A New Local Score Based Method Applied to Behavior-divergent Quail Lines Sequenced in Pools Precisely Detects Selection Signatures on Genes Related to Autism

TL;DR

This paper introduces a novel local score method for detecting selection signatures in pooled sequencing data, effectively identifying genes related to behavior and autism, outperforming existing methods in accuracy and biological relevance.

Contribution

The paper presents a new local score approach that accounts for linkage disequilibrium in pooled sequencing data, enabling precise detection of selection signals without windowing.

Findings

Method performs similarly to haplotype-based methods on benchmark datasets.

Successfully detects genes linked to social behavior and autism in quail.

Outperforms competing methods in biological coherence of results.

Abstract

Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans allows increasing the detection power, but haplotype-based methods require individual genotypes and are not applicable on pool-sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium, accumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal, avoiding windowing methods. This method provided results similar to haplotype-based methods on two benchmark data sets with individual genotypes. Results obtained for a divergent selection experiment on behavior in quail, where two lines were sequenced in pools, are precise and biologically coherent, while competing methods failed: our approach led to the detection of signals involving genes known to act on social responsiveness or autistic traits. This local score approach is general and can be applied to other genome-wide analyzes such as GWAS or genome scans for selection.