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Hybrid fitness effects modify fixation probabilities of introgressed alleles.

ABSTRACT: Hybridization is a common occurrence in natural populations, and introgression is a major source of genetic variation. Despite the evolutionary importance of adaptive introgression, classical population genetics theory does not take into account hybrid fitness effects. Specifically, heterosis (i.e. hybrid vigor) and Dobzhansky-Muller incompatibilities influence the fates of introgressed alleles. Here, we explicitly account for polygenic, unlinked hybrid fitness effects when tracking a rare introgressed marker allele. These hybrid fitness effects quickly decay over time due to repeated backcrossing, enabling a separation-of-timescales approach. Using diffusion and branching process theory in combination with computer simulations, we formalize the intuition behind how hybrid fitness effects affect introgressed alleles. We find that hybrid fitness effects can significantly hinder or boost the fixation probability of introgressed alleles, depending on the relative strength of heterosis and Dobzhansky-Muller incompatibilities effects. We show that the inclusion of a correction factor (α, representing the compounded effects of hybrid fitness effects over time) into classic population genetics theory yields accurate fixation probabilities. Despite having a strong impact on the probability of fixation, hybrid fitness effects only subtly change the distribution of fitness effects of introgressed alleles that reach fixation. Although strong Dobzhansky-Muller incompatibility effects may expedite the loss of introgressed alleles, fixation times are largely unchanged by hybrid fitness effects.

SUBMITTER: Pfennig A

PROVIDER: S-EPMC9258535 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient-ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.

Project description:Infections by parasitic nematodes inflict a huge burden on the health of humans and livestock throughout the world. Anthelmintic drugs are the first line of defense against these infections. Unfortunately, resistance to these drugs is rampant and continues to spread. To improve treatment strategies, we must understand the genetics and molecular mechanisms that underlie resistance. Studies of the fungus Aspergillus nidulans and the free-living nematode Caenorhabditis elegans discovered that a beta-tubulin gene is mutated in benzimidazole (BZ) resistant strains. In parasitic nematode populations, three beta-tubulin alleles, F167Y, E198A, and F200Y, have long been correlated with resistance. Additionally, improvements in sequencing technologies have identified new alleles - E198V, E198L, E198K, E198I, and E198Stop - also correlated with BZ resistance. However, none of these alleles have been proven to cause resistance. To empirically demonstrate this point, we independently introduced the F167Y, E198A, and F200Y alleles as well as two of the newly identified alleles, E198V and E198L, into the BZ susceptible C. elegans N2 genetic background using the CRISPR-Cas9 system. These genome-edited strains were exposed to both albendazole and fenbendazole to quantitatively measure animal responses to BZs. We used a range of concentrations for each BZ compound to define response curves and found that all five of the alleles conferred resistance to BZ compounds equal to a loss of the entire beta-tubulin gene. These results prove that the parasite beta-tubulin alleles cause resistance. The E198V allele is found at low frequencies along with the E198L allele in natural parasite populations, suggesting that it could affect fitness. We performed competitive fitness assays and demonstrated that the E198V allele reduces animal health, supporting the hypothesis that this allele might be less fit in field populations. Overall, we present a powerful platform to quantitatively assess anthelmintic resistance and effects of specific resistance alleles on organismal fitness in the presence or absence of the drug.

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Hybrid fitness effects modify fixation probabilities of introgressed alleles.

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