Biology subjects

Julienne, H.

Publications and source records attributed to Julienne, H..

12 recordsLinked to original sources

bioRxiv2026

Evolution of the genetic architecture of uterine disorders

Uterine disorders and menstrual abnormalities are prevalent reproductive conditions with significant clinical consequences. Recent genome-wide association studies have identified hundreds of variants contributing to different uterine disorders, while epidemiological evidence suggests that these disorders co-occur more frequently than expected, implying shared genetic mechanisms. However, the specific genetic variants and biological pathways underlying this shared architecture remain poorly characterized. Here, we conducted a uterus-centric, multi-trait genome-wide association analysis across ten uterine disorders in a European cohort to elucidate this shared genetic architecture. Further, we embedded this architecture in a functional and population genomics framework to investigate its plausible biological mechanisms and its evolutionary history in humans. We confirm strong positive genetic correlations between major uterine disorders and identify 31 independent susceptibility loci jointly affecting the genetic risk of multiple uterine pathologies, substantiating an intertwined biological basis. Populational analyses demonstrated that several of these susceptibility variants exhibit pronounced allele frequency differentiation across global populations suggestive of recent polygenic selection, including variants with well-supported regulatory functions at the ESR1-CCDC170, WNT4, SFR1, FOXO1, ITPR1, DMRT1 and CDKN2B loci. Notably, we show that most derived alleles acquired during recent human evolution increase risk across multiple uterine disorders and may evolve under antagonistic selection. These findings provide functional annotation and population-level prioritization of genetic variants influencing multiple uterine disorders. They also highlight how past evolutionary histories may contribute to population differences in uterine disease prevalence and pathogenesis.

genetics↗

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

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with anincreased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearsons {rho} equal to 0.43 between the observed and predicted gain, P < 1.6 x 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

genetics↗

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

Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap

BackgroundApproximately 95% of samples analyzed in univariate genome-wide association studies (GWAS) are of European ancestry. This bias toward European ancestry populations in association screening also exists for other analyses and methods that are often developed and tested on European ancestry only. However, existing data in non-European populations, which are often of modest sample size, could benefit from innovative approaches as recently illustrated in the context of polygenic risk scores. MethodsHere, we extend and assess the potential limitations and gain of our multi-trait GWAS pipeline, JASS (Joint Analysis of Summary Statistics), for the analysis of non-European ancestries. To this end, we conducted the joint GWAS of 19 hematological traits and glycemic traits across five ancestries (European (EUR), admixed American (AMR), African (AFR), East Asian (EAS), South-East Asian (SAS)). ResultsWe detected 367 new genome-wide significant associations in non-European populations (15 in Admixed American (AMR), 72 in African (AFR) and 280 in East Asian (EAS)). New associations detected represent 5%, 17% and 13% of associations in the AFR, AMR and EAS populations, respectively. Overall, multi-trait testing increases the replication of European associated loci in non-European ancestry by 15%. Pleiotropic effects were highly similar at significant loci across ancestries (e.g. the mean correlation between multi-trait genetic effects of EUR and EAS ancestries was 0.88). For hematological traits, strong discrepancies in multitrait genetic effects are tied to known evolutionary divergences: the ARKC1 loci, which is adaptive to overcome the p.vivax induced malaria. ConclusionsMulti-trait GWAS can be a valuable tool to narrow the genetic knowledge gap between European and non-European populations.

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↗

bioRxiv2022

Gender-based disparities and biases in science: observational study of a virtual conference

Success in STEM (Science, Technology, Engineering, and Math) remains influenced by race, gender, and socioeconomic status. Here, we focus on the impact of gender on question-asking behavior during the 2021 JOBIM virtual conference (Journees Ouvertes en Biologie et Mathematiques). We gathered quantitative and qualitative data including : demographic information, question asking motivations, live observations and interviews of participants. Quantitative analyses include unprecedented figures such as the fraction of the audience identifying as LGBTQIA+ and an increased attendance of women in virtual conferences. Although parity was reached in the audience, women asked half as many questions as men. This under-representation persisted after accounting for seniority of the asker. Interviews of participants highlighted several barriers to oral expression encountered by women and gender minorities : negative reactions to their speech, discouragement to pursue a career in research, and gender discrimination/sexual harassment. Informed by the study, guidelines for conference organizers have been written.

scientific communication and education↗

bioRxiv2020

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

genetics↗

bioRxiv2019

MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Genomics Data

Although missing data are prevalent in applications, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates. Here we present MGMM, an R package for fitting GMMs in the presence of missing data. Using three case studies on real and simulated data sets, we demonstrate that, when the underlying distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than state of the art imputation followed by standard GMM. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty even when the generative distribution is not a GMM. This assessment may be used to identify unassignable observations. MGMM is available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM.

bioinformatics↗

bioRxiv2019

JASS: Command Line and Web interface for the joint analysis of GWAS results

Genome Wide Association Study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available, and studies have demonstrated their utility for a range of secondary analyses. This includes in particular the joint analysis of multiple GWAS to identify new genetic variants missed by univariate screenings. However, although several methods have been proposed, there are very few large scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package solves all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of various joint statistics, an optimized data management process, and a web interface for exploration purposes. Benchmark analyses confirmed the strong performances of JASS. We also performed multiple real data analyses demonstrating the strong potential of JASS for the detection of new associated genetic variants across various scenarios. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

bioinformatics↗

bioRxiv2018

RAISS: Robust and Accurate imputation from Summary Statistics

MotivationMulti-trait analyses using public summary statistics from genome-wide association studies (GWAS) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. While methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses.\n\nResultsWe fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for small size-effect variants on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel.\n\nAvailabilityThe python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/.\n\nContacthanna.julienne@pasteur.fr

bioinformatics↗