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Nyholt D.R.,QIMR Berghofer Medical Research Institute
Bioinformatics | Year: 2014

The genomics era provides opportunities to assess the genetic overlap across phenotypes at the measured genotype level; however, current approaches require individual-level genome-wide association (GWA) single nucleotide polymorphism (SNP) genotype data in one or both of a pair of GWA samples. To facilitate the discovery of pleiotropic effects and examine genetic overlap across two phenotypes, I have developed a user-friendly web-based application called SECA to perform SNP effect concordance analysis using GWA summary results. The method is validated using publicly available summary data from the Psychiatric Genomics Consortium. © 2014 The Author 2014. Source


Fornito A.,Monash University | Zalesky A.,University of Melbourne | Breakspear M.,QIMR Berghofer Medical Research Institute
Nature Reviews Neuroscience | Year: 2015

Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease. © 2015 Macmillan Publishers Limited. All rights reserved. Source


Lane S.W.,QIMR Berghofer Medical Research Institute | Williams D.A.,Dana-Farber Cancer Institute | Williams D.A.,Harvard Stem Cell Institute | Watt F.M.,Kings College London
Nature Biotechnology | Year: 2014

The field of regenerative medicine holds considerable promise for treating diseases that are currently intractable. Although many researchers are adopting the strategy of cell transplantation for tissue repair, an alternative approach to therapy is to manipulate the stem cell microenvironment, or niche, to facilitate repair by endogenous stem cells. The niche is highly dynamic, with multiple opportunities for intervention. These include administration of small molecules, biologics or biomaterials that target specific aspects of the niche, such as cell-cell and cell-extracellular matrix interactions, to stimulate expansion or differentiation of stem cells, or to cause reversion of differentiated cells to stem cells. Nevertheless, there are several challenges in targeting the niche therapeutically, not least that of achieving specificity of delivery and responses. We envisage that successful treatments in regenerative medicine will involve different combinations of factors to target stem cells and niche cells, applied at different times to effect recovery according to the dynamics of stem cell-niche interactions. © 2014 Nature America, Inc. Source


Cloonan N.,QIMR Berghofer Medical Research Institute
BioEssays | Year: 2015

Despite a library full of literature on miRNA biology, core issues relating to miRNA target detection, biological effect, and mode of action remain controversial. This essay proposes that the predominant mechanism of direct miRNA action is translational inhibition, whereas the bulk of miRNA effects are mRNA based. It explores several issues confounding miRNA target detection, and discusses their impact on the dominance of "miRNA seed" dogma and the exploration of non-canonical binding sites. Finally, it makes comparisons between miRNA target prediction and transcription factor binding prediction, and questions the value of characterizing miRNA binding sites based on which miRNA nucleotides are paired with an mRNA. © 2015 The Author. Bioessays published by WILEY Periodicals, Inc. Source


Whitfield J.B.,QIMR Berghofer Medical Research Institute
Clinical Biochemist Reviews | Year: 2014

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as 'cardiometabolic' risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification. Source

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