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通过多omics网络分析,探索与Bos indicus中的料效率和甲排放相关的基因组,转录组和微生物组相互作用

Tainã Figueiredo Cardoso¹, Jennifer Jessica Bruscadin^2,3, Juliana Afonso²

¹Embrapa Southeast Livestock, São Carlos, São Paulo, Brazil. taina.cardoso@colaborador.embrapa.br.

Mammalian genome : official journal of the International Mammalian Genome Society

|September 25, 2025

概括

遗传相互作用,或表观作用,影响复杂的特征,如牛料效率和甲排放. 这项研究确定了关键的遗传模块和途径,揭示了免疫系统的功能.

科学领域:

动物基因组学动物基因组学
量化遗传学量化遗传学
系统生物学系统生物学

背景情况:

牛的复杂特征,如残留料摄入量 (RFI) 和残留甲排放 (RME),受到多种遗传因素的影响.
遗传变异之间的相互作用 (epistasis) 在确定这些复杂的特征方面起着至关重要的作用,代表非添加性遗传效应.

研究的目的:

调查Nelore牛对RFI和RME的表性影响.
评估这些基因相互作用在多个奥米克层的影响,包括微生物概况和基因表达 (mRNA,miRNA).

主要方法:

全基因组SNP-SNP相互作用分析,以确定与RFI和RME相关的重要模块.
整合多omics数据 (微生物群,mRNA,miRNA) 与已识别的SNP-SNP模块相关联.
相关基因和SNP的途径和功能丰富分析.

主要成果:

对RME和RFI分别确定了14个和10个重要的SNP-SNP模块,其中大多数SNP位于内基/跨基区域.
相关途径包括免疫系统和actin细胞骨组织.
发现了SNP-SNP模块与其他omics数据之间的相关性,特别是微生物组成和基因表达.

结论:

免疫反应和组织被认为对牛的料效率很重要.
这些发现提供了通过微生物群和转录调节改善牛效率和甲排放的新型候选者.
网络分析有效地揭示了与复杂特征相关的功能相互作用.

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