Haplotype-aware inference of human chromosome abnormalities

Significance Whole-chromosome gains and losses (aneuploidies) are the leading cause of human pregnancy loss and congenital disorders. Recent work has demonstrated that in addition to harmful meiotic aneuploidies, mitotic aneuploidies (which lead to mosaic embryos harboring cells with different numbers of chromosomes) may also be common in preimplantation embryos but potentially compatible with healthy birth. Here we developed and tested a method for distinguishing these forms of aneuploidy using genetic testing data from 8,154 in vitro fertilization (IVF) embryos. We reclassified embryos based on signatures of meiotic and mitotic error, while also revealing lethal forms of chromosome abnormality that were previously hidden. Our method complements standard protocols for preimplantation genetic testing, while offering insight into the biology of early development. Extra or missing chromosomes—a phenomenon termed aneuploidy—frequently arise during human meiosis and embryonic mitosis and are 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 aneuploidy (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. Here, we describe 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 sparse nature of the data 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 0.05 × and at higher coverage can also distinguish between meiosis I and meiosis II errors based on signatures spanning the centromeres. LD-PGTA provides fundamental insight into the origins of human chromosome abnormalities, as well as a practical tool with the potential to improve genetic testing during IVF.