Ultra-low-coverage genome-wide association study-insights into gestational age using 17,844 embryo samples with preimplantation genetic testing

Low-pass whole genome sequencing is a reliable and cost-effective approach for copy number variant analysis in the clinical setting

INTRODUCTION

Next generation sequencing technology has greatly reduced the cost and time required for sequencing a genome. An approach that is rapidly being adopted as an alternative method for CNV analysis is the low-pass whole genome sequencing (LP-WGS). Here, we evaluated the performance of LP-WGS to detect copy number variants (CNVs) in clinical cytogenetics.

MATERIALS AND METHODS

DNA samples with known CNVs detected by chromosomal microarray analyses (CMA) were selected for comparison and used as positive controls; our panel included 44 DNA samples (12 prenatal and 32 postnatal), comprising a total of 55 chromosome imbalances. The selected cases were chosen to provide a wide range of clinically relevant CNVs, the vast majority being associated with intellectual disability or recognizable syndromes. The chromosome imbalances ranged in size from 75 kb to 90.3 Mb, including aneuploidies and two cases of mosaicism.

RESULTS

All CNVs were successfully detected by LP-WGS, showing a high level of consistency and robust performance of the sequencing method. Notably, the size of chromosome imbalances detected by CMA and LP-WGS were compatible between the two different platforms, which indicates that the resolution and sensitivity of the LP-WGS approach are at least similar to those provided by CMA.

DISCUSSION

Our data show the potential use of LP-WGS to detect CNVs in clinical diagnosis and confirm the method as an alternative for chromosome imbalances detection. The diagnostic effectiveness and feasibility of LP-WGS, in this technical validation study, were evidenced by a clinically representative dataset of CNVs that allowed a systematic assessment of the detection power and the accuracy of the sequencing approach. Further, since the software used in this study is commercially available, the method can easily be tested and implemented in a routine diagnostic setting.

Identifying risk variants for embryo aneuploidy using ultra-low coverage whole-genome sequencing from preimplantation genetic testing

Aneuploidy frequently arises during human meiosis and is the primary cause of early miscarriage and in vitro fertilization (IVF) failure. Individuals undergoing IVF exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using next-generation sequencing is a standard test for identifying and selecting IVF-derived euploid embryos. The wealth of embryo aneuploidy data and ultra-low coverage whole-genome sequencing (ulc-WGS) data from PGT-A have the potential to discover variants in parental genomes that are associated with aneuploidy risk in their embryos. Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in embryo genomes. We then used the imputed variants and embryo aneuploidy calls to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system. We identified one locus on chromosome 3 that is significantly associated with meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice. Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that has potential to be leveraged for similar association studies that use ulc-WGS data.

Identifying risk genes for embryo aneuploidy using ultra-low coverage whole-genome sequencing

Background

Aneuploidy, the state of a cell containing extra or missing chromosomes, frequently arises during human meiosis and is the primary cause of early miscarriage and maternal age-related in vitro fertilization (IVF) failure. IVF patients exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using ultra-low coverage whole-genome sequencing (ulc-WGS) is a standard test for identifying and selecting IVF-derived embryos with a normal chromosome complement. The wealth of embryo aneuploidy data and ulc-WGS data from PGT-A has potential for discovering variants in paternal genomes that are associated with aneuploidy risk in their embryos.

Methods

Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in parental genomes. We then used the imputed variants and aneuploidy calls from the embryos to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system.

Results

We identified one locus on chromosome 3 that is significantly associated with maternal meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice.

Conclusions

Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that can be leveraged for similar association studies using ulc-WGS data.