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HLA allele frequencies in tab-delimited format

DOI

Kamil Slowikowski

2024-04-22

Table of Contents

Introduction

Here, we share a single file afnd.tsv (5.99MB) in tab-delimited format with all allele frequencies for 8 HLA genes, 18 KIR genes, 2 MIC genes, and 29 cytokine genes from Allele Frequency Net Database (AFND).

The script allelefrequencies.py automatically downloads allele frequencies from the website.

What is the Allele Frequency Net Database?

The Allele Frequency Net Database (AFND) is a public database which contains frequency information of several immune genes such as Human Leukocyte Antigens (HLA), Killer-cell Immunoglobulin-like Receptors (KIR), Major histocompatibility complex class I chain-related (MIC) genes, and a number of cytokine gene polymorphisms.

The afnd.tsv file looks like this:

d <- fread("afnd.tsv")
head(d)
##    group gene  allele                              population indivs_over_n alleles_over_2n   n
## 1:   hla    A A*01:01                  Argentina Rosario Toba          15.1          0.0760  86
## 2:   hla    A A*01:01                Armenia combined Regions                        0.1250 100
## 3:   hla    A A*01:01 Australia Cape York Peninsula Aborigine                        0.0530 103
## 4:   hla    A A*01:01      Australia Groote Eylandt Aborigine                        0.0270  75
## 5:   hla    A A*01:01     Australia New South Wales Caucasian                        0.1870 134
## 6:   hla    A A*01:01            Australia Yuendumu Aborigine                        0.0080 191

Definitions:

  • alleles_over_2n (Alleles / 2n) Allele Frequency: total number of copies of the allele in the population sample in three decimal format.

  • indivs_over_n (100 * Individuals / n) Percentage of individuals who have the allele or gene.

  • n (Individuals) Number of individuals sampled from the population.

Examples

Here are a few examples of how we can use R to analyze these data.

View the largest and smallest populations available in the data:

d %>%
  mutate(n = parse_number(n)) %>%
  select(population, n) %>%
  unique() %>%
  arrange(-n)
##                                     population       n
##    1:             Germany DKMS - German donors 3456066
##    2:              USA NMDP European Caucasian 1242890
##    3:          USA NMDP African American pop 2  416581
##    4:              USA NMDP Mexican or Chicano  261235
##    5:              USA NMDP South Asian Indian  185391
##   ---                                                 
## 1489:                            Cameroon Sawa      13
## 1490:    Paraguay/Argentina Ache NA-DHS_24 (G)      13
## 1491:            Malaysia Orang Kanaq Cytokine      11
## 1492:                      Cameroon Baka Pygmy      10
## 1493: Paraguay/Argentina Guarani NA-DHS_23 (G)      10

Count the number of alleles for each gene:

d %>%
  count(group, gene, allele) %>%
  count(group, gene) %>%
  arrange(-n) %>%
  head(15)
##     group  gene    n
##  1:   hla     B 1979
##  2:   hla     A 1394
##  3:   hla     C 1209
##  4:   hla  DRB1  954
##  5:   hla  DPB1  384
##  6:   hla  DQB1  351
##  7:   kir  3DL1   90
##  8:   mic  MICA   69
##  9:   kir  3DL3   67
## 10:   kir  2DL1   52
## 11:   kir  2DL4   35
## 12:   mic  MICB   34
## 13:   hla  DQA1   30
## 14:   kir  3DL2   30
## 15:   kir 2DL5B   24

Sum the allele frequencies for each gene in each population. This allows us to see which populations have a set of allele frequencies that adds up to 100 percent:

d %>%
  mutate(alleles_over_2n = parse_number(alleles_over_2n)) %>%
  filter(alleles_over_2n > 0) %>%
  group_by(group, gene, population) %>%
  summarize(sum = sum(alleles_over_2n)) %>%
  count(sum == 1)
## `summarise()` has grouped output by 'group', 'gene'. You can override using the `.groups` argument.

## # A tibble: 44 × 4
## # Groups:   group, gene [28]
##    group gene  `sum == 1`     n
##    <chr> <chr> <lgl>      <int>
##  1 hla   A     FALSE        420
##  2 hla   A     TRUE          18
##  3 hla   B     FALSE        513
##  4 hla   B     TRUE          19
##  5 hla   C     FALSE        323
##  6 hla   C     TRUE          19
##  7 hla   DPA1  FALSE         54
##  8 hla   DPA1  TRUE           6
##  9 hla   DPB1  FALSE        207
## 10 hla   DPB1  TRUE          39
## # ℹ 34 more rows

Plot the frequency of a specific allele in populations with more than 1000 sampled individuals:

my_allele <- "DQB1*02:01"
my_d <- d %>% filter(allele == my_allele) %>%
  mutate(
    n = parse_number(n),
    alleles_over_2n = parse_number(alleles_over_2n)
  ) %>%
  filter(n > 1000) %>%
  arrange(-alleles_over_2n)

ggplot(my_d) +
  aes(x = alleles_over_2n, y = reorder(population, alleles_over_2n)) +
  scale_y_discrete(position = "right") +
  geom_colh() +
  labs(
    x = "Allele Frequency (Alleles / 2N)",
    y = NULL,
    title =  glue("Frequency of {my_allele} across populations"),
    caption = "Data from AFND http://allelefrequencies.net"
  )

Citation

If you use this data, please cite the latest manuscript about Allele Frequency Net Database:

@ARTICLE{Gonzalez-Galarza2020,
  title    = "{Allele frequency net database (AFND) 2020 update: gold-standard
              data classification, open access genotype data and new query
              tools}",
  author   = "Gonzalez-Galarza, Faviel F and McCabe, Antony and Santos, Eduardo
              J Melo Dos and Jones, James and Takeshita, Louise and
              Ortega-Rivera, Nestor D and Cid-Pavon, Glenda M Del and
              Ramsbottom, Kerry and Ghattaoraya, Gurpreet and Alfirevic, Ana
              and Middleton, Derek and Jones, Andrew R",
  journal  = "Nucleic acids research",
  volume   =  48,
  number   = "D1",
  pages    = "D783--D788",
  month    =  jan,
  year     =  2020,
  language = "en",
  issn     = "0305-1048, 1362-4962",
  pmid     = "31722398",
  doi      = "10.1093/nar/gkz1029",
  pmc      = "PMC7145554"
}

Related work

Here are all of the resources I could find that have information about HLA allele frequencies in different populations.

HLAfreq

https://github.com/Vaccitech/HLAfreq/

CIWD version 3.0.0

The authors provide xlsx files on this website:

But the frequency information is binned into categories:

  • C, common
  • I, intermediate
  • WD, well-documented
  • NA, not applicable

There is a tool called HLA-Net that provides a visualization of the CIWD data.

IEDB Tools

http://tools.iedb.org/population/download

At the IEDB Tools page, we can find a tool called Population Coverage. The authors have downloaded the HLA frequency information from AFND and saved it in a Python pickle file.

dbMHC

https://www.ncbi.nlm.nih.gov/gv/mhc

The dbMHC database and website appears to be discontinued. But an archive of old files is still available via FTP.

NMDP

https://bioinformatics.bethematchclinical.org/hla-resources/haplotype-frequencies/high-resolution-hla-alleles-and-haplotypes-in-the-us-population/

Acknowledgments

Thanks to David A. Wells for sharing scrapeAF, which inspired me to work on this project.