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Detecting fetal subchromosomal aberrations by MPS: An unexpected discrepancy between amniocyte DNA and ccffDNA

机译:MPS检测胎儿亚染色体畸变:羊膜细胞DNA与ccffDNA之间的意外差异

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Because of apoptosis of placental cytotrophoblast cells, cell-free fetal DNA is released into the blood circulation of pregnant women. This circulating cell-free fetal DNA (ccffDNA) comprises about 10% of DNA in maternal plasma, making it a valuable source for noninvasive prenatal testing (NIPT).1 With the advent of massively parallel sequencing (MPS), it became feasible to overcome the challenge of analyzing fetal DNA in a predominant maternal background,2'3 opening the way for implementation of NIPT for whole chromosome aneuploidy detection in clinical practice. To date, the NIPT focus has been primarily on the detection of fetal whole chromosome aneuploidies, especially trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). The detection of subchromosomal aberrations is more challenging as it requires a higher sequencing depth and/or a relatively high percentage of fetal DNA in order to be feasible.4'5 A first proof-of-concept study6 used -243 x 10~6 reads to detect a 4.2-Mb deletion on chromosome 12, whereas in a second study, a 3-Mb 22qll.2 deletion was detected in two samples with 18% fetal DNA at a fourfold genomic coverage.7 However, in routine clinical practice, the genome-wide sequencing depth for fetal aneuploidy detection may be onefold or less. Chen et al. developed a bioinformatic approach for the detection of large deletions and duplications in such low coverage sequencing data.
机译:由于胎盘滋养层细胞的凋亡,无细胞的胎儿DNA被释放到孕妇的血液循环中。这种循环的无细胞胎儿DNA(ccffDNA)占母体血浆中DNA的约10%,使其成为无创产前检测(NIPT)的宝贵来源。1随着大规模并行测序(MPS)的出现,克服这一难题变得可行在主要的母体背景下分析胎儿DNA的挑战,2'3为临床实践中实施NIPT进行全染色体非整倍性检测提供了途径。迄今为止,NIPT的重点主要是检测胎儿的全染色体非整倍性,特别是21三体(唐氏综合征),18三体(爱德华兹综合征)和13三体(Patau综合征)。亚染色体畸变的检测更具挑战性,因为它需要更高的测序深度和/或相对较高的胎儿DNA百分比才能可行。4'5第一项概念验证研究6使用了-243 x 10〜6个读数以检测12号染色体上的4.2 Mb缺失,而在第二项研究中,在两个基因组覆盖率为四倍的18%胎儿DNA样品中检测到3-Mb 22qll.2缺失。7然而,在常规临床实践中,用于胎儿非整倍性检测的全基因组测序深度可以是一倍或更小。 Chen等。开发了一种生物信息学方法来检测这种低覆盖率测序数据中的大缺失和重复。

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