PoMoSelect: Disentangling Modes of Selection
Lead Research Organisation:
University of St Andrews
Department Name: Biology
Abstract
Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Alternative models accounting for multi-individual data have nevertheless been proposed and are known as polymorphism-aware phylogenetic models (PoMo). PoMo adds a new layer of complexity to the standard nucleotide substitution models by accounting for the population-level (so far, genetic drift and mutations) processes to describe the evolutionary process. To do so, PoMo expands the standard substitution models to include polymorphic states. We have previously shown that PoMo accounts for incomplete lineage sorting (ILS), and improve the estimation of species tree inference.
For this project, we will develop an approach that accounts for balancing selection called PoMoSelect. Genetic drift removes polymorphism from populations over time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. PoMo is a mutation-selection model which we will use disentangle balancing selection from directional selection as well as mutational effects, fixation biases, and demographic effects. Furthermore, PoMo combination of polymorphism with divergence data allows it to model short as well as long-term balancing selection. We will introduce a new mechanistic parameters to quantify the strength of balancing selection on a loci. For the inference of the new parameters we will develop a new Bayesian framework and software package PoMoSelect. Together with our collaborators, we will analyse DNA sequences of African hunter-gather and farmer populations as well as great ape species to understand the role of blood parasites on the alpha and beta globin cluster (thalasiamias). Finally, we will apply our new methodology genome-wide to study the extent and patterns of balancing selection.
For this project, we will develop an approach that accounts for balancing selection called PoMoSelect. Genetic drift removes polymorphism from populations over time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. PoMo is a mutation-selection model which we will use disentangle balancing selection from directional selection as well as mutational effects, fixation biases, and demographic effects. Furthermore, PoMo combination of polymorphism with divergence data allows it to model short as well as long-term balancing selection. We will introduce a new mechanistic parameters to quantify the strength of balancing selection on a loci. For the inference of the new parameters we will develop a new Bayesian framework and software package PoMoSelect. Together with our collaborators, we will analyse DNA sequences of African hunter-gather and farmer populations as well as great ape species to understand the role of blood parasites on the alpha and beta globin cluster (thalasiamias). Finally, we will apply our new methodology genome-wide to study the extent and patterns of balancing selection.
Technical Summary
Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Alternative models accounting for multi-individual data have nevertheless been proposed and are known as polymorphism-aware phylogenetic models (PoMo). PoMo adds a new layer of complexity to the standard nucleotide substitution models by accounting for the population-level (so far, genetic drift and mutations) processes to describe the evolutionary process. To do so, PoMo expands the standard substitution models to include polymorphic states. We have previously shown that PoMo accounts for incomplete lineage sorting (ILS), and improve the estimation of species tree inference.
For this project, we will develop an approach that accounts for balancing selection called PoMoSelect. Genetic drift removes polymorphism from populations over time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. PoMo is a mutation-selection model which we will use disentangle balancing selection from directional selection as well as mutational effects, fixation biases, and demographic effects. Furthermore, PoMo combination of polymorphism with divergence data allows it to model short as well as long-term balancing selection. We will introduce a new mechanistic parameters to quantify the strength of balancing selection on a loci. For the inference of the new parameters we will develop a new Bayesian framework and software package PoMoSelect. Together with our collaborators, we will analyse DNA sequences of African hunter-gather and farmer populations as well as great ape species to understand the role of blood parasites on the alpha and beta globin cluster (thalasiamias). Finally, we will apply our new methodology genome-wide to study the extent and patterns of balancing selection.
For this project, we will develop an approach that accounts for balancing selection called PoMoSelect. Genetic drift removes polymorphism from populations over time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. PoMo is a mutation-selection model which we will use disentangle balancing selection from directional selection as well as mutational effects, fixation biases, and demographic effects. Furthermore, PoMo combination of polymorphism with divergence data allows it to model short as well as long-term balancing selection. We will introduce a new mechanistic parameters to quantify the strength of balancing selection on a loci. For the inference of the new parameters we will develop a new Bayesian framework and software package PoMoSelect. Together with our collaborators, we will analyse DNA sequences of African hunter-gather and farmer populations as well as great ape species to understand the role of blood parasites on the alpha and beta globin cluster (thalasiamias). Finally, we will apply our new methodology genome-wide to study the extent and patterns of balancing selection.
Publications
Barata C
(2023)
Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments.
in Journal of evolutionary biology
Borges R
(2022)
Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes
in Methods in Ecology and Evolution
Description | Single cell multi-omics sequencing platform to understand the building blocks of life. |
Amount | £288,106 (GBP) |
Funding ID | BB/W019493/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 08/2022 |
End | 07/2023 |
Title | Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes |
Description | Supplementary files of Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes by Borges, Boussau, Höhna, Pereira and Kosiol |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | This data set allows the reproducibility of the results in the publication. It also provides users of the software package with benchmark data sets. |
URL | https://zenodo.org/record/6592395 |
Description | Dr Ricardo Pereira |
Organisation | Ludwig Maximilian University of Munich (LMU Munich) |
Country | Germany |
Sector | Academic/University |
PI Contribution | Help with analysis of next generation sequencing data set, in particular reconstruction of species tree. |
Collaborator Contribution | Provided interesting data set on grass hoppers as a showcase for our methods. |
Impact | One publication: https://doi.org/10.1111/2041-210X.13980 |
Start Year | 2021 |
Description | Dr Rui Borges, Group Leader |
Organisation | University of Veterinary Medicine Vienna |
Country | Austria |
Sector | Academic/University |
PI Contribution | Rui Borges received training on methods development in population genetics and phylogenomics as postdoctoral researcher. He has recently been offered a position as Group Leader, and we continue to collaborate. |
Collaborator Contribution | Contributions of C++ code of our software package, and the writing of related papers. |
Impact | Two publications that are listed with this grant (https://doi.org/10.1111/2041-210X.13980 and https://doi.org/10.1111/jeb.14134) |
Start Year | 2017 |
Description | Dr Sebastian Hoehna |
Organisation | Ludwig Maximilian University of Munich (LMU Munich) |
Country | Germany |
Sector | Academic/University |
PI Contribution | Development of new tutorials for applying the RevBayes programming language in the specific context of polymorphism-aware phylogentic models. |
Collaborator Contribution | Training in the RevBayes programming language provided to the PDRA Svitlana Braichenko on the grant. |
Impact | Publication that is listed with this grant: https://doi.org/10.1111/2041-210X.13980 |
Start Year | 2019 |
Title | Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes |
Description | We implemented polymorphism-aware phylogenetic models (PoMos), an alter- native approach for species tree estimation, in the Bayesian phylogenetic software RevBayes. PoMos naturally account for incomplete lineage sorting, which is known to cause difficulties for phylogenetic inference in species radiations, and scale well with genome-wide data. Simultaneously, PoMos can estimate mutation and selection biases. |
Type Of Technology | Software |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | Software enables biologists to infer species trees while accounting for selection |
URL | https://revbayes.github.io/tutorials/pomos/ |