Strategies for integrated analysis in imaging genetics studies

Vilor‐Tejedor, Natàlia; Alemany, Silvia; Cáceres, Alejandro; Pujol, Jesús; Sunyer, Jordi; González, Juan R.

doi:10.1016/j.neubiorev.2018.06.013

Cited by 7 publications

(5 citation statements)

References 121 publications

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“…59 Yet, this represents an extra burden on data dimensionality and may therefore require the development of novel analytical methods. 60 Following a similar line of reasoning, designs focused on the integration of imaging gene-environmental interactions open up new perspectives of analysis potentially leading to a better understanding of the conditional mechanisms through which genes and environment interact to exert an impact on the hippocampal subfields and may develop further into neurodegenerative diseases. 61 Such designs represent an opportunity, not only to integrate different sources of omics and imaging features but also to integrate target environmental sources relevant to the brain's structure and function and in particular in hippocampal subfields.…”

Section: Discussionmentioning

confidence: 99%

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Genetic Influences on Hippocampal Subfields

et al. 2021

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There is clear evidence that hippocampal subfield volumes have partly distinct genetic determinants associated with specific biological processes. The identification of genetic correlates of hippocampal subfield volumes may help to elucidate the mechanisms of neurologic diseases, as well as aging and neurodegenerative processes. However, despite the emerging interest in this area of research, the current knowledge of the genetic architecture of hippocampal subfields has not yet been consolidated. We aimed to provide a review of the current evidence from genetic studies of hippocampal subfields, highlighting current priorities and upcoming challenges. The limited number of studies investigating the influential genetic effects on hippocampal subfields, a lack of replicated results and longitudinal designs, and modest sample sizes combined with insufficient standardization of protocols are identified as the most pressing challenges in this emerging area of research.

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Section: Discussionmentioning

confidence: 99%

“…59 Yet, this represents an extra burden on data dimensionality and may therefore require the development of novel analytical methods. 60…”

Section: Discussionmentioning

confidence: 99%

Genetic Influences on Hippocampal Subfields

et al. 2021

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“…Also, even though batcheffect corrections have thoroughly been applied here, the inevitable effect of merging multiple datasets on the results could still be a limitation. However, despite the limitations of the current analyses, molecular signatures here described across ND-NPs can provide target leads for the development of therapeutic interventions targeted to common pathological mechanisms which may overcome indications solely based on clinical manifestations, thus paving the way for the rational design of personalized and mechanistically-based therapies 118 .…”

Section: Discussionmentioning

confidence: 99%

Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders

Sadeghi

Gispert

Palumbo

et al. 2021

Preprint

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Neurodegenerative and neuropsychiatric disorders (ND-NPs) are multifactorial, polygenic and complex behavioral phenotypes caused by brain abnormalities. Large-scale collaborative efforts have tried to identify the genetic architecture of these conditions. However, specific and shared underlying molecular pathobiology of brain illnesses is not clear. Here, we examine transcriptome-wide characterization of eight conditions, using a total of 2633 post-mortem brain samples from patients with Alzheimer's disease (AD), Parkinson's disease (PD), Progressive Supranuclear Palsy (PSP), Pathological Aging (PA), Autism Spectrum Disorder (ASD), Schizophrenia (Scz), Major Depressive Disorder (MDD), and Bipolar Disorder (BP)–in comparison with 2,078 brain samples from matched control subjects. Similar transcriptome alterations were observed between NDs and NPs with the top correlations obtained between Scz-BP, ASD-PD, AD-PD, and Scz-ASD. Region-specific comparisons also revealed shared transcriptome alterations in frontal and temporal lobes across NPs and NDs. Co-expression network analysis identified coordinated dysregulations of cell-type-specific modules across NDs and NPs. This study provides a transcriptomic framework to understand the molecular alterations of NPs and NDs through their shared- and specific gene expression in the brain.

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“…Currently, development is underway to use Galaxy to design additional workflows, and further optimize the PCEV 27 pipeline. This pipeline is however only one amongst many other analysis methods that can be used in imaging genetics (Vilor-Tejedor et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Integration of “omics” Data and Phenotypic Data Within a Unified Extensible Multimodal Framework

Das

Boucher

Rogers

et al. 2018

Front. Neuroinform.

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Analysis of “omics” data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable “omics” framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling.

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Strategies for integrated analysis in imaging genetics studies

Cited by 7 publications

References 121 publications

Genetic Influences on Hippocampal Subfields

Genetic Influences on Hippocampal Subfields

Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders

Integration of “omics” Data and Phenotypic Data Within a Unified Extensible Multimodal Framework

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