Selection on the Fly: Short-Term Adaptation to an Altered Sexual Selection Regime in Drosophila pseudoobscura.

Experimental evolution studies are powerful approaches to examine the evolutionary history of lab populations. Such studies have shed light on how selection changes phenotypes and genotypes. Most of these studies have not examined the time course of adaptation under sexual selection manipulation, by resequencing the populations' genomes at multiple time points. Here, we analyze allele frequency trajectories in Drosophila pseudoobscura where we altered their sexual selection regime for 200 generations and sequenced pooled populations at 5 time points. The intensity of sexual selection was either relaxed in monogamous populations (M) or elevated in polyandrous lines (E). We present a comprehensive study of how selection alters population genetics parameters at the chromosome and gene level. We investigate differences in the effective population size-Ne-between the treatments, and perform a genome-wide scan to identify signatures of selection from the time-series data. We found genomic signatures of adaptation to both regimes in D. pseudoobscura. There are more significant variants in E lines as expected from stronger sexual selection. However, we found that the response on the X chromosome was substantial in both treatments, more pronounced in E and restricted to the more recently sex-linked chromosome arm XR in M. In the first generations of experimental evolution, we estimate Ne to be lower on the X in E lines, which might indicate a swift adaptive response at the onset of selection. Additionally, the third chromosome was affected by elevated polyandry whereby its distal end harbors a region showing a strong signal of adaptive evolution especially in E lines.

Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments.

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques. In so-called Evolve-and-Resequence (E&R) experiments, populations are kept in the laboratory under controlled experimental conditions where their genomes are sampled and allele frequencies monitored. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER - a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data. We implemented and released our method in a new open-source software package that can be accessed at https://doi.org/10.5281/zenodo.7351736.

Predicting Biologic Therapy Outcome of Patients With Spondyloarthritis: Joint Models for Longitudinal and Survival Analysis.

BACKGROUND: Rheumatic diseases are one of the most common chronic diseases worldwide. Among them, spondyloarthritis (SpA) is a group of highly debilitating diseases, with an early onset age, which significantly impacts patients' quality of life, health care systems, and society in general. Recent treatment options consist of using biologic therapies, and establishing the most beneficial option according to the patients' characteristics is a challenge that needs to be overcome. Meanwhile, the emerging availability of electronic medical records has made necessary the development of methods that can extract insightful information while handling all the challenges of dealing with complex, real-world data. OBJECTIVE: The aim of this study was to achieve a better understanding of SpA patients' therapy responses and identify the predictors that affect them, thereby enabling the prognosis of therapy success or failure. METHODS: A data mining approach based on joint models for the survival analysis of the biologic therapy failure is proposed, which considers the information of both baseline and time-varying variables extracted from the electronic medical records of SpA patients from the database, Reuma.pt. RESULTS: Our results show that being a male, starting biologic therapy at an older age, having a larger time interval between disease start and initiation of the first biologic drug, and being human leukocyte antigen (HLA)-B27 positive are indicators of a good prognosis for the biological drug survival; meanwhile, having disease onset or biologic therapy initiation occur in more recent years, a larger number of education years, and higher values of C-reactive protein or Bath Ankylosing Spondylitis Functional Index (BASFI) at baseline are all predictors of a greater risk of failure of the first biologic therapy. CONCLUSIONS: Among this Portuguese subpopulation of SpA patients, those who were male, HLA-B27 positive, and with a later biologic therapy starting date or a larger time interval between disease start and initiation of the first biologic therapy showed longer therapy adherence. Joint models proved to be a valuable tool for the analysis of electronic medical records in the field of rheumatic diseases and may allow for the identification of potential predictors of biologic therapy failure.