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.

Genomics and epigenetics guided identification of tissue-specific genomic safe harbors.

BACKGROUND: Genomic safe harbors are regions of the genome that can maintain transgene expression without disrupting the function of host cells. Genomic safe harbors play an increasingly important role in improving the efficiency and safety of genome engineering. However, limited safe harbors have been identified. RESULTS: Here, we develop a framework to facilitate searches for genomic safe harbors by integrating information from polymorphic mobile element insertions that naturally occur in human populations, epigenomic signatures, and 3D chromatin organization. By applying our framework to polymorphic mobile element insertions identified in the 1000 Genomes project and the Genotype-Tissue Expression (GTEx) project, we identify 19 candidate safe harbors in blood cells and 5 in brain cells. For three candidate sites in blood, we demonstrate the stable expression of transgene without disrupting nearby genes in host erythroid cells. We also develop a computer program, Genomics and Epigenetic Guided Safe Harbor mapper (GEG-SH mapper), for knowledge-based tissue-specific genomic safe harbor selection. CONCLUSIONS: Our study provides a new knowledge-based framework to identify tissue-specific genomic safe harbors. In combination with the fast-growing genome engineering technologies, our approach has the potential to improve the overall safety and efficiency of gene and cell-based therapy in the near future.

Exome sequencing links CEP120 mutation to maternally derived aneuploid conception risk.

STUDY QUESTION: What are the genetic factors that increase the risk of aneuploid egg production? SUMMARY ANSWER: A non-synonymous variant rs2303720 within centrosomal protein 120 (CEP120) disrupts female meiosis in vitro in mouse. WHAT IS KNOWN ALREADY: The production of aneuploid eggs, with an advanced maternal age as an established contributing factor, is the major cause of IVF failure, early miscarriage and developmental anomalies. The identity of maternal genetic variants contributing to egg aneuploidy irrespective of age is missing. STUDY DESIGN, SIZE, DURATION: Patients undergoing fertility treatment (n = 166) were deidentified and selected for whole-exome sequencing. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients self-identified their ethnic groups and their ages ranged from 22 to 49 years old. The study was performed using genomes from White, non-Hispanic patients divided into controls (97) and cases (69) according to the number of aneuploid blastocysts derived during each IVF procedure. Following a gene prioritization strategy, a mouse oocyte system was used to validate the functional significance of the discovered associated genetic variants. MAIN RESULTS AND THE ROLE OF CHANCE: Patients producing a high proportion of aneuploid blastocysts (considered aneuploid if they missed any of the 40 chromatids or had extra copies) were found to carry a higher mutational burden in genes functioning in cytoskeleton and microtubule pathways. Validation of the functional significance of a non-synonymous variant rs2303720 within Cep120 on mouse oocyte meiotic maturation revealed that ectopic expression of CEP120:p.Arg947His caused decreased spindle microtubule nucleation efficiency and increased incidence of aneuploidy. LIMITATIONS, REASONS FOR CAUTION: Functional validation was performed using the mouse oocyte system. Because spindle building pathways differ between mouse and human oocytes, the defects we observed upon ectopic expression of the Cep120 variant may alter mouse oocyte meiosis differently than human oocyte meiosis. Further studies using knock-in 'humanized' mouse models and in human oocytes will be needed to translate our findings to human system. Possible functional differences of the variant between ethnic groups also need to be investigated. WIDER IMPLICATIONS OF THE FINDINGS: Variants in centrosomal genes appear to be important contributors to the risk of maternal aneuploidy. Functional validation of these variants will eventually allow prescreening to select patients that have better chances to benefit from preimplantation genetic testing. STUDY FUNDING/COMPETING INTEREST(S): This study was funded through R01-HD091331 to K.S. and J.X. and EMD Serono Grant for Fertility Innovation to N.R.T. N.R.T. is a shareholder and an employee of Genomic Prediction. TRIAL REGISTRATION NUMBER: N/A.