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

The Promise of Inferring the Past using the Ancestral Recombination Graph (ARG)

The Ancestral Recombination Graph (ARG) is a structure that represents the history of coalescent and recombination events connecting a set of sequences (Hudson 1991). The full ARG can be represented as a set of genealogical trees at every locus in the genome, annotated with recombination events that change the topology of the trees between adjacent loci and the mutations that occurred along the branches of those trees (Griffiths & Marjoram 1997). Valuable insights can be gained into past evolutionary processes, such as demographic events or the influence of natural selection, by studying the ARG. It is regarded as the “holy grail” of population genetics (Hubisz & Siepel 2020) since it encodes the processes that generate all patterns of allelic and haplotypic variation from which all commonly used summary statistics in population genetic research (e.g. heterozygosity, linkage disequilibrium, etc.) can be derived. Many previous evolutionary inferences relied on summary statistics extracted from the genotype matrix. Evolutionary inferences using the ARG represent a significant advancement as the ARG is a representation of the evolutionary history of a sample that shows the past history of recombination, coalescent and mutation events across a particular sequence. This representation in theory contains as much information, if not more, than the combination of all independent summary statistics that could be derived from the genotype matrix. Consistent with this idea, some of the first ARG-based analyses have allowed more powerful analysis than summary statistic-based analyses (Stern et al. 2019; Speidel et al. 2019; Hubisz et al. 2020; Hejase et al. 2022; Fan et al. 2022, 2023; Link et al. 2023; Zhang et al. 2023). As such, there has been significant interest in the field to investigate two main problems related to the ARG: 1) How can we estimate the ARG based on genomic data, and 2) How can we extract information of past evolutionary processes from the ARG? In this perspective we highlight three topics that pertain to these main issues: The development of computational innovations that enable the estimation of the ARG; remaining challenges in estimating the ARG; and methodological advances for deducing evolutionary forces and mechanisms using the ARG. This perspective serves to introduce the readers to the types of questions that can be explored using the ARG, and to highlight some of the most pressing issues that must be addressed in order to make ARG-based inference an indispensable tool for evolutionary research

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