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Whole-genome risk prediction of common diseases in human preimplantation embryos

Preimplantation genetic testing (PGT) of in-vitro-fertilized embryos has been proposed as a method to reduce transmission of common disease; however, more comprehensive embryo genetic assessment, combining the effects of common variants and rare variants, remains unavailable. Here, we used a combina...

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Autores principales: Kumar, Akash, Im, Kate, Banjevic, Milena, Ng, Pauline C., Tunstall, Tate, Garcia, Geronimo, Galhardo, Luisa, Sun, Jiayi, Schaedel, Oren N., Levy, Brynn, Hongo, Donna, Kijacic, Dusan, Kiehl, Michelle, Tran, Nam D., Klatsky, Peter C., Rabinowitz, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938270/
https://www.ncbi.nlm.nih.gov/pubmed/35314819
http://dx.doi.org/10.1038/s41591-022-01735-0
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author Kumar, Akash
Im, Kate
Banjevic, Milena
Ng, Pauline C.
Tunstall, Tate
Garcia, Geronimo
Galhardo, Luisa
Sun, Jiayi
Schaedel, Oren N.
Levy, Brynn
Hongo, Donna
Kijacic, Dusan
Kiehl, Michelle
Tran, Nam D.
Klatsky, Peter C.
Rabinowitz, Matthew
author_facet Kumar, Akash
Im, Kate
Banjevic, Milena
Ng, Pauline C.
Tunstall, Tate
Garcia, Geronimo
Galhardo, Luisa
Sun, Jiayi
Schaedel, Oren N.
Levy, Brynn
Hongo, Donna
Kijacic, Dusan
Kiehl, Michelle
Tran, Nam D.
Klatsky, Peter C.
Rabinowitz, Matthew
author_sort Kumar, Akash
collection PubMed
description Preimplantation genetic testing (PGT) of in-vitro-fertilized embryos has been proposed as a method to reduce transmission of common disease; however, more comprehensive embryo genetic assessment, combining the effects of common variants and rare variants, remains unavailable. Here, we used a combination of molecular and statistical techniques to reliably infer inherited genome sequence in 110 embryos and model susceptibility across 12 common conditions. We observed a genotype accuracy of 99.0–99.4% at sites relevant to polygenic risk scoring in cases from day-5 embryo biopsies and 97.2–99.1% in cases from day-3 embryo biopsies. Combining rare variants with polygenic risk score (PRS) magnifies predicted differences across sibling embryos. For example, in a couple with a pathogenic BRCA1 variant, we predicted a 15-fold difference in odds ratio (OR) across siblings when combining versus a 4.5-fold or 3-fold difference with BRCA1 or PRS alone. Our findings may inform the discussion of utility and implementation of genome-based PGT in clinical practice.
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spelling pubmed-89382702022-04-07 Whole-genome risk prediction of common diseases in human preimplantation embryos Kumar, Akash Im, Kate Banjevic, Milena Ng, Pauline C. Tunstall, Tate Garcia, Geronimo Galhardo, Luisa Sun, Jiayi Schaedel, Oren N. Levy, Brynn Hongo, Donna Kijacic, Dusan Kiehl, Michelle Tran, Nam D. Klatsky, Peter C. Rabinowitz, Matthew Nat Med Article Preimplantation genetic testing (PGT) of in-vitro-fertilized embryos has been proposed as a method to reduce transmission of common disease; however, more comprehensive embryo genetic assessment, combining the effects of common variants and rare variants, remains unavailable. Here, we used a combination of molecular and statistical techniques to reliably infer inherited genome sequence in 110 embryos and model susceptibility across 12 common conditions. We observed a genotype accuracy of 99.0–99.4% at sites relevant to polygenic risk scoring in cases from day-5 embryo biopsies and 97.2–99.1% in cases from day-3 embryo biopsies. Combining rare variants with polygenic risk score (PRS) magnifies predicted differences across sibling embryos. For example, in a couple with a pathogenic BRCA1 variant, we predicted a 15-fold difference in odds ratio (OR) across siblings when combining versus a 4.5-fold or 3-fold difference with BRCA1 or PRS alone. Our findings may inform the discussion of utility and implementation of genome-based PGT in clinical practice. Nature Publishing Group US 2022-03-21 2022 /pmc/articles/PMC8938270/ /pubmed/35314819 http://dx.doi.org/10.1038/s41591-022-01735-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kumar, Akash
Im, Kate
Banjevic, Milena
Ng, Pauline C.
Tunstall, Tate
Garcia, Geronimo
Galhardo, Luisa
Sun, Jiayi
Schaedel, Oren N.
Levy, Brynn
Hongo, Donna
Kijacic, Dusan
Kiehl, Michelle
Tran, Nam D.
Klatsky, Peter C.
Rabinowitz, Matthew
Whole-genome risk prediction of common diseases in human preimplantation embryos
title Whole-genome risk prediction of common diseases in human preimplantation embryos
title_full Whole-genome risk prediction of common diseases in human preimplantation embryos
title_fullStr Whole-genome risk prediction of common diseases in human preimplantation embryos
title_full_unstemmed Whole-genome risk prediction of common diseases in human preimplantation embryos
title_short Whole-genome risk prediction of common diseases in human preimplantation embryos
title_sort whole-genome risk prediction of common diseases in human preimplantation embryos
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938270/
https://www.ncbi.nlm.nih.gov/pubmed/35314819
http://dx.doi.org/10.1038/s41591-022-01735-0
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