Statistical methods for assessing the effects of de novo variants on birth defects

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY
Yuhan Xie, Ruoxuan Wu, Hongyu Li, Weilai Dong, Geyu Zhou, Hongyu Zhao
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引用次数: 0

Abstract

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.
评估新生变异对出生缺陷影响的统计方法
随着下一代测序技术的发展,具有有害影响的新生变异(DNVs)可以被识别出来,并研究它们对先天性心脏病(CHD)等出生缺陷的影响。然而,由于招募和测序样本的成本较高,样本量较小,且 DNV 的发生率较低,因此此类研究的统计能力仍然有限。患病个体间的遗传异质性使 DNV 分析变得更加复杂。因此,将 DNV 与其他类型的基因组/生物学信息联合分析,以提高识别出生缺陷相关基因的统计能力至关重要。在这篇综述中,我们将讨论 DNV 分析的一般工作流程、统计方法的最新进展和未来方向。
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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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