山东农业大学学报（自然科学版）2025，Vol.56Issue(6)：929-937,9.DOI:10.3969/j.issn.1000-2324.2025.06.003
基于高效混合模型的全基因组关联分析方法对鲶鱼头尺性状的遗传解析
Genetic Analysis of Catfish Head-size Traits Using a Genome-Wide Association Analysis Method Based on an Efficient Mixed Model
摘要
Abstract
Skull morphology is fundamental to biological evolution and species adaptation to their environment.For aquaculture fish species,head size is also a critical economic trait linked to fillet yield and ornamental value.Therefore,identifying the genetic loci controlling the catfish head size can provide target molecular markers and functional genes for genetic selection and breeding.This study identifies quantitative trait nucleotides(QTNs)and genes associated with catfish head size.It performs genotyping using a hybrid population derived from 10 catfish families,conducts genome-wide association study(GWAS)with the efficient and robust mixed model-based Optim-GRAMMAR method,and compares the results with those from the EMMAX method.Based on 206,763 high-quality single nucleotide polymorphisms(SNPs)obtained,the EMMAX method detects no significantly associated SNPs under the same conditions.In contrast,Optim-GRAMMAR identifies 1,6,and 1 SNPs associated with head length,head depth,and head width,respectively.Most of these genes are involved in growth and development,cell proliferation,metabolic promotion,skeletal morphology,and feeding regulation.Among these genes,slc12a5a,slc7a10a,and Rab2a exert positive effects on growth and development in species such as zebrafish.The QTNs and genes reported in this study enhance our understanding of the genetic architecture underlying head size traits in catfish and will facilitate marker-assisted selection breeding in aquaculture of catfish.关键词
全基因组关联分析/Optim-GRAMMAR/EMMAX/鲶鱼/头部大小Key words
Genome-wide association study/Optim-GRAMMAR/EMMAX/catfish/head size分类
农业科技引用本文复制引用
陈澳,常中宇,赵兴宁,李宁,蒋丽..基于高效混合模型的全基因组关联分析方法对鲶鱼头尺性状的遗传解析[J].山东农业大学学报（自然科学版）,2025,56(6):929-937,9.基金项目
农业部黄河渔业资源与环境调查项目(HHDC-2022-06) （HHDC-2022-06）
中国水产科学研究院水产生物遗传大数据研究与应用创新团队项目(2023TD25) （2023TD25）
