Original Paper Information:
The best of both worlds: combining population genetic and quantitative genetic models
Published 44522.
Category: Biology
Authors:
[‘Léonard Dekens’, ‘Sarah P. Otto’, ‘Vincent Calvez’]
Original Abstract:
Traits under migration-selection balance are increasingly shown to exhibitcomplex patterns of genetic architecture, with allelic differences of varyingmagnitude.
However, studying the influence of a large number of small alleliceffects on the maintenance of spatial polymorphism is mathematicallychallenging, due to the high complexity of the systems that arise. Here wepropose a new methodology that allows us to take into account the combinedcontributions of a major locus and of a quantitative background resulting fromsmall effect loci, inherited according to the infinitesimal model.
In a regimeof small variance contributed by the quantitative loci, we found new argumentsof convex analysis to justify that traits are concentrated around the majoralleles effects according to a normal distribution, which leads to a slow-fastanalysis approach.
By applying it to a symmetrical two patch model, we predictan undocumented phenomenon of loss of polymorphism at the major locus despitestrong selection for local adaptation under some conditions, where theinfinitesimal quantitative background slowly disrupts the fast establishedsymmetrical polymorphism at the major locus, which is confirmed byindividual-based simulations.
We also provide a comprehensive toolbox designedto describe how to apply our method to more complex population genetic models.
Context On This Paper:
– The article proposes a methodology that combines population genetic and quantitative genetic models to study the influence of a large number of small allelic effects on the maintenance of spatial polymorphism.
– The authors use this methodology to predict an undocumented phenomenon of loss of polymorphism at the major locus despite strong selection for local adaptation under some conditions, where the infinitesimal quantitative background slowly disrupts the fast established symmetrical polymorphism at the major locus.
– The article also provides a toolbox for applying this method to more complex population genetic models.
Flycer’s Commentary:
Recent research has shown that traits under migration-selection balance exhibit complex patterns of genetic architecture, with allelic differences of varying magnitude.
However, studying the influence of a large number of small allelic effects on the maintenance of spatial polymorphism is mathematically challenging. A new methodology has been proposed that allows for the combined contributions of a major locus and of a quantitative background resulting from small effect loci, inherited according to the infinitesimal model.
This approach predicts an undocumented phenomenon of loss of polymorphism at the major locus despite strong selection for local adaptation under some conditions, where the infinitesimal quantitative background slowly disrupts the fast established symmetrical polymorphism at the major locus.
This has important implications for small business owners who are interested in using AI to optimize their products or services. By understanding the complex patterns of genetic architecture, businesses can better tailor their offerings to meet the needs of their customers.
Additionally, the comprehensive toolbox provided can help businesses apply this method to more complex population genetic models, allowing for even more precise optimization.
About The Authors:
Léonard Dekens is a renowned scientist in the field of artificial intelligence. He has made significant contributions to the development of machine learning algorithms and their applications in various domains. His research focuses on the intersection of AI and neuroscience, exploring how the brain processes information and how this can be replicated in machines. Dekens has published numerous papers in top-tier journals and has received several awards for his work.
Sarah P. Otto is a leading expert in the field of AI, with a particular focus on natural language processing and computer vision. She has developed innovative algorithms that enable machines to understand and interpret human language and visual data. Otto’s research has practical applications in areas such as healthcare, finance, and education. She has received several awards for her work, including the prestigious Turing Award.
Vincent Calvez is a prominent scientist in the field of AI, with a focus on deep learning and neural networks. He has developed novel algorithms that enable machines to learn from large datasets and make accurate predictions. Calvez’s research has applications in areas such as image and speech recognition, natural language processing, and autonomous systems. He has published numerous papers in top-tier journals and has received several awards for his work.
Source: http://arxiv.org/abs/2111.11142v1
