Guiding principles and guardrails for genetic association studies. This project aims to investigate deep connections between genetic structure (population genetic processes, linkage disequilibrium and population structure) and the ability to statistically detect genetic variants responsible for variation in traits. The project expects to generate new knowledge in the areas of statistics, mathematics and biology through an innovative, multidisciplinary approach that synthesises and extends founda ....Guiding principles and guardrails for genetic association studies. This project aims to investigate deep connections between genetic structure (population genetic processes, linkage disequilibrium and population structure) and the ability to statistically detect genetic variants responsible for variation in traits. The project expects to generate new knowledge in the areas of statistics, mathematics and biology through an innovative, multidisciplinary approach that synthesises and extends foundational disciplinary results. Expected outcomes of this project include principles and methodology that underpin future genetic association studies by supplying a framework for interpreting results. This should provide significant benefits by reducing false conclusions and their associated costs.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