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Genome-wide survey of parent-of-origin specific associations across clinical traits derived from electronic health records

By Hye In Kim, Bin Ye, Jeffrey Staples, Anthony Marcketta, Chuan Gao, Regeneron Genetics Center, Geisinger Regeneron DiscovEHR Collaboration, Alan R Shuldiner, Cristopher Van Hout

Posted 11 Dec 2020
medRxiv DOI: 10.1101/2020.12.08.20246199

Parent-of-origin (PoO) effects refer to the differential phenotypic impact of genetic variants dependent on their parental inheritance. Genetic variants in imprinted genes can have PoO specific effects on complex traits, but these effects may be poorly captured by models that do not differentiate the parental origin of the variant. The aim of this study was to screen genome-wide imputed sequence for PoO effects on electronic health records (EHR) derived clinical traits in 134,049 individuals of European ancestry from the DiscovEHR study. Using pairwise kinship estimates from genetic data and demographic data, we identified 22,051 offspring with at least one parent present in the DiscovEHR study. We then assigned the PoO of ~9 million variants in the heterozygous offspring using two methods. First, when one of the parental genotypes was homozygous, we determined PoO based on apparent Mendelian segregation. Second, we estimated PoO by comparing parental and offspring haplotypes around the variant allele. Using these PoO assignments, we performed genome-wide PoO association analyses across 154 quantitative traits including lab test results and biometric measures and 612 binary traits of ICD10 3-digit codes extracted from EHR in the DiscovEHR study. Out of 732 PoO associations meeting a significance threshold of P <5x10-8, we attempted to replicate 274 PoO associations in the UK Biobank study, consisting of 462,453 individuals and including 5,015 offspring with at least one parent, and replicated 9 PoO associations with nominal significance threshold P <0.05. In summary, the current study characterizes PoO effects of genetic variants genome-wide on a broad range of clinical traits derived from EHR in a large population study enriched for familial relationships. Our results suggest that 1) PoO specific effects are frequently captured by a standard additive model and that 2) statistical power to detect PoO specific effects remains modest even in large studies. Nonetheless, accurately modeling PoO effects of genetic variants has the potential to improve our understanding of the mechanism of the association and finding new associations that are not captured by the additive model.

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