GeT-RM Reference Materials

At a glance

This extensive list of resources includes publications and data about characterized genomic DNA reference materials from the Genetic Testing Reference Materials Coordination Program (GeT-RM).

Pharmacogenetics and HLA

This resource contains the up-to-date consensus PGx genotypes for 363 DNA samples characterized by GeT-RM. These samples were characterized during 9 GeT-RM pharmacogenetic or Human Leukocyte Antigen (HLA) studies covering 34 genes/loci.123456789

Consolidated Table of all GeT-RM Pharmacogenetic and HLA Reference Material Genotypes

A searchable web-based tool, GeT-RM PGx Search, based on these data, is available on the Coriell Institute for Medical Research website.

Note: The files in the sections below contain the same data as the consolidated Excel table in this section.

This table shows the up-to-date consensus PGx genotypes for 137 samples that were characterized in the original GeT-RM PGx study published in 2016: PMID 266211017. These samples have been recharacterized for several genes, including CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5, with newer methods and technologies during subsequent studies234569.

GeT-RM used a variety of methods to characterize DNA from 33 Coriell cell lines to create a panel of reference materials for DPYD. 9 The data from each test method and the consensus genotype for each sample are available for review.

DPYD genotypes and data table

DNA from 49 cell lines was characterized for alleles in CYP3A4 and CYP3A5 using a variety of methods.2 The document includes data from each method and consensus diplotypes for each sample.

CYP3A4, CYP3A5 Table

The alleles were tested using genotyping assays.

Summary of CYP3A4 and CYP3A5 Platforms and Genotyping Assays

GeT-RM used a variety of methods to characterize DNA from 30 Coriell cell lines to create a panel of reference materials for TMPT and NUDT15.3 The data from each method and the consensus diplotypes for each sample are available for review.

Lab Data and Consensus TMPT and NUDT15 Diplotypes

Details on the assays used and the alleles tested can be found separately.

Summary of TPMT and NUDT15 Platforms and Genotyping Assays

GeT-RM used targeted and whole genome sequencing analysis to recharacterize 137 DNA samples that were previously characterized using a variety of targeted genotyping assays to identify star allele diplotypes for CYP2C8, CYP2C9, and CYP2C19.47 Data from each test method and consensus diplotypes for each sample are available for review.

CYP2C8, CYP2C9 and CYP2C19 (Next Generation Sequencing) Results by Test Method

GeT-RM characterized 18 cell line DNA samples to create reference materials containing pharmacogenetic alleles classified as "Tier 2" by the Association for Molecular Pathology PGx Work Group.5 Data from each test method and consensus diplotypes for each sample are available for review.

GeT-RM Lab Data and Consensus Genotypes for Tier 2 Variants

The CYP2D6 gene in DNA from 179 cell lines including rare and/or difficult to analyze alleles and/or diplotypes were characterized by the GeT-RM using a variety of methods.6 The data from each test method and consensus diplotypes for each sample, assays used, and the tested alleles are available for review.

CYP2D6 Test Methods and Consensus Genotypes

DNA from 137 cell lines was characterized by the GeT-RM for 28 PGx genes using a variety of methods.7 Consensus genotype data and data by platform are presented in the tables, covering all 28 loci collectively and individually for each locus. Details on the assays used and the alleles tested are provided separately.

  • WGS data (FASTQ and BAM files) for 70 of these samples are freely available for download and use from the .
  • Tables showing data from each of the platforms used to characterize each of the 28 PGx genes.

Lab Results and Consensus Genotypes for 28 PGx Genes

PGx GeT-RM - 28 Genes and Alleles Tested

DNA from 107 cell lines was characterized for CYP2D6, CYP2C19, CYP2C9, VKORC1, and UGT1A1 by the GeT-RM using a variety of methods.8 The assays used and the tested alleles in this study are in the Summary of Assays and Alleles Tested spreadsheet linked below. (106 of these samples were also characterized by GeT-RM for HLA-A, B, C, DRB1, DRB3, DRB4, DRB5, DQA1, DQB1, DPA1, and DPB1.1) Data are presented in the documents listed below for all 5 loci together and for each of the 5 loci individually.

Summary of Assays and Alleles Test

CYP2D6, CYP2C19, CYP2C9, VKORC1 and UGT1A1 Characterized by GeT-RM

CYP2D6

CYP2C19

CYP2C9

CYP2C9 and VKORC1

VKORC1

UGT1A1

All 108 cell lines in this table have been characterized using various methods, including NGS by the GeT-RM program. 106 of these cell lines were previously characterized by GeT-RM for CYP2D6, CYP2C19, CYP2C9, VKORC1 and UGT1A1.

Inherited genetic disease

GeT-RM partnered with to develop a publicly available list of expert curated, clinically important variants. This list serves as a resource for designing comprehensive validation studies. It is also used for creating in silico reference materials for clinical genomic test development and validation.10 ClinGen Variant Curation Expert Panels nominated 546 pathogenic and difficult to detect variants in 84 disease-associated genes.

GeT-RM/ClinGen Table of Variants

GeT-RM/ClinGen Table of Genes

The Ashkenazi Jewish Reference Material Panel includes the following diseases:

  • Bloom syndrome
  • Canavan disease
  • Fanconi anemia type C
  • Familial dysautonomia
  • Gaucher disease
  • Glycogen storage disease type 1a
  • Mucolipidosis IV
  • Neimann-Pick disease
  • Tay-Sachs disease.

All cell lines in this table have been characterized using various methods by the GeT-RM program.11

All cell lines in this table have been characterized using various methods by the GeT-RM program.12

All cell lines in this table have been characterized using various methods by the GeT-RM program.13

All cell lines in this table have been characterized using various methods by the GeT-RM program and AMP.14

All cell lines in this table have been characterized using various methods by the GeT-RM program.15

All cell lines in this table have been characterized using various methods by the GeT-RM program.16

All cell lines in this table have been characterized using various methods by the GeT-RM program.17

All cell lines in this table have been characterized using various methods by the GeT-RM program.18

All cell lines in this table have been characterized using various methods by the GeT-RM program. 19

Additional data

These are additional data for 137 DNA samples obtained during the GeT-RM PGx characterization study.7 Data are from one assay only. The assay used to characterize each gene is indicated.

These are additional data about 107 DNA samples obtained during the GeT-RM PGx characterization study.8 Data are from only one laboratory.

These are additional data about the DNA characterized in the GeT-RM MTHFR, SERPINA1, RET, BRCA1, and BRCA2 characterization study.16 These data come from only one laboratory.

These are additional data about 107 DNA samples in the GeT-RM PGx characterization study.8 Data are from only one or two (HFE) laboratories.

This document includes reference materials for conditions involving nonsyndromic deafness, craniosynostosis/Muenke, hemochromatosis, MTHFR, alpha-thalassemia, factor V, prothrombin, sickle cell disease.20

Contact information

For inquiries about the information on this page, contact [email protected].

For assistance reading the data tables in the PDF and Excel files, use the subject line “GeT-RM Data Tables: Assistance with Reading” in your email.

  1. Bettinotti MP, Ferriola D, Duke JL, Mosbruger TL, Tairis N, Jennings L, et al. . J Mol Diag 2018, 20:703-715.
  2. Gaedigk A, Boone EC, Turner A, van Schaik R, Cheranova D, Wang WY, et al. J Mol Diag 2023 25:655-664.
  3. Pratt VM, Wang WY, Boone EC, Broeckel U, Cody N, Edelmann L, et al. . J Mol Diag 2022, 24:1079-1088.
  4. Gaedigk A, Boone EC, Scherer S, Lee S, Numanagic I, Sahinalp C, et al. . J Mol Diag 2022, 24:337-350.
  5. Pratt, VM, Turner A, Broeckel U, Dawson BD, Gaedigk A, Lynnes, et al. . J Mol Diag 2021, 23:952-958.
  6. Gaedigk A, Turner A, Everts RE, Scott SA, Aggarwal P, Broeckel U, et al. . J Mol Diag 2019 21:1034-1052.
  7. Pratt VM, Everts RE, Aggarwal P, Beyer BN, Broeckel U, Epstein-Baak R, et al. . J Mol Diag 2016 18:109-123.
  8. Pratt VM, Zehnbauer B, Wilson JA, Baak R, Babic N, Bettinotti M, et al. . J Mol Diag 2010 12:835-846.
  9. Gaedigk A, Turner AJ, Moyer AM, Zubiaur P, Boone EC, Wang WY, Broeckel U, Kalman LV. . Journal of Molecular Diagnostics. 2024, 26:864-875.
  10. Wilcox E, Harrison SM, Lockhart E, Voelkerding K, Lubin IM, Clingen Expert Panels, et al. . J Mol Diag 2021 23:1501-1506.
  11. Kalman LV, Amos Wilson J, Buller A, Dixon J, Edelmann L, Geller L, et al. . J Mol Diag 2009 Nov;11(6):530-6.
  12. Pratt VM, Caggana M, Bridges C, Buller AM, DiAntonio L, Highsmith EW, et al. . J Mol Diag 2009 11:186-93.
  13. Kalman L, Leonard J, Gerry N, Tarleton J, Bridges C, Gastier-Foster JM, et al. . J Mol Diag 2011 Mar;13(2):167-74.
  14. Wilson JA, Pratt VM, Phansalkar A, Muralidharan K, Highsmith Jr WE, Beck JC, et al. . J Mol Diag 2008 10:2-12.
  15. Kalman L, Johnson MA, Beck J, Berry-Kravis E, Buller A, Casey B, et al. Genetics in Medicine 2007 9:719-723.
  16. Barker SD, Bale S, Booker J, Buller A, Das S, Friedman K, Godwin AK, et al. J Mol Diag 2009 Nov;11(6):553-61.
  17. Kalman L, Tarleton J, Hitch M, Hegde M, Hjelm N, Berry-Kravis E, et al. . J Mol Diag 2013 15:518-525.
  18. Kalman L, Tarleton J, Percy A, Aradhya S, Bale S, Barker S, et al. . J Mol Diag 2014 16:273-279.
  19. Prior TW, Bayrak-Toydemir P, Lynnes TC, Mao R, Metcalf JD, Muralidharan K, et al. . J Mol Diag 2021, 23:103-110.
  20. Bernacki SH, Beck JC, Stankovic AK, Williams LO, Amos J, Snow-Bailey K, et al. (2005). . Clinical chemistry, 51(11), 2013-2024.