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
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