Composite trait Mendelian randomization reveals distinct metabolic and lifestyle consequences of differences in body shape

Obesity is a major risk factor for a wide range of cardiometabolic diseases, however the impact of specific aspects of body morphology remains poorly understood. We combined the GWAS summary statistics of fourteen anthropometric traits from UK Biobank through principal component analysis to reveal four major independent axes: body size, adiposity, predisposition to abdominal fat deposition, and lean mass. Mendelian randomization analysis showed that although body size and adiposity both contribute to the consequences of BMI, many of their effects are distinct, such as body size increasing the risk of cardiac arrhythmia (b = 0.06, p = 4.2 ∗ 10 <sup>-17</sup> ) while adiposity instead increased that of ischemic heart disease (b = 0.079, p = 8.2 ∗ 10 <sup>-21</sup> ). The body mass-neutral component predisposing to abdominal fat deposition, likely reflecting a shift from subcutaneous to visceral fat, exhibited health effects that were weaker but specifically linked to lipotoxicity, such as ischemic heart disease (b = 0.067, p = 9.4 ∗ 10 <sup>-14</sup> ) and diabetes (b = 0.082, p = 5.9 ∗ 10 <sup>-19</sup> ). Combining their independent predicted effects significantly improved the prediction of obesity-related diseases (p < 10 <sup>-10</sup> ). The presented decomposition approach sheds light on the biological mechanisms underlying the heterogeneity of body morphology and its consequences on health and lifestyle

Robust Causal Inference Methods to Assess Risk Factors for Common Diseases

The study of complex traits, those influenced by multiple genetic and environmental factors, has long been a cornerstone of genetic research, where scientists have sought to untangle this complexity. These traits include a vast array of human characteristics, from molecular phenotypes to diseases. The advent of Genome-Wide Association Studies (GWAS) following human genome sequencing marked an essential moment in this pursuit. These studies, characterised by their large sample size and examination of millions of genetic variants, have significantly advanced our understanding of the genetic architecture underlying complex traits. GWAS have unearthed numerous genetic markers associated with various traits, providing vital clues for further exploration. GWAS have not only identified genetic associations to complex traits, but have also helped re- searchers explore the relationships between these traits. Understanding the causal relationships among traits is essential due to its potential to improve medical practices and public health interventions. In response, Mendelian Randomisation (MR) emerged as a genetically-informed version of previous causal inference methods, such as Randomised Control Trials (RCTs). MR uses genetic variants as instrumental variables to elucidate causal relationships between traits, distinguishing true causation from mere correlation. As a statistical method, MR comes with several assumptions that must hold for accurate estimation. However, validating some of these assumptions can be challenging, potentially introducing bias in the estimation of causal effects. During my thesis, I investigated assumption violations that MR often faces, particularly in two scenarios: (i) the presence of unmeasured heritable confounding factors introducing spurious causal relationships and (ii) the heterogeneity of causal effects due to potential underlying pleiotropic pathways or confounder mechanisms. To address the first assumption violation, I developed an extension to the MR model known as LHC-MR, which accounts for the presence of a Latent Heritable Confounder. LHC-MR is applicable to association summary statistics of trait pairs, allowing simultaneous estimation of bi-directional causal effects, direct heritabilities, and confounder effects on the pair. For the second assumption violation, I proposed an approach, PWC-MR, that leverages Phenome-Wide association data across several traits to perform informative Clustering of the focal trait instruments. PWC-MR revealed that for body mass index (BMI), distinct clusters of instruments exist with heterogeneous causal effects on educational attainment. Lastly, I explored indirect genetic effects using individual-level genetic data of sibling pairs. The aim was to estimate the causal effect of the parental environment/rearing on offspring traits in later life, using MR. In summary, this journey from the study of complex traits to the emergence of GWAS and MR as tools for causal inference has reshaped our understanding of genetics. While MR offers great promise, its often-violated assumptions necessitate careful consideration, and my work aimed to address some of these challenges. -- L’étude des traits complexes, qui sont influencés par de multiples facteurs génétiques et environnemen- taux, a toujours été un pilier de la recherche en génétique, où les scientifiques ont cherché à démêler cette complexité. Ces traits englobent une vaste gamme de caractéristiques humaines, comme des phénotypes moléculaires mais aussi certaines maladies courantes. L’avènement des études d’association pangénomique (GWAS) à la suite du séquençage du génome humain, a marqué un moment essentiel dans cette quête. Ces études, caractérisées par leur grande taille d’échantillon et l’analyse de millions de variants génétiques, ont considérablement avancé notre compréhension de l’architecture génétique des traits complexes, en permettant d’identifier de nombreux marqueurs génétiques associés à divers traits, fournissant ainsi des indices essentiels pour de futures explorations. Les GWAS ont non seulement permis d’identifier des associations génétiques, mais elles ont également aidé les chercheurs à explorer les relations entre ces traits. Il est essentiel de comprendre les relations de cause à effet entre les traits pour pouvoir améliorer les pratiques médicales et les interventions de santé publique. En réponse, la Randomisation Mendélienne (MR), version génétiquement informée des méthodes précédentes d’inférence causale, telles que les Essais Contrôlés Randomisés, a émergé. La MR utilise des variants génétiques en tant que variables instrumentales pour élucider les relations de cause à effet entre les traits, distinguant ainsi véritable causalité et simple corrélation. C’est une méthode statistique qui repose sur plusieurs hypothèses qui doivent être respectées afin d’obtenir une estimation précise. Cependant, la validation de certaines de ces hypothèses peut s’avérer difficile et leur violation peut introduire un biais dans l’estimation des effets de causalité. Au cours de ma thèse, j’ai examiné les violations d’hypothèses auxquelles la MR est souvent confrontée, en particulier dans deux scénarios : (i) la présence de facteurs confondants héréditaires non mesurés introduisant des relations de causalité fallacieuses et (ii) l’hétérogénéité des effets de causalité due à d’éventuels effets pléiotropiques ou à des facteurs confondants. Concernant la première violation d’hypothèse, j’ai développé une extension du modèle MR appelée LHC-MR, qui prend en compte la présence d’un facteur Confondant Héréditaire Latent. LHC-MR utilise des statistiques synthétiques issues des GWAS pour étudier la relation entre deux traits, via l’estimation simultanée d’effets de causalité bidirectionnels, d’héritabilités directes et des effets du facteur confondant sur chacun des traits. Pour aborder le deuxième scénario, j’ai proposé une approche, PWC-MR, qui permet d’effectuer un regroupement informatif des instruments, sélectionnés pour leur association avec le facteur de risqué d’intérêt, en exploitant des données d’association génétique avec plusieurs autres traits. PWC-MR a révélé que, pour l’indice de masse corporelle (IMC), il existe des groupes distincts d’instruments avec des effets de causalité hétérogènes sur le niveau d’éducation. Enfin, j’ai exploré les effets génétiques indirects en utilisant des données génétiques d’individus issus d’une même fratrie. L’objectif était d’utiliser la MR pour estimer l’effet de causalité de l’environnement parental sur les traits des enfants à un stade ultérieur de leur vie. En résumé, l’étude des traits complexes, depuis l’émergence des GWAS jusqu’à l’utilisation de la MR en tant qu’outil pour l’inférence de causalité, a remodelé notre compréhension de la génétique. Bien que la MR offre de grandes promesses, ses hypothèses souvent violées nécessitent une réflexion minutieuse, et mon travail de doctorat a permis de proposer des solutions pour relever certains de ces défis

PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment

Mendelian Randomisation (MR) estimates causal effects between risk factors and complex outcomes using genetic instruments. Pleiotropy, heritable confounders, and heterogeneous causal effects violate MR assumptions and can lead to biases. To alleviate these, we propose an approach employing a Phenome-Wide association Clustering of the MR instruments (PWC-MR) and apply this method to revisit the surprisingly large apparent causal effect of body mass index (BMI) on educational attainment (EDU): [Formula: see text] = -0.19 [-0.22, -0.16]. First, we cluster 324 BMI-associated genetic instruments based on their association with 407 traits in the UK Biobank, which yields six distinct groups. Subsequent cluster-specific MR reveals heterogeneous causal effect estimates on EDU. A cluster enriched for socio-economic indicators yields the largest BMI-on-EDU causal effect estimate ([Formula: see text] = -0.49 [-0.56, -0.42]) whereas a cluster enriched for body-mass specific traits provides a more likely estimate ([Formula: see text] = -0.09 [-0.13, -0.05]). Follow-up analyses confirms these findings: within-sibling MR ([Formula: see text] = -0.05 [-0.09, -0.01]); MR for childhood BMI on EDU ([Formula: see text] = -0.03 [-0.06, -0.002]); step-wise multivariable MR ([Formula: see text] = -0.05 [-0.07, -0.02]) where socio-economic indicators are jointly modelled. Here we show how the in-depth examination of the BMI-EDU causal relationship demonstrates the utility of our PWC-MR approach in revealing distinct pleiotropic pathways and confounder mechanisms.</p

Genetic insights into the causal relationship between physical activity and cognitive functioning

Abstract Physical activity and cognitive functioning are strongly intertwined. However, the causal relationships underlying this association are still unclear. Physical activity can enhance brain functions, but healthy cognition may also promote engagement in physical activity. Here, we assessed the bidirectional relationships between physical activity and general cognitive functioning using Latent Heritable Confounder Mendelian Randomization (LHC-MR). Association data were drawn from two large-scale genome-wide association studies (UK Biobank and COGENT) on accelerometer-measured moderate, vigorous, and average physical activity (N = 91,084) and cognitive functioning (N = 257,841). After Bonferroni correction, we observed significant LHC-MR associations suggesting that increased fraction of both moderate (b = 0.32, CI95% = [0.17,0.47], P = 2.89e − 05) and vigorous physical activity (b = 0.22, CI95% = [0.06,0.37], P = 0.007) lead to increased cognitive functioning. In contrast, we found no evidence of a causal effect of average physical activity on cognitive functioning, and no evidence of a reverse causal effect (cognitive functioning on any physical activity measures). These findings provide new evidence supporting a beneficial role of moderate and vigorous physical activity (MVPA) on cognitive functioning