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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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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相关实验视频

Updated: Jun 2, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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全基因组关联研究对相互作用的基因进行了丰富的研究.

Peter T Nguyen1, Simon G Coetzee2, Irina Silacheva1

  • 1The Department of Biomedical and Translational Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.

BioData mining
|January 15, 2025
PubMed
概括

基因算法应用于多omics数据,通过识别关键基因和细胞类型,揭示了遗传变异如何促进疾病风险. 这种方法产生了疾病的细胞模型,突出了乳腺癌等疾病中的变异性相互作用.

关键词:
乳腺癌 乳腺癌 乳腺癌复杂的疾病复杂的疾病.病理起因 病理起因 病理起因在GWAS中,GWAS就是GWAS.基因网络 基因网络遗传算法 遗传算法 遗传算法多个omics的多个omics.易感性 易感性 易感性变体优先级的排序方式

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 单细胞技术提供了关于疾病机制和细胞类型起源的见解.
  • 全基因组关联研究 (GWAS) 确定与疾病相关的遗传变异.
  • 整合多omics数据对于理解疾病发展的遗传影响至关重要.

研究的目的:

  • 开发一种方法来了解来自GWAS的遗传变异如何影响疾病发展.
  • 利用基因算法与多omics数据来建模基因和细胞类型对疾病风险的贡献.
  • 探索基因和细胞类型对增加疾病易感性的集体影响.

主要方法:

  • 采用基因算法与配对的单核RNA-seq和ATAC-seq数据.
  • 综合基因组注释和蛋白质与蛋白质相互作用数据.
  • 评估了使用客观功能,包括蛋白质与蛋白质相互作用的基因细胞组建议.

主要成果:

  • 与对照组相比,遗传算法识别了具有显著更高健身分数的基因细胞集.
  • 该模型成功地确定了已知的基因标和联结体-受体相互作用.
  • 分析显示,与疾病相关的变体表现出比偶然预期的更多的物理相互作用,以乳腺癌为例.

结论:

  • 遗传算法可以从易感变异中生成连贯的疾病风险细胞模型.
  • 这种计算方法通过将变异与细胞机制联系起来,增强了对GWAS发现的解释.
  • 这项研究证明了多omics整合的实用性,用于剖析复杂的疾病病因.