Inference of human chromosome abnormalities taking into account the haplotype

Abstract

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.