The impact of genomics on pediatric research and medicine

A Genomically Informed Education System? Challenges for Behavioral Genetics

The exponential growth of genetic knowledge and precision medicine research raises hopes for improved prevention, diagnosis, and treatment options for children with behavioral and psychiatric conditions. Although well-intended, this prospect also raise the possibility-and concern-that behavioral, including psychiatric genetic data would be increasingly used-or misused-outside the clinical context, such as educational settings. Indeed, there are ongoing calls to endorse a "personalized education" model that would tailor educational interventions to children's behavioral and psychiatric genetic makeup. This article explores the justifications for, and prospects and pitfalls of such endeavors. It considers the scientific challenges and highlights the ethical, legal, and social issues that will likely arise should behavioral genetic data become available (or be perceived as such) and are routinely incorporated in student education records. These include: when to disclose students' behavioral and psychiatric genetic profile; whose genomic privacy is protected and by whom; and how students' genetic data may affect education-related decisions. I argue that the introduction of behavioral genetics in schools may overshadow the need to address underlying structural and environmental factors that increase the risk for psychiatric conditions of all students, and that the unregulated use of student behavioral genetic profiles may lead to unintended consequences that are detrimental for individuals, families and communities. Relevant stakeholders-from parents and students to health professionals, educators, and policy-makers-ought to consider these issues before we forge ahead with a genomically informed education system.

Copy number variation analysis in the context of electronic medical records and large-scale genomics consortium efforts

The goal of this paper is to review recent research on copy number variations (CNVs) and their association with complex and rare diseases. In the latter part of this paper, we focus on how large biorepositories such as the electronic medical record and genomics (eMERGE) consortium may be best leveraged to systematically mine for potentially pathogenic CNVs, and we end with a discussion of how such variants might be reported back for inclusion in electronic medical records as part of medical history.

Metabolomic Analysis of the Effect of Postnatal Hypoxia on the Retina in a Newly Born Piglet Model

The availability of reliable biomarkers of brain injury secondary to birth asphyxia could substantially improve clinical grading, therapeutic intervention strategies, and prognosis. In this study, changes in the metabolome of retinal tissue caused by profound hypoxia in an established neonatal piglet model were investigated using an ultra performance liquid chromatography - quadrupole time of flight mass spectrometry (UPLC-QTOFMS) untargeted metabolomic approach, which included Partial Least Squares - Discriminant Analysis (PLSDA) multivariate data analysis. The initial identification of a set of discriminant metabolites from UPLC-QTOFMS data was confirmed by target UPLC-MS/MS and allowed the selection of endogenous CDP-choline as a promising candidate biomarker for hypoxia-derived brain damage assessing intensity of retinal hypoxia. Results from this study will foster further research on CDP-choline changes occurring during resuscitation.

Metabolomics as a tool for personalizing medicine: 2012 update

Numerous factors in conjunction with an individual’s genetic make up will determine predisposition to disease, adverse or beneficial effects of drug treatment or therapy, and disease progression. A major limitation of current clinical measures is that the disease phenotype, which is comprised of the genotype and other environmental factors, is underestimated. Rather, each disease is treated similarly even though the disease process is highly complex. Methods that evaluate the interaction of genotype and environmental factors would likely be a better indicator of patients’ response to medical treatments. The omics technologies, specifically metabolomics, will play a major role in the movement towards personalized medicine. Metabolomics is phenotype driven and should provide better clinical biomarkers. Furthermore, recent studies have shown that associations between genetic variants and downstream metabolite changes can provide a unique description of an individual’s genotype and phenotype, which will further enhance the movement towards personalized medicine.

Disclosure of incidental findings from next-generation sequencing in pediatric genomic research

Next-generation sequencing technologies will likely be used with increasing frequency in pediatric research. One consequence will be the increased identification of individual genomic research findings that are incidental to the aims of the research. Although researchers and ethicists have raised theoretical concerns about incidental findings in the context of genetic research, next-generation sequencing will make this once largely hypothetical concern an increasing reality. Most commentators have begun to accept the notion that there is some duty to disclose individual genetic research results to research subjects; however, the scope of that duty remains unclear. These issues are especially complicated in the pediatric setting, where subjects cannot currently but typically will eventually be able to make their own medical decisions at the age of adulthood. This article discusses the management of incidental findings in the context of pediatric genomic research. We provide an overview of the current literature and propose a framework to manage incidental findings in this unique context, based on what we believe is a limited responsibility to disclose. We hope this will be a useful source of guidance for investigators, institutional review boards, and bioethicists that anticipates the complicated ethical issues raised by advances in genomic technology.