Kashyap, M. | Search bioRxiv

bioRxiv2020

Evolutionary genetics of Drosophila melanogaster immunity: role of the X chromosome and sex-specific dominance

Intralocus Sexual Conflict (IaSC) ensues when males and females of the same species experience divergent selection on shared traits. A large number of traits have been implicated in IaSC and there is growing evidence for sexual antagonism associated with immunity. X chromosomes are thought to be hotspots of sexually antagonistic genetic variation and have been shown to harbour substantial immunity-related genetic variation. Here, using interpopulation crosses and cytogenetic cloning, we investigated the role of the X chromosome in improved immune response of laboratory populations of the fruit-fly Drosophila melanogaster selected against systemic infection by Pseudomonas entomophila. We could not detect any contribution of the X chromosome in the evolved immune response of our selected populations. However, we found strong evidence of sex-specific dominance related to immunity in our populations. Our results indicate that alleles that confer a superior immune response to the selected populations are, on average, partially dominant in females but partially recessive in males. We argue that sex-specific dominance over immunity evolved as a by-product of sexually antagonistic selection in the wild ancestors of our populations. We also highlight sex-specific dominance as a potential mechanism of sex differences in immunity, with population-level sex differences primarily driven by sex differences in heterozygotes.

evolutionary biology↗

bioRxiv2019

Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) Network

ObjectiveAccurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed ten phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Sciences and Informatics (OHDSI) network.\n\nMaterials and MethodsWe constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the OMOP CDM. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across three medical centers, including one international site.\n\nResultsCompared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site.\n\nDiscussion and ConclusionWe demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across multiple sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and consequently, sharing the classifier-building recipe, rather than the pre-trained classifiers, may be more useful for facilitating collaborative observational research.

bioinformatics↗