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en:projects [2023/12/12 03:10] – ariaden:projects [2023/12/12 03:12] (current) – ariad
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 Meiotic recombination is a fundamental source of human genetic diversity and is also critical for ensuring the accuracy of chromosome segregation. Understanding the landscape of meiotic recombination, its variation across individuals, and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring the landscape of recombination either rely on population genetic patterns of linkage disequilibrium (LD)—capturing a time-averaged view—or direct detection of crossovers in gametes or multi-generation pedigrees, limiting the scale and availability of relevant datasets. Here, we introduce an approach for inferring sex-specific landscapes of recombination from retrospective analysis of data from preimplantation genetic testing for aneuploidy (PGT-A), which is based on low-coverage (<0.05x) whole-genome sequencing of biopsies from in vitro fertilized (IVF) embryos. To overcome the sparsity of these data, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of chromosome loss in embryos, whereby the remaining chromosome is phased by default. LD-CHASE is a generalizable tool for mapping crossovers in low-coverage sequencing data from multiple siblings, toward a better understanding of the factors that modulate the meiotic crossover landscape and the role of recombination in the origins of aneuploidies. Meiotic recombination is a fundamental source of human genetic diversity and is also critical for ensuring the accuracy of chromosome segregation. Understanding the landscape of meiotic recombination, its variation across individuals, and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring the landscape of recombination either rely on population genetic patterns of linkage disequilibrium (LD)—capturing a time-averaged view—or direct detection of crossovers in gametes or multi-generation pedigrees, limiting the scale and availability of relevant datasets. Here, we introduce an approach for inferring sex-specific landscapes of recombination from retrospective analysis of data from preimplantation genetic testing for aneuploidy (PGT-A), which is based on low-coverage (<0.05x) whole-genome sequencing of biopsies from in vitro fertilized (IVF) embryos. To overcome the sparsity of these data, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of chromosome loss in embryos, whereby the remaining chromosome is phased by default. LD-CHASE is a generalizable tool for mapping crossovers in low-coverage sequencing data from multiple siblings, toward a better understanding of the factors that modulate the meiotic crossover landscape and the role of recombination in the origins of aneuploidies.
  
-[[https://www.doi.org/10.1101/gr.278168.123|Daniel Ariad, Svetlana Madjunkova, Mitko Madjunkov, Siwei Chen, Rina Abramov, Clifford Librach and Rajiv C McCoy. "Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos", Genome Res. January 2023]] | [[https://www.biorxiv.org/content/10.1101/2023.06.07.543910v2|bioRxiv, 2023.06.07.543910]]. +[[https://www.doi.org/10.1101/gr.278168.123|Daniel Ariad, Svetlana Madjunkova, Mitko Madjunkov, Siwei Chen, Rina Abramov, Clifford Librach and Rajiv C McCoy. "Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos." Genome Res. January 2023]] | [[https://www.biorxiv.org/content/10.1101/2023.06.07.543910v2|bioRxiv, 2023.06.07.543910]]. 
  
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 Extra or missing chromosomes—a phenomenon termed aneuploidy—frequently arises during human meiosis and embryonic mitosis and is the leading cause of pregnancy loss, including in the context of in vitro fertilization (IVF). While meiotic aneuploidies affect all cells and are deleterious, mitotic errors generate mosaicism, which may be compatible with healthy live birth. Large-scale abnormalities such as triploidy and haploidy also contribute to adverse pregnancy outcomes, but remain hidden from standard sequencing-based approaches to preimplantation genetic testing for aneuploidies (PGT-A). The ability to reliably distinguish meiotic and mitotic aneuploidies, as well as abnormalities in genome-wide ploidy may thus prove valuable for enhancing IVF outcomes. We developed a statistical method for distinguishing these forms of aneuploidy based on analysis of low-coverage whole-genome sequencing data, which is the current standard in the field. Our approach overcomes the data sparsity by leveraging allele frequencies and linkage disequilibrium (LD) measured in a population reference panel. The method, which we term LD- informed PGT-A (LD-PGTA), retains high accuracy down to coverage as low as 0.05x and at higher coverage can also distinguish between meiosis I and meiosis II errors based on signatures spanning the centromeres. In summary, our method complements current approaches for PGT-A, while also offering insight into the origins of chromosome abnormalities in human development. Extra or missing chromosomes—a phenomenon termed aneuploidy—frequently arises during human meiosis and embryonic mitosis and is the leading cause of pregnancy loss, including in the context of in vitro fertilization (IVF). While meiotic aneuploidies affect all cells and are deleterious, mitotic errors generate mosaicism, which may be compatible with healthy live birth. Large-scale abnormalities such as triploidy and haploidy also contribute to adverse pregnancy outcomes, but remain hidden from standard sequencing-based approaches to preimplantation genetic testing for aneuploidies (PGT-A). The ability to reliably distinguish meiotic and mitotic aneuploidies, as well as abnormalities in genome-wide ploidy may thus prove valuable for enhancing IVF outcomes. We developed a statistical method for distinguishing these forms of aneuploidy based on analysis of low-coverage whole-genome sequencing data, which is the current standard in the field. Our approach overcomes the data sparsity by leveraging allele frequencies and linkage disequilibrium (LD) measured in a population reference panel. The method, which we term LD- informed PGT-A (LD-PGTA), retains high accuracy down to coverage as low as 0.05x and at higher coverage can also distinguish between meiosis I and meiosis II errors based on signatures spanning the centromeres. In summary, our method complements current approaches for PGT-A, while also offering insight into the origins of chromosome abnormalities in human development.
  
-[[https://doi.org/10.1073/pnas.2109307118|Daniel Ariad, Stephanie M. Yan, Andrea R. Victor, Frank L. Barnes, Christo G. Zouves, Manuel Viotti and Rajiv C. McCoy. "Haplotype-aware inference of human chromosome abnormalities", PNAS November 16, 2021 118 (46) e2109307118]] | [[https://www.biorxiv.org/content/10.1101/2021.05.18.444721v3| bioRxiv:10.1101/2021.05.18.444721]] | The study is summarized in {{:research:posters:ld_pgta_poster.pdf|this poster}}+[[https://doi.org/10.1073/pnas.2109307118|Daniel Ariad, Stephanie M. Yan, Andrea R. Victor, Frank L. Barnes, Christo G. Zouves, Manuel Viotti and Rajiv C. McCoy. "Haplotype-aware inference of human chromosome abnormalities." PNAS November 16, 2021 118 (46) e2109307118]] | [[https://www.biorxiv.org/content/10.1101/2021.05.18.444721v3| bioRxiv:10.1101/2021.05.18.444721]] | The study is summarized in {{:research:posters:ld_pgta_poster.pdf|this poster}}
  
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en/projects.txt · Last modified: 2023/12/12 03:12 by ariad