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The combined impact of persistent infections and human genetic variation on C-reactive protein levels

Clara Bonnet, Jacques Fellay, Flavia Aurelia Shoko Hodel, Olivier Noël Marie Naret

Multiple human pathogens establish chronic, sometimes life-long infections. Even if they are often latent, these infections can trigger some degree of local or systemic immune response, resulting in chronic low-grade inflammation. There remains an incomplete understanding of the potential contribution of both persistent infections and human genetic variation on chronic low-grade inflammation. We searched for potential associations between seropositivity for 13 persistent pathogens and the plasma levels of the inflammatory biomarker C-reactive protein (CRP), using data collected in the context of the UK Biobank and the CoLaus|PsyCoLaus Study, two large population-based cohorts. We performed backward stepwise regression starting with the following potential predictors: serostatus for each pathogen, polygenic risk score for CRP, and demographic and clinical factors known to be associated with CRP. We found evidence for an association between Chlamydia trachomatis (P-value = 5.04e - 3) and Helicobacter pylori (P-value = 8.63e - 4) seropositivity and higher plasma levels of CRP. We also found an association between pathogen burden and CRP levels (P-value = 4.12e - 4). These results improve our understanding of the relationship between persistent infections and chronic inflammation, an important determinant of long-term morbidity in humans.

BMC2022

Human genomics of the humoral immune response against polyomaviruses

Jacques Fellay, Christian Hammer, Flavia Aurelia Shoko Hodel, Olivier Noël Marie Naret, Petar Scepanovic, Christian Axel Wandall Thorball, Zhi Ming Xu

Human polyomaviruses are widespread in humans and can cause severe disease in immunocompromised individuals. To identify human genetic determinants of the humoral immune response against polyomaviruses, we performed genome-wide association studies and meta-analyses of qualitative and quantitative immunoglobulin G responses against BK polyomavirus (BKPyV), JC polyomavirus (JCPyV), Merkel cellpolyomavirus (MCPyV), WU polyomavirus (WUPyV), and human polyomavirus 6 (HPyV6) in 15,660 individuals of European ancestry from three independent studies. We observed significant associations for all tested viruses: JCPyV, HPyV6, and MCPyV associated with human leukocyte antigen class II variation, BKPyV and JCPyV with variants in FUT2, responsible for secretor status, MCPyV with variants in STING1, involved in interferon induction, and WUPyV with a functional variant in MUC1, previously associated with risk for gastric cancer. These results provide insights into the genetic control of a family of very prevalent human viruses, highlighting genes and pathways that play a modulating role in human humoral immunity.

OXFORD UNIV PRESS2021

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

The focus of the work presented in this thesis is the exploration of the genetic architecture of complex human traits - at the dawn of genomic medicine. The underlying mechanisms explaining the enormously polygenic nature of most human complex traits are still unknown. The first chapter explores a possible explanatory model in which variant effects are due to an indirect mechanism, namely competition among genes for shared intracellular resources such as ribosomes. Our findings show that under most reasonable assumptions, resource competition should not be expected to have much impact on either protein expression levels of individual genes or on complex trait outcomes. The prediction accuracy of polygenic scores (PGS) remains relatively modest compared to what is expected given the estimated heritability of traits. Traditionally, the construction of PGS uses a large number of genetic variations, most of which have weak additive effects. Recent machine learning methods could improve PGS by also aggregating epistatic effects. To evaluate these different methods, we conducted an experiment based on an innovative concept of crowdsourcing, detailed in the second chapter. We collaborated with opensnp.org, an open repository where people share their genotyping data and phenotypic information, and with crowdai.org, a platform that allowed us to create a public competition for the genomic prediction of height. The challenge lasted three months and attracted 138 participants. This was the first crowd-sourcing challenge based on publicly available genome-wide genotyping data. Due to the enormous number of potential combinations of variants, it is difficult to integrate epistatic effects into PGS. In the third chapter, we present a method where we limit the possible combinations to the boundaries of each topologically associated domain (TAD) independently. With the UK Biobank, for the height phenotype, we included 17,560 variants in an artificial neural network (ANN) and compared the variance explained (

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

for n=20). We concluded that it should be possible to build better PGS using ANNs and epistasis in TADs. The effect of genetic ancestry on phenotypes is not taken into account in PGS-based risk estimates. Doing so could accelerate the adoption of genomic medicine for underrepresented populations and mixed-race individuals. The fourth chapter presents a method for its integration through a secondary score derived from genome-wide genotyping data, the PC score (PCS). We compared two models, one using only the PGS and the other using both the PGS and the PCS. Using the UK Biobank, we found an improvement in genetic prediction for all phenotypes tested:

EPFL2021

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

EPFL2021