Mendelian randomization and its application to genome-wide association studies

Genetics aims is to study heredity: how traits are passed from one generation to the next and how genetic variations can lead to changes in phenotypes. Some phenotypes, called complex or quantitative traits, are under the control of both genetic and environmental factors. Examples of complex traits include quantitative phenotypes, such as height or cholesterol levels, as well as certain diseases, like diabetes or cardiovascular diseases. Genome-wide association studies (GWASs) are used to statistically test for the association between each genetic variant and a given phenotype. These studies confirmed that most complex traits are influenced by a large number of genetic variants, often exhibiting small effect sizes that can only be detected using large numbers of individuals. They also permitted the estimation of narrow-sense heritability, which is the proportion of phenotypic variation that can be attributed to these genetic variations. The results of such GWASs (association results for every genetic variant) are often made publicly available and they can be used to perform follow-up analyses, for example Mendelian randomization. Mendelian randomization aims at investigating causal relationships between complex traits and estimating the causal effect of one exposure on an outcome. This method mimicks randomized controlled trials and takes advantage of the fact that genetic variations are randomly distributed across the population. By using association results for genetic variants strongly associated with a given risk factor and measuring the effect of these variants on another trait or disease, Mendelian randomization can infer the existence and the strength of the causal relationship between them. Analyses helping to understand the genetics underlying complex traits and the relationships between them are key to precision medicine. Precision medicine is an approach that takes into account the genome sequence and the environmental exposures of each patient, to provide personalized prevention and treatment to each individual. During my thesis, I have been involved in several projects aiming at developing statistical methods that rely on Mendelian randomization. In the first part, I worked on a Bayesian GWAS approach (bGWAS). The goal of this approach is to increase statistical power to discover variants associated with a trait by leveraging data from correlated risk factors. The idea is to combine (1) the causal effects of the risk factors on the trait of interest (estimated using Mendelian randomization) with (2) the association results of genetic variants with these risk factors, in order to estimate the prior effect of each variant on the trait of interest. This approach has been used to study the genetics underlying lifespan, taking into account various potential risk factors, such as body mass index, cholesterol levels, and several diseases for example. In the second part, I worked on developing Mendelian randomization extensions (MRlap and LHC-MR) that aim at tackling some of the most common sources of bias. These extensions allow for more robust causal effect estimates, when some of the Mendelian randomization assumptions are violated, as well as for an extension of the scope of application of Mendelian randomization. -- La génétique est l’étude de la transmission de traits héréditaires au sein d’une population. Un défi majeur de la génétique moderne est cependant d’expliquer le mécanisme exact par lequel les variations génétiques peuvent, ou non, se traduire par des variations phénotypiques. Ce défi est d’autant plus important dans le cas des traits dits «complexes», qui sont affectés à la fois par des facteurs génétiques et par des facteurs environnementaux. C’est le cas par exemple de la taille adulte, du taux de cholestérol ou encore de certaines maladies, comme le diabète. Les études d’association pangénomique, en anglais genome-wide association studies (GWASs), permettent de tester si des variants génétiques sont statistiquement associés à un phénotype donné. Ces études ont confirmé que la plupart des traits complexes sont influencés par un très large nombre de variants génétiques, dont chacun a souvent un faible effet qui n’aurait pas été détecté sans l’accès à de larges jeux de données. Elles ont également permis d’estimer la part de la variation phénotypique expliquée par l’ensemble des variants (héritabilité au sens étroit). Les résultats de ces GWASs sont souvent publiés sous forme de statistiques synthétiques (pour chaque variant génétique) qui peuvent être utilisées pour réaliser des analyses additionnelles, notamment des analyses de randomisation mendélienne. Celles-ci permettent d’étudier les relations de cause à effet entre différents traits complexes et d’estimer l’effet de causalité d’un trait sur un autre. Les variations génétiques étant théoriquement réparties de façon aléatoire dans une population, la randomisation mendélienne est une alternative aux essais cliniques randomisés. En utilisant les résultats d’association de variants génétiques associés spécifiquement avec un facteur de risque et en mesurant leurs effets sur un autre trait, la randomisation mendélienne permet d’établir une relation de cause à effet entre deux traits. Ces études, permettant la compréhension des causes génétiques à l’origine des traits complexes ainsi que des relations de cause à effet pouvant exister entre ceux-ci, ouvrent la voie au développement de la médecine de précision, une approche prenant en compte toutes les informations concernant un individu (génétiques et environnementales) pour proposer à chacun un diagnostic et un traitement personnalisés. Durant mon doctorat, j’ai été impliquée dans différents projets visant à développer des approches techniques basées sur la randomisation mendélienne. Dans un premier temps, j’ai travaillé sur une méthode appelée GWAS bayésien (bGWAS). Cette méthode utilise des informations provenant de potentiel facteurs de risques identifiés a priori de façon à augmenter la puissance statistique de l’identification de variants génétiques associés à un trait d’intérêt. L’idée est de combiner (1) les effets de causalité des risques facteurs sur le trait d’intérêt (estimés en utilisant la randomisation mendélienne) et (2) les résultats d’association des variants génétiques avec ces facteurs de risque, pour estimer leur effet a priori sur le trait d’intérêt. Cette méthode a notamment été utilisée pour étudier les causes génétiques influençant l’espérance de vie, en prenant en compte plusieurs facteurs de risques tel que certaines maladies ou encore l’indice de masse corporel. Dans un second temps, j’ai travaillé sur des projets visant à proposer des extensions aux méthodes classiques de randomisation mendélienne (MRlap et LHC-MR) pour les rendre plus robustes à certaines sources de biais communément observées, avec pour but d’élargir les possibilités d’application de ces méthodes

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

GWAS of retinal vessel tortuosity identifies 85 novel loci recovering variants associated with disease

Fundus pictures of the eye allow for non-invasive inspection of the microvasculature system of the retina which is informative on cardiovascular health. Automated image processing enables the extraction of morphometric properties of this system as quantitative features that can be used for modelling disease risks. Here we report the results of the largest genome-wide association study (GWAS) of retinal vessel tortuosity conducted to date using data from the UK Biobank (N=63,899). We identified 87 loci associated with this trait (85 of which are novel). The heritability of the trait was h2=0.23 (0.02). We carried out a replication study on a small independent population-based cohort, SKIPOGH (N=436). While the power of this study was too small to replicate individual hits, the effect size estimates correlated significantly between the two studies (Pearson correlation r=0.55, p=4.6E-6). We showed that the alleles associated with retinal vessel tortuosity point to a common genetic architecture of this trait with CVD and related traits. Our results shed new light on the genetics of cardiovascular risk factors and disease

Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases

Purpose: To identify novel susceptibility loci for retinal vascular tortuosity, to better understand the molecular mechanisms modulating this trait, and reveal causal relationships with diseases and their risk factors. Design: Genome-wide Association Studies (GWAS) of vascular tortuosity of retinal arteries and veins followed by replication meta-analysis and Mendelian randomization (MR). Participants: We analyzed 116 639 fundus images of suitable quality from 63 662 participants from 3 cohorts, namely the UK Biobank (n = 62 751), the Swiss Kidney Project on Genes in Hypertension (n = 397), and OphtalmoLaus (n = 512). Methods: Using a fully automated retina image processing pipeline to annotate vessels and a deep learning algorithm to determine the vessel type, we computed the median arterial, venous and combined vessel tortuosity measured by the distance factor (the length of a vessel segment over its chord length), as well as by 6 alternative measures that integrate over vessel curvature. We then performed the largest GWAS of these traits to date and assessed gene set enrichment using the novel high-precision statistical method PascalX. Main Outcome Measure: We evaluated the genetic association of retinal tortuosity, measured by the distance factor. Results: Higher retinal tortuosity was significantly associated with higher incidence of angina, myocardial infarction, stroke, deep vein thrombosis, and hypertension. We identified 175 significantly associated genetic loci in the UK Biobank; 173 of these were novel and 4 replicated in our second, much smaller, metacohort. We estimated heritability at ∼25% using linkage disequilibrium score regression. Vessel type specific GWAS revealed 116 loci for arteries and 63 for veins. Genes with significant association signals included COL4A2, ACTN4, LGALS4, LGALS7, LGALS7B, TNS1, MAP4K1, EIF3K, CAPN12, ECH1, and SYNPO2. These tortuosity genes were overexpressed in arteries and heart muscle and linked to pathways related to the structural properties of the vasculature. We demonstrated that retinal tortuosity loci served pleiotropic functions as cardiometabolic disease variants and risk factors. Concordantly, MR revealed causal effects between tortuosity, body mass index, and low-density lipoprotein. Conclusions: Several alleles associated with retinal vessel tortuosity suggest a common genetic architecture of this trait with ocular diseases (glaucoma, myopia), cardiovascular diseases, and metabolic syndrome. Our results shed new light on the genetics of vascular diseases and their pathomechanisms and highlight how GWASs and heritability can be used to improve phenotype extraction from high-dimensional data, such as images. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article

Using genetic variation to disentangle the complex relationship between food intake and health outcomes.

Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes