Inference of human chromosome abnormalities taking into account the haplotype



Gains and losses of whole chromosomes (aneuploidies) are the leading cause of miscarriages in humans and birth defects. Recent work has shown that in addition to harmful meiotic aneuploidies, mitotic aneuploidies (which lead to mosaic embryos harboring cells with different chromosome numbers) can also be common in preimplantation embryos but potentially compatible with healthy birth. Here, we have developed and tested a method to distinguish these forms of aneuploidy using genetic test data from 8,154 in vitro fertilization (IVF) embryos. We reclassified the embryos based on meiotic and mitotic error signatures, while also revealing fatal forms of chromosomal abnormalities that were previously hidden. Our method complements standard protocols for preimplantation genetic testing, while providing insight into the biology of early development.


Extra or missing chromosomes – a phenomenon called aneuploidy – frequently occur during human meiosis and embryonic mitosis and are the main cause of miscarriage, 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 a 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 for preimplantation genetic testing for aneuploidy (PGT-A). The ability to reliably distinguish between meiotic and mitotic aneuploidies, as well as genome-wide ploidy abnormalities, may therefore prove useful in improving IVF outcomes. Here, we describe a statistical method to distinguish 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 sparse nature of the data by taking advantage of allele frequencies and binding imbalance (LD) measured in a population reference panel. The method, which we call LD-informed PGT-A (LD-PGTA), keeps high precision down to coverage as low as 0.05 × and at higher coverage, it can also distinguish between errors meiosis I and meiosis II based on signatures spanning the centromeres. LD-PGTA provides fundamental insight into the origins of human chromosomal abnormalities, as well as a practical tool with the potential to improve genetic testing during IVF.


    • Accepted September 16, 2021.
  • Author contributions: research designed by DA, MV and RCM; DA and RCM have done research; ARV, FLB and CGZ provided new reagents / analytical tools; DA and RCM analyzed the data; DA, SMY, MV and RCM wrote the article; and ARV, FLB, CGZ and MV carried out the data collection and retention.

  • Declaration of Competing Interests: DA, MV and RCM are the co-inventors of the method described here, which is the subject of a provisional patent application by Johns Hopkins University.

  • This article is a direct PNAS submission.

  • This article contains additional information online at


About Alma Ackerman

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