Hérédité

Résumé

L’ est la transmission, au sein d'une espèce vivante ou d'une lignée de cellules, de caractéristiques d'une génération à la suivante. Les mécanismes de l'hérédité sont au cœur de la théorie de l'évolution car ils permettent l'accumulation des variations au fil des générations qui conduit à l'apparition de nouvelles espèces. En général, on associe l'hérédité aux gènes tels qu'ils furent découverts par Gregor Mendel mais d'autres mécanismes dits non mendéliens et épigénétiques peuvent aussi intervenir dans la transmission des caractères biologiques. S'agissant d'espèces animales, y compris l'être humain, l'hérédité peut concerner des traits physiques mais aussi comportementaux bien que ces derniers puissent aussi être acquis par apprentissage ou . La transmission des caractères acquis fit l'objet de nombreuses controverses dans l'histoire de la pensée évolutionniste, en particulier dans son application à l'homme et dans les tentatives eugénistes. Étymologie Le terme hérédi

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https://fr.wikipedia.org/wiki/H%C3%A9r%C3%A9dit%C3%A9

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Background: Resistance to enteric pathogens is a complex trait at the crossroads of multiple biological processes. We have previously shown in the Drosophila Genetic Reference Panel (DGRP) that resistance to infection is highly heritable, but our understanding of how the effects of genetic variants affect different molecular mechanisms to determine gut immunocompetence is still limited. Results: To address this, we perform a systems genetics analysis of the gut transcriptomes from 38 DGRP lines that were orally infected with Pseudomonas entomophila. We identify a large number of condition-specific, expression quantitative trait loci (local-eQTLs) with infection-specific ones located in regions enriched for FOX transcription factor motifs. By assessing the allelic imbalance in the transcriptomes of 19 F1 hybrid lines from a large round robin design, we independently attribute a robust cis-regulatory effect to only 10% of these detected local-eQTLs. However, additional analyses indicate that many local-eQTLs may act in trans instead. Comparison of the transcriptomes of DGRP lines that were either susceptible or resistant to Pseudomonas entomophila infection reveals nutcracker as the only differentially expressed gene. Interestingly, we find that nutcracker is linked to infection-specific eQTLs that correlate with its expression level and to enteric infection susceptibility. Further regulatory analysis reveals one particular eQTL that significantly decreases the binding affinity for the repressor Broad, driving differential allele-specific nutcracker expression. Conclusions: Our collective findings point to a large number of infection-specific cis- and trans-acting eQTLs in the DGRP, including one common non-coding variant that lowers enteric infection susceptibility.

2020

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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

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For more than a century, it has been recognized that our genetic inheritance and our environment interact to shape who we are and how we act (nature vs. nurture), and the study of genetics has allowed us to explain why traits can vary dramatically between individuals (i.e. trait variance), and yet often be strongly shared within families (i.e. trait heritability). Scientists, statisticians, and physicians can calculate heritability, and can observe how both genes and environment influence health, but relatively few interventions or treatments have stemmed from this expanded knowledge. In large part this is because testing biomedical hypotheses is difficult, because we are all so different, and because environmental factors are so hard to control. In 2001 the first (nearly) complete DNA sequence of the human genome was generated after years of technical development and brute force effort. This accomplishment has ushered in a new field of personalized medicine, as prevention and treatment can both be tailored based on genome and environment. Unfortunately, the science of genomic sequencing has greatly outpaced our ability to actually understand the genetic code, and it remains difficult to make accurate predictions of an individual's characteristics and susceptibilities except in a few clear-cut cases such as eye color or risk of Huntington's disease. There are several reasons for this disconnect: (1) many traits and diseases are driven by complex interactions among environmental causes and genetic risks, (2) there are many aspects of genetics which we do not fully understand and that cannot be easily observed (e.g. non-coding RNAs, epigenetic modifications, and complex metagenomic cohorts that we all carry), and (3) gene products (such as mRNA and protein) change dramatically over time and across cell types in complex and even unpredictable ways. Today, myriad developments promise to improve our capacity to predict resistance and susceptibility to diseases based on our individual genome. This technical capability has also provided new avenues for developing therapeutic agents and/or lifestyle changes. However, no matter our developments in scientific understanding, personalized medicine will never allow perfect predictionâenvironmental influences and personal choices will always affect our health, and medical treatments will still require information gained from standard medical checkups. Rather than perfect prediction, personalized medicine will instead provide more accurate predictions on health than those previously possible. Thus, those at high risk for (e.g.) diabetes will know better to watch their diet, while those at low risk should remember that biology is never completely predictable. In this thesis, I have analyzed 45 strains of mice from a genetic reference population called the BXD with the goal of identifying major gene regulators of metabolic phenotypes, such as exercise capacity and glucose response. Each member of this BXD family, which contains ~150 distinct but related lines (or strains), has a unique genetic makeup that has been fixed by inbreeding. Each family member is thus available as an unlimited supply of identical twins which may be studied over years and among laboratories. With this population, it is possible to both (1) test what would occur to a single individual in different environmental conditions and (2) analyze how much environmental influences vary across genetically-diverse phenotypes.

EPFL2014

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EPFL2014