Étude d'association pangénomique

Une étude d'association pangénomique (en anglais genome-wide association study, GWAS) est une analyse de nombreuses variations génétiques chez de nombreux individus, afin d'étudier leurs corrélations avec des traits phénotypiques. Ces études se concentrent généralement sur les associations entre les polymorphismes nucléotidiques (SNP) et des phénotypes tels que les maladies humaines majeures. En effet, quand elle est appliquée sur des données humaines, une comparaison de séquences d’ADN se fait entre individus ayant plusieurs phénotypes différents pour un même caractère, la taille par exemple. Cette comparaison a pour but d’identifier les variants génétiques les plus fréquemment associés avec le phénotype observé, on parle alors d’une approche « phénotype en premier ». Cette approche consiste à catégoriser les patients selon leur signes cliniques, et de les corréler avec des SNP à l’inverse de l’approche « génotype en premier ». Par exemple, l'étude d'association pangénomique « CoLaus » (cohorte lausannoise) cherche des associations entre les données médicales et génétiques d'une cohorte de plus de personnes. Les échantillons d’ADN de chaque personne sont alors collectés et déposés sur des puces à ADN contenant des millions de sondes auxquelles les variants génétiques vont s’hybrider et être détectés par fluorescence. Une GWAS est appliquée sur l’entièreté du génome, ce qui permet d’obtenir un pourcentage plus élevé de variance expliquée pour des traits quantitatifs contrôlés par plusieurs gènes. Néanmoins, les GWAS ne donnent pas une relation de causalité du SNP à son caractère mais renseignent plutôt sur les variants corrélés avec ce trait. Pour faire une étude d’association pangénomique il faut disposer d'une cohorte, c’est-à-dire des milliers d'individus à partir desquels de l’ADN pourra être collecté. L'approche la plus commune est l’étude cas-témoins, qui compare deux grands groupes d'individus, un groupe témoin en bonne santé et un groupe de cas affecté par une maladie. Il faut également disposer de marqueurs génétiques, ici des SNPs connus.

Genome-Wide Association Study of Alzheimer's Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Dataset

Christopher Clark

Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

FRONTIERS MEDIA SA2022

The interplay between human genetic variation, chronic inflammation and persistent infection: relevance for cardiovascular morbidity

Flavia Aurelia Shoko Hodel

Contemporary genomic approaches allow us to seek answers to biological questions that were previously out of reach. Genome-wide association studies (GWAS) have identified numerous genetic polymorphisms associated with human diseases, providing new insight into the genes and pathways involved in pathogenesis. The work presented in this thesis aimed to harness the power of large-scale genomics, statistical methods and bioinformatic tools to explore the interactions between persistent infections, chronic low-grade inflammation, and coronary heart disease (CHD). To achieve this, we used data collected from large population-based studies of individuals of European ancestry.To identify human genetic determinants of the humoral immune response against persistent or frequently recurring infections, with a special focus on human polyomaviruses, we performed GWAS and meta-analyses of serostatus and quantitative immunoglobulin G responses. We identified NFKB1 as a common locus associated with antibody response to multiple pathogens. For polyomaviruses, we found strong associations of HLA class II, FUT2, STING1, and MUC1 genetic variants with the intensity of the humoral response. Together, these results demonstrate the modulating contribution of host genetic variation to the individual response against some of the most prevalent human viruses.Even when they are latent, chronic infections can trigger some degree of local or systemic immune response, resulting in low-grade inflammation. In an effort to better understand the variability of humoral immune response and inflammation patterns in response to pathogen exposure, we found evidence for an association between Chlamydia trachomatis and Helicobacter pylori seropositivity, and higher plasma levels of C-reactive protein (CRP), a sensitive indicator of inflammation. High polygenic risk was also significantly associated with CRP levels, confirming that human genetic variation plays a modulating role in systemic inflammation. These results improve our understanding of the relationship between persistent infections and chronic inflammation, an important determinant of long-term morbidity in humans.Finally, we developed statistical models to explore the influences of genetic variation, persistent infections and low-grade inflammation on incident CHD. We identified high polygenic risk and Fusobacterium nucleatum infection as associated with an increased risk of developing CHD, in addition to conventional risk factors. These results confirm that CHD is a multicomponent disease that is caused by demographic, genetic and environmental factors. Moreover, they might allow for better identification of individuals at high risk for CHD and provide a rationale for future anti-infective prevention trials.Together, this research demonstrates that current genomic and serological technology, bioinformatic analysis and functional follow-up studies have the potential to provide new insight into the molecular basis of host-pathogen interactions, and their role in chronic diseases. From a translational perspective, the results of this project include new targets for diagnosis and therapeutic development, new disease biomarkers, and better predictive models. Every advance in the understanding of complex disease has the potential to improve genomic and clinical medicine, and I am pleased to have had the opportunity to act at different levels to participate in this much needed transition to precision medicine.

EPFL2022

A Phylogeny-aware GWAS Framework to Correct for Heritable Pathogen Effects on Infectious Disease Traits

Jacques Fellay, Christian Axel Wandall Thorball

Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host-pathogen systems, HIV in humans and X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.

OXFORD UNIV PRESS2022

Christopher Clark

FRONTIERS MEDIA SA2022

The interplay between human genetic variation, chronic inflammation and persistent infection: relevance for cardiovascular morbidity

Flavia Aurelia Shoko Hodel

EPFL2022