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]

PURPOSE To develop and validate a cross-ancestry integrated risk score (caIRS) that combines a cross-ancestry polygenic risk score (caPRS) with a clinical estimator for breast cancer (BC) risk. We hypothesized that the caIRS is a better predictor of BC risk than clinical risk factors across diverse ancestry groups. METHODS We used diverse retrospective cohort data with longitudinal follow-up to develop a caPRS and integrate it with the Tyrer-Cuzick (T-C) clinical model. We tested the association between the caIRS and BC risk in two validation cohorts including > 130,000 women. We compared model discrimination for 5-year and remaining lifetime BC risk between the caIRS and T-C and assessed how the caIRS would affect screening in the clinic. RESULTS The caIRS outperformed T-C alone for all populations tested in both validation cohorts and contributed significantly to risk prediction beyond T-C. The area under the receiver operating characteristic curve improved from 0.57 to 0.65, and the odds ratio per standard deviation increased from 1.35 (95% CI, 1.27 to 1.43) to 1.79 (95% CI, 1.70 to 1.88) in validation cohort 1 with similar improvements observed in validation cohort 2. We observed the largest gain in positive predictive value using the caIRS in Black/African American women across both validation cohorts, with an approximately two-fold increase and an equivalent negative predictive value as the T-C. In a multivariate, age-adjusted logistic regression model including both caIRS and T-C, caIRS remained significant, indicating that caIRS provides information over T-C alone. CONCLUSION Adding a caPRS to the T-C model improves BC risk stratification for women of multiple ancestries, which could have implications for screening recommendations and prevention.