifications in the development of diabetes in subjects exposed to a diabetic in utero environment. Nevertheless, this study demonstrates the importance of in utero exposure and provides a rationale for pursuing additional research in this area.

M. C. del Rosario et al., Potential epigenetic dysregulation of genes associated with Mody and type 2 diabetes in humans exposed to a diabetic intrauterine environment: An analysis of genome-wide DNA methylation.Metabolism, published online 21 January 2014 (10.1016/j.metabol.2014.01.007)


Genomics, Type 2 Diabetes, and Obesity

Mark I. McCarthy, M.D.

N Engl J Med 2010; 363:2339-2350December 9, 2010DOI: 10.1056/NEJMra0906948


Interactive Graphic

Proposed Mechanisms of Some Susceptibility Variants Associated with Type 2 Diabetes.

Proposed Mechanisms of Some Susceptibility Variants Associated with Type 2 Diabetes.

Type 2 diabetes, though poorly understood, is known to be a disease characterized by an inadequate beta-cell response to the progressive insulin resistance that typically accompanies advancing age, inactivity, and weight gain.1 The disease accounts for substantial morbidity and mortality from adverse effects on cardiovascular risk and disease-specific complications such as blindness and renal failure.2 The increasing global prevalence of type 2 diabetes is tied to rising rates of obesity2 — in part a consequence of social trends toward higher energy intake and reduced energy expenditure. However, the mechanisms that underlie individual differences in the predisposition to obesity remain obscure.

Failure to understand the pathophysiology of diseases such as type 2 diabetes and obesity frustrates efforts to develop improved therapeutic and preventive strategies. The identification of DNA variants influencing disease predisposition will, it is hoped, deliver clues to the processes involved in disease pathogenesis. This would not only spur translational innovation but also provide opportunities for personalized medicine through stratification according to an individual person’s risk and more precise classification of the disease subtype. In this article, I consider the extent to which these objectives have been realized.

Discovery of Susceptibility Genes

For type 2 diabetes and obesity, the discovery of causal genes (Figure 1Figure 1Genomic Locations of Proven Signals of Nonautoimmune Forms of Diabetes. and Figure 2Figure 2Genomic Locations of Proven Signals of Body-Mass Index (BMI), Obesity, and Related Phenotypes.) has followed three main waves. The first wave consisted of family-based linkage analyses (see the Glossary) and focused candidate-gene studies. These proved effective in identifying genes responsible for extreme forms of early-onset disease segregating as single-gene (mendelian) disorders. Genes underlying several distinct, familial forms of nonautoimmune diabetes — including maturity-onset diabetes of the young, mitochondrial diabetes with deafness, and neonatal diabetes — were characterized (see the review by Waterfield and Gloyn3). Similar approaches revealed mutations in genes responsible for rare forms of severe childhood obesity, including the genes encoding leptin, the leptin receptor, and proopiomelanocortin (see the review by O’Rahilly4). These discoveries have provided insights into processes critical for the maintenance of normal glucose homeostasis and energy balance and clues to the inner workings of the pancreatic beta cell and hypothalamus. For many families, this information has led to improved diagnostic and therapeutic options (described in more detail below).

Attempts to apply similar approaches to families in which either common forms of diabetes or obesity is segregating have proved to be largely unrewarding,5 and the second wave of discovery involved a switch to tests of association. Although intrinsically more powerful than linkage analysis, association analysis suffers from the disadvantage that the signal can be detected only if one examines the causal variant itself or a nearby marker with which it is tightly correlated. Until the advent of methods that enabled genomewide surveys of association, researchers were therefore obliged to direct their attention to specific candidate variants or genes of interest.6 In retrospect, it is obvious that most such studies were seriously underpowered or focused on inappropriate candidates.6 Nevertheless, by accruing data over the course of multiple studies, some genuine susceptibility variants were identified. Common coding variants in PPARG and KCNJ11 (each of which encodes a protein that acts as a target for classes of therapeutic agents widely used in diabetes management) were shown to have modest effects on the risk of type 2 diabetes.7,8 Resequencing of the gene encoding the melanocortin-4 receptor (MC4R) resulted in the identification of low-frequency coding variants that explain approximately 2 to 3% of cases of severe obesity.9

The third, and most successful, wave of discovery has been driven by systematic, large-scale surveys of association between common DNA sequence variants and disease. The first demonstration that unbiased discovery efforts could reveal new insights into the pathogenesis of type 2 diabetes resulted from identification of the association between type 2 diabetes and variants within TCF7L2(encoding transcription factor 7–like 2, a protein not previously identified as a biologic candidate).10 TCF7L2 has now been shown to modulate pancreatic islet function.11

The number of loci for which there is convincing evidence that they confer susceptibility to type 2 diabetes started to grow in early 2007 with the publication of the first genomewide association studies.12-18 Together, these studies revealed six new associations, including variants near CDKAL1, CDKN2A, and CDKN2B (which encode putative or known regulators of cyclin-dependent kinases) and HHEX (which is transcribed into a homeobox protein implicated in beta-cell development). Typically each copy of a susceptibility allele at one of these loci is associated with a 15 to 20% increase in the risk of diabetes. Since then, the dominant approach to discovery has involved ever-larger aggregations of genomewide association data from multiple samples so as to improve the power to identify variants of modest effect: these studies have revealed more than 20 additional confirmed signals of susceptibility to type 2 diabetes19-22 (Table 1Table 1Major Genomewide Association (GWA) Studies of Type 2 Diabetes. and Figure 1). Though early studies were restricted to samples obtained from persons of European descent, genomewide association analyses conducted in other ethnic groups are now emerging.23,24,29 The current total of approximately 40 confirmed type 2 diabetes loci includes variants in or near WFS1 (wolframin) and the hepatocyte nuclear factors HNF1A and HNF1B (genes that also harbor rare mutations responsible for monogenic forms of diabetes)30-33; the melatonin-receptor gene MTNR1B (which highlights the link between circadian and metabolic regulation)26-28; and IRS1 (encoding insulin-receptor substrate 1), one of a limited number of type 2 diabetes loci with a primary effect on insulin action rather than on secretion.25

Genomewide association studies of genetic variants influencing body-mass index (BMI) and obesity have been similarly productive, with three main strategies being adopted (Table 2Table 2Major Genomewide Association (GWA) Studies of Body-Mass Index (BMI), Risk of Obesity, and Fat Distribution. and Figure 2). Genomewide association studies of population-based samples to examine the full range of BMI values have identified approximately 30 loci influencing BMI and the risk of obesity. The strongest signal remains the association with variants within FTO(the fat-mass and obesity–related gene).13,34,45 Other signals near BDNF, SH2B1, and NEGR1 (all implicated in aspects of neuronal function) reinforce the view of obesity as a disorder of hypothalamic function.35,37,38,43 A second approach, focusing on case–control analysis of persons selected from the extremes of the BMI distribution, has delivered a complementary, only partly overlapping, set of loci.39,42,46,47 Finally, genomewide analyses of patterns of fat distribution, prompted by the particularly deleterious health effects of visceral fat accumulation, have characterized approximately 15 loci that are largely distinct from those influencing overall adiposity36,40,41,44: many of the 15 display markedly stronger associations in women than in men.

From Genes to Clinical Practice

Despite the growing number of loci discovered, the contribution of genetic discoveries to the clinical management of diabetes and obesity remains limited to the small proportion of cases with monogenic forms of disease. What, then, are the obstacles impeding the clinical translation of the scores of multifactorial variants now defined?

The first is the modest effect size of the implicated variants. The common variants with the greatest effects on the risk of type 2 diabetes (TCF7L2 in Europeans, KCNQ1 in Asians) result in lifetime prevalence rates that are, in persons carrying two copies of the risk allele, roughly double those seen in persons with none.10,23,24 The association signal at FTO accounts for less than 0.5% of the overall variance in BMI, equivalent to a difference of 2 to 3 kg between adults homozygous for the risk allele and those homozygous for the alternative allele.13 Most other variants associated with type 2 diabetes and BMI have effects considerably smaller than these. More detailed analysis of the associated regions may reveal that some of these associations are driven by causal variants with larger effects, although empirical evidence supporting this assertion is limited.22 In contrast, the mutations underlying monogenic forms of diabetes and obesity have far more dramatic clinical consequences: in pedigrees segregating these conditions, knowing whether a family member has inherited a given causal allele generally allows for the confident prediction of disease status.

A second obstacle to the translation of variants implicated in multifactorial forms of diabetes and obesity relates to the speed with which risk-allele discovery has led to an improved understanding of the biologic basis of disease. Most alleles implicated in monogenic and syndromic forms of diabetes and obesity alter the coding sequence and therefore have dramatic and largely predictable effects on the function of the gene. The use of molecular diagnostics to derive clinically useful prognostic and therapeutic information relies on this relatively straightforward assignment of functional significance. In multifactorial disease, however, most susceptibility variants lie outside the coding regions of genes and are assumed to influence transcript regulation rather than gene function.

Characterization of the downstream consequences of these “noncoding” variants is difficult, given our rudimentary knowledge of the mechanics of gene regulation. Detailed functional studies are required to translate these genomic “signposts” into biologic knowledge that can spur translational development, and there have been relatively few successes.48 Indeed, at most susceptibility loci, it remains far from clear even which transcripts mediate the susceptibility effects that have been observed.

The time required to achieve clinical translation is often underestimated, 49 and most of the discoveries in multifactorial disease have simply been too recent for their full translational potential to be realized. That potential lies in three main areas: the characterization of disease mechanisms that provide new targets for treatment and prevention, improved risk prediction and differential diagnosis, and personalized treatment and prevention.

From Genetics to Biology

An improved understanding of pathophysiology achieved through genetic discovery provides new opportunities for treatment, diagnosis, and monitoring. Studies of risk variants for type 2 diabetes in healthy populations have shown that most variants act through perturbation of insulin secretion rather than insulin action, establishing inherited abnormalities of beta-cell function or mass (or both) as critical components of the progression to type 2 diabetes (Figure 3Figure 3Pathways to Type 2 Diabetes Implicated by Identified Common Variant Associations.).22,50 (Aninteractive graphic depicting proposed mechanisms of some susceptibility variants associated with type 2 diabetes is available at At loci for which there is evidence of a primary effect driven by abnormalities of insulin action, both obesity-dependent and obesity-independent mechanisms are involved (Figure 3).22 As described above, it is not always easy to link association signals to specific transcripts, but some of the genes more confidently assigned to type 2 diabetes susceptibility — TCF7L2, SLC30A8, and CDKN2A and CDKN2B — relate to Wnt signaling, zinc transport, and cell-cycle regulation, respectively, suggesting that these functions have roles in the maintenance of normal islet function.22,51 Beyond that, efforts to identify key processes in the pathogenesis of type 2 diabetes — for example, by showing that genes encoding members of particular pathways are overrepresented at susceptibility loci — have not been particularly rewarding.22 Either type 2 diabetes is highly heterogeneous, or those fundamental disease processes are poorly captured by existing biologic knowledge.

Efforts to achieve therapeutic modification of weight have had little success. The identification of new pathways amenable to safe and effective weight manipulation would be a valuable “deliverable” from genetic-discovery efforts. However, the transition from association signal to causal mechanism has not been straightforward, especially when the disease involves tissues as inaccessible to direct study as the human hypothalamus. Consider the example of FTO.13 Although the association signal maps to a clearly defined region of the gene, and the effect is comparatively large, there is still some doubt as to whether FTO itself is responsible for the weight phenotype, rather than one of the nearby genes such as RPGRIP1L (also expressed in the hypothalamus, with responses to alterations in nutritional and hormonal status similar to those of FTO 52). Studies of mice with disruptions of Fto sequence53,54 are consistent with the hypothesis that FTO mediates the BMI effect in humans, whereas studies of human FTO mutations have been less clear-cut.55,56 Notwithstanding these data, the story emerging from the growing number of loci supports the view of overall obesity as a disease of hypothalamic dysregulation.37,43 In contrast, variation in patterns of fat distribution is associated with variants within genes that influence adipocyte development and function.40,41,44 How best to use this information to effect early translation into new therapeutic or preventive approaches remains uncertain.

One characteristic of metabolic disease is the cluster of traits referred to as the metabolic syndrome. However, the genetic evidence to date provides limited support for the metabolic syndrome as a defined pathophysiological entity, perhaps indicating that this clustering is driven by environmental factors. Though BMI-associated variants such as FTO modulate the risk of type 2 diabetes and hyperlipidemia,57 and loci altering lipid levels have secondary effects on the risk of coronary artery disease,58,59 there is little suggestion that the variants implicated in individual components of the metabolic syndrome overlap. At some loci, the patterns of association actually run counter to the broader correlative patterns of the metabolic syndrome. At the glucokinase regulator gene GCKR, for example, one common variant allele increases triglyceride levels yet lowers glucose levels.15,60,61 The complexity of the relations that can exist at the genetic level between closely related phenotypes is further illustrated by the observation that alleles associated with similar degrees of fasting hyperglycemia in healthy populations have highly variable effects on the risk of type 2 diabetes later in life.20

Prediction and Differential Diagnostics

In cases of monogenic disease, genetic information can provide powerful diagnostic and predictive value for selected patients. Since subtypes of monogenic diabetes and obesity vary in their prognostic implications and therapeutic recommendations, a definitive molecular diagnosis is an important component of clinical management (Table 3 Table 3. Initial Treatments for Various Diabetes Subtypes. ).3,62 To date, the use of molecular diagnostic tools has been limited by the expense of using conventional sequencing technologies to screen known causal genes for mutations that are often specific to a given family. Next-generation sequencing technologies are likely to be transformative in the medium term, though distinguishing pathogenic mutation from incidental variation will remain a challenge. In the meantime, improved biomarkers of diabetes subtypes that enable the more precise targeting of diagnostic resequencing would be valuable. For example, patients with maturity-onset diabetes of the young caused by HNF1A mutations have recently been shown to have C-reactive protein (CRP) levels well below those of patients with other subtypes of diabetes, suggesting that CRP could form the basis of a useful diagnostic test.63 This observation also exemplifies the early translation of genetic discoveries, since it came directly from genomewide association studies showing that CRP levels are influenced by common variants near HNF1A.

The effect sizes of the known, common variants influencing the risk of type 2 diabetes and variation in adult BMI are modest, and the proportion of overall predisposition explained is small: approximately 5 to 10% for type 2 diabetes and 1% for BMI.22,43 As a result, the ability to perform individual-level prediction with respect to these traits is limited. By combining data from multiple loci, one can identify persons who have inherited especially high or low numbers of risk alleles: the risk of type 2 diabetes differs by a factor of approximately 4 between persons in the top 1% and those in the bottom 1% of the “risk-score” distribution.64-67 However, the risk profiles of many such persons are already discernible on the basis of conventional risk factors (e.g., BMI or family history), and there is limited evidence to suggest that information about genetic predisposition can be used effectively to guide the modification of long-term behavior. The discriminative accuracy of genetic profiling of known type 2 diabetes risk variants (as measured by means of receiver-operating-characteristic curves) is only approximately 60%,64-67 well below the threshold required for clinical usefulness and the degree of prediction achievable on the basis of nongenetic risk factors.68 Furthermore, estimates of risk can depend crucially on exactly which variants are included in the risk profile.69 The key to improved performance will be the identification of risk variants with greater effect sizes than those discovered so far. Since existing genomewide association studies have most likely captured any common variants of large effect, the search is now focused on less-common variants.

A person’s risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures. Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone)70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power.71 Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important. After all, environmental factors can be modified more readily than genetic factors.

Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity.3,4 Will the same be true for the common forms of these conditions? Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual “barcode” of susceptibility alleles and protective alleles across many loci. It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification.22,72 If efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information. For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes. Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3).

Targeted Treatment and Prevention

Recommended therapies for the various subtypes of diabetes differ (Table 3).3,4,62,73-75 In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy. For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient. In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73 Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas. In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option.74,75 In children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving.76

As yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity.77 Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment,78 the effect is too modest to guide the care of individual patients. For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline. Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention),32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications.


Given the substantial time it takes to translate basic biomedical discoveries into clinical tools,49 any current assessment of the clinical value of recent advances in the genetic basis of common diseases is probably an underestimate. An improved understanding of fundamental disease mechanisms is already emerging; this will underpin future therapeutic advances. But the expansion of personalized medicine beyond monogenic forms of disease awaits a more complete description of predisposition. The boundaries of personalized medicine will be much clearer in a few years, after large-scale genomewide resequencing efforts (now under way) provide a systematic, comprehensive description of the relations between genome sequence variation and major clinical phenotypes.

Dr. McCarthy reports receiving consulting fees from Prosidion Pharmaceuticals and lecture fees from Novo Nordisk. No other potential conflict of interest relevant to this article was reported.

Disclosure forms provided by the author are available with the full text of this article at

Source Information

From the Oxford Centre for Diabetes, Endocrinology and Metabolism; the Oxford National Institute of Health Research Biomedical Research Centre; and the Wellcome Trust Centre for Human Genetics, University of Oxford — all in Oxford, United Kingdom.

Address reprint requests to Dr. McCarthy at the Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford OX3 7LJ, United Kingdom.



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