Improved models to understand the genomic architecture of complex traits. This project aims to improve modelling of the genetics underlying complex traits. The project will develop and test models for using genome-wide genetic data to investigate how much heritability (genetic effect) underlies traits of interest, where it lies in the genome, and how much of it is shared across traits. The new models will be implemented in statistical algorithms in a freely-available software package. This proj ....Improved models to understand the genomic architecture of complex traits. This project aims to improve modelling of the genetics underlying complex traits. The project will develop and test models for using genome-wide genetic data to investigate how much heritability (genetic effect) underlies traits of interest, where it lies in the genome, and how much of it is shared across traits. The new models will be implemented in statistical algorithms in a freely-available software package. This project expects to increase understanding of biological mechanisms, the efficiency of genetic association analyses and the accuracy of genomic prediction, including the effects of interventions. The project will adapt human models to a wider range of organisms, in particular bacteria.Read moreRead less
Demographic and evolutionary inferences from large, whole-genome datasets. A new data structure for genome-wide datasets has allowed great improvements in the efficiency of genomic data storage and in population genomics simulations, which are crucial to developing and testing mathematical models of population history and species evolution. We will take these advances in new directions, using efficient data structures to dramatically improve inferences about: the demographic histories of popul .... Demographic and evolutionary inferences from large, whole-genome datasets. A new data structure for genome-wide datasets has allowed great improvements in the efficiency of genomic data storage and in population genomics simulations, which are crucial to developing and testing mathematical models of population history and species evolution. We will take these advances in new directions, using efficient data structures to dramatically improve inferences about: the demographic histories of populations, rates of genome change, and phylogenetic networks, and we will develop the first inference methods for the multispecies coalescent with recombination. Outcomes will include advances in understanding the evolutionary histories of humans and other species, including pathogens of importance for global health.Read moreRead less
Evolutionary analyses of short-read sequences from pooled samples. This project aims to provide biologists with a means of making sound, statistical inferences about evolution by using next-generation data from mixed samples. When biologists make statements about history, they use evolutionary trees, frequently reconstructed from the genetic data of many individuals. Next-generation sequencing provides large amounts of genetic data at low cost, but biologists have difficulty using these data for ....Evolutionary analyses of short-read sequences from pooled samples. This project aims to provide biologists with a means of making sound, statistical inferences about evolution by using next-generation data from mixed samples. When biologists make statements about history, they use evolutionary trees, frequently reconstructed from the genetic data of many individuals. Next-generation sequencing provides large amounts of genetic data at low cost, but biologists have difficulty using these data for evolutionary research, particularly when they sample mixtures of DNA from many individuals. The anticipated value of this project is that it allows evolutionary biologists to capitalise on the benefits of next-generation sequencing, without sacrificing their ability to make reliable inferences about history.Read moreRead less