Background Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS 2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS 2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10 − 135 ) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10 − 129 ). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS 2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10 − 13 ). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS 2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS 2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women. Electronic supplementary material The online version of this article (10.1186/s12885-019-5783-1) contains supplementary material, which is available to authorized users.

Purpose Large-scale, population-based biobanks integrating health records and genomic profiles may provide a platform to identify individuals with disease-predisposing genetic variants. Here, we recall probands carrying familial hypercholesterolemia (FH)-associated variants, perform cascade screening of family members, and describe health outcomes affected by such a strategy. Methods The Estonian Biobank of Estonian Genome Center, University of Tartu, comprises 52,274 individuals. Among 4776 participants with exome or genome sequences, we identified 27 individuals who carried FH-associated variants in the LDLR , APOB , or PCSK9 genes. Cascade screening of 64 family members identified an additional 20 carriers of FH-associated variants. Results Via genetic counseling and clinical management of carriers, we were able to reclassify 51% of the study participants from having previously established nonspecific hypercholesterolemia to having FH and identify 32% who were completely unaware of harboring a high-risk disease-associated genetic variant. Imaging-based risk stratification targeted 86% of the variant carriers for statin treatment recommendations. Conclusion Genotype-guided recall of probands and subsequent cascade screening for familial hypercholesterolemia is feasible within a population-based biobank and may facilitate more appropriate clinical management.

doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Genotype-first approach allows to systematically identify carriers of pathogenic variants in BRCA1/2 genes conferring a high risk of familial breast and ovarian cancer. Participants of the Estonian biobank have expressed support for the disclosure of clinically significant findings. With an Estonian biobank cohort, we applied a genotype-first approach, contacted carriers and offered return of results with genetic counseling. We evaluated participants responses to and the clinical utility of the reporting of actionable genetic findings. Twenty-two of 40 contacted carriers of 17 pathogenic BRCA1/2 variants responded and chose to receive results. Eight of these 22 participants qualified for high-risk assessment based on National Comprehensive Cancer Network criteria. Twenty of 21 counseled participants appreciated being contacted. Relatives of 10 participants underwent cascade screening. Five of 16 eligible female BRCA1/2 variant carriers chose to undergo risk-reducing surgery, and 10 adhered to surveillance recommendations over the 30-month follow-up period. We recommend the return of results to population-based biobank participants; this approach could be viewed as a model for population-wide genetic testing. The genotype-first approach permits the identification of individuals at high risk who would not be identified by application of an approach based on personal and family histories only.

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