Haplotype-aware inference of human chromosome abnormalities

Abstract

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 (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 0.05x 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.

Parental genomes segregate into distinct blastomeres during multipolar zygotic divisions leading to mixoploid and chimeric blastocysts

“…To select for euploid embryos, which have a higher implantation and baby-take home rate as compared to aneuploid embryos, preimplantation genetic testing for aneuploidies (PGT-A) has become a standard procedure in many human IVF laboratories world-wide. However, most PGT-A methods only interrogate chromosomal copy number changes and do not determine the haplotypes [73][74][75]. Thanks to the implementation of single-cell haplotyping methods in PGT, the existence of androgenetic, gynogenetic, and triploid embryo biopsies has been uncovered [14,73,[76][77][78].…”

Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos

“…Because data from PGT-A typically fall well below these coverage requirements, they are generally assumed unsuitable for applications that demand genotypes, including the study of recombination landscapes in embryos. However, as exemplified by common methods such as genotype imputation [27], knowledge of patterns of LD from external population genetic reference panels may facilitate the extraction of meaningful signal from sparse, low-coverage datasets, including in the context of prenatal genetics [28,29].…”

“…Abnormal number or location of meiotic crossovers between homologous chromosomes may predispose oocytes to aneuploidy, as demonstrated by several previous studies [reviewed in 40]. Our published method, LD-PGTA [28], facilitates the mapping of crossovers on trisomic chromosomes, which can then be compared to the crossover map for disomic chromosomes obtained via LD-CHASE. One caveat of this comparison is that LD-PGTA and LD-CHASE possess different sensitivities and specificities, which also vary along the genome, as evident from our simulation-based benchmarking analyses (Figures S3, S4, S13 and S14).…”

“…In extreme cases such as triploidy and haploidy/GW-isoUPD, where coverage is uniform across the genome despite the ploidy aberration, embryos may be erroneously classified as euploid. To overcome this limitation, we applied our published haplotype-aware method, LD-PGTA [28],…”

“…We define true positives (negatives) as simulations where sequences generated under the "unmatched" ("matched") haplotypes hypothesis are correctly classified. Based on these simulations, we generate balanced receiver operating characteristic (ROC) curves for each bin [28]. The balanced true and false positive rates for a bin are defined as…”