中国畜禽种业2026，Vol.22Issue(1)：18-31,14.
多组学技术在鸡数量性状中的研究进展
Research progress of multi-omics technology in quantitative traits of chicken
摘要
Abstract
As an important economic and model animal,chickens have quantitative traits(growth traits,reproductive traits,disease resistance traits,meat quality traits,etc.)which are the core research content in the field of animal genetics and breeding.With the rapid advancement of sequencing technologies,multi-omics approaches—including genomics,epigenomics,transcriptomics,and proteomics—have been widely employed in molecular genetic studies of quantitative traits in chickens.These methodologies have systematically uncovered key genes,regulatory elements,and signaling pathways critical to the formation of quantitative traits,providing mechanistic insights into their genetic architecture.Compared to traditional breeding methods,omics analysis can explore the regulatory mechanisms of quantitative traits in chickens at the micro level.Moreover,the combined analysis of multiple omics technologies can provide multi-level and multi-dimensional studies of quantitative traits in chickens,enabling precise selection of superior traits.This review systematically summarizes the applications of individual omics technologies in chicken quantitative trait research and synthesizes current advances in multi-omics integration.Furthermore,it outlines three principal strategies for multi-omics data analysis:network-based integration(e.g.,co-expression networks,ceRNA networks,3D genomics),statistical modeling(e.g.,Bayesian networks,PCA,PLSR),and machine learning-based approaches.By consolidating these analytical frameworks,this work aims to provide valuable references and perspectives for future investigations into the molecular mechanisms underlying quantitative traits in chickens.关键词
鸡/多组学技术/数量性状Key words
Chicken/Multi-omics technologies/Quantitative traits分类
农业科技引用本文复制引用
许瑞龙,张志远,何富民,田亚东,李文婷,李转见,康相涛,李东华..多组学技术在鸡数量性状中的研究进展[J].中国畜禽种业,2026,22(1):18-31,14.基金项目
河南省高等学校重点科研项目服务产业发展专项计划(25CY015) （25CY015）
国家自然科学基金面上项目(32573210). （32573210）
