Biology subjects

Belloy, M. E.

Publications and source records attributed to Belloy, M. E..

7 recordsLinked to original sources

bioRxiv2026

Topographic reference modelling of white matter hyperintensities across the adult lifespan

White matter hyperintensities (WMH) are among the most common MRI findings in older adults. They are widely regarded as markers of brain aging and cerebral small-vessel disease, and associated with risk for cognitive decline, Alzheimers disease and related dementias (ADRD), and stroke. White matter hyperintensities (WMH) are typically interpreted by total lesion burden, but lesions with similar volumes can occupy different anatomical locations, potentially reflecting distinct pathological processes. Here we present voxelwise topographic reference modelling (vTRM), a framework for characterizing individual WMH masks against age- and sex-stratified frequency maps derived from cognitively unimpaired individuals. Using 42,469 FLAIR-derived lesion masks from twelve adult cohorts, we mapped population-level WMH spatial vulnerability, characterized age-related changes in its distribution across periventricular, lobar and arterial-territory compartments, and constructed paired percentile charts for lesion burden and spatial typicality. A quadrant-based classification defined by these two percentile dimensions revealed graded relationships with ADRD pathophysiology, neurodegeneration, and cognitive impairment, while identifying WMH patterns differentially associated with ageing-related, vascular and neurodegenerative processes among individuals with similar lesion volumes. These reference charts establish a benchmark for normative WMH ageing, enabling quantification of individual deviations from typical age-, sex- and burden-related patterns and identification of WMH phenotypes associated with vascular, neurodegenerative or mixed etiologies. The framework, together with a deployable 3D U-Net model trained on these data for automated WMH segmentation, is available through the Brain Lesion Analytics platform (https://brainlesionanalytics.org/).

neuroscience↗

bioRxiv2026

Massive-scale single-nucleus multi-omics identifies novel rare noncoding drivers of Parkinson's disease

Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association studies often fail to explain much of the observed heritability of these diseases, rare variants often have higher effect sizes and cumulatively explain a larger portion of heritability. However, rare variants, particularly rare noncoding variants, have remained under-characterized largely due to the difficulties of accurately predicting variant functionality at scale, given that each individual carries an average of [~]10,000 rare variants. Here, we generated multi-omic data from >3.3 million nuclei sampled from five brain regions across a cohort of 80 individuals with Parkinsons disease (PD) and 21 neurologically normal control individuals with matched 30x whole-genome sequencing. We use this data to identify cell type-specific features of PD, map cell type-specific chromatin accessibility and expression quantitative trait loci, and train machine learning models to predict the effect of variants on gene regulation. We identify rare noncoding variants statistically associated with sporadic PD and extend our approaches to predict drivers of familial PD of unknown genetic origin. Our results underscore the significance of rare noncoding variants in complex diseases and provide a roadmap for applying similar approaches in other disease systems.

genetics↗

bioRxiv2025

Genetic Associations with Longevity in a Calabrian Cohort: A Genome-Wide Study

Human longevity is a complex trait shaped by genetic background and population-specific factors. Calabria, a region in Southern Italy with a high prevalence of centenarians and relative genetic isolation, is a valuable model for investigating the genetic architecture of extreme survival. Here, we performed a genome-wide association study of longevity in 705 Calabrian individuals, comparing long-lived subjects to younger controls using a mixed-model approach that accounts for relatedness and population structure. We identified 267 candidate longevity-associated variants, including 23 suggestive genomic risk loci, of which one reached genome-wide significance. Although most loci did not replicate in external datasets, one intronic variant regulating proteasome-related gene expression was confirmed by meta-analysis. Gene- and pathway-based analyses highlighted biological processes central to aging, including proteostasis, DNA repair, telomere maintenance, apoptosis, insulin signaling, inflammation, and cancer-related pathways. Notably, established longevity loci such as APOE and FOXO3 were not associated, underscoring population-specific genetic effects. Overall, our findings suggest that longevity in the Calabrian population arises from a combination of unique genetic influences and conserved aging-related mechanisms, providing new insights into the molecular basis of human lifespan extension.

genomics↗

bioRxiv2024

In silico identification of putative causal genetic variants

Understanding the causal genetic architecture of complex phenotypes will fuel future research into disease mechanisms and potential therapies. Here, we illustrate the power of a novel framework: it detects, starting from summary statistics, and across the entire genome, sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. The approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform existing methods in false discovery rate control, statistical power and various fine-mapping criteria. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimers disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. Massively parallel reporter assays and CRISPR-Cas9 experiments have confirmed the functionality of the putative causal variants our method points to. Finally, we retrospectively analyzed summary statistics from 67 large-scale GWAS for a variety of phenotypes. Results reveal the methods capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

genetics↗

bioRxiv2021

Summary statistics knockoff inference empowers identification of putative causal variants in genome-wide association studies

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) analysis of 1,403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry, and (2) a meta-analysis for Alzheimers disease (AD) comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies. The UK Biobank analysis demonstrates superior performance of the proposed method compared to conventional GWAS in both statistical power (2.05-fold more discoveries) and localization of putative causal variants at each locus (46% less proxy variants due to linkage disequilibrium). The AD meta-analysis identified 55 risk loci (including 31 new loci) with ~70% of the proximal genes at these loci showing suggestive signal in downstream single-cell transcriptomic analyses. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

genetics↗

bioRxiv2020

Resting-state Co-activation Patterns as Accurate Predictors of Alzheimer's Disease in Aged Mice.

Alzheimers disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-beta (A{beta}) plaques leads to progressive loss of memory and cognitive function. Resting state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task positive resting state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed resting state networks (RSNs) and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and 8 wild type littermates at 18 months of age. Subsequently we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly activated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate for a diagnostic, and potentially, prognostic biomarker of Alzheimers disease.

neuroscience↗

bioRxiv2019

Resting brain fluctuations are intrinsically coupled to visual response dynamics

How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional (f)MRI to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) govern mouse resting brain dynamics. QPPs captured the temporal alignment of global brain fluctuations, anti-correlation of the Default Mode (DMN)- and Task Positive (TPN)-like networks, and activity in neuromodulatory nuclei of the reticular formation. While visual stimulation could trigger a transient spatiotemporal pattern highly similar to intrinsic QPPs, global signal fluctuations and QPPs during rest periods could explain variance in the following visual responses. QPPs and the global signal thus appeared to capture a common arousal-related brain-state fluctuation, orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.

neuroscience↗