Inferring Balancing Selection From Genome-Scale Data

The identification of genomic regions and genes that have evolved under natural selection is a fundamental objective in the field of evolutionary genetics. While various approaches have been established for the detection of targets of positive selection, methods for identifying targets of balancing selection, a form of natural selection that preserves genetic and phenotypic diversity within populations, have yet to be fully developed. Despite this, balancing selection is increasingly acknowledged as a significant driver of diversity within populations, and the identification of its signatures in genomes is essential for understanding its role in evolution. In recent years, a plethora of sophisticated methods has been developed for the detection of patterns of linked variation produced by balancing selection, such as high levels of polymorphism, altered allele-frequency distributions, and polymorphism sharing across divergent populations. In this review, we provide a comprehensive overview of classical and contemporary methods, offer guidance on the choice of appropriate methods, and discuss the importance of avoiding artifacts and of considering alternative evolutionary processes. The increasing availability of genome-scale datasets holds the potential to assist in the identification of new targets and the quantification of the prevalence of balancing selection, thus enhancing our understanding of its role in natural populations

Overcoming Language Barriers in Academia: Machine Translation Tools and a Vision for a Multilingual Future.

Having a central scientific language remains crucial for advancing and globally sharing science. Nevertheless, maintaining one dominant language also creates barriers to accessing scientific careers and knowledge. From an interdisciplinary perspective, we describe how, when, and why to make scientific literature more readily available in multiple languages through the practice of translation. We broadly review the advantages and limitations of neural machine translation systems and propose that translation can serve as both a short- and a long-term solution for making science more resilient, accessible, globally representative, and impactful beyond the academy. We outline actions that individuals and institutions can take to support multilingual science and scientists, including structural changes that encourage and value translating scientific literature. In the long term, improvements to machine translation technologies and collective efforts to change academic norms can transform a monolingual scientific hub into a multilingual scientific network. Translations are available in the supplemental material

The genetic architecture of phenotypic diversity in the Betta fish (Betta splendens).

The Betta fish displays a remarkable variety of phenotypes selected during domestication. However, the genetic basis underlying these traits remains largely unexplored. Here, we report a high-quality genome assembly and resequencing of 727 individuals representing diverse morphotypes of the Betta fish. We show that current breeds have a complex domestication history with extensive introgression with wild species. Using a genome-wide association study, we identify the genetic basis of multiple traits, including coloration patterns, the "Dumbo" phenotype with pectoral fin outgrowth, extraordinary enlargement of body size that we map to a major locus on chromosome 8, the sex determination locus that we map to dmrt1, and the long-fin phenotype that maps to the locus containing kcnj15. We also identify a polygenic signal related to aggression, involving multiple neural system-related genes such as esyt2, apbb2, and pank2. Our study provides a resource for developing the Betta fish as a genetic model for morphological and behavioral research in vertebrates