Weng, Chunhua

Abstract Objectives To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions. Materials and Methods A five-stage process was executed at ten sites. Data streams from REDCap surveys, PRS and monogenic reports, and MeTree pedigrees were normalized and forwarded through a REDCap plug-in to the CanRisk API. Results Integrated risk was returned to >10 000 women; 3.6% were ≥25 % lifetime risk and 0.9% carried pathogenic variants. Pipeline generated score aligns well with manual generated ones. Major barriers such as heterogeneous pedigree formats, missing data, edge-case handling, and evolving model versions were identified and resolved through mapping rules, imputations, and iterative testing. Discussion Cross-platform data harmonization and stakeholder alignment were decisive for success. Borderline-risk communication and model-version drift remain open issues. Conclusion Large-scale PRS-integrated breast-cancer risk reporting is feasible but requires robust interoperability standards and iterative governance.

Abstract Importance Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. The most common forms of monogenic kidney disorders include autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN), caused by mutations in COL4A3, COL4A4, or COL4A5 genes. These disorders have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors. Objective Our objectives were to investigate the interplay of polygenic and monogenic risk of kidney disease and to test the utility of polygenic risk scores for risk stratification in ADPKD and COL4-AN. Design We combined exome/genome sequencing, SNP micro-array, and electronic health record data from the UK Biobank (N = 469,835 participants) and the All of Us (N = 98,622 participants) datasets to test the effects of the genome-wide polygenic score (GPS) for CKD in ADPKD and COL4-AN variant carriers defined by strict variant classification criteria. We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 or kidney failure) and controls (eGFR > 90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The GPS was tested as a predictor of CKD in qualifying variant carriers and non-carriers using logistic regression after adjustment for age, sex, diabetes, and genetic ancestry. Results The GPS was predictive of CKD in ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55–3.37, P = 2.6E-05) as well as non-carriers (ORmeta=1.72 per SD, 95% CI = 1.69–1.76, P < E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN carriers (ORmeta=1.78, 95% CI = 1.22–2.58, P = 2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68–1.73 P < E-300). The carriers in the top GPS tertile had a 2.5-fold higher risk of CKD while the risk for carriers in the bottom GPS tertile was not different from the average population risk. Conclusions and Relevance Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by the differences in polygenic risk profiles. Accounting for polygenic factors improves risk stratification in monogenic kidney disease and may have clinical implications for genetic counseling.

There is a need for multimodal strategies to keep research participants informed about study results. Our aim was to characterize preferences of genomic research participants from two institutions along four dimensions of general research result updates: content, timing, mechanism, and frequency. Methods: We conducted a web-based cross-sectional survey that was administered from 25 June 2018 to 5 December 2018. Results: 397 participants completed the survey, most of whom (96%) expressed a desire to receive research updates. Preferences with high endorsement included: update content (brief descriptions of major findings, descriptions of purpose and goals, and educational material); update timing (when the research is completed, when findings are reviewed, when findings are published, and when the study status changes); update mechanism (email with updates, and email newsletter); and update frequency (every three months). Hierarchical cluster analyses based on the four update preferences identified four profiles of participants with similar preference patterns. Very few participants in the largest profile were comfortable with budgeting less money for research activities so that researchers have money to set up services to send research result updates to study participants. Conclusion: Future studies may benefit from exploring preferences for research result updates, as we have in our study. In addition, this work provides evidence of a need for funders to incentivize researchers to communicate results to participants.