| Title: | Genome-wide survey of parent-of-origin-specific associations across clinical traits derived from electronic health records |
| Journal: | Human Genetics and Genomics Advances |
| Published: | 11 Jun 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35047837/ |
| DOI: | https://doi.org/10.1016/j.xhgg.2021.100039 |
| Citations: | 3 as of 8 Aug 2024 |
Publication 9744
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Abstract
Parent-of-origin (PoO) effects refer to the differential phenotypic impacts of genetic variants dependent on their parental inheritance due to imprinting. While PoO effects can influence complex traits, they may be poorly captured by models that do not differentiate the parental origin of the variant. The aim of this study was to conduct a genome-wide screen for PoO effects on a broad range of clinical traits derived from electronic health records (EHR) in the DiscovEHR study enriched with familial relationships. Using pairwise kinship estimates from genetic data and demographic data, we identified 22,051 offspring among 134,049 individuals in the DiscovEHR study. PoO of ~9 million variants was assigned in the offspring by comparing offspring and parental genotypes and haplotypes. We then performed genome-wide PoO association analyses across 154 quantitative and 611 binary traits extracted from EHR. Of the 732 significant PoO associations identified (p < 5 × 10-8), we attempted to replicate 274 PoO associations in the UK Biobank study with 5,015 offspring and replicated 9 PoO associations (p < 0.05). In summary, our study implements a bioinformatic and statistical approach to examine PoO effects genome-wide in a large population study enriched with familial relationships and systematically characterizes PoO effects on hundreds of clinical traits derived from EHR. Our results suggest that, while the statistical power to detect PoO effects remains modest yet, accurately modeling PoO effects has the potential to find new associations that may have been missed by the standard additive model, further enhancing the mechanistic understanding of genetic influence on complex traits.</p>
9 Authors
- Hye In Kim
- Bin Ye
- Jeffrey Staples
- Anthony Marcketta
- Chuan Gao
- Regeneron Genetics Center
- Geisinger Regeneron DiscovEHR Collaboration
- Alan R. Shuldiner
- Cristopher V. Van Hout
1 Application
| Application ID | Title |
|---|---|
| 26041 | Large-Scale Sequencing in the UK Biobank to Facilitate Gene Discovery, Genome Sciences, and Precision Medicine |
Enabling scientific discoveries that improve human health