Guillaume Huguet

Worrawat Engchuan

Jakub Kopal

Thomas Renne

Martineau Jean-Louis

Omar Shanta

Zohra Saci

Bhooma Thiruvahindrapuram

Jeffrey MacDonald

Josephine Mollon

Laura M Schultz

Emma E M Knowles

David Porteous

Gail Davies

Paul Redmond

Sarah Harris

Simon Cox

Gunter Schumann … (voir 9 de plus)

Zdenka Pausova

Celia Greenwood

Tomáš Paus

Stephen Scherer

Laura Almasy

Jonathan Sebat

David Glahn

Sébastien Jacquemont

Cognitive deficits are common across many neurodevelopmental and psychiatric conditions, including those studied in the current set of PGC-C… (voir plus)NV papers. How changes in regional gene expression across the cerebral cortex influence cognitive ability remains unknown. Population variation in gene dosage—which significantly impacts gene expression—represents a unique paradigm to address this question. We developed a cerebral-cortex gene-set burden analysis (CC-GSBA) to associate a trait with genomic deletions and duplications that disrupt genes with similar expression profiles across 180 cortical regions. We performed CC-GSBA across 180 cortical regions to test associations with cognitive ability in 260,000 individuals from general population cohorts. Most cortical gene sets were associated with a decrease in cognitive ability when deleted or duplicated, and this novel approach revealed opposing cortical patterns for the effect sizes of deletions and duplications. These cortical patterns of effect sizes followed the cortical gradient previously characterized at the molecular, cellular, and functional levels. We show that genes with preferential expression in sensorimotor regions demonstrated the largest effect on cognition when deleted. At the opposing end of the cortical gradient, genes with preferential expression in multimodal association regions affected cognition the most when duplicated. These two gene dosage cortical patterns could not be explained by particular cell types, developmental epochs, or genetic constraints, highlighting the fact that the macroscopic network organization of the cerebral cortex is key to understanding the effects of gene dosage on cognitive traits.

2025-11-27

Research Square (prépublication)

Determinants of pleiotropy and monotonic gene dosage responses across human traits

Sayeh Kazem

Kuldeep Kumar

Josephine Mollon

Thomas Renne

Laura M. Schultz

Emma E.M. Knowles

Worrawat Engchuan

Omar Shanta

Bhooma Thiruvahindrapuram

Jeffrey R. MacDonald

Celia M. T. Greenwood

Stephen W. Scherer

Laura Almasy

Jonathan Sebat

David C. Glahn

Sébastien Jacquemont

While pleiotropic effects of gene dosage are of particular relevance for comorbidities observed in the developmental pediatric and psychiatr… (voir plus)ic clinic, the biological processes underlying such pleiotropy remain unknown. We developed a new functional burden analysis (FunBurd) to investigate all CNVs, genome-wide, beyond well-studied recurrent CNVs. In ~500,000 UK-Biobank participants, we tested the association between 43 traits and CNVs disrupting 172 tissue or cell-type gene-sets. CNVs affected all traits. Pleiotropy was correlated with genetic constraint and was higher in the brain compared to non-brain functions, even after normalizing for genetic constraint. The levels of pleiotropy, measured by burden correlation, were similar in deletions and loss-of-function SNVs and higher compared to common variants and duplications. Gene sets under high genetic constraint showed less monotonic gene dosage responses across traits. Even in the absence of a monotonic response, we observed a negative correlation between deletion and duplication effect sizes across most traits. Overall, functional gene sets are preferentially associated with a given trait when either deleted or duplicated, but rarely both.

2025-11-18

Research Square (prépublication)

A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B. T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging-genetics field has st… (voir plus)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

2025-07-17

Nature Biomedical Engineering (publié)

A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B. T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging-genetics field has st… (voir plus)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

2025-07-17

Nature Biomedical Engineering (publié)

Cortical differences across psychiatric disorders and associated common and rare genetic variants

Kuldeep Kumar

Zhijie Liao

Jakub Kopal

Clara Moreau

Christopher R. K. Ching

Claudia Modenato

Will Snyder

Sayeh Kazem

Charles-Olivier Martin

C.O. Martin

Anne-Marie Bélanger

Valérie K. Fontaine

Khadije Jizi

Rune Boen

Zohra Saci

Leila Kushan

Ana I. Silva

Marianne B.M. van den Bree

David E.J. Linden … (voir 16 de plus)

Michael J. Owen

Jeremy Hall

Sarah Lippé

Bogdan Draganski

Laura Almasy

Sophia I. Thomopoulos

Neda Jahanshad

Ida E. Sønderby

Ole A. Andreassen

David C. Glahn

Armin Raznahan

Carrie Bearden

Tomáš Paus

Paul M. Thompson

Sébastien Jacquemont

2025-04-18

medRxiv (prépublication)

Determinants of pleiotropy and monotonic gene dosage responses across human traits

Sayeh Kazem

Kuldeep Kumar

Martineau Jean‐Louis

Thomas Renne

Zohra Saci

Worrawat Engchuan

Omar Shanta

Bhooma Thiruvahindrapuram

Jeffrey R. MacDonald

Celia M. T. Greenwood

Stephen W. Scherer

Laura Almasy

Jonathan Sebat

David C. Glahn

Sébastien Jacquemont

Pleiotropic effects of gene dosage are central to understanding comorbidities in developmental pediatric and psychiatric disorders, yet the … (voir plus)underlying biological processes are unknown. We developed Functional Burden analysis (FunBurd) to investigate the association of all protein-coding copy-number-variants (CNVs), genome-wide, with 43 complex traits in ∼500,000 UK-Biobank participants. We tested CNV associations disrupting 172 tissue or cell-type gene-sets, observing associations across all traits. Pleiotropy was correlated with genetic constraint and was higher in the brain compared to non-brain functions, even after normalizing for genetic constraint. Cognition and mental health traits showed specific gene-dosage effects across cortical/sub-cortical and neuronal/glial functional categories. The levels of pleiotropy, measured by burden correlation, were similar in deletions and loss-of-function SNVs, and higher compared to common variants and duplications. Gene sets under high genetic constraint showed less monotonic gene dosage responses across traits. Across most traits, we observed a negative deletion-duplication effect size correlation, indicating that functional gene sets are preferentially sensitive to either deletion or duplication, but rarely both. Our results highlight the key role of genetic constraint and brain-specific mechanisms in shaping CNV-driven pleiotropy, providing a mechanistic basis for the whole-body multimorbidity observed in neurodevelopmental and psychiatric conditions.

2025-02-25

medRxiv (publié)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Chen Liu

Guy Wolf

Ian Adelstein

Tim G. J. Rudner

Smita Krishnaswamy

Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportu… (voir plus)nities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and transporting populations via feasible paths. To address these issues, we introduce Geometry-Aware Generative Autoencoder (GAGA), a novel framework that combines extensible manifold learning with generative modeling. GAGA constructs a neural network embedding space that respects the intrinsic geometries discovered by manifold learning and learns a novel warped Riemannian metric on the data space. This warped metric is derived from both the points on the data manifold and negative samples off the manifold, allowing it to characterize a meaningful geometry across the entire latent space. Using this metric, GAGA can uniformly sample points on the manifold, generate points along geodesics, and interpolate between populations across the learned manifold using geodesic-guided flows. GAGA shows competitive performance in simulated and real-world datasets, including a 30% improvement over the state-of-the-art methods in single-cell population-level trajectory inference.

2025-01-21

aistats.org/AISTATS/2025/Conference (poster)

proceedings.mlr.press

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Thomas Renne

Cécile Poulain

Alma Dubuc

Kuldeep Kumar

Sayeh Kazem

Worrawat Engchuan

Omar Shanta

Elise Douard

Catherine Proulx

Martineau Jean-Louis

Zohra Saci

Josephine Mollon

Laura M. Schultz

Emma E.M. Knowles

Simon R. Cox

David Porteous

Gail Davies

Paul Redmond

Sarah E. Harris … (voir 10 de plus)

Gunter Schumann

Aurélie Labbe

Zdenka Pausova

Tomáš Paus

Stephen W. Scherer

Jonathan Sebat

Laura Almasy

David C. Glahn

Sébastien Jacquemont

Copy-number variants (CNVs) that increase the risk for neurodevelopmental disorders also affect cognitive ability. However, such CNVs remain… (voir plus) challenging to study due to their scarcity, limiting our understanding of gene-dosage-sensitive biological processes linked to cognitive ability. We performed a genome-wide association study (GWAS) in 258,292 individuals, which identified—for the first time—a duplication at 2q12.3 associated with higher cognitive performance. We developed a functional-burden analysis, which tested the association between cognition and CNVs disrupting 6,502 gene sets biologically defined across tissues, cell types, and ontologies. Among those, 864 gene sets were associated with cognition, and effect sizes of deletion and duplication were negatively correlated. The latter suggested that functions across all biological processes were sensitive to either deletions (e.g., subcortical regions, postsynaptic) or duplications (e.g., cerebral cortex, presynaptic). Associations between non-brain tissues and cognition were driven partly by constrained genes, which may shed light on medical comorbidities in neurodevelopmental disorders.

2024-11-30

Cell Genomics (publié)

ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images

Chen Liu

Ke Xu

Liangbo L. Shen

Jay Stewart

Jay C. Wang

Lucian V. Del Priore

Smita Krishnaswamy

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same pat… (voir plus)ients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose ImageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. ImageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate ImageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of ImageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets. The official implementation is available at https://github.com/KrishnaswamyLab/ImageFlowNet.

2024-06-19

ArXiv (prépublication)

arxiv.org

Geometry-Aware Generative Autoencoders for Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Guy Wolf

Ian Adelstein

Tim G. J. Rudner

Smita Krishnaswamy

Non-linear dimensionality reduction methods have proven successful at learning low-dimensional representations of high-dimensional point clo… (voir plus)uds on or near data manifolds. However, existing methods are not easily extensible—that is, for large datasets, it is prohibitively expensive to add new points to these embeddings. As a result, it is very difficult to use existing embeddings generatively, to sample new points on and along these manifolds. In this paper, we propose GAGA (geometry-aware generative autoencoders) a framework which merges the power of generative deep learning with non-linear manifold learning by: 1) learning generalizable geometry-aware neural network embeddings based on non-linear dimensionality reduction methods like PHATE and diffusion maps, 2) deriving a non-euclidean pullback metric on the embedded space to generate points faithfully along manifold geodesics, and 3) learning a flow on the manifold that allows us to transport populations. We provide illustration on easily-interpretable synthetic datasets and showcase results on simulated and real single cell datasets. In particular, we show that the geodesic-based generation can be especially important for scientific datasets where the manifold represents a state space and geodesics can represent dynamics of entities over this space.

2024-06-16

ICML.cc/2024/Workshop/GRaM (publié)

openreview.net

High-effect gene-coding variants impact cognition, mental well-being, and neighborhood safety substrates in brain morphology

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B.T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging genetics field has st… (voir plus)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, environment, human health, daily living skills and outcomes. Hence, we interrogated the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and corresponding implications on cognitive, behavioral, psychosocial, and socioeconomic traits. Specifically, we designed a holistic pattern-learning algorithm that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 962 behavioral variables spanning 20 categories in 7,657 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks, and specific parameters of the family well-being (increased parental and child stress, anxiety and depression) or neighborhood dynamics (decreased safety); effects extending beyond the impairment of cognitive ability or language capacity, dominantly reported in the CNV literature. Our investigation thus spotlights a far-reaching interplay between genetic variation and subjective life quality in adolescents and their families.

2024-05-21

medRxiv (publié)

295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population

Kuldeep Kumar

Sayeh Kazem

Zhijie Liao

Jakub Kopal

Thomas Renne

Martineau Jean‐Louis

Zhe Xie

Zohra Saci

Laura Almasy

David C. Glahn

Tomáš Paus

Carrie E. Bearden

Paul M. Thompson

Richard A. I. Bethlehem

Varun Warrier

Sébastien Jacquemont

2024-04-28

Biological Psychiatry (inconnu)