一个框架,用于识别和优先级SNP在基因的下丘脑 - 垂体 - 淋巴腺轴在古泽拉特牛使用安普利康基于NGS的基因
Olivia Marcuzzi1,2, Francisco Calcaterra1,2, Leónidas H Olivera1,2
1Instituto de Genética Veterinaria (IGEVET, CONICET), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 60 Y 118 S/N, La Plata, 1900, Argentina.
BMC genomic data
|October 29, 2025
概括
研究人员开发了一种新方法,结合了amplicon下一代测序 (NGS) 和生物信息学,以识别影响牛繁殖的遗传变异. 这种方法确切地指出了三个关键的基因多态性,可能会影响诸如古泽拉特牛的青春期发作等特征.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 动物遗传学动物遗传学
背景情况:
- 下一代测序 (NGS) 和生物信息学使全面的基因组和转录组分析成为可能.
- 有针对性的NGS方法对于研究遗传疾病,表观遗传学和微生物组至关重要.
- 格泽拉特牛是巴西的泽布 (Zebu) 品种,表现出适应能力,但有延迟青春期等生殖局限性.
研究的目的:
- 创建用于检测和选择候选基因中的多态的路线图,使用NGS-Target和生物信息学.
- 为了识别可能影响古泽拉特牛的生殖效率的下垂体-垂体-淋巴腺 (HPG) 轴基因内的遗传变异.
主要方法:
- 开发了一种针对68个HPG轴基因 (730个区域,136,274个基因组) 的amplicon NGS试验.
- 通过测量和GATK协议对75头古泽拉特牛进行了测序,检测到2600个SNP和1615个INDEL.
- 应用过步骤 (maf,非同义替代,保存,生物化学性质) 和生物信息工具 (SIFT,PANTHER,PolyPhen2,MutPred) 来识别候选SNP.
主要成果:
- 在过后确定了30个候选SNP,其中5个显示出高预测的蛋白质效应.
- 使用AlphaFold和DDMut.使用候选SNP的估计蛋白质结构和稳定性.
- 选择了3个候选多态 (在IGF1R,LHCGR,TAC3R基因中) 具有潜在显著的蛋白质效应,以便进一步验证.
结论:
- 集成的NGS和生物信息学方法有效地确定了HPG轴基因中的候选多态性.
- 在IGF1R,LHCGR和TAC3R中,有三种特定的多态性,需要进一步研究它们在牛生殖特征中的作用.
- 这份路线图为未来的基因研究提供了框架,旨在提高牛养殖和生殖效率.
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