Age is the primary risk factor for many common human diseases. Here, we quantify the relative contributions of genetics and aging to gene expression patterns across 27 tissues from 948 humans. We show that the predictive power of expression quantitative trait loci is impacted by age in many tissues. Jointly modelling the contributions of age and genetics to transcript level variation we find expression heritability (h²) is consistent among tissues while the contribution of aging varies by >20-fold with [Formula: see text] in 5 tissues. We find that while the force of purifying selection is stronger on genes expressed early versus late in life (Medawar's hypothesis), several highly proliferative tissues exhibit the opposite pattern. These non-Medawarian tissues exhibit high rates of cancer and age-of-expression-associated somatic mutations. In contrast, genes under genetic control are under relaxed constraint. Together, we demonstrate the distinct roles of aging and genetics on expression phenotypes.

The Yuma myotis bat (Myotis yumanensis) is a small vespertilionid bat and one of 52 species of new world Myotis bats in the subgenus Pizonyx. While M. yumanensis populations currently appear relatively stable, it is one of 12 bat species known or suspected to be susceptible to white-nose syndrome, the fungal disease causing declines in bat populations across North America. Only two of these 12 species have genome resources available, which limits the ability of resource managers to use genomic techniques to track the responses of bat populations to white-nose syndrome generally. Here we present the first de novo genome assembly for Yuma myotis, generated as a part of the California Conservation Genomics Project. The M. yumanensis genome was generated using a combination of PacBio HiFi long reads and Omni-C chromatin-proximity sequencing technology. This high-quality genome is one of the most complete bat assemblies available, with a contig N50 of 28.03 Mb, scaffold N50 of 99.14 Mb, and BUSCO completeness score of 93.7%. The Yuma myotis genome provides a high-quality resource that will aid in comparative genomic and evolutionary studies, as well as inform conservation management related to white-nose syndrome.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.