Dataset Information

Opportunities for an enhanced integration of neuroscience and genomics.

ABSTRACT: Neuroimaging and genetics are two rapidly expanding fields of research. Thoughtful integration of these areas is critical for ongoing large-scale research into the genetic mechanisms underlying brain structure, function, and development. Neuroimaging genetics has been slow to evolve relative to psychiatric genetics research, and some may be unaware that new statistical methods allow for the genomic analysis of more modestly-sized imaging samples. We present a broad overview of the extant imaging genetics literature, provide an interpretation of the major problems surrounding the integration of neuroimaging and genetics, discuss the influence and impact of genetics consortia, and suggest statistical genetic analyses that expand the repertoire of imaging researchers amassing rich behavioral data in modestly-sized samples. Specific attention is paid to the creative use of polygenic risk scoring in imaging genetic analyses, with primers on the most current risk scoring applications.

SUBMITTER: Moore AA

PROVIDER: S-EPMC5912999 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Opportunities for an enhanced integration of neuroscience and genomics.

Moore Ashlee A AA Sawyers Chelsea C Adkins Daniel E DE Docherty Anna R AR

Brain imaging and behavior 20180801 4

Neuroimaging and genetics are two rapidly expanding fields of research. Thoughtful integration of these areas is critical for ongoing large-scale research into the genetic mechanisms underlying brain structure, function, and development. Neuroimaging genetics has been slow to evolve relative to psychiatric genetics research, and some may be unaware that new statistical methods allow for the genomic analysis of more modestly-sized imaging samples. We present a broad overview of the extant imaging ...[more]

PMID: 29063506

Similar Datasets

Project description:Rationale & objectiveThe etiology of kidney disease remains unknown in many individuals with chronic kidney disease (CKD). We created the Mayo Clinic Nephrology Genomics Clinic to improve our ability to integrate genomic and clinical data to identify the etiology of unexplained CKD.Study designRetrospective study.Setting & participantsAn essential component of our program is the Nephrology Genomics Board which consists of nephrologists, geneticists, pathologists, translational omics scientists, and trainees who interpret the patient's clinical and genetic data. Since September 2016, the Board has reviewed 163 cases (15 cystic, 100 glomerular, 6 congenital anomalies of kidney and urinary tract (CAKUT), 20 stones, 15 tubulointerstitial, and 13 other).Analytical approachTesting was performed with targeted panels, single gene analysis, or analysis of kidney-related genes from exome sequencing. Variant classification was obtained based on the 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines.ResultsA definitive genetic diagnosis was achieved for 50 families (30.7%). The highest diagnostic yield was obtained in individuals with tubulointerstitial diseases (53.3%), followed by congenital anomalies of the kidney and urological tract (33.3%), glomerular (31%), cysts (26.7%), stones (25%), and others (15.4%). A further 20 (12.3%) patients had variants of interest, and variant segregation, and research activities (exome, genome, or transcriptome sequencing) are ongoing for 44 (40%) unresolved families.LimitationsPossible overestimation of diagnostic rate due to inclusion of individuals with variants with evidence of pathogenicity but classified as of uncertain significance by the clinical laboratory.ConclusionsIntegration of genomic and research testing and multidisciplinary evaluation in a nephrology cohort with CKD of unknown etiology or suspected monogenic disease provided a diagnosis in a third of families. These diagnoses had prognostic implications, and often changes in management were implemented.

Project description:High-quality antibodies (Abs) are critical to neuroscience research, as they remain the primary affinity proteomics reagent used to label and capture endogenously expressed protein targets in the nervous system. As in other fields, neuroscientists are frequently confronted with inaccurate and irreproducible Ab-based results and/or reporting. The UC Davis/NIH NeuroMab Facility was created with the mission of addressing the unmet need for high-quality Abs in neuroscience research by applying a unique approach to generate and validate mouse monoclonal antibodies (mAbs) optimized for use against mammalian brain (i.e., NeuroMabs). Here we describe our methodology of multi-step mAb screening focused on identifying mAbs exhibiting efficacy and specificity in labeling mammalian brain samples. We provide examples from NeuroMab screens, and from the subsequent specialized validation of those selected as NeuroMabs. We highlight the particular challenges and considerations of determining specificity for brain immunolabeling. We also describe why our emphasis on extensive validation of large numbers of candidates by immunoblotting and immunohistochemistry against brain samples is essential for identifying those that exhibit efficacy and specificity in those applications to become NeuroMabs. We describe the special attention given to candidates with less common non-IgG1 IgG subclasses that can facilitate simultaneous multiplex labeling with subclass-specific secondary antibodies. We detail our recent use of recombinant cloning of NeuroMabs as a method to archive all NeuroMabs, to unambiguously define NeuroMabs at the DNA sequence level, and to re-engineer IgG1 NeuroMabs to less common IgG subclasses to facilitate their use in multiplex labeling. Finally, we provide suggestions to facilitate Ab development and use, as to design, execution and interpretation of Ab-based neuroscience experiments. Reproducibility in neuroscience research will improve with enhanced Ab validation, unambiguous identification of Abs used in published experiments, and end user proficiency in Ab-based assays.

Project description:The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Dataset Information

Opportunities for an enhanced integration of neuroscience and genomics.

Publications

Opportunities for an enhanced integration of neuroscience and genomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets