Periodic Reporting for period 1 - HaplotypeStructure (Understanding the evolution of continuous genomes)
Berichtszeitraum: 2022-09-01 bis 2025-02-28
An organism's phenotype depends on a multitude of genetic variants, spread over a linear genome. This is widely understood, and yet in practice, has hardly been incorporated into population genetic analysis. Recent developments in theory, computation, and sequencing technology now make it possible to obtain and analyse whole haploid genomes on a large scale. This proposal is to develop and apply a theoretical analysis of genetic variation that is spread over continuous linear genomes. Theory and methods will be developed in close interaction with empirical data from artificial selection experiments and from an intensely studied hybrid zone in Antirrhinum; for both, we have a known pedigree, and phased whole-genome sequence. Population structure will be analysed by following blocks of genome through pedigrees, and across two-dimensional landscapes. Selection on discrete loci will be analysed by finding its effect on surrounding haplotypes, by analysing how favoured alleles become disentangled from heterogeneous backgrounds, and by seeing how haplotype blocks flow past selected clines. The contribution of variants that are spread across the genome to GWA, to selection response, and to hybrid zones will be modelled, and the overall effect of inherited fitness variance on haplotype structure will be determined. This work will establish a new framework for population genomics that goes beyond the current focus on individual loci. It will help bridge the distinct communities within genomics, quantitative genetics, and population genetics, which currently tackle these problems largely in isolation. The project will develop better tools for inferring selection and population structure from DNA sequence data, and more fundamentally, will give us a deeper understanding of how the abundant variation that is carried on linear genomes is shaped by evolution.
Our most significant achievements so far are:
1) to provide a perspective on the “ancestral recombination graph” (ARG), which describes the ancestry that underlies the haplotype structure that we observe.
2) to extend our analysis of the infinitesimal model to diploid organisms, giving a simple and general theory for the relation between genotype and phenotype
3) to finalise whole-genome sequence data from more than 1000 snapdragons, and develop new methods for phasing these data.
4) For the first time, to give a full analysis of how selection acts on variation in an asexual
population
5) to publish an analysis of a 30-year experiment, successfully predicting how a small population of marine snails (Littorina) adapted to a new environment.
1) to provide a perspective on the “ancestral recombination graph” (ARG), which describes the ancestry that underlies the haplotype structure that we observe.
2) to extend our analysis of the infinitesimal model to diploid organisms, giving a simple and general theory for the relation between genotype and phenotype
3) to finalise whole-genome sequence data from more than 1000 snapdragons, and develop new methods for phasing these data.
4) For the first time, to give a full analysis of how selection acts on variation in an asexual
population
5) to publish an analysis of a 30-year experiment, successfully predicting how a small population of marine snails (Littorina) adapted to a new environment.
We have an exceptionally large dataset, which allows us to build a pedigree that gives the relations between individuals in a natural population. This is (to our knowledge) the largest such study in plants, and amongst the largest in any organism. Moreover, our population spans a hybrid zone for flower colour, where we know the major genes responsible, and the selection that acts on them. This provides a unique resource, and a model, for future work on selection in natural populations.
Our analysis of the infinitesimal model is the first mathematical demonstration that justifies this model beyond the (implausible and restrictive) additive assumption. In this ERC project, we
will extend this to linkage, and connect with our analysis of the ancestral recombination graph. Currently, there is considerable interest in using the ARG to make inferences about selection
and population structure. However, almost all applications so far are equivalent to (and based on) single-locus theory. The methods which we propose will be amongst the first to use the full
genealogical structure.
Our analysis of the infinitesimal model is the first mathematical demonstration that justifies this model beyond the (implausible and restrictive) additive assumption. In this ERC project, we
will extend this to linkage, and connect with our analysis of the ancestral recombination graph. Currently, there is considerable interest in using the ARG to make inferences about selection
and population structure. However, almost all applications so far are equivalent to (and based on) single-locus theory. The methods which we propose will be amongst the first to use the full
genealogical structure.