bioRxivGenomicsPreprint
Improved two-stage GWAS identifies rare genetic variants associated with dermo susceptibility in the eastern oyster
Paul Coÿne, Henry Sun, Zhenwei WangiD, Sandra Casa, Jérôme F. La PeyreiD, Mason L. Williams, Scott Rikard, David BushekiD, Ximing GuoiD
Abstract
Perkinsus marinus is a protist that causes the wasting disease dermo in the Eastern oyster, Crassostrea virginica. The disease causes high mortality of both wild and cultured oysters, impacting ecosystem health and aquaculture. Despite decades of research, the genetic mechanisms of dermo resistance remain poorly understood. Traditional selective breeding based on phenotypes has yielded improvements in dermo resistance, but progress has been slow, likely due to the trait's complex genetic architecture. Understanding the genetic mechanisms underlying dermo resistance may facilitate advanced breeding to accelerate improvement. This study conducted genome-wide association studies (GWAS) of dermo resistance, leveraging a multi-generational dataset comprising 2,423 oysters phenotyped for survival following a dermo challenge and genotyped using a high-density 66K SNP array. A two-stage GWAS with more inclusive quality control identified 48 dermo-resistance markers, including many associated with genes for innate immune response and energy metabolism. Most dermo-resistance markers showed rare minor alleles occurring at higher frequencies in oysters that died after challenge, suggesting that these rare alleles are deleterious and associated with dermo susceptibility. The strongest association was with a polymorphism in the mucin-5AC-like gene, explaining 8.1% of phenotypic variation and suggesting that mucus production plays a crucial role in dermo resistance. The inclusion of these markers has improved genomic prediction accuracy, and their identification provides new insights into genomic variation and the architecture of dermo resistance, informing future strategies for genetic improvement.
- organism: non-human primate
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
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