A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Genomics and proteomics methodologies for vulnerable populations research

This chapter describes common genomic and proteomic methods and their application to the study of vulnerable population groups. The International HapMap project is discussed in relation to unique Haplotype single nucleotide polymorphisms (htSNPs) in population groups. In addition, studies, which have used these methods to investigate aging, ethnic, and racial specific conditions, as well as psychiatric diseases, are reviewed. Advantages and limitations of various genomic and proteomic approaches are discussed in relation to population admixture and sample selection.