Erlich Henry A, Ko Lily, Lee Jiyae, Eaton Katrina, Calloway Cassandra D, Lal Ashutosh, Das Reena, Jamwal Manu, Lopez-Pena Christian, Mack Steven J
Henry A. Erlich, UCSF Benioff Children's Hospital Oakland Research Institute, 747 52nd St, Oakland, CA 94609, USA,
Croat Med J. 2024 Jun 13;65(3):180-188. doi: 10.3325/cmj.2024.65.180.
To develop a non-invasive prenatal test for beta-hemoglobinopathies based on analyzing maternal plasma by using next generation sequencing.
We applied next generation sequencing (NGS) of maternal plasma to the non-invasive prenatal testing (NIPT) of autosomal recessive diseases, sickle cell disease and beta-thalassemia. Using the Illumina MiSeq, we sequenced plasma libraries obtained via a Twist Bioscience probe capture panel covering 4 Kb of chromosome 11, including the beta-globin (HBB) gene and >450 genomic single-nucleotide polymorphisms (SNPs) used to estimate the fetal fraction (FF). The FF is estimated by counting paternally transmitted allelic sequence reads present in the plasma but absent in the mother. We inferred fetal beta-globin genotypes by comparing the observed mutation (Mut) and reference (Ref) read ratios to those expected for the three possible fetal genotypes (Mut/Mut; Mut/Ref; Ref/Ref), based on the FF.
We bioinformatically enriched the FF by excluding reads over a specified length via in-silico size selection (ISS), favoring the shorter fetal reads, which increased fetal genotype prediction accuracy. Finally, we determined the parental HBB haplotypes, which allowed us to use the read ratios observed at linked SNPs to help predict the fetal genotype at the mutation site(s). We determined HBB haplotypes via Oxford Nanopore MinION sequencing of a 2.2 kb amplicon and aligned these sequences using Soft Genetics' NextGENe LR software.
The combined use of ISS and HBB haplotypes enabled us to correctly predict fetal genotypes in cases where the prediction based on variant read ratios alone was incorrect.
基于下一代测序分析孕妇血浆,开发一种用于β-地中海贫血的非侵入性产前检测方法。
我们将孕妇血浆的下一代测序(NGS)应用于常染色体隐性疾病、镰状细胞病和β-地中海贫血的非侵入性产前检测(NIPT)。使用Illumina MiSeq,我们对通过Twist Bioscience探针捕获面板获得的血浆文库进行测序,该面板覆盖11号染色体的4 kb,包括β-珠蛋白(HBB)基因和>450个用于估计胎儿分数(FF)的基因组单核苷酸多态性(SNP)。通过计算血浆中存在但母亲中不存在的父系遗传等位基因序列读数来估计FF。基于FF,通过将观察到的突变(Mut)和参考(Ref)读数比率与三种可能的胎儿基因型(Mut/Mut;Mut/Ref;Ref/Ref)预期的比率进行比较,推断胎儿β-珠蛋白基因型。
我们通过基于计算机模拟的大小选择(ISS)排除指定长度以上的读数,在生物信息学上富集了FF,有利于较短的胎儿读数,这提高了胎儿基因型预测的准确性。最后,我们确定了父母的HBB单倍型,这使我们能够利用在连锁SNP处观察到的读数比率来帮助预测突变位点的胎儿基因型。我们通过对一个2.2 kb扩增子进行牛津纳米孔MinION测序确定HBB单倍型,并使用Soft Genetics的NextGENe LR软件对这些序列进行比对。
ISS和HBB单倍型的联合使用使我们能够在仅基于变异读数比率的预测不正确的情况下正确预测胎儿基因型。