Characterizing selection on complex traits through conditional frequency spectra.

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. Recognizing the biases inherent to GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort.

Exploring the factors influencing the adult population's reluctance to accept the COVID-19 vaccine in Tehran.

Background: While evidence of efficacy, safety, and technical feasibility is crucial when introducing a vaccine, it is equally important to consider the psychological, social, and political factors influencing vaccine acceptance. This study aims to identify the factors contributing to COVID-19 vaccine hesitancy among adults in Tehran, Iran.

Methods: The study employed a descriptive and analytical cross-sectional design carried out from 2021 to 2022.

Essential core competencies for health policy graduates: a multi-method consensus type study.

Background: In light of the multi-faceted challenges confronting health systems worldwide and the imperative to advance towards development goals, the contribution of health policy graduates is of paramount importance, facilitating the attainment of health and well-being objectives. This paper delineates a set of core skills and competencies that are requisite for health policy graduates, with the objective of preparing these graduates for a spectrum of future roles, including both academic and non-academic positions.

Methods: The study was conducted in three phases: a scoping review, qualitative interviews and the validation of identified competencies through brainstorming with experts.

Bayesian estimation of gene constraint from an evolutionary model with gene features.

Measures of selective constraint on genes have been used for many applications, including clinical interpretation of rare coding variants, disease gene discovery and studies of genome evolution. However, widely used metrics are severely underpowered at detecting constraints for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. Here we developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, s.