Preimplantation genetic testing (PGT) of in-vitro-fertilized embryos has been proposed as a method to reduce transmission of common disease; however, more comprehensive embryo genetic assessment, combining the effects of common variants and rare variants, remains unavailable. Here, we used a combination of molecular and statistical techniques to reliably infer inherited genome sequence in 110 embryos and model susceptibility across 12 common conditions. We observed a genotype accuracy of 99.0–99.4% at sites relevant to polygenic risk scoring in cases from day-5 embryo biopsies and 97.2–99.1% in cases from day-3 embryo biopsies. Combining rare variants with polygenic risk score (PRS) magnifies predicted differences across sibling embryos. For example, in a couple with a pathogenic BRCA1 variant, we predicted a 15-fold difference in odds ratio (OR) across siblings when combining versus a 4.5-fold or 3-fold difference with BRCA1 or PRS alone. Our findings may inform the discussion of utility and implementation of genome-based PGT in clinical practice.

10540 Background: Breast cancer (BC) risk is influenced by many common variants with small effects. Polygenic risk scores (PRS) weight these variants based on genome-wide association studies (GWAS) and aggregate them into a single measure. PRS has primarily shown benefit in Caucasian women. We established a cross-ancestry polygenic model (caPRS) which assesses risk of breast cancer across multiple ancestries. Methods: Performance of multiple BC polygenic models, both published and developed in-house, were evaluated for each of five ancestry groups: European, African, South Asian, East Asian, and Admixed American. To account for ancestry-specific mean and variance, we computed principal components (PCs) for all women by projecting their genotypes onto PCs calculated on individuals in the 1000 Genomes Project (1KGP). We next centered each ancestry-specific PRS by subtracting the PRS predicted from a linear regression of PRS against the first four PCs in unaffected individuals. Each centered PRS was then divided by the SD of the corresponding 1KGP population. We defined a cross-ancestry polygenic model as a linear combination of the best performing PRS model for each ancestry group weighted by fractional ancestry. Association of the caPRS with breast cancer risk was tested in a validation cohort of >130,000 women consisting of multiple independent cohorts (the Women’s Health Initiative, the Multiethnic Cohort, the ROOT cohort and the UK Biobank) using a multivariate logistic regression model that included caPRS, age, self-reported ancestry, personal history of ovarian cancer (when available) and first-degree family history of BC. Discrimination was assessed by the odds ratio (OR) per SD and the area under the receiver-operator curve (AUC). Results: This study included women with African/Black, East Asian, Caucasian/White, Hispanic/Latino, South Asian and ‘Other’ self-reported ancestry. The ancestry-specific models included in the caPRS ranged in size from 173 to >800,000 variants. The caPRS was associated with BC risk for women in each self-reported ancestry (Table). The caPRS offered a modest increase in performance over a commonly implemented 313-SNP PRS in non-European ancestries, most significantly in African/Black women where the OR per SD increased from 1.24 (1.08 - 1.43), p-value 2.3x10-3. Conclusions: The caPRS performed well for women of any ancestry and allows flexibility to update ancestry-specific models. These results suggest the caPRS has the potential to improve the clinical utility of existing clinical risk predictors. [Table: see text]

Background Diagnosis of rare genetic diseases can be a long, expensive and complex process, involving an array of tests in the hope of obtaining an actionable result. Long-read sequencing platforms offer the opportunity to make definitive molecular diagnoses using a single assay capable of detecting variants, characterizing methylation patterns, resolving complex rearrangements, and assigning findings to long-range haplotypes. Here, we demonstrate the clinical utility of Nanopore long-read sequencing by validating a confirmatory test for copy number variants (CNVs) in neurodevelopmental disorders and illustrate the broader applications of this platform to assess genomic features with significant clinical implications. Methods We used adaptive sampling on the Oxford Nanopore platform to sequence 25 genomic DNA samples and 5 blood samples collected from patients with known or false-positive copy number changes originally detected using short-read sequencing. Across the 30 samples (a total of 50 with replicates), we assayed 35 known unique CNVs (a total of 55 with replicates) and one false-positive CNV, ranging in size from 40 kb to 155 Mb, and assessed the presence or absence of suspected CNVs using normalized read depth. Results Across 50 samples (including replicates) sequenced on individual MinION flow cells, we achieved an average on-target mean depth of 9.5X and an average on-target read length of 4805 bp. Using a custom read depth-based analysis, we successfully confirmed the presence of all 55 known CNVs (including replicates) and the absence of one false-positive CNV. Using the same CNV-targeted data, we compared genotypes of single nucleotide variant loci to verify that no sample mix-ups occurred between assays. For one case, we also used methylation detection and phasing to investigate the parental origin of a 15q11.2-q13 duplication with implications for clinical prognosis. Conclusions We present an assay that efficiently targets genomic regions to confirm clinically relevant CNVs with a concordance rate of 100%. Furthermore, we demonstrate how integration of genotype, methylation, and phasing data from the Nanopore sequencing platform can potentially simplify and shorten the diagnostic odyssey.