Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing

Common variation in meiosis genes shapes human recombination phenotypes and aneuploidy risk

The leading cause of human pregnancy loss is aneuploidy, often tracing to errors in chromosome segregation during female meiosis. While abnormal crossover recombination is known to confer risk for aneuploidy, limited data have hindered understanding of the potential shared genetic basis of these key molecular phenotypes. To address this gap, we performed retrospective analysis of preimplantation genetic testing data from 139,416 in vitro fertilized embryos from 22,850 sets of biological parents. By tracing transmission of haplotypes, we identified 3,656,198 crossovers, as well as 92,485 aneuploid chromosomes. Counts of crossovers were lower in aneuploid versus euploid embryos, consistent with their role in chromosome pairing and segregation. Our analyses further revealed that a common haplotype spanning the meiotic cohesin SMC1B is significantly associated with both crossover count and maternal meiotic aneuploidy, with evidence supporting a non-coding cis-regulatory mechanism. Transcriptome- and phenome-wide association tests also implicated variation in the synaptonemal complex component C14orf39 and crossover-regulating ubiquitin ligases CCNB1IP1 and RNF212 in meiotic aneuploidy risk. More broadly, recombination and aneuploidy possess a partially shared genetic basis that also overlaps with reproductive aging traits. Our findings highlight the dual role of recombination in generating genetic diversity, while ensuring meiotic fidelity.

Maternal genetic variants in kinesin motor domains prematurely increase egg aneuploidy

The female reproductive lifespan is highly dependent on egg quality, especially the presence of a normal number of chromosomes in an egg, known as euploidy. Mistakes in meiosis leading to egg aneuploidy are frequent in humans. Yet, knowledge of the precise genetic landscape that causes egg aneuploidy in women is limited, as phenotypic data on the frequency of human egg aneuploidy are difficult to obtain and therefore absent in public genetic datasets. Here, we identify genetic determinants of reproductive aging via egg aneuploidy in women using a biobank of individual maternal exomes linked with maternal age and embryonic aneuploidy data. Using the exome data, we identified 404 genes bearing variants enriched in individuals with pathologically elevated egg aneuploidy rates. Analysis of the gene ontology and protein-protein interaction network implicated genes encoding the kinesin protein family in egg aneuploidy. We interrogate the causal relationship of the human variants within candidate kinesin genes via experimental perturbations and demonstrate that motor domain variants increase aneuploidy in mouse oocytes. Finally, using a knock-in mouse model, we validate that a specific variant in kinesin KIF18A accelerates reproductive aging and diminishes fertility. These findings reveal additional functional mechanisms of reproductive aging and shed light on how genetic variation underlies individual heterogeneity in the female reproductive lifespan, which might be leveraged to predict reproductive longevity. Together, these results lay the groundwork for the noninvasive biomarkers for egg quality, a first step toward personalized fertility medicine.

Maternal genetic variants in kinesin motor domains prematurely increase egg aneuploidy

The female reproductive lifespan depends on egg quality, particularly euploidy. Mistakes in meiosis leading to egg aneuploidy are common, but the genetic landscape causing this is not well understood due to limited phenotypic data. We identify genetic determinants of reproductive aging via egg aneuploidy using a biobank of maternal exomes linked with maternal age and embryonic aneuploidy data. We found 404 genes with variants enriched in individuals with high egg aneuploidy rates and implicate kinesin protein family genes in aneuploidy risk. Experimental perturbations showed that motor domain variants in these genes increase aneuploidy in mouse oocytes. A knock-in mouse model validated that a specific variant in kinesin KIF18A accelerates reproductive aging and diminishes fertility. These findings suggest potential non-invasive biomarkers for egg quality, aiding personalized fertility medicine.

One sentence summary

The study identifies novel genetic determinants of reproductive aging linked to egg aneuploidy by analyzing maternal exomes and demonstrates that variants in kinesin genes, specifically KIF18A , contribute to increased aneuploidy and accelerated reproductive aging, offering potential for personalized fertility medicine.

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.

Exome sequencing to explore the possibility of predicting genetic susceptibility to the joint occurrence of polycystic ovary syndrome and Hashimoto's thyroiditis

A large body of evidence indicates that women with polycystic ovary syndrome (PCOS) have a higher risk of developing Hashimoto's thyroiditis (HT) than healthy individuals. Given the strong genetic impact on both diseases, common predisposing genetic factors are possibly involved but are not fully understood. Here, we performed whole-exome sequencing (WES) for 250 women with sporadic PCOS, HT, combined PCOS and HT (PCOS+HT), and healthy controls to explore the genetic background of the joint occurrence of PCOS and HT. Based on relevant comparative analyses, multivariate logistic regression prediction modeling, and the most informative feature selection using the Monte Carlo feature selection and interdependency discovery algorithm, 77 variants were selected for further validation by TaqMan genotyping in a group of 533 patients. In the allele frequency test, variants in RAB6A, GBP3, and FNDC7 genes were found to significantly (padjusted < 0.05) differentiated the PCOS+HT and PCOS groups, variant in HIF3A differentiated the PCOS+HT and HT groups, whereas variants in CDK20 and CCDC71 differentiated the PCOS+HT and both single disorder groups. TaqMan genotyping data were used to create final prediction models, which differentiated between PCOS+HT and PCOS or HT with a prediction accuracy of AUC = 0.78. Using a 70% cutoff of the prediction score improved the model parameters, increasing the AUC value to 0.87. In summary, we demonstrated the polygenic burden of both PCOS and HT, and many common and intersecting signaling pathways and biological processes whose disorders mutually predispose patients to the development of both diseases.

Identification of novel candidate genes associated with meiotic aneuploidy in human embryos by whole-exome sequencing

PURPOSE

To identify novel genetic variants responsible for meiotic embryonic aneuploidy.

METHODS

A prospective observational cohort study that included 29 couples who underwent trophectoderm biopsies from 127 embryos and performed whole-exome sequencing (WES) between November 2019 and March 2022. Patients were divided into two groups according to the expected embryo aneuploidy rate based on maternal age.

RESULTS

After variant filtering in the WES analysis of 58 patients/donors, five heterozygous variants were identified in female partners from the study group that had an impact on embryo aneuploidy. Additionally, a slowdown in embryo development and a decrease in the number of blastocysts available for biopsy were observed in the study group embryos.

CONCLUSION

This study has identified new candidate genes and variants not previously associated with meiotic embryo aneuploidy, but which are involved in important biological processes related to cell division and chromosome segregation. WES may be an efficient tool to identify patients with a higher-than-expected risk of embryo aneuploidy based on maternal age and allow for individualized genetic counselling prior to treatment.

Common genetic risk factors in ASD and ADHD co-occurring families

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two major neurodevelopmental disorders that frequently co-occur. However, the genetic mechanism of the co-occurrence remains unclear. The New Jersey Language and Autism Genetics Study (NJLAGS) collected more than 100 families with at least one member affected by ASD. NJLAGS families show a high prevalence of ADHD and provide a good opportunity to study shared genetic risk factors for ASD and ADHD. The linkage study of the NJLAGS families revealed regions on chromosomes 12 and 17 that are significantly associated with ADHD. Using whole-genome sequencing data on 272 samples from 73 NJLAGS families, we identified potential risk genes for ASD and ADHD. Within the linkage regions, we identified 36 genes that are associated with ADHD using a pedigree-based gene prioritization approach. KDM6B (Lysine Demethylase 6B) is the highest-ranking gene, which is a known risk gene for neurodevelopmental disorders, including ASD and ADHD. At the whole-genome level, we identified 207 candidate genes from the analysis of both small variants and structure variants, including both known and novel genes. Using enrichment and protein-protein interaction network analyses, we identified gene ontology terms and pathways enriched for ASD and ADHD candidate genes, such as cilia function and cation channel activity. Candidate genes and pathways identified in our study improve the understanding of the genetic etiology of ASD and ADHD and will lead to new diagnostic or therapeutic interventions for ASD and ADHD in the future.

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3' untranslated region (3' UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3' UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein-protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies.