Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
Department of Genetics, Yale School of Medicine, New Haven, CT, 06520, USA.
Hum Genomics. 2024 Mar 14;18(1):25. doi: 10.1186/s40246-024-00590-z.
With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.
随着下一代测序技术的发展,可以识别具有有害影响的新生变异(DNVs),并研究其对先天性心脏病(CHD)等出生缺陷的影响。然而,由于招募和测序样本的成本高,以及 DNV 的发生率低,导致样本量小,因此此类研究的统计能力仍然有限。DNV 分析因患病个体之间的遗传异质性而变得更加复杂。因此,联合分析 DNV 与其他类型的基因组/生物学信息对于提高识别与出生缺陷相关基因的统计能力至关重要。在这篇综述中,我们讨论了 DNV 分析的一般工作流程、统计方法的最新进展以及未来的发展方向。