Genetic variation | EPFL Graph Search

Genetic variation is the difference in DNA among individuals or the differences between populations among the same species. The multiple sources of genetic variation include mutation and genetic recombination. Mutations are the ultimate sources of genetic variation, but other mechanisms, such as genetic drift, contribute to it, as well. Among individuals within a population Genetic variation can be identified at many levels. Identifying genetic variation is possible from observations of phenotypic variation in either quantitative traits (traits that vary continuously and are coded for by many genes (e.g., leg length in dogs)) or discrete traits (traits that fall into discrete categories and are coded for by one or a few genes (e.g., white, pink, or red petal color in certain flowers)). Genetic variation can also be identified by examining variation at the level of enzymes using the process of protein electrophoresis. Polymorphic genes

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

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

Design and automated fabrication scenario for a wood shingle-based envelope system

Aymeric Thierry Damien Broyet

The building cultures of regions where plant resources abound show the adaptation of these local bio-based materials to the roofing of buildings: thatch, reed, bark or shingles. Despite a low environmental impact, these techniques require skilled manual work and remain unused compared to mainstream industrialised solutions based on terracotta, metal sheets or bituminous products. The emergence of digital tools capable of appreciating and manipulating objects with variable characteristics creates an opportunity for developing partially automated construction techniques by capitalising on pre-existing know-how. In particular, a type of wood shingle known as tavillon in Romandy, Switzerland is the primary object of this study. The objective is to describe and validate an entire construction scenario based on automation tools. The research plan paving the way for scenario implementation is another deliverable. We first carry out a state of the art of traditional tavillon technique before translating those rules into an algorithm. This instruction set is implemented in a parametric geometric model to design such roofs. Towards automation, the geometrical variability of the shingles makes it necessary to specify the model to these individual variations. A field study then demonstrates benefits from the automated installation are secured when framed into an industrial context, leading to the investigation of a panel-based prefabrication scenario.

2021