School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; Shenzhen Key Laboratory of Pathogenic Microbes and Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; BGI-Shenzhen, Shenzhen 518083, Guangdong, China; Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Cell Genom. 2024 Oct 9;4(10):100669. doi: 10.1016/j.xgen.2024.100669.
Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.
非侵入性产前检测 (NIPT) 采用母体血浆游离 DNA 的超低深度测序来检测胎儿三体。它的全球应用使 NIPT 成为一个大型人类遗传资源，用于探索遗传变异及其与表型的关联。在这里，我们提出了分析大规模、低深度 NIPT 数据的方法，包括用于遗传变异检测、基因型推断、家族相关性、群体结构推断和母体基因组全基因组关联分析的定制算法和软件。我们的结果表明，对于 0.1×至 0.3×的 NIPT 测序深度，能够准确估计等位基因频率并实现高基因型推断准确性（R>0.84）。我们还能够有效地对重复样本和一级亲属进行分类，并进行稳健的主成分分析。此外，我们还可以在具有足够样本量的基因分型和测序平台上，以 R>0.81 的精度估计遗传效应大小。这些方法为在医学遗传研究中利用 NIPT 数据提供了坚实的理论和实践基础。
