Inferring Balancing Selection From Genome-Scale Data

Abstract

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 have 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.

Ecology, the pace‐of‐life, epistatic selection and the maintenance of genetic variation in life‐history genes

“…Single major effect loci may of course also represent POL loci. However, associating variation in complex LH traits even with loci with major effects is difficult (Schielzeth et al, 2018) and detecting NFDS at particular sites is even more difficult (Bitarello et al, 2023; Fijarczyk & Babik, 2015). Hence, it is perhaps not surprising that there are few well‐studied examples.…”

“…Detecting balancing selection in general, and NFDS in particular, is very challenging (Bitarello et al, 2023; Fijarczyk & Babik, 2015). The standard empirical toolbox we use to better understand selection and adaptation using genomic data, based largely on various forms of outlier detection, will typically not able to detect and shed much light on associative NFDS (Fijarczyk & Babik, 2015; Wellenreuther & Hansson, 2016).…”

“…For example, we would predict that many key LH genes should show a relatively even frequency spectrum of segregating sites. Yet, several confounding processes can generate such spectra (Bitarello et al, 2023; Fijarczyk & Babik, 2015) and, to further complicate matters, the polygenic nature of LH variation predicts substantial genetic redundancy. One strategy is to interrogate sets of genes showing hallmarks of balancing selection for functional enrichment.…”

Polymorphism-aware models in RevBayes: Species trees, disentangling Balancing Selection and CG-biased gene conversion

“…Although the classical theory, describing population evolution through the interplay of mutations and selections with varying strengths and effects whether positive or purifying, has diminished the ubiquity of the balancing hypothesis, it remains a valuable concept for explaining the persistence of polymorphisms over extended periods. Based on their temporal span, three types of balancing selection exist according to Bitarello et al (2023): ultra long-term (> 7 × 10 6 years, e.g., MHC locus (Kelley et al, 2005)), long-term (10 6 years, e.g., ERAP2 (Andrés et al, 2010)) and recent (< 10 6 years, e.g., sickle-cell disease (Laval et al, 2019)).…”

“…This approach provides a more realistic null model, thereby enhancing the estimation of BS on experimental data. PoMos prove to be valuable for modelling and detecting BS, as they are rooted in polymorphisms characterized by the prolonged existence of multiple genetic variations -markers of BS (Bitarello et al, 2023). This phenomenon manifests in a shift in the site frequency spectrum (SFS) towards an excess of intermediate frequency variants.…”