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Multilevel selection and the integrity of mitochondrial DNA. This project aims to investigate the evolutionary conundrum of how and why organelles remain asexual. The widespread occurrence of sexual reproduction suggests that sex is beneficial to organisms. Yet we all carry an ancient genome that never had sex, the mitochondrial genome (mtDNA). Theory predicts that mtDNA should no longer exist, because without sex it accumulates deleterious mutations and cannot accumulate beneficial ones. Yet mt ....Multilevel selection and the integrity of mitochondrial DNA. This project aims to investigate the evolutionary conundrum of how and why organelles remain asexual. The widespread occurrence of sexual reproduction suggests that sex is beneficial to organisms. Yet we all carry an ancient genome that never had sex, the mitochondrial genome (mtDNA). Theory predicts that mtDNA should no longer exist, because without sex it accumulates deleterious mutations and cannot accumulate beneficial ones. Yet mtDNA does not suffer mutational meltdown and is shown to adapt. This project will explain how, proposing that the combination of two traits, uniparental inheritance and multiple genomes per cell, make up for the lack of sex. This project expects to provide an explanation for the evolutionary question of what keeps mitochondria healthy, important as mitochondria affect ageing and health.Read moreRead less
Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is un ....Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is unknown. This is useful for selecting the best parents for breeding in agriculture and for predicting the future phenotype of animals, crops and people. The proposed method uses data on very many traits to identify sequence variants that have a function and to predict the traits affected by each variant.Read moreRead less