Highly Efficient Methods with a Generalized Linear Mixed Model for the Quantitative Trait Locus Mapping of Resistance to Columnaris Disease in Rainbow Trout (Oncorhynchus mykiss)

Abstract

Linear mixed models (LMMs) are commonly used in genome-wide association studies (GWASs) to evaluate population structures and relatedness. However, LMMs have been shown to be ineffective in controlling false positive errors for the analysis of resistance to Columnaris disease in Rainbow Trout. To solve this problem, we conducted a series of studies using generalized linear mixed-model association software such as GMMAT (v1.4.0) (generalized linear mixed-model association tests), SAIGE (v1.4.0) (Scalable and Accurate Implementation of Generalized mixed model), and Optim-GRAMMAR for scanning a total of 25,853 SNPs. Seven different SNPs (single-nucleotide polymorphisms) associated with the trait of resistance to Columnaris were detected by Optim-GRAMMAR, four SNPs were detected by GMMAT, and three SNPs were detected by SAIGE, and all of these SNPs can explain 8.87% of the genetic variance of the trait of resistance to Columnaris disease. The heritability of the trait of resistance to Columnaris re-evaluated by GMMAT was calibrated and was found to amount to a total of 0.71 other than 0.35, which was seriously underestimated in previous research. The identification of LOC110520307, LOC110520314, and LOC110520317 associated with the resistance to Columnaris disease will provide us more genes to improve the genetic breeding by molecular markers. Finally, we continued the haplotype and gene-based analysis and successfully identified some haplotypes and a gene (TTf-2) associated with resistance to Columnaris disease.