P517: Utilization of a REDCap GeneResultTool for returning HFE pathogenic results

well-established variant-disease associations that have been mapped to phecodes.We then ran a binomial logistic regression associating known genetic variants with disease status, adjusted by age, sex at birth, and 10 principal components.We performed this regression for cases defined as those with either survey or EHR evidence of disease, and again for cases defined as those with concordant survey and EHR evidence.Results: The proportion of participants with both billing codes and PFHH survey data for a condition ranged from 1% (Chickenpox) to 66.3% (HIV/AIDS).The proportion of participants with only EHR codes compared to only survey responses also varied greatly.For example, in blindness, 74.5% of participants had a billing code without reporting it in the survey, while for sexually transmitted infections, 81.2% of participants reported this in the survey without having a corresponding billing code.Among the 245,388 All of Us participants with short read genome sequencing available, 86,424 took the PFHH survey and had at least one billing code.We found an R 2 = 0.5756 for the correlation between odds ratios (ORs) when using either survey responses or phecodes vs using both.In some SNPs, we found a considerably higher OR when using both survey responses and EHR billing codes.For a known Type 1 Diabetes pathogenic variant (rs9273363), we found an OR of 4. 046 (3.641, 4.496) with both survey and billing codes, compared to 1.692 (1.592, 1.780) using either survey responses or phecodes (PGRM OR = 5.48 (4.80, 6.27)).For a known Celiac pathogenic variant (rs2187668), we found an OR of 4.276 (3.736, 4.894) using both survey and billing codes, compared to 2.441 (2.225, 2.678) using either survey responses or phecodes (PGRM OR = 6.326 (5.95, 6.52)). Conclusion:We present here a first map of personal and family history conditions to phecodes, which we hope will facilitate genetic and other analyses that combine billing and survey data.When comparing phenotyping methods using either phecodes or survey responses vs requiring both, we found that the inclusion of survey data strengths genetic association in known variants.The varied distributions of EHR and survey data in our cohort suggest that further study will be needed to compare the phenotyping performance of survey data across different conditions.