Skip to main content

Understanding how selection for body weight in mouse operates at the RNA level

Periodic Reporting for period 1 - MICEXPRESS (Understanding how selection for body weight in mouse operates at the RNA level)

Reporting period: 2015-09-01 to 2017-08-31

The aim of this project I to enhance our understanding of how genetic selection for body weight in mouse drives changes at the RNA level. This is a key point for quantitative genetics because a better knowledge of the genetic control of gene expression could imply a better prediction of changes in the regulatory networks and subsequently in the phenotype. Moreover, the biological insight gained by this understanding could, in turn, be used by the animal breeding industry to design more efficient breeding strategies. Thus, this research has used a system approach to model the genetic control of transcript expression and identify genes that can be modify by selection. The second aim was to elucidate whether maternal effects shape the transcripts expression and gain insight into the mechanisms underpinning gene by environment interactions. Answering this question has required experimental designs and biological resources not usually available. Four inbred lines of mice derived from long-term divergent selection and kept at Roslin Institute were used in the current project. Results of the project indicate that directional selection drives correlated changes at the mean RNA level and the maternal environment can have a significant impact at on gene expression. It implies that transcript’s expression can be a good predictor of the phenotypes. Moreover, the results of this project support that idea of genome functional conservation between mammals. It makes divergent selection experiments a powerful tool for unravelling the genetic regulation of complex traits.
In this project, we use embryos of four inbred lines of mice derived from long-term divergent selection for growth to enhance how selection for body weight in mouse drives changes at the RNA level. We tested whether selection for body weight has a correlated response to the mean and the variance of the gene expression. The results showed that selection for body weight has a clear correlated response to the mean of the gene expression but not to the variance. The analysis performed allowed us to identify genes with transcript expression correlated to selection. Additionally, knowledge of whether the expression of these genes is regulating equivalent complex traits in mouse and human is still limited. Thus, we also explored whether those genes with transcript expression correlated to selection are associated with human traits linked to the selected one. Results showed 3871 transcripts differential expressed (DE) between lines and replicating in both experiments at False discovery rate <0.05. The genes of these transcripts showed a clear enrichment for embryo weight, embryo growth retardation and body weight in mouse and for height and body mass index (BMI) in GWAS studies in human. From these genes, we identified 417 transcripts with expression correlated to embryo weight after adjusting for line. These genes showed an enrichment with abnormal long bone morphology in mouse and BMI in human. We computed the heritability of each one of these genes for BMI and height in human using the UK Biobank data (~130,000 individuals genotyped). The results reveal novel genes explaining a high percentage of the total heritability for BMI or height, and some of them with pleiotropic effects. In conclusion, results indicate that directional selection drives correlated changes at the mean RNA level. It implies that transcript’s expression can be a good predictor of the phenotypes. Moreover, the results of this project support that idea of genome functional conservation between mammals. It makes divergent selection experiments a powerful tool for unravelling the genetic regulation of complex traits.

On the other hand, important environmental effects, such as maternal effects, play a key role on the response to selection for growth in mammals. In a second part of the project, we study the differential expression between genes in a reciprocal cross of the inbred lines of mice derived from long-term divergent selection for growth. The differences in gene expression between both crosses with identical genotypes indicate maternal effect on the gene expression. Results of these analysis identified 1057 genes with differential expression between the reciprocal crosses that also replicate in the comparison between High and Low lines. However, from these genes, only 41 have missense SNPs in the coding region which are target genes for allelic specific expression. These results indicate that maternal environment has an important impact on the gene expression that affects the body weight in mouse. These findings are crucial to understand the genetic regulation of growth across mammals and for a better prediction of the phenotypes.
The use of long-term divergent in mice with next generation sequence, and statistical and bioinformatics tool has been this project very powerful to explore the control of phenotypic variability of RNA levels and transcript variation. This project has addressed important but poorly understand question as how selection for a particular trait operates at the gene level in mammals. This project has been novel and original because, to my knowledge, no one has been studied how artificial selection could lead response on the mean and variance of cellular phenotypes variation or the effect as the maternal effect at transcriptomic level. Results showed a correlated response to the mean but not to the variance of the gene expression. Moreover, also showed that maternal environmental effects can have an impact at mean transcriptomic level. These results can have a strong impact on agriculture, medicine or evolution because help to understand the genetic control of growth across mammals as well as its phenotypic prediction. In fact, the results of this project support that idea of genome functional conservation between mammals which makes the results of this project invaluable for unravelling the genetic regulation of growth. Moreover, this project has identified genes which expression has been modified by body weight selection in mouse. It can be use directly to improve breeding strategies using the genomic information generated as well in human to predict height and body mass index in human.
postn-gene-expr.jpg