Hugues Aschard

Asthma patients suffer from neuropsychiatric disorders (NPDs) more often than expected and NPD patients have a higher prevalence of smoking, which can lead to asthma. To investigate the genetic mechanisms underlying this complex relationship, we performed an integrative analysis of GWAS summary statistics from four asthma subtypes, 12 NPDs, and six substance use behaviors (SUBs). Asthma shares an important genetic component with post-traumatic stress disorder (PTSD), major depressive disorder, and attention-deficit/hyperactivity disorder (ADHD) that cannot be fully accounted for by smoking. Multi-trait GWAS identified 354 independent variants mapping to 178 pleiotropic genes capturing the core of the shared genetics of asthma, NPD, and SUB. Single-cell transcriptomic analysis across blood, brain, and lung tissues revealed pleiotropic gene overexpression in 57 cell types and three candidate mechanisms linking asthma and NPDs. First, immune-related pleiotropic genes are expressed in microglia, blood and lung immune cells, suggesting shared immune dysfunction. Second, the pleiotropic genes EP300 , RERE , and MAML3 regulate NOTCH signaling and are expressed in neurons, microglia, and lymphoid progenitors. Third, pleiotropic genes expressed in ciliated cells of both lung and brain implicate ciliary dysfunction. These findings may help identify new treatment strategies for patients with co-occurring asthma and neuropsychiatric disorders.

Genome-wide Association Studies (GWAS) have played a crucial role in uncovering the genetics underlying complex human traits. Recently, there has been considerable interest in adapting GWAS-like methodologies to investigate pathogenic bacteria. Despite the variety of methods proposed, there remains a lack of clarity on how to effectively model the intricate population structures found in bacterial cohorts. In this study, we analyze the genetic architecture of whole-genome sequencing data from three distinct bacterial species, showing that the standard models used in human genetics, typically employed by existing bacterial GWAS methods, fall short when applied to organisms with highly structured genomes. Building on these findings, we introduce ChoruMM, a robust and powerful multi-component linear mixed model. This model infers components through hierarchical clustering of the bacterial genetic relatedness matrix. Extensive simulations show that our approach reduces false positives while maintaining, or even improving, detection rates compared to current pipelines. The ChoruMM package includes post-processing and visualization tools designed to address the prevalent issue of long-range correlations in bacterial genomes, enabling accurate assessment and calibration of type I error rates.

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.

Both short and long sleep duration have been associated with poor glycemic control and an increased risk of developing type 2 diabetes mellitus. Although sleep duration may differentially modify the effects of genetic risk factors for type 2 diabetes, this has not been systematically investigated. In the present study, we conducted genome-wide gene by sleep duration meta-analyses, separately assessing interactions of short and long sleep, for fasting glucose, fasting insulin, and hemoglobin A1c in up to 489,309 individuals without diabetes from seven different population groups. In total, 16 loci were identified to interact with sleep duration — six with short sleep and ten with long sleep. Of these, four loci were identified through cross-population meta-analysis. Mapped genes exhibit pathway connections to pericyte apoptosis, NMDA receptor activity, the GLUT1 receptor, neurological health, and sleep architecture. Eleven loci ( VRK2, PCDH7, TFAP2A, CAP2, PAPPA, ZCCHC2, MYH9, SGIP1, JAKMIP3, RRAS2, MAPT ) have not been reported in previous glycemic trait genome-wide association studies. Interaction loci identify divergent biological mechanisms for short and long sleep duration influencing glycemic control, suggesting specific pathways of intervention for precision medicine approaches to diabetes prevention and management. Article Highlights: The biological mechanisms of how sleep duration impacts type 2 diabetes pathogenesis and glycemic control are unclear. This study reveals 16 loci (11 novel) that interact with either short or long sleep duration to influence hemoglobin A1c, fasting glucose, or fasting insulin. Short and long sleep duration loci were non-overlapping. Regulation of copper and diacylglycerol levels appear as distinct cellular mechanisms implicated by long and short sleep duration loci respectively. Identified gene targets present insight for potential type 2 diabetes therapeutic design approaches related to JIP1-JNK interaction disruption, pericyte health, NMDA receptor activity, anti-inflammatory and leptin-enhancing dietary supplements, and serpins.

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.

The gut microbiome is a complex ecosystem characterized not only by its marginal taxonomic composition but also by its emergent properties. Bacteria develop local interactions to form coherent functional communities, whose effects on health and diseases cannot be predicted from the behavior of individual members. Understanding the factors underlying variability in these communities may therefore provide critical insights on the biological links between the gut microbiome and human phenotypes. Here, we examined the effect of a range of host factors, including demographics, medical history, and dietary habits, on these communities in 938 healthy individuals using MANOCCA, a covariance-based approach developed to address existing limitations. Increased age and smoking were associated with a significant overall decrease in co-abundance, and conversely a higher body mass index was associated with increased co-abundance. At the taxon level, a core of 200 genera were systematically impacted in their co-abundance with other taxa, suggesting a central role in structuring the network. Finally, we demonstrate that our approach offers a powerful framework for prediction purposes, with taxa co-abundance being able to predict the age of participants with an accuracy three-fold higher than a model based on abundance only.

Cigarette smoking influences blood pressure (BP) levels. Studying and accounting for potential gene-smoking interactions can help discover novel loci and provide insights into biological pathways for smoking-associated BP regulation. We conducted a genome-wide association meta-analysis involving 1,188,241 individuals from 66 studies in five ancestry groups, analyzing systolic BP, diastolic BP, and pulse pressure while considering interactions between genetic variants and three smoking exposures: smoking status, cigarettes per day, and pack years. These analyses identified twelve novel loci for BP at genome-wide significance ( P < 5 × 10 ⁻⁹ ), and highlighted biological processes including tight junction integrity, mitochondrial health, vascular relaxation, and endothelial function. In smoking status-stratified analyses, smoking modifies the genetic effect of six variants on BP. To prioritize likely causal, we developed and applied SuSiEgxe, a fine-mapping method based on a two-degree-of-freedom joint test using gene-environment interaction summary statistics. Fine-mapped loci uncovered immune-related pathway for smoking-associated BP regulation.

Serum lipid levels, which are influenced by both genetic and environmental factors, are key determinants of cardiometabolic health and are influenced by both genetic and environmental factors. Improving our understanding of their underlying biological mechanisms can have important public health and therapeutic implications. Although psychosocial factors, including depression, anxiety, and perceived social support, are associated with serum lipid levels, it is unknown if they modify the effect of genetic loci that influence lipids. We conducted a genome-wide gene-by-psychosocial factor interaction (G×Psy) study in up to 133,157 individuals to evaluate if G×Psy influences serum lipid levels. We conducted a two-stage meta-analysis of G×Psy using both a one-degree of freedom (1df) interaction test and a joint 2df test of the main and interaction effects. In Stage 1, we performed G×Psy analyses on up to 77,413 individuals and promising associations (P < 10⁻⁵) were evaluated in up to 55,744 independent samples in Stage 2. Significant findings (P < 5 × 10⁻⁸) were identified based on meta-analyses of the two stages. There were 10,230 variants from 120 loci significantly associated with serum lipids. We identified novel associations for variants in four loci using the 1df test of interaction, and five additional loci using the 2df joint test that were independent of known lipid loci. Of these 9 loci, 7 could not have been detected without modeling the interaction as there was no evidence of association in a standard GWAS model. The genetic diversity of included samples was key in identifying these novel loci: four of the lead variants displayed very low frequency in European ancestry populations. Functional annotation highlighted promising loci for further experimental follow-up, particularly rs73597733 (MACROD2), rs59808825 (GRAMD1B), and rs11702544 (RRP1B). Notably, one of the genes in identified loci (RRP1B) was found to be a target of the approved drug Atenolol suggesting potential for drug repurposing. Overall, our findings suggest that taking interaction between genetic variants and psychosocial factors into account and including genetically diverse populations can lead to novel discoveries for serum lipids.

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.