RHtyper

RHtyper genotypes human RHD and RHCE genes from whole-genome sequencing (WGS) data to detect complex genetic variations affecting Rh blood group antigen expression in sickle cell disease.


Key Features:

  • High-throughput genotyping speed: Determines RHD genotypes in an average of 3.4 minutes per sample and RHCE genotypes in an average of 3.3 minutes per sample from WGS data.
  • Validation and accuracy: Achieved 100% accuracy for RHD and 98.2% accuracy for RHCE in a validation cohort of 57 patients with sickle cell disease compared against RH BeadChip, targeted molecular assays, Sanger sequencing, and independent next-generation sequencing assays.
  • Comprehensive allele detection: Identified 38 distinct RHD alleles and 28 distinct RHCE alleles across a combined dataset of 884 patients (57 validation + 827 additional), including the novel RHD* 602G, 733C, 744T, 1136T allele.
  • Characterization of antigen variation: Provides detailed characterization of RH genetic variations that can lead to loss or emergence of antigen epitopes relevant to Rh alloimmunization risk in sickle cell disease patients.

Scientific Applications:

  • Sickle cell disease genotyping: Enables precise RHD and RHCE genotyping in sickle cell disease patients to identify variant antigen expressions.
  • Rh alloimmunization risk assessment: Supports identification of patients at risk for Rh alloimmunization by detecting variant antigen expressions and alleles.
  • Allele discovery and population genetics: Contributes to discovery of novel alleles and expands understanding of the genetic landscape of Rh blood group antigens.

Methodology:

Performs genotyping of human RHD and RHCE genes using whole-genome sequencing (WGS) data.

Topics

Details

Added:
1/18/2021
Last Updated:
2/6/2021

Operations

Publications

Chang T, Haupfear KM, Yu J, Rampersaud E, Sheehan VA, Flanagan JM, Hankins JS, Weiss MJ, Wu G, Vege S, Westhoff CM, Chou ST, Zheng Y. A novel algorithm comprehensively characterizes human RH genes using whole-genome sequencing data. Blood Advances. 2020;4(18):4347-4357. doi:10.1182/bloodadvances.2020002148. PMID:32915977. PMCID:PMC7509869.