Comparative study of population genomic approaches for mapping colony-level traits.

• Main Authors: Shani Inbar, Pnina Cohen, Tal Yahav, Eyal Privman
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-03-01
• Series: PLoS Computational Biology
• Online Access: https://doi.org/10.1371/journal.pcbi.1007653

Social insect colonies exhibit colony-level phenotypes such as social immunity and task coordination, which are produced by the individual phenotypes. Mapping the genetic basis of such phenotypes requires associating the colony-level phenotype with the genotypes in the colony. In this paper, we examine alternative approaches to DNA extraction, library construction, and sequencing for genome wide association studies (GWAS) of colony-level traits using a population sample of Cataglyphis niger ants. We evaluate the accuracy of allele frequency estimation from sequencing a pool of individuals (pool-seq) from each colony using either whole-genome sequencing or reduced representation genomic sequencing. Based on empirical measurement of the experimental noise in sequenced DNA pools, we show that reduced representation pool-seq is drastically less accurate than whole-genome pool-seq. Surprisingly, normalized pooling of samples did not result in greater accuracy than un-normalized pooling. Subsequently, we evaluate the power of the alternative approaches for detecting quantitative trait loci (QTL) of colony-level traits by using simulations that account for an environmental effect on the phenotype. Our results can inform experimental designs and enable optimizing the power of GWAS depending on budget, availability of samples and research goals. We conclude that for a given budget, sequencing un-normalized pools of individuals from each colony provides optimal QTL detection power.