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相关概念视频

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Incomplete Dominance01:43

Incomplete Dominance

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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相关实验视频

Updated: Jul 13, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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基于序列的GWAS对牛肉生产特征的元分析.

Marie-Pierre Sanchez1, Thierry Tribout2, Naveen K Kadri3

  • 1Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France. marie-pierre.sanchez@inrae.fr.

Genetics, selection, evolution : GSE
|October 12, 2023
PubMed
概括
此摘要是机器生成的。

这项研究使用了序列级的元分析 (MA) 来识别牛肉生产特征的遗传变异. 该方法成功地确定了候选基因和潜在的因果变异,为这些复杂特征的遗传结构提供了洞察力.

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科学领域:

  • 动物遗传学动物遗传学
  • 基因组学就是基因组学.
  • 量化遗传学 量化遗传学

背景情况:

  • 全基因组关联研究 (GWAS) 有助于识别与复杂特征相关的变异.
  • 序列级GWAS的元分析 (MA) 提高了准确性和功率.
  • 在H2020BovReg项目中,重点是牛肉生产特征.

研究的目的:

  • 为牛肉生产特征执行序列级MA.
  • 识别与牛的生长,形态和尸体特征相关的遗传变异.
  • 为了利用多种群的数据进行强大的遗传发现.

主要方法:

  • 从基于序列的GWAS对15个种群 (54,782只动物) 的综合总结统计数据.
  • 对生长,形态和尸体特征进行了16次元分析.
  • 使用固定效应和z-score方法进行元分析.

主要成果:

  • 在元分析中,与人群内GWAS相比,确定了更多的定量特征位点 (QTL).
  • 在已知的生长和肉类特征的基因组区域中突出显示QTL,每个QTL的变异较少.
  • 确定了MSTN,LCORL和PLAG1等基因的候选变异,并确定了可能调节肉类生产的基因的新型变异.
  • 观察到与表达QTL重叠,表明已识别的变体的调节作用.

结论:

  • 分析是识别肉牛候选基因和因果变异的有力工具.
  • 这种方法可以更深入地了解复杂的牛肉生产特征的遗传结构.
  • MA补充了人群内GWAS,增强了对遗传机制的理解.