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Concept

Génétique médicale

Résumé

La génétique médicale est la spécialité médicale qui étudie l'hérédité chez les individus et les causes génétiques des maladies. Le travail de la génétique médicale est d'abord d'étudier la présence de maladies dans une famille. Cela permet de faire des pronostics et donc de la prévention sur les enfants à naître. On parle de diagnostic génétique. Elle utilise l'ensemble de la sémiologie clinique pour trouver les signes d'une maladie génétique chez un patient, elle utilise également la Biologie médicale avec des disciplines telles que la cytogénétique, ou des outils comme l'amniocentèse et diverses techniques d'analyse génétique pour détecter la présence d'anomalies dans les chromosomes ou les gènes. La médecine génomique est une branche de la médecine qui utilise la génétique, notamment le séquençage du génome humain à la recherche de mutations associées à des maladies. Elle comprend plusieurs disciplines (nutrigénomique, , pharmacogénomique, etc.). Cette médecine personnalisée p

Source officielle

https://fr.wikipedia.org/wiki/G%C3%A9n%C3%A9tique_m%C3%A9dicale

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Séances de cours associées (15)

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Séances de cours associées

Chargement

A privacy-preserving solution for compressed storage and selective retrieval of genomic data

Erman Ayday, Jacques Fellay, Zhicong Huang, Jean-Pierre Hubaux, Huang Lin, Adam Molyneaux, Zhi Ming Xu

In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients' complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (elective retrieval on Encrypted and Compressed Reference oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared with BAM, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based instead of read-based, and (2) it allows random querying of a subregion from a BAM-like file in an encrypted form. Our method thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data.

Cold Spring Harbor Laboratory2016

Chargement

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

Chargement

Privacy-preserving genomic testing in the clinic: a model using HIV treatment

Erman Ayday, Istvan Bartha, Jacques Fellay, Jean-Pierre Hubaux, Jean Louis Raisaro, Amalio Telenti

The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.

Nature Publishing Group2016

Chargement

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