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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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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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In 1866, Gregor Mendel published the results of his pea plant breeding experiments, providing evidence for predictable patterns in the inheritance of physical characteristics. The significance of his findings was not immediately recognized. In fact, the existence of genes was unknown at the time. Mendel referred to hereditary units as “factors.”
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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ti-scMR:基于轨迹推理的动态单细胞门德尔随机化确定了表型差异背后的因果基因.

Jianle Sun1,2, Qun Dong1, Jialu Wei1

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

NAR genomics and bioinformatics
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概括

我们开发了基于轨迹推断的动态单细胞孟德尔随机化 (ti-scMR) 来发现因果基因,将基因型与表型联系起来. 这种方法整合了单细胞数据和遗传变异,以确定影响细胞发育和疾病的基因.

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科学领域:

  • 基因组学就是基因组学.
  • 文字转录学 (Transcriptomics) 是一个学科.
  • 系统生物学 系统生物学

背景情况:

  • 基因表达是细胞和个体表型变化的基础,将基因型与表型连接起来.
  • 单细胞差异表达分析可以识别细胞类型,但由于混因素,无法确定因果关系.
  • 传统的门德尔随机化方法经常忽略细胞异质性和动态表达变化.

研究的目的:

  • 开发一种新的方法,基于轨迹推理的动态单细胞孟德尔随机化 (ti-scMR),用于因果基因发现.
  • 整合种群基因组学和单细胞转录组学,以探索转录特征和表型之间的因果关系.
  • 确定影响细胞发育和相关疾病的因果基因.

主要方法:

  • 杆轨迹推断和功能主要组件分析以建模累积基因表达效应.
  • 使用单细胞表达量的特征位置 (eQTL) 映射选择的遗传仪器变量.
  • 采用转录组水平的门德尔随机化来优先考虑因果基因的表型.

主要成果:

  • 通过模拟,证明了ti-scMR在因果基因鉴定中的卓越性能.
  • 将ti-scMR应用于两个真实单细胞数据集,揭示了潜在的因果基因.
  • 确定了与免疫细胞分化和相关疾病相关的因果基因.

结论:

  • ti-scMR有效地解决了单细胞研究中因果推断现有方法的局限性.
  • 轨迹推断,eQTL和门德尔随机化的集成为阐明因果机制提供了一个强大的方法.
  • 这个框架通过发现与细胞发育和疾病相关的因果基因,提高了我们对复杂特征的理解.