Department of Computer Science, The University of Hong Kong, Hong Kong, China.
Department of Obstetrics & Gynecology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China.
Genome Med. 2023 Feb 14;15(1):10. doi: 10.1186/s13073-023-01158-7.
Very low-coverage (0.1 to 1×) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for genome-wide association study (GWAS). To support genetic screening using preimplantation genetic testing (PGT) in a large population, the sequencing coverage goes below 0.1× to an ultra-low level. However, the feasibility and effectiveness of ultra-low-coverage WGS (ulcWGS) for GWAS remains undetermined.
We built a pipeline to carry out analysis of ulcWGS data for GWAS. To examine its effectiveness, we benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1× and sample sizes from 2000 to 16,000, using 17,844 embryo PGT samples with approximately 0.04× average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1744 transferred embryos who have gestational ages and complete follow-up records to GWAS.
The accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus, and chromatin interaction strategies. Among these mapped genes, CRHBP, ICAM1, and OXTR were more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainly CRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1, and EGR2 with preterm birth, infant disease, and breast cancer.
This study not only demonstrates that ulcWGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into the associations between gestational age and genetic variations of the fetal embryos from Chinese population.
极低覆盖度（0.1 到 1×）全基因组测序（WGS）已成为一种有前途且经济实惠的方法，可用于发现人类群体的基因组变异以进行全基因组关联研究（GWAS）。为了支持大规模人群中使用植入前遗传学检测（PGT）进行遗传筛查，测序覆盖度降至 0.1×以下的超低水平。然而，超低覆盖度 WGS（ulcWGS）用于 GWAS 的可行性和有效性仍未确定。
我们构建了一个用于 ulcWGS 数据 GWAS 分析的管道。为了检验其有效性，我们使用 17844 个胚胎 PGT 样本（平均覆盖度约为 0.04×）和已知基因型的标准中国样本 HG005，在低于 0.1×的不同覆盖度和从 2000 到 16000 的样本量组合下，对不同覆盖度下的基因型推断准确性进行了基准测试。然后，我们将具有妊娠年龄和完整随访记录的 1744 个转移胚胎的推断基因型应用于 GWAS。
通过增加样本量和应用一系列过滤器，可以提高超低覆盖度下的基因型推断准确性。从 1744 个出生的胚胎中，我们确定了 11 个与妊娠年龄相关的基因组风险位点和 166 个映射到这些位点的基因，这些基因是根据位置、表达数量性状基因座和染色质相互作用策略确定的。在这些映射的基因中，CRHBP、ICAM1 和 OXTR 更频繁地被报道与早产有关。通过对先前研究的基因表达数据进行联合分析，我们构建了主要由 CRHBP、ICAM1、PLAGL1、DNMT1、CNTNL、DKK1 和 EGR2 组成的与早产、婴儿疾病和乳腺癌的相互关系。
本研究不仅证明 ulcWGS 可以实现足够高的基因型推断准确性，并且能够进行 GWAS，还提供了有关中国人群胎儿胚胎妊娠年龄和遗传变异之间关联的见解。
