A model-based approach for identifying signatures of balancing selection in genetic data

TL;DR

This paper introduces two new composite likelihood ratio tests for detecting balancing selection in genetic data, demonstrating their effectiveness through simulations and application to human genome data, revealing both known and novel loci under balancing selection.

Contribution

The paper develops and validates two innovative methods for identifying balancing selection, outperforming existing approaches and uncovering new candidate loci in human genomes.

Findings

Successfully identified known balancing selection loci like HLA genes.

Discovered novel candidates such as FANK1 with signs of balancing selection.

Methods outperform existing techniques across various models.

Abstract

While much effort has focused on detecting positive and negative directional selection in the human genome, relatively little work has been devoted to balancing selection. This lack of attention is likely due to the paucity of sophisticated methods for identifying sites under balancing selection. Here we develop two composite likelihood ratio tests for detecting balancing selection. Using simulations, we show that these methods outperform competing methods under a variety of assumptions and demographic models. We apply the new methods to whole-genome human data, and find a number of previously-identified loci with strong evidence of balancing selection, including several HLA genes. Additionally, we find evidence for many novel candidates, the strongest of which is FANK1, an imprinted gene that suppresses apoptosis, is expressed during meiosis in males, and displays marginal signs of segregation distortion. We hypothesize that balancing selection acts on this locus to stabilize the segregation distortion and negative fitness effects of the distorter allele. Thus, our methods are able to reproduce many previously-hypothesized signals of balancing selection, as well as discover novel interesting candidates.