通过整合单细胞多基因组方法和基因组距离,将调节变异与目标基因联系起来
medRxiv : the preprint server for health sciences
|June 3, 2024
概括
pgBoost通过整合多种策略,包括基因组距离,以提高单细胞多组数据分析的准确性来改善基因调节元件的连接. 这种方法增强了对各种细胞类型的遗传变异的目标基因的识别.
科学领域:
- 基因组学和生物信息学
- 单细胞多组学分析
- 监管元素-基因连接链接
背景情况:
- 单细胞配对RNA-seq和ATAC-seq多基因数据分析有望将调节元素与基因联系起来.
- 由于不同的建模假设和未能考虑基因组距离,现有方法的一致性较低.
- 生物和技术噪音使得准确的基因调节元素关联变得复杂.
研究的目的:
- 开发一个集成建模框架,pgBoost,用于强大的SNP-基因链接分配.
- 使用单细胞多组数据,提高将调节元素与基因联系的准确性和一致性.
- 为了捕捉基因距离在调节元素-基因关联中的影响.
主要方法:
- 提出了pgBoost,这是一个整合性建模框架,结合了现有的链接策略.
- 纳入基因组距离作为非线性组合模型的关键特征.
- 在精细映射的eQTL数据上训练了pgBoost,并将其应用于85k个单细胞多组免疫/血细胞样本.
主要成果:
- 与现有方法相比,pgBoost 对精细映射的eSNP-eGene对实现了显著更高的丰富度,特别是在更长的距离 (> 10kb 和 > 100kb) 上.
- 在丰富CRISPR验证链接,ABC分数和GWAS数据方面表现出卓越的性能,在更大的基因组距离上有显著的改进.
- 表明将pgBoost专注于细胞类型特定的特征可以增强对该细胞类型相关SNP基因链接的识别.
结论:
- 链接策略的非线性组合,包括基因组距离至关重要,大大提高了确定GWAS关联的目标基因的能力.
- pgBoost提供了一种更准确和更强大的方法,用于将单细胞多组数据中的调节元素与基因联系起来.
- 该框架成功地确定了其他方法遗漏的生物可信的基因变异链接,突出了其在遗传研究中的实用性.
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