bioRxivGeneticsPreprint
A rare variant on a common risk haplotype of HFE causes increased risk of hereditary hemochromatosis
Andrew Michael GlazeriD, Lisa A. BastaracheiD, Lynn D. HalliD, Laura L ShortiD, Tiffany Shields, Brett M. KronckeiD, Joshua C. DennyiD, Dan M. RodeniD
Abstract
Hereditary hemochromatosis (HH) is an autosomal recessive disorder of excess iron absorption. The most common form, HH1, is caused by loss of function variants in HFE. HFE encodes a cell surface protein that binds to the Transferrin Receptor (TfR1), reducing TfR1s affinity for the transferrin/iron complex and thereby limiting cellular iron uptake. Two common missense alleles for HH1 have been identified, HFE C282Y and HFE H63D; H63D is considered to be a less penetrant allele. When we deployed Phenotype Risk Scores (PheRS), a method that aggregates multiple symptoms together in Electronic Health Records (EHRs), we identified HFE E168Q as a novel variant associated with HH. E168Q is on the same haplotype as H63D, and in a crystal structure HFE E168 lies at the interface of the HFE-TfR1 interaction and makes multiple salt bridge connections with TfR1. In in vitro cell surface abundance experiments, the HFE E168Q+H63D double mutation surprisingly increased cell surface abundance of HFE by 10-fold compared to wildtype. In coimmunoprecipitation experiments, however, HFE C282Y, E168Q, and E168Q+H63D completely abolished the interaction between HFE and TfR1, while H63D alone only partially reduced binding. These findings provide mechanistic insight to validate the PheRS result that HFE E168Q is an HH1-associated allele and lead to the reclassification of E168Q from a variant of uncertain significance to a pathogenic variant, according to ACMG guidelines. HFE E168Q results in loss of HFE function by disrupting the HFE-TfR1 interaction. In addition, some disease manifestations attributed to H63D may reflect the functional effects of E168Q.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: database / resource
- approach: statistical model
- Citations
- 0
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