Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci

Scelsi, Marzia Antonella; Khan, Rehan; Lorenzi, Marco; Christopher, Leigh; Greicius, Michael D.; Schott, Jonathan M.; Ourselin, Sébastien; Altmann, André

doi:10.1093/brain/awy141

Cited by 64 publications

(54 citation statements)

References 64 publications

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“…We began using an established FluoroGold (FG) retrograde tracing approach (16) combined with oxytocin antibody labeling (17,18) to carry out the first comprehensive quantification of the anatomical distribution and proportion of subtypes of OT neuron in the paraventricular nucleus (PVN) of the hypothalamus in male mice (postnatal day [30][31][32][33][34][35][36][37][38][39][40]. OT-Ab labeled neurons were classified as magnocellular if they were FG positive, and parvocellular if they were FG negative ( Fig.…”

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confidence: 99%

Parallel social information processing circuits are differentially impacted in autism

Lewis

Stein-O’Brien

Patino

et al. 2020

Preprint

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Parallel processing circuits are thought to dramatically expand the network capabilities of the nervous system. Magnocellular and parvocellular oxytocin neurons have been proposed to subserve two parallel streams of social information processing, which allow a single molecule to encode a diverse array of ethologically distinct behaviors, although to date direct evidence to support this hypothesis is lacking. Here we provide the first comprehensive characterization of magnocellular and parvocellular oxytocin neurons, validated across anatomical, projection target, electrophysiological, and transcriptional criteria. We next used novel multiple feature selection tools in Fmr1 KO mice to provide direct evidence that normal functioning of the parvocellular but not magnocellular oxytocin pathway is required for autism-relevant social reward behavior. Finally, we demonstrate that autism risk genes are uniquely enriched in parvocellular oxytocin neurons. Taken together these results provide the first evidence that oxytocin pathway specific pathogenic mechanisms account for social impairments across a broad range of autism etiologies.

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confidence: 99%

Parallel social information processing circuits are differentially impacted in autism

Lewis

Stein-O’Brien

Patino

et al. 2020

Preprint

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“…We processed the PET scans to derive standardized uptake value ratio (SUVR) values in cortical gray matter as measures of cortical amyloid deposition at baseline, defined here as the first Florbetapir scan available. Details of PET image processing can be found in Scelsi et al (). Briefly, tracer uptakes in the cortical ROI were standardized to the uptake in a composite reference region following recommendations from (Landau et al, ).…”

Section: Methodsmentioning

confidence: 99%

Modeling longitudinal imaging biomarkers with parametric Bayesian multi‐task learning

Aksman

Scelsi

Marquand

et al. 2019

Human Brain Mapping

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Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross‐sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under‐sampling and measurement errors and should be able to integrate multi‐modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi‐task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling , across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel‐based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI‐derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET‐based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori , we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.

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“…Ancestry and population structure on the ADNI dataset were previously analysed from GWAS data, using SNPweights version 2.1 [48] and a two-step procedure described in [49]. We leveraged the results of this analysis to retain only study participants showing a probability of being of Caucasian ancestry greater than 80% (N = 439 AD cases and controls).…”

Section: Methodsmentioning

confidence: 99%

Network propagation of rare mutations in Alzheimer’s disease reveals tissue-specific hub genes and communities

Scelsi

Napolioni

Greicius

et al. 2019

Preprint

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BackgroundState-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes.ResultsWe applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through gene networks. The result of network propagation is a set of smoothed gene scores used to predict disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed mutation profile acted as a robust predictor of case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we showed tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms and gene sets with known connections to AD.ConclusionsThe presented framework enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

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Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci

Cited by 64 publications

References 64 publications

Parallel social information processing circuits are differentially impacted in autism

Parallel social information processing circuits are differentially impacted in autism

Modeling longitudinal imaging biomarkers with parametric Bayesian multi‐task learning

Network propagation of rare mutations in Alzheimer’s disease reveals tissue-specific hub genes and communities

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