Biology subjects

Frouin, A.

Publications and source records attributed to Frouin, A..

9 recordsLinked to original sources

bioRxiv2026

Taxonomic-free metagenome GWAS to identify gut microbiome functions influencing host phenotypes

Genome-wide association studies (GWAS) have been pivotal for uncovering the genetics of human phenotypes, and there is now growing interest in applying GWAS-like methods to explore the role of the microbiome in human health. Here, we developed a taxonomy free GWAS approach that uses k-mers, i.e. DNA words of length k that can capture single nucleotide polymorphisms, insertion or deletion events, and gene presence-absence, to interrogate the gut microbiome. We applied this method to 26 traits spanning demographics, physiological measurements, health, and lifestyle in 938 healthy participants from the Milieu Interieur cohort. We generated a k-mer abundance matrix encompassing 97 million distinct k-mers. GWAS of the 26 traits identified significant associations for seven of them: age, sex, depression, appetite, cooked meat consumption, soda intake, and smoking. By modeling the correlation structure among k-mers, we identified a modest number of independent signals and conducted a comprehensive in silico functional annotation of these signals, revealing potential mechanisms of host-microbiota interaction. Overall, our analyses demonstrate that k-mers can capture biologically relevant functions shared across multiple taxa and provide a refined modeling framework that complements the standard taxonomic-based screening approach.

microbiology↗

bioRxiv2025

Empowering multitrait brain phenotypes GWAS

Brain magnetic resonance imaging (MRI) data are now becoming available in human genetic cohorts including thousands of participants, providing a powerful mean to decipher the genetic architecture of brain phenotypes. Univariates genome-wide association screenings (GWAS) are typically applied as a baseline approach, but there are increasing interest for multitrait approaches to both increase statistical power and uncover shared structure underlying those phenotypes. However, there are limited guidelines on how to select phenotypes to be analyzed jointly and optimize power. Here we investigated factors impacting variant discoverability of state-of-the art multitrait GWAS using 1,010 MRI-derived phenotypes measured in 32,947 participants from the UK Biobank. Our study shows that the expected gain of multitrait approaches over univariate GWAS is highly predictable. We further propose data-driven strategy to define optimize sets and compare its performances with biologically driven approaches.

neuroscience↗

bioRxiv2023

Linking the genetic structure of neuroanatomical phenotypes with psychiatric disorders

There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Here, we demonstrate how multivariate analyses can help reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches. We first conducted univariate and multivariate genome-wide association studies (GWAS) for eight MRI-derived brain volume phenotypes in 20K UK Biobank participants. We performed various enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders at all the levels we investigated. Multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. We further clustered top associated variants based on their MRI multitrait association using an optimized k-medoids approach and detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophin pathways on both MRI and schizophrenia. Altogether our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.

genetics↗

bioRxiv2023

A multivariate outcome test of covariance

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance, and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1,000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

bioinformatics↗

bioRxiv2023

Polygenic risk score prediction accuracy convergence

Polygenic risk scores (PRS) trained from genome-wide association study (GWAS) results are set to play a pivotal role in biomedical research addressing multifactorial human diseases. The prospect of using these risk scores in clinical care and public health is generating both enthusiasm and controversy, with varying opinions about strengths and limitations across experts1. The performances of existing polygenic scores are still limited, and although it is expected to improve with increasing sample size of GWAS and the development of new powerful methods, it remains unclear how much prediction can be ultimately achieved. Here, we conducted a retrospective analysis to assess the progress in PRS prediction accuracy since the publication of the first large-scale GWASs using six common human diseases with sufficient GWAS data. We show that while PRS accuracy has grown rapidly for years, the improvement pace from recent GWAS has decreased substantially, suggesting that further increasing GWAS sample size may translate into very modest risk discrimination improvement. We next investigated the factors influencing the maximum achievable prediction using recently released whole genome-sequencing data from 125K UK Biobank participants, and state-of-the-art modeling of polygenic outcomes. Our analyses point toward increasing the variant coverage of PRS, using either more imputed variants or sequencing data, as a key component for future improvement in prediction accuracy.

genetics↗

bioRxiv2023

ChoruMM: a versatile multi-components mixed model for bacterial-GWAS

Genome-wide Association Studies (GWAS) have been central to studying the genetics of complex human outcomes, and there is now tremendous interest in implementing GWAS-like approaches to study pathogenic bacteria. A variety of methods have been proposed to address the complex linkage structure of bacterial genomes, however, some questions remain about to optimize the genetic modelling of bacteria to decipher causal variations from correlated ones. Here we examined the genetic structure underlying whole-genome sequencing data from 3,824 Listeria monocytogenes strains, and demonstrate that the standard human genetics model, commonly assumed by existing bacterial GWAS methods, is inadequate for studying such highly structured organisms. We leverage these results to develop ChoruMM, a robust and powerful approach that consists of a multi-component linear mixed model, where components are inferred from a hierarchical clustering of the bacteria genetic relatedness matrix. Our ChoruMM approach also includes post-processing and visualization tools that address the pervasive long-range correlation observed in bacteria genome and allow to assess the type I error rate calibration.

genetics↗

bioRxiv2021

Microglia Mediate Early Corticostriatal Synapse Loss and Cognitive Dysfunction in Huntingtons Disease Through Complement-Dependent Mechanisms

Huntingtons disease (HD) is a devastating monogenic neurodegenerative disease characterized by early, selective pathology in the basal ganglia despite the ubiquitous expression of mutant huntingtin. The molecular mechanisms underlying this region-specific neuronal degeneration and how these relate to the development of early cognitive phenotypes are poorly understood. Here, we show that there is selective loss of synaptic connections between the cortex and striatum in postmortem tissue from HD patients that is associated with the increased activation and localization of complement proteins, innate immune molecules, to markers of these synaptic elements. We also find that levels of these secreted innate immune molecules are elevated in the CSF of premanifest HD patients and correlate with established measures of disease burden. In preclinical genetic models of HD we show that complement proteins mediate the selective elimination of corticostriatal synapses at an early stage in disease pathogenesis marking them for removal by microglia, the brains resident macrophage population. This process requires mutant huntingtin to be expressed in both cortical and striatal neurons and inhibition of this complement-dependent elimination mechanism through administration of a therapeutically relevant C1q function blocking antibody or genetic ablation of a complement receptor on microglia, prevented synapse loss, increased excitatory input to the striatum and rescued the early development of visual discrimination learning and cognitive flexibility deficits in these models. Together, our findings implicate microglia and the complement cascade in the selective, early degeneration of corticostriatal synapses and the development of cognitive deficits in presymptomatic HD, and also provide new preclinical data to support complement as a therapeutic target for early intervention.

neuroscience↗

bioRxiv2020

CUB and Sushi Multiple Domains 1 (CSMD1) opposes the complement cascade in neural tissues

Schizophrenia risk is associated with increased gene copy number and brain expression of complement component 4 (C4). Because the complement system facilitates synaptic pruning, the C4 association has renewed interest in a hypothesis that excessive pruning contributes to schizophrenia pathogenesis. However, little is known about complement regulation in neural tissues or whether such regulation could be relevant to psychiatric illness. Intriguingly, common variation within CSMD1, which encodes a putative complement inhibitor, has consistently associated with schizophrenia at genome-wide significance. We found that Csmd1 is predominantly expressed in the brain by neurons, and is enriched at synapses; that human stem cell-derived neurons lacking CSMD1 are more vulnerable to complement deposition; and that mice lacking Csmd1 have increased brain complement activity, fewer synapses, aberrant complement-dependent development of a neural circuit, and synaptic elements that are preferentially engulfed by cultured microglia. These data suggest that CSMD1 opposes the complement cascade in neural tissues. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/291427v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@172c857org.highwire.dtl.DTLVardef@bee84corg.highwire.dtl.DTLVardef@184da10org.highwire.dtl.DTLVardef@10867c8_HPS_FORMAT_FIGEXP M_FIG Graphic Abstract.Our findings support a model in which CSMD1 opposes actions of the complement cascade in neural tissues (top left). We investigated two models in which Csmd1 was genetically ablated: human cortical neurons derived from embryonic stem cells, and a back-crossed C57bl6-Tac mouse line (top right). Csmd1 is normally expressed by neurons and present at synapses where it can protect them from complement (bottom left); in the absence of Csmd1 (bottom right), we find more deposition of complement (on cultured human cortical neurons and in the mouse visual system), reduced numbers of synapses (in the mouse visual system), and synaptic fractions that are more readily engulfed by microglia (ex vivo). Created with BioRender.com. C_FIG

neuroscience↗

bioRxiv2017

A new organotypic model of synaptic competition reveals activity-dependent localization of C1q

Immature neural circuits undergo synaptic refinement, in which activity-dependent competition between synapses results in pruning of inappropriate connections and maintenance of appropriate ones. A longstanding question is how neuronal activity eliminates specific synapses based on their strength. The technical challenges of in vivo studies have made it difficult to identify a molecular link between decreased activity and synapse elimination. We developed an organotypic coculture model of the mouse retinogeniculate system that facilitates real-time imaging and elucidation of molecular mechanisms underlying the removal of less active synapses during synaptic competition. Using this model we show for the first time that complement component C1q is necessary for activity-dependent synaptic competition and preferentially localizes to less active, competing presynaptic inputs. In conjunction with classic in vivo and ex vivo models, this coculture model is a new tool to reveal molecular pathways underlying CNS circuit refinement.

neuroscience↗