2022 - Identifying genes associated with genetic control of color polymorphism in the pearl oyster Pinctada margaritifera var. cumingii (Linnaeus 1758) using a comparative whole genome pool-sequencing approach

For hundreds of years, the color diversity of Mollusca shells has been a topic of interest for humanity. However, the genetic control underlying color expression is still poorly understood in mollusks. The pearl oyster Pinctada margaritifera is increasingly becoming a biological model to study this process due to its ability to produce a large range of colors. Previous breeding experiments demonstrated that color phenotypes were partly under genetic control, and while a few genes were found in comparative transcriptomics and epigenetic experiments, genetic variants associated with the phenotypes have not yet been investigated. Here, we used a pooled-sequencing approach on 172 individuals to investigate color-associated variants on three color phenotypes of economic interest for pearl farming, in three wild and one hatchery populations. While our results uncovered SNPs targeting pigment-related genes already identified in previous studies, such as PBGD, tyrosinases, GST, or FECH, we also identified new color-related genes occurring in the same pathways, like CYP4F8, CYP3A4, and CYP2R1. Moreover, we identified new genes involved in novel pathways unknown to be involved in shell coloration for P. margaritifera, like the carotenoid pathway, BCO1. These findings are essential to possibly implement future breeding programs focused on individual selection for specific color production in pearl oysters and improve the footprint of perliculture on the Polynesian lagoon by producing less but with a better quality. Download paper here

Recommended citation: ‘Stenger PL., Ky CL., Vidal Dupiol J., Planes S., Reisser C. (2022) - Identifying genes associated with genetic control of color polymorphism in the pearl oyster Pinctada margaritifera var. cumingii (Linnaeus 1758) using a comparative whole genome pool-sequencing approach – Evolutionary Applications, https://doi.org/10.1111/eva.13464’