immunogenetr

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immunogenetr is a comprehensive toolkit for clinical HLA informatics. It is built on tidyverse principles and makes use of genotype list string (GL string, https://glstring.org/) for storing and using HLA genotype data.

Specific functionalities of this library include:

Table of Contents

Installation

You may install immunogenetr from GitHub with the below lines of code. Devtools is necessary for installation. If devtools is not installed, you may run install.packages("devtools") first.

devtools::install_github("k96nb01/immunogenetr_package")

Usage

To demonstrate some functionality of immunogenetr we will use an internal dataset to perform match grades for a putative recipient/donor pair.

library(immunogenetr)
library(tidyverse)

# The "HLA_typing_1" dataset is installed with immunogenetr, and contains high resolution typing at all classical 
# HLA loci for ten individuals.

print(HLA_typing_1)
patient A1 A2 C1 C2 B1 B2 DRB345_1 DRB345_2 DRB1_1 DRB1_2 DQA1_1 DQA1_2 DQB1_1 DQB1_2 DPA1_1 DPA1_2 DPB1_1 DPB1_2
1 A*24:02 A*29:02 C*07:04 C*16:01 B*44:02 B*44:03 DRB5*01:01 DRB5*01:01 DRB1*15:01 DRB1*15:01 DQA1*01:02 DQA1*01:02 DQB1*06:02 DQB1*06:02 DPA1*01:03 DPA1*01:03 DPB1*03:01 DPB1*04:01
2 A*02:01 A*11:05 C*07:01 C*07:02 B*07:02 B*08:01 DRB3*01:01 DRB4*01:03 DRB1*03:01 DRB1*04:01 DQA1*03:03 DQA1*05:01 DQB1*02:01 DQB1*03:01 DPA1*01:03 DPA1*01:03 DPB1*04:01 DPB1*04:01
3 A*02:01 A*26:18 C*02:02 C*03:04 B*27:05 B*54:01 DRB3*02:02 DRB4*01:03 DRB1*04:04 DRB1*14:54 DQA1*01:04 DQA1*03:01 DQB1*03:02 DQB1*05:02 DPA1*01:03 DPA1*02:02 DPB1*02:01 DPB1*05:01
4 A*29:02 A*30:02 C*06:02 C*07:01 B*08:01 B*13:02 DRB4*01:03 DRB4*01:03 DRB1*04:01 DRB1*07:01 DQA1*02:01 DQA1*03:01 DQB1*02:02 DQB1*03:02 DPA1*01:03 DPA1*02:01 DPB1*01:01 DPB1*16:01
5 A*02:05 A*24:02 C*07:18 C*12:03 B*35:03 B*58:01 DRB3*02:02 DRB3*02:02 DRB1*03:01 DRB1*14:54 DQA1*01:04 DQA1*05:01 DQB1*02:01 DQB1*05:03 DPA1*01:03 DPA1*02:01 DPB1*10:01 DPB1*124:01
6 A*01:01 A*24:02 C*07:01 C*14:02 B*49:01 B*51:01 DRB3*03:01 DRBX*NNNN DRB1*08:01 DRB1*13:02 DQA1*01:02 DQA1*04:01 DQB1*04:02 DQB1*06:04 DPA1*01:03 DPA1*01:04 DPB1*04:01 DPB1*15:01
7 A*03:01 A*03:01 C*03:03 C*16:01 B*15:01 B*51:01 DRB4*01:01 DRBX*NNNN DRB1*01:01 DRB1*07:01 DQA1*01:01 DQA1*02:01 DQB1*02:02 DQB1*05:01 DPA1*01:03 DPA1*01:03 DPB1*04:01 DPB1*04:01
8 A*01:01 A*32:01 C*06:02 C*07:02 B*08:01 B*37:01 DRB3*02:02 DRB5*01:01 DRB1*03:01 DRB1*15:01 DQA1*01:02 DQA1*05:01 DQB1*02:01 DQB1*06:02 DPA1*01:03 DPA1*02:01 DPB1*04:01 DPB1*14:01
9 A*03:01 A*30:01 C*07:02 C*12:03 B*07:02 B*38:01 DRB3*01:01 DRB5*01:01 DRB1*03:01 DRB1*15:01 DQA1*01:02 DQA1*05:01 DQB1*02:01 DQB1*06:02 DPA1*01:03 DPA1*01:03 DPB1*04:01 DPB1*04:01
10 A*02:05 A*11:01 C*07:18 C*16:02 B*51:01 B*58:01 DRB3*03:01 DRB5*01:01 DRB1*13:02 DRB1*15:01 DQA1*01:02 DQA1*01:03 DQB1*06:01 DQB1*06:09 DPA1*01:03 DPA1*01:03 DPB1*02:01 DPB1*104:01

immunogenetr uses genotype list strings (GL strings) for most functions, including the matching and mismatching functions. To easily convert the genotypes found in “HLA_typing_1” to GL strings we can use the HLA_columns_to_GLstring function:

HLA_typing_1_GLstring <- HLA_typing_1 %>% 
  mutate(GL_string = HLA_columns_to_GLstring(., HLA_typing_columns = A1:DPB1_2), .after = patient) %>% 
  # Note the syntax for the `HLA_columns_to_GLstring` arguments - when this function is used inside 
  # of a `mutate` function to make a new column in a data frame, "." is used in the first argument 
  # to tell the function to use the working data frame as the source of the HLA typing columns.
  select(patient, GL_string)

print(HLA_typing_1_GLstring)
patient GL_string
1 HLA-A*24:02+HLA-A*29:02HLA-C*07:04+HLA-C*16:01HLA-B*44:02+HLA-B*44:03HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*15:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:02HLA-DQB1*06:02+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*03:01+HLA-DPB1*04:01
2 HLA-A*02:01+HLA-A*11:05HLA-C*07:01+HLA-C*07:02HLA-B*07:02+HLA-B*08:01HLA-DRB3*01:01+HLA-DRB3*01:03HLA-DRB1*03:01+HLA-DRB1*04:01HLA-DQA1*03:03+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*03:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01
3 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
4 HLA-A*29:02+HLA-A*30:02HLA-C*06:02+HLA-C*07:01HLA-B*08:01+HLA-B*13:02HLA-DRB3*01:03+HLA-DRB3*01:03HLA-DRB1*04:01+HLA-DRB1*07:01HLA-DQA1*02:01+HLA-DQA1*03:01HLA-DQB1*02:02+HLA-DQB1*03:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*01:01+HLA-DPB1*16:01
5 HLA-A*02:05+HLA-A*24:02HLA-C*07:18+HLA-C*12:03HLA-B*35:03+HLA-B*58:01HLA-DRB3*02:02+HLA-DRB3*02:02HLA-DRB1*03:01+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*05:03HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*10:01+HLA-DPB1*124:01
6 HLA-A*01:01+HLA-A*24:02HLA-C*07:01+HLA-C*14:02HLA-B*49:01+HLA-B*51:01HLA-DRB3*03:01HLA-DRB1*08:01+HLA-DRB1*13:02HLA-DQA1*01:02+HLA-DQA1*04:01HLA-DQB1*04:02+HLA-DQB1*06:04HLA-DPA1*01:03+HLA-DPA1*01:04HLA-DPB1*04:01+HLA-DPB1*15:01
7 HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01
8 HLA-A*01:01+HLA-A*32:01HLA-C*06:02+HLA-C*07:02HLA-B*08:01+HLA-B*37:01HLA-DRB3*02:02+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*04:01+HLA-DPB1*14:01
9 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01
10 HLA-A*02:05+HLA-A*11:01HLA-C*07:18+HLA-C*16:02HLA-B*51:01+HLA-B*58:01HLA-DRB3*03:01+HLA-DRB3*01:01HLA-DRB1*13:02+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:03HLA-DQB1*06:01+HLA-DQB1*06:09HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*02:01+HLA-DPB1*104:01

The “HLA_typing_1_GLstring” data frame now contains a row with a GL string for each individual, containing their full HLA genotype in a single string. Let’s select one individual to act as a recipient, and one to act as a donor.

# Select one case each for recipient and donor.
HLA_typing_1_GLstring_recipient <- HLA_typing_1_GLstring %>% 
  filter(patient == 7) %>% 
  rename(GL_string_recipient = GL_string, case = patient)

HLA_typing_1_GLstring_donor <- HLA_typing_1_GLstring %>% 
  filter(patient == 9) %>% 
  rename(GL_string_donor = GL_string) %>% 
  select(-patient)

# Combine the tables so recipient and donor are on the same row.
HLA_typing_1_recip_donor <- bind_cols(
  HLA_typing_1_GLstring_recipient, 
  HLA_typing_1_GLstring_donor
  )

print(HLA_typing_1_recip_donor)
case GL_string_recipient GL_string_donor
7 HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01

We now have a data frame with a recipient and donor HLA genotype on one row. Let’s try out some of the mismatching functions on this data.

HLA_typing_1_recip_donor_mismatches <- HLA_typing_1_recip_donor %>% 
  mutate(A_MM_GvH = HLA_mismatch_logical(
                      GL_string_recipient, 
                      GL_string_donor, 
                      "HLA-A", 
                      direction = "GvH"), 
                    .after = case) %>% 
  mutate(A_MM_HvG = HLA_mismatch_logical(
                      GL_string_recipient, 
                      GL_string_donor, 
                      "HLA-A", 
                      direction = "HvG"), 
                    .after = A_MM_GvH)

print(HLA_typing_1_recip_donor_mismatches)
case A_MM_GvH A_MM_HvG GL_string_recipient GL_string_donor
7 TRUE TRUE HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01

The HLA_mismatch_logical function determines if there are any mismatches at a particular locus. We’ve determined that at the HLA-A locus there are not any mismatches in the graft-versus-host direction, but are in the host-versus-graft direction. We can use the HLA_mismatched_alleles function to tell us what those mismatches are:

HLA_typing_1_recip_donor_mismatched_allles <- HLA_typing_1_recip_donor %>% 
  mutate(A_HvG_MMs = HLA_mismatched_alleles(
                        GL_string_recipient, 
                        GL_string_donor, 
                        "HLA-A", 
                        direction = "HvG"), 
                      .after = case)

print(HLA_typing_1_recip_donor_mismatched_allles)
case A_HvG_MMs GL_string_recipient GL_string_donor
7 HLA-A*30:01 HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01

The HLA_mismatched_alleles function reported that the “HLA-A*30:01” allele was mismatched in the HvG direction. Sometimes, however, we simply want to know how many mismatches are at a particular locus. We can do that with the HLA_mismatch_number function:

# Determine the number of bidirectional mismatches at several loci.
HLA_typing_1_recip_donor_MM_number <- HLA_typing_1_recip_donor %>% 
  mutate(ABCDRB1_MM = HLA_mismatch_number(
                        GL_string_recipient, 
                        GL_string_donor, 
                        c("HLA-A", "HLA-B", "HLA-C", "HLA-DRB1"), 
                        direction = "bidirectional"), 
                      .after = case)

print(HLA_typing_1_recip_donor_MM_number)
case ABCDRB1_MM GL_string_recipient GL_string_donor
7 HLA-A=1 HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01

We might want to calculate an HLA match summary for stem cell transplantation. We can use the HLA_match_summarry_HCT function for this:

# The match_grade argument of "Xof8" will return the number of matches at the HLA-A, B, C, and DRB1 loci.
HLA_typing_1_recip_donor_8of8_matching <- HLA_typing_1_recip_donor %>% 
  mutate(ABCDRB1_matching = HLA_match_summary_HCT(
                              GL_string_recipient, 
                              GL_string_donor, 
                              direction = "bidirectional", 
                              match_grade = "Xof8"), 
                            .after = case)

print(HLA_typing_1_recip_donor_8of8_matching)
case ABCDRB1_matching GL_string_recipient GL_string_donor
7 1 HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01

Clearly, this recipient and donor are not a great match. Let’s see how we could use this workflow to find the best-matched donor from several options. To do this, we’ll choose a case from “HLA_typing_1” and compare it to all the cases in that data set:

# Select one case to be the recipient.
HLA_typing_1_GLstring_candidate <- HLA_typing_1_GLstring %>% 
  filter(patient == 3) %>% 
  select(GL_string) %>% 
  rename(GL_string_recip = GL_string)

# Join the recipient to the 10-donor list and perform matching
HLA_typing_1_GLstring_donors <- HLA_typing_1_GLstring %>% 
  rename(GL_string_donor = GL_string, donor = patient) %>% 
  cross_join(HLA_typing_1_GLstring_candidate) %>%
  mutate(ABCDRB1_matching = HLA_match_summary_HCT(
                              GL_string_recip, 
                              GL_string_donor, 
                              direction = "bidirectional", 
                              match_grade = "Xof8"), 
                            .after = donor) %>%
  arrange(desc(ABCDRB1_matching))

print(HLA_typing_1_GLstring_donors)
donor ABCDRB1_matching GL_string_donor GL_string_recip
3 8 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
2 1 HLA-A*02:01+HLA-A*11:05HLA-C*07:01+HLA-C*07:02HLA-B*07:02+HLA-B*08:01HLA-DRB3*01:01+HLA-DRB3*01:03HLA-DRB1*03:01+HLA-DRB1*04:01HLA-DQA1*03:03+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*03:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
5 1 HLA-A*02:05+HLA-A*24:02HLA-C*07:18+HLA-C*12:03HLA-B*35:03+HLA-B*58:01HLA-DRB3*02:02+HLA-DRB3*02:02HLA-DRB1*03:01+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*05:03HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*10:01+HLA-DPB1*124:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
1 0 HLA-A*24:02+HLA-A*29:02HLA-C*07:04+HLA-C*16:01HLA-B*44:02+HLA-B*44:03HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*15:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:02HLA-DQB1*06:02+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*03:01+HLA-DPB1*04:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
4 0 HLA-A*29:02+HLA-A*30:02HLA-C*06:02+HLA-C*07:01HLA-B*08:01+HLA-B*13:02HLA-DRB3*01:03+HLA-DRB3*01:03HLA-DRB1*04:01+HLA-DRB1*07:01HLA-DQA1*02:01+HLA-DQA1*03:01HLA-DQB1*02:02+HLA-DQB1*03:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*01:01+HLA-DPB1*16:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
6 0 HLA-A*01:01+HLA-A*24:02HLA-C*07:01+HLA-C*14:02HLA-B*49:01+HLA-B*51:01HLA-DRB3*03:01HLA-DRB1*08:01+HLA-DRB1*13:02HLA-DQA1*01:02+HLA-DQA1*04:01HLA-DQB1*04:02+HLA-DQB1*06:04HLA-DPA1*01:03+HLA-DPA1*01:04HLA-DPB1*04:01+HLA-DPB1*15:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
7 0 HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
8 0 HLA-A*01:01+HLA-A*32:01HLA-C*06:02+HLA-C*07:02HLA-B*08:01+HLA-B*37:01HLA-DRB3*02:02+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*04:01+HLA-DPB1*14:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
9 0 HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01
10 0 HLA-A*02:05+HLA-A*11:01HLA-C*07:18+HLA-C*16:02HLA-B*51:01+HLA-B*58:01HLA-DRB3*03:01+HLA-DRB3*01:01HLA-DRB1*13:02+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:03HLA-DQB1*06:01+HLA-DQB1*06:09HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*02:01+HLA-DPB1*104:01 HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01

We can see that donor 3 is the only donor with an 8/8 match for the recipient.

License

This project is licensed under the GNU General Public License v3.0.