Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone

How Can We Move Clinical Genomics Beyond the Hype?

Examines the debate over increased use of genetic testing, due in part to lax regulation, and its consequences: wasteful spending, patient harm, and health system challenges. Makes recommendations for implementation of and data on promising technologies

Propagation of pharmacogenetic differences in cytochrome P450 into pharmacokinetic and pharmacodynamic measures

Literature reports of studies that investigate the impact of CYP polymorphisms on drug pharmacokinetics and response are often conflicting and the importance of genetic variation in drug metabolism for drug response remains unclear. Johnsson & Sheiner (2002) have advocated the need for 'smarter clinical trial design' and showed that simulation techniques can help in this process by integrating all the available information. However, current examples of clinical trial simulation rely heavily on data already available from in vivo studies and there is a need for utilising pharmacokinetic information gathered earlier on during drug development. The aim of the current work was to integrate early preclinical data on drug metabolism into a clinical trial simulation paradigm in order to investigate (A) the impact of genetic polymorphisms in the cytochromes P450 on the pharmacokinetics and pharmacodynamics of 5 model drugs: dextromethorphan, (S)-warfarin, midazolam, omeprazole and tolbutamide, and (B) the predicted power of studies to detect the effects of such polymorphisms. SimcypS algorithms incorporate information on in vitro metabolism and in vivo kinetics with interindividual variability in the genetics of drug metabolising enzymes and other physiological and demographic features. In the current study these algorithms were linked to pharmacokinetic-pharmacodynamic models to describe the time course of concentration and effect of the model drugs in virtual populations of subjects. The probability of detecting a statistically significant difference in the pharmacokinetics or response between CYP phenotypes/genotypes was assessed and the power of studies to detect such differences was calculated. Various aspects of study design (study size and enrichment) and drug characteristics (active metabolites, PD variability etc) were investigated in each case. The study powers calculated from the simulations where largely consistent with the observed in vivo outcomes and helped to explain the aforementioned literature discrepancies. In conclusion, the simulations described have demonstrated the usefulness of clinical trial simulations, incorporating preclinical information on the genetics of drug metabolism for the prediction of drug pharmacokinetics and dynamics in virtual populations of individuals of varying drug metabolizing capability. In the future, clinical trial simulation may increasingly use prior in vitro data

Integrating basic research with prevention/intervention to reduce risky substance use among college students

Too often basic research on etiological processes that contribute to substance use outcomes is disconnected from efforts to develop prevention and intervention programming. Substance use on college campuses is an area of concern where translational efforts that bring together basic scientists and prevention/intervention practitioners have potential for high impact. We describe an effort at a large, public, urban university in the United States to bring together researchers across the campus with expertise in college behavioral health with university administration and health/wellness practitioners to address college student substance use and mental health. The project “Spit for Science” examines how genetic and environmental influences contribute to behavioral health outcomes across the college years. We argue that findings coming out of basic research can be used to develop more tailored prevention and intervention programming that incorporates both biologically and psychosocially influenced risk factors. Examples of personalized programming suggest this may be a fruitful way to advance the field and reduce risky substance use

Relevance of pharmacogenomics for developing countries in Europe : implementation in the Maltese population

Pharmacogenomics is a promising new discipline that can realize personalized treatment for patients suffering from many common diseases, particularly those with multiple treatment modalities. Recent advances in the deciphering of the human genome sequence and high throughput genotyping technology have led to the reduction of the overall genotyping costs and enabled the inclusion of genotype-related dosing recommendations into drug package inserts, hence allowing the integration of pharmacogenomics into clinical practice. Although, pharmacogenomics gradually assumes an integral part in mainstream medical practice in developed countries, many countries, particularly from the developing world, still do not have access either to the knowledge or the resources to individualize drug treatment. The PharmacoGenetics for Every Nation Initiative (PGENI) aims to fill in this gap, by making pharmacogenomics globally applicable, not only by defining population-specific pharmacogenomic marker frequency profiles and formulating country-specific recommendations for drug efficacy and safety but also by increasing general public and healthcare professionals’ awareness over pharmacogenomics and genomic medicine. This article highlights the PGENI activities in Europe and its implementation in the Maltese population, in an effort to make pharmacogenomics readily applicable in European healthcare systems.peer-reviewe

Optimizing the identification of risk-relevant mutations by multigene panel testing in selected hereditary breast/ovarian cancer families

The introduction of multigene panel testing for hereditary breast/ovarian cancer screening has greatly improved efficiency, speed, and costs. However, its clinical utility is still debated, mostly due to the lack of conclusive evidences on the impact of newly discovered genetic variants on cancer risk and lack of evidence-based guidelines for the clinical management of their carriers. In this pilot study, we aimed to test whether a systematic and multiparametric characterization of newly discovered mutations could enhance the clinical utility of multigene panel sequencing. Out of a pool of 367 breast/ovarian cancer families Sanger-sequenced for BRCA1 and BRCA2 gene mutations, we selected a cohort of 20 BRCA1/2-negative families to be subjected to the BROCA-Cancer Risk Panel massive parallel sequencing. As a strategy for the systematic characterization of newly discovered genetic variants, we collected blood and cancer tissue samples and established lymphoblastoid cell lines from all available individuals in these families, to perform segregation analysis, loss-of-heterozygosity and further molecular studies. We identified loss-of-function mutations in 6 out 20 high-risk families, 5 of which occurred on BRCA1, CHEK2 and ATM and are esteemed to be risk-relevant. In contrast, a novel RAD50 truncating mutation is most likely unrelated to breast cancer. Our data suggest that integrating multigene panel testing with a pre-organized, multiparametric characterization of newly discovered genetic variants improves the identification of risk-relevant alleles impacting on the clinical management of their carriers

Health Care Savings from Personalizing Medicine Using Genetic Testing: The Case of Warfarin

Progress towards realizing a vision of personalized medicine - drugs and drug doses that are safer and more effective because they are chosen based on an individual's genetic makeup - has been slower than once forecast. The Food and Drug Administration has a key role to play in facilitating the use of genetic information in drug therapies because it approves labels, and labels influence how doctors use drugs. Here we evaluate one example of how using genetic information in drug therapy may improve public health and lower health care costs. Warfarin, an anticoagulant commonly used to prevent and control blood clots, is complicated to use because the optimal dose varies greatly among patients. If the dose is too strong the risk of serious bleeding increases and if the dose is too weak, the risk of stroke increases. We estimate the health benefits and the resulting savings in health care costs by using personalized warfarin dosing decisions based on appropriate genetic testing. We estimate that formally integrating genetic testing into routine warfarin therapy could allow American warfarin users to avoid 85,000 serious bleeding events and 17,000 strokes annually. We estimate the reduced health care spending from integrating genetic testing into warfarin therapy to be $1.1 billion annually, with a range of about$100 million to $2 billion.

Are providers prepared for genomic medicine: interpretation of Direct-to-Consumer genetic testing (DTC-GT) results and genetic self-efficacy by medical professionals.

BACKGROUND:Precision medicine is set to deliver a rich new data set of genomic information. However, the number of certified specialists in the United States is small, with only 4244 genetic counselors and 1302 clinical geneticists. We conducted a national survey of 264 medical professionals to evaluate how they interpret genetic test results, determine their confidence and self-efficacy of interpreting genetic test results with patients, and capture their opinions and experiences with direct-to-consumer genetic tests (DTC-GT). METHODS:Participants were grouped into two categories, genetic specialists (genetic counselors and clinical geneticists) and medical providers (primary care, internists, physicians assistants, advanced nurse practitioners, etc.). The survey (full instrument can be found in the Additional file 1) presented three genetic test report scenarios for interpretation: a genetic risk for diabetes, genomic sequencing for symptoms report implicating a potential HMN7B: distal hereditary motor neuropathy VIIB diagnosis, and a statin-induced myopathy risk. Participants were also asked about their opinions on DTC-GT results and rank their own perceived level of preparedness to review genetic test results with patients. RESULTS:The rates of correctly interpreting results were relatively high (74.4% for the providers compared to the specialist's 83.4%) and age, prior genetic test consultation experience, and level of trust assigned to the reports were associated with higher correct interpretation rates. The self-selected efficacy and the level of preparedness to consult on a patient's genetic results were higher for the specialists than the provider group. CONCLUSION:Specialists remain the best group to assist patients with DTC-GT, however, primary care providers may still provide accurate interpretation of test results when specialists are unavailable