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Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing

BACKGROUND: 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 popul...

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Autores principales: Li, Shumin, Yan, Bin, Li, Thomas K. T., Lu, Jianliang, Gu, Yifan, Tan, Yueqiu, Gong, Fei, Lam, Tak-Wah, Xie, Pingyuan, Wang, Yuexuan, Lin, Ge, Luo, Ruibang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926832/
https://www.ncbi.nlm.nih.gov/pubmed/36788602
http://dx.doi.org/10.1186/s13073-023-01158-7
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author Li, Shumin
Yan, Bin
Li, Thomas K. T.
Lu, Jianliang
Gu, Yifan
Tan, Yueqiu
Gong, Fei
Lam, Tak-Wah
Xie, Pingyuan
Wang, Yuexuan
Lin, Ge
Luo, Ruibang
author_facet Li, Shumin
Yan, Bin
Li, Thomas K. T.
Lu, Jianliang
Gu, Yifan
Tan, Yueqiu
Gong, Fei
Lam, Tak-Wah
Xie, Pingyuan
Wang, Yuexuan
Lin, Ge
Luo, Ruibang
author_sort Li, Shumin
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-023-01158-7.
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spelling pubmed-99268322023-02-15 Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing Li, Shumin Yan, Bin Li, Thomas K. T. Lu, Jianliang Gu, Yifan Tan, Yueqiu Gong, Fei Lam, Tak-Wah Xie, Pingyuan Wang, Yuexuan Lin, Ge Luo, Ruibang Genome Med Research BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-023-01158-7. BioMed Central 2023-02-14 /pmc/articles/PMC9926832/ /pubmed/36788602 http://dx.doi.org/10.1186/s13073-023-01158-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Shumin
Yan, Bin
Li, Thomas K. T.
Lu, Jianliang
Gu, Yifan
Tan, Yueqiu
Gong, Fei
Lam, Tak-Wah
Xie, Pingyuan
Wang, Yuexuan
Lin, Ge
Luo, Ruibang
Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
title Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
title_full Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
title_fullStr Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
title_full_unstemmed Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
title_short Ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
title_sort ultra-low-coverage genome-wide association study—insights into gestational age using 17,844 embryo samples with preimplantation genetic testing
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926832/
https://www.ncbi.nlm.nih.gov/pubmed/36788602
http://dx.doi.org/10.1186/s13073-023-01158-7
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