Periodic Reporting for period 5 - DENOVOMUT (An integrated approach to understanding the impact of de novo mutations on the mammalian genome)
Periodo di rendicontazione: 2023-01-01 al 2024-07-31
The ERC Advanced Grant project addresses two broad issues:
1. What is the influence of new mutations on reproductive traits, other quantitative traits and genomic variation in mammals?
2. What explains variation in the amount of nucleotide diversity across the genome?
This is potentially important for society because:
1. Understanding the nature of genetic variation is important for understanding the genetic basis of human complex disease.
2. It is argued that the fitness of human populations is at long term risk from deleterious mutation accumulation.
3. The input of variation from mutations has implications for understanding continued responses to artificial selection in farm animals and crops and the nature of long term response to artificial selection.
The overall objectives of the project are:
1. To determine the rate at which reproductive ability and other quantitative traits (such as growth rate) change as a consequence of the accumulation of new mutations in the house mouse.
2. To determine the properties of new mutations affecting the mouse genome, including the rate at which mutations appear per generation, and the extent to which the properties of mutations vary among different strains.
3. Based on our results from mice, predict the rate of change of traits related to reproductive ability in humans and the impact of new mutations on response to artificial selection in farm animals.
4. Explain why nucleotide diversity varies across the mammalian genome and to determine the relative contributions of mutations in coding and noncoding DNA to fitness change.
5. To infer the distribution of fitness effects of new mutations.
1. What is the influence of new mutations on reproductive traits, other quantitative traits and genomic variation in mammals?
2. What explains variation in the amount of nucleotide diversity across the genome?
This is potentially important for society because:
1. Understanding the nature of genetic variation is important for understanding the genetic basis of human complex disease.
2. It is argued that the fitness of human populations is at long term risk from deleterious mutation accumulation.
3. The input of variation from mutations has implications for understanding continued responses to artificial selection in farm animals and crops and the nature of long term response to artificial selection.
The overall objectives of the project are:
1. To determine the rate at which reproductive ability and other quantitative traits (such as growth rate) change as a consequence of the accumulation of new mutations in the house mouse.
2. To determine the properties of new mutations affecting the mouse genome, including the rate at which mutations appear per generation, and the extent to which the properties of mutations vary among different strains.
3. Based on our results from mice, predict the rate of change of traits related to reproductive ability in humans and the impact of new mutations on response to artificial selection in farm animals.
4. Explain why nucleotide diversity varies across the mammalian genome and to determine the relative contributions of mutations in coding and noncoding DNA to fitness change.
5. To infer the distribution of fitness effects of new mutations.
1. Spontaneous mutation accumulation in the house mouse.
We set up a replicated mutation accumulation (MA) lines, and controls consisting of frozen embryos to allow us to determine whether the mean values of quantitative traits have changed.
2. Distinguishing low from high frequency variant sites in the genome.
We developed a statistical approach to estimate the frequencies of beneficial and deleterious mutations based on polymorphism data from a sample of individuals. This method has been widely used (Keightley and Jackson 2018).
3. Understanding the causes of variation in nucleotide diversity across the genome.
We attempted to quantify the contributions of background selection and selective sweeps to diversity dips around protein-coding genes and gene regulatory elements in the mouse. Selection in favour of strongly advantageous mutations has been important in shaping patterns of diversity across the genome (Booker and Keightley 2018).
4. The distribution of fitness effects (DFE) for new mutations.
We attempted to characterize properties of the DFE in the single-celled green alga Chlamydomonas reinhardtii by crossing lines carrying known complements of mutations with their ancestral strains (Boendel et al 2019, 2022). Our results suggest that the distribution is L-shaped, and that a high proportion of mutations increase fitness.
5. Sequencing of MA lines and their ancestors to estimate the mutation rate.
The variation present in the ancestors of our MA lines provided a reasonable per nucleotide mutation rate estimate for the colony nucleus (~7.9 × 10^-9) (Chebib et al 2021). In the MA lines, we estimated that the average rate of new SNMs is ∼μ = 6.7 × 10^-9. We followed this up with PacBio sequencing. Among the different types of structural mutations, tandem repeat mutations have the highest mutation rate, followed by insertions of transposable elements (Lopez-Cortegano et al 2025). Studies were complemented by experiments in MA lines of Chlamydomonas (Lopez-Cortegano at al 2021).
6. The fitness consequences of mutation accumulation in the house mouse.
Prior to our study, the phenotypic consequences of MA in vertebrates were largely unknown. We studied the impact of spontaneous MA on the mean and genetic variation for quantitative and fitness-related traits using the MA experimental design, with a cryopreserved control to account for environmental influences. Variation accumulates at a sufficiently high rate to maintain genetic variation and selection response. When extrapolated to humans, our results imply that the rate of fitness loss should not be of concern in the foreseeable future (Chebib et al 2021).
We set up a replicated mutation accumulation (MA) lines, and controls consisting of frozen embryos to allow us to determine whether the mean values of quantitative traits have changed.
2. Distinguishing low from high frequency variant sites in the genome.
We developed a statistical approach to estimate the frequencies of beneficial and deleterious mutations based on polymorphism data from a sample of individuals. This method has been widely used (Keightley and Jackson 2018).
3. Understanding the causes of variation in nucleotide diversity across the genome.
We attempted to quantify the contributions of background selection and selective sweeps to diversity dips around protein-coding genes and gene regulatory elements in the mouse. Selection in favour of strongly advantageous mutations has been important in shaping patterns of diversity across the genome (Booker and Keightley 2018).
4. The distribution of fitness effects (DFE) for new mutations.
We attempted to characterize properties of the DFE in the single-celled green alga Chlamydomonas reinhardtii by crossing lines carrying known complements of mutations with their ancestral strains (Boendel et al 2019, 2022). Our results suggest that the distribution is L-shaped, and that a high proportion of mutations increase fitness.
5. Sequencing of MA lines and their ancestors to estimate the mutation rate.
The variation present in the ancestors of our MA lines provided a reasonable per nucleotide mutation rate estimate for the colony nucleus (~7.9 × 10^-9) (Chebib et al 2021). In the MA lines, we estimated that the average rate of new SNMs is ∼μ = 6.7 × 10^-9. We followed this up with PacBio sequencing. Among the different types of structural mutations, tandem repeat mutations have the highest mutation rate, followed by insertions of transposable elements (Lopez-Cortegano et al 2025). Studies were complemented by experiments in MA lines of Chlamydomonas (Lopez-Cortegano at al 2021).
6. The fitness consequences of mutation accumulation in the house mouse.
Prior to our study, the phenotypic consequences of MA in vertebrates were largely unknown. We studied the impact of spontaneous MA on the mean and genetic variation for quantitative and fitness-related traits using the MA experimental design, with a cryopreserved control to account for environmental influences. Variation accumulates at a sufficiently high rate to maintain genetic variation and selection response. When extrapolated to humans, our results imply that the rate of fitness loss should not be of concern in the foreseeable future (Chebib et al 2021).
1. We made the first attempt to carry out a highly replicated mutation accumulation experiment with a cryopreserved control that allowed us to determine the rate of change of fitness per generation in a model mammalian species. We also determined the rate of increase of genetic variation from mutation for quantitative traits.
2. We sequenced a large cohort of individuals from MA lines of four different strains using shor- and long-read technology, which allowd us to determine the extent of variation in the rate of mutation between different inbred mouse strains, and to determine the factors that influence variation in the mutation rate across the genome.
3. We used long-range sequencing technology to infer the rate of large scale rearrangements and transposition events in the genome.
4. Our study on the causes of diversity variation across the mouse genome attempted to estimate the relative contributions of mutations in coding and noncoding elements of the genome to fitness change.
5. We used sequencing of recombinants and changes of frequency of mutations segregating in large population of Chlamydomonas to estimate the distribution of fitness effects mutations under natural selection.
2. We sequenced a large cohort of individuals from MA lines of four different strains using shor- and long-read technology, which allowd us to determine the extent of variation in the rate of mutation between different inbred mouse strains, and to determine the factors that influence variation in the mutation rate across the genome.
3. We used long-range sequencing technology to infer the rate of large scale rearrangements and transposition events in the genome.
4. Our study on the causes of diversity variation across the mouse genome attempted to estimate the relative contributions of mutations in coding and noncoding elements of the genome to fitness change.
5. We used sequencing of recombinants and changes of frequency of mutations segregating in large population of Chlamydomonas to estimate the distribution of fitness effects mutations under natural selection.