Song Yuxin, Chang Zhongyu, Chen Ao, Zhao Yunfeng, Jiang Yanliang, Jiang Li
Wuxi Fisheries College, Nanjing Agricultural University, Wuxi 214081, China.
Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Int J Mol Sci. 2024 Nov 27;25(23):12758. doi: 10.3390/ijms252312758.
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 , , and 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 () associated with resistance to Columnaris disease.
线性混合模型(LMMs)常用于全基因组关联研究(GWASs)中,以评估群体结构和相关性。然而,在虹鳟柱状病抗性分析中,LMMs已被证明在控制假阳性错误方面效果不佳。为了解决这个问题,我们使用了广义线性混合模型关联软件,如GMMAT(v1.4.0)(广义线性混合模型关联测试)、SAIGE(v1.4.0)(广义混合模型的可扩展精确实现)和Optim-GRAMMAR,进行了一系列研究,共扫描了25,853个单核苷酸多态性(SNPs)。通过Optim-GRAMMAR检测到7个与柱状病抗性性状相关的不同SNPs,通过GMMAT检测到4个SNPs,通过SAIGE检测到3个SNPs,所有这些SNPs可以解释柱状病抗性性状8.87%的遗传变异。通过GMMAT重新评估的柱状病抗性性状的遗传力经过校准,发现总计为0.71,而不是之前研究中严重低估的0.35。与柱状病抗性相关的 、 和 的鉴定将为我们提供更多通过分子标记改善遗传育种的基因。最后,我们继续进行单倍型和基于基因的分析,并成功鉴定出一些与柱状病抗性相关的单倍型和一个基因( )。