用长时间读取的全基因组测序和特征相关的多基因分数映射罕见疾病基因的结构变异
Cas LeMaster1, Carl Schwendinger-Schreck1, Bing Ge2
1Genomic Medicine Center, Children's Mercy Research Institute and Children's Mercy Kansas City, Kansas City, MO, USA.
medRxiv : the preprint server for health sciences
|April 2, 2024
概括
将罕见结构变异 (rSV) 与多基因分数 (PGS) 整合起来,有助于优先考虑罕见疾病的候选基因. 这种方法识别了与疾病相关的基因组区域,有助于对复杂遗传疾病中rSV影响的功能验证.
科学领域:
- 基因组学就是基因组学.
- 人类遗传学 人类遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 罕见的结构变异 (rSV) 越来越多地被识别在人类基因组中,并与罕见疾病有关,特别是当单核酸变异 (SNV) 不被诊断时.
- 目前整合RSV的方法仅限于针对已知的孟德尔罕见病基因的有针对性的方法,这对于复杂的遗传疾病是不够的.
- 多基因分数 (PGS) 总结了常见变异的效应,但它们与罕见疾病基因优先级的RSV的整合仍未得到充分探索.
研究的目的:
- 通过将罕见结构变异 (rSV) 与特征相关的多基因分数 (PGS) 整合起来,开发和实施一种新的框架来优先考虑候选疾病基因.
- 在PGS中识别和排名基因,RSV可能对疾病风险产生重大影响,从而减少功能验证的搜索空间.
- 利用长期阅读的全基因组测序 (lrWGS) 数据,在罕见疾病队列中进行全面的RSV检测和注释.
主要方法:
- 利用PacBio HiFi长读全基因组测序 (lrWGS) 来识别来自儿童基因组答案 (GA4K) 罕见病计划的497名患者的RSV.
- 综合特征相关多基因分数 (PGS) 定义一组核心/关键基因用于RSV影响评估.
- 应用了gnomAD的基因组约束注释,以评估核心/关键PGS基因中识别的RSV的意义,并将其与控制基因组进行比较.
主要成果:
- 确定了大量的RSVs,包括删除,重复,插入和反转,与自闭症病例中的假定核心/关键PGS基因重叠 (N=54).
- 观察到,与对照组相比,核心/关键PGS基因的罕见重复在较高的约束区域显著丰富 (P = 1×10-03).
- 证明这种丰富性并不存在于排名最低的基因组 (P = 0.15),突出了基于PGS的优先级的实用性.
结论:
- 该研究提供了一个强大的框架,用于注释长时间阅读的RSV,并优先考虑与疾病相关的基因组区域进行功能验证.
- 将rSV与PGS集成为减少候选基因列表和改善复杂遗传架构的罕见疾病诊断产量提供了一种强大的策略.
- 自由可用的SV等位基因频率和基因关联将促进更广泛的研究,加快罕见疾病遗传学的发现.
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