辅助生殖领域中的精准医学

doi:10.12280/gjszjk.20200412

摘要/Abstract

摘要：

精准医学及其前沿技术促进了生殖医学的发展。高通量测序、全基因组关联研究（GWAS）、蛋白质组学技术成功用于不孕不育症的病因诊断、遗传病检测和胚胎植入前遗传学检测（PGT）的临床实践,单细胞全基因组扩增（eWGA）、生物大数据分析等近年也应用于人类配子和胚胎早期发育的研究。例如通过GWAS确定了11个基因座、17个单核苷酸多态性（SNPs）与多囊卵巢综合征（PCOS）有很强的相关性;eWGA应用到PGT中,就可通过分析植入前胚胎中的单个细胞推断出卵子本身的全部基因组信息,从而减少先天性遗传缺陷婴儿的出生,降低辅助生殖的医源性风险;人工智能辅助（AI）方法在体外受精-胚胎移植过程中用于卵母细胞或胚胎选择,显示出越来越大的优势。本文综述精准医学在辅助生殖领域中的应用。

关键词: 精准医学, 生殖技术,辅助, 高通量核苷酸序列分析, 不育, 基因组学, 人工智能

Abstract:

Precision medicine and its advanced technologies have promoted the development of reproductive medicine. The high-throughput sequencing, genome-wide association studies (GWAS), proteomics technology have been successfully used in the etiological diagnosis of infertility, the detection of genetic disease and the pre-implantation genetic testing (PGT) in clinical practice, and the single-celled whole genome amplification (eWGA), biological big data analysis and so on have been also applied in the research of human gamete and early embryo development in recent years. For example, 11 loci and 17 single nucleotide polymorphisms (SNPs) were identified by GWAS, which are strongly correlated with PCOS. When eWGA is applied to PGT, the whole genome information of the egg itself can be deduced by analyzing a single cell obtained from the pre-implantation embryo, so as to reduce the birth with congenital genetic defects and to reduce the iatrogenic risk of assisted reproduction. The artificial intelligence-assisted (AI) method for oocyte or embryo selection during in vitro fertilization-embryo transfer are showing increasing its advantages. This paper reviews the application of precision medicine in assisted reproduction.

Key words: Precision medicine, Reproductive techniques,assisted, High-throughput nucleotide sequencing, Infertility, Genomics, Artificial intelligence

郎鹏, 时晓丹, 张军强. 辅助生殖领域中的精准医学[J]. 国际生殖健康/计划生育, 2021, 40(2): 121-125.

LANG Peng, SHI Xiao-dan, ZHANG Jun-qiang. Precision Medicine in the Field of Assisted Reproduction[J]. Journal of International Reproductive Health/Family Planning, 2021, 40(2): 121-125.

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