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kgp

CRAN status Lifecycle: stable arXiv

This kgp data package provides metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios.

Installation

You can install the released version of kgp from CRAN with:

install.packages("kgp")

You can install the development version of kgp from GitHub with:

# install.packages("devtools")
devtools::install_github("stephenturner/kgp")

About the data

The 1000 Genomes Project data Phase 3 data contains 2,504 samples with sequence data available, and was later expanded to 3,202 samples with high coverage adding 602 trios. Data is available through the 1000 Genomes FTP site and GitHub.

There are three data sets available in the kgp package.

library(kgp)
data(kgp)

The kgp3 data contains pedigree and population information for the 2,504 samples included in the Phase 3 release of the 1000 Genomes Project data.

kgp3
#> # A tibble: 2,504 × 10
#>    fid     id      pid   mid     sex sexf   pop   reg   population        region
#>    <chr>   <chr>   <chr> <chr> <int> <fct>  <chr> <chr> <chr>             <chr> 
#>  1 HG00096 HG00096 0     0         1 male   GBR   EUR   British in Engla… Europe
#>  2 HG00097 HG00097 0     0         2 female GBR   EUR   British in Engla… Europe
#>  3 HG00099 HG00099 0     0         2 female GBR   EUR   British in Engla… Europe
#>  4 HG00100 HG00100 0     0         2 female GBR   EUR   British in Engla… Europe
#>  5 HG00101 HG00101 0     0         1 male   GBR   EUR   British in Engla… Europe
#>  6 HG00102 HG00102 0     0         2 female GBR   EUR   British in Engla… Europe
#>  7 HG00103 HG00103 0     0         1 male   GBR   EUR   British in Engla… Europe
#>  8 HG00105 HG00105 0     0         1 male   GBR   EUR   British in Engla… Europe
#>  9 HG00106 HG00106 0     0         2 female GBR   EUR   British in Engla… Europe
#> 10 HG00107 HG00107 0     0         1 male   GBR   EUR   British in Engla… Europe
#> # … with 2,494 more rows
#> # ℹ Use `print(n = ...)` to see more rows

The kgpe data contains pedigree and population information all 3,202 samples included in the expanded 1000 Genomes Project data, which includes 602 trios.

kgpe
#> # A tibble: 3,202 × 11
#>    fid     id      pid   mid     sex sexf   pop   reg   population region phase3
#>    <chr>   <chr>   <chr> <chr> <int> <fct>  <chr> <chr> <chr>      <chr>  <lgl> 
#>  1 HG00096 HG00096 0     0         1 male   GBR   EUR   British i… Europe TRUE  
#>  2 HG00097 HG00097 0     0         2 female GBR   EUR   British i… Europe TRUE  
#>  3 HG00099 HG00099 0     0         2 female GBR   EUR   British i… Europe TRUE  
#>  4 HG00100 HG00100 0     0         2 female GBR   EUR   British i… Europe TRUE  
#>  5 HG00101 HG00101 0     0         1 male   GBR   EUR   British i… Europe TRUE  
#>  6 HG00102 HG00102 0     0         2 female GBR   EUR   British i… Europe TRUE  
#>  7 HG00103 HG00103 0     0         1 male   GBR   EUR   British i… Europe TRUE  
#>  8 HG00105 HG00105 0     0         1 male   GBR   EUR   British i… Europe TRUE  
#>  9 HG00106 HG00106 0     0         2 female GBR   EUR   British i… Europe TRUE  
#> 10 HG00107 HG00107 0     0         1 male   GBR   EUR   British i… Europe TRUE  
#> # … with 3,192 more rows
#> # ℹ Use `print(n = ...)` to see more rows

The kgpmeta contains population metadata for the 26 populations across five continental regions.

kgpmeta
#> # A tibble: 26 × 7
#>    pop   population                          reg   region   regco…¹   lat    lng
#>    <chr> <chr>                               <chr> <chr>    <chr>   <dbl>  <dbl>
#>  1 CDX   Chinese Dai in Xishuangbanna, China EAS   East As… #778500  22   101.  
#>  2 CHB   Han Chinese in Bejing, China        EAS   East As… #778500  39.9 116.  
#>  3 JPT   Japanese in Tokyo, Japan            EAS   East As… #778500  35.7 140.  
#>  4 KHV   Kinh in Ho Chi Minh City, Vietnam   EAS   East As… #778500  10.8 107.  
#>  5 CHS   Southern Han Chinese, China         EAS   East As… #778500  23.1 113.  
#>  6 BEB   Bengali in Bangladesh               SAS   South A… #c44cfd  23.7  90.4 
#>  7 GIH   Gujarati Indian in Houston,TX       SAS   South A… #c44cfd  29.8 -95.4 
#>  8 ITU   Indian Telugu in the UK             SAS   South A… #c44cfd  52.5  -1.89
#>  9 PJL   Punjabi in Lahore,Pakistan          SAS   South A… #c44cfd  31.6  74.4 
#> 10 STU   Sri Lankan Tamil in the UK          SAS   South A… #c44cfd  52.5  -1.89
#> # … with 16 more rows, and abbreviated variable name ¹​regcolor
#> # ℹ Use `print(n = ...)` to see more rows

Examples

library(dplyr)
library(ggplot2)
library(kgp)
data(kgp)

Count the number of samples in each region, or in each population:

kgp3 %>% 
  count(region) %>% 
  knitr::kable()
region n
Africa 661
America 347
East Asia 504
Europe 503
South Asia 489 
kgp3 %>% 
  count(region, population) %>% 
  knitr::kable()
region population n
Africa African Ancestry in Southwest US 61
Africa African Caribbean in Barbados 96
Africa Esan in Nigeria 99
Africa Gambian in Western Division, The Gambia 113
Africa Luhya in Webuye, Kenya 99
Africa Mende in Sierra Leone 85
Africa Yoruba in Ibadan, Nigeria 108
America Colombian in Medellin, Colombia 94
America Mexican Ancestry in Los Angeles, California 64
America Peruvian in Lima, Peru 85
America Puerto Rican in Puerto Rico 104
East Asia Chinese Dai in Xishuangbanna, China 93
East Asia Han Chinese in Bejing, China 103
East Asia Japanese in Tokyo, Japan 104
East Asia Kinh in Ho Chi Minh City, Vietnam 99
East Asia Southern Han Chinese, China 105
Europe British in England and Scotland 91
Europe Finnish in Finland 99
Europe Iberian populations in Spain 107
Europe Toscani in Italy 107
Europe Utah residents with Northern and Western European ancestry 99
South Asia Bengali in Bangladesh 86
South Asia Gujarati Indian in Houston,TX 103
South Asia Indian Telugu in the UK 102
South Asia Punjabi in Lahore,Pakistan 96
South Asia Sri Lankan Tamil in the UK 102 
kgp3 %>% 
  count(region, population) %>% 
  arrange(region, n) %>% 
  mutate(population=forcats::fct_inorder(population)) %>% 
  ggplot(aes(population, n)) + 
  geom_col(aes(fill=region)) + 
  labs(fill=NULL, x=NULL, x="N") + 
  coord_flip() + 
  theme_bw() + 
  theme(legend.position="bottom")

The latitude and longitude coordinates in kgpmeta can be used to plot a map of the locations of the 1000 Genomes populations. There is also a column for region color, which provides a hexadecimal color code to enable reproduction of the population data map as shown on the IGSR population data page. The figure below shows a static map produced using ggplot2, but interactive maps such as that shown on the IGSR population data portal can be created with the leaflet package.

pal <- kgpmeta %>% distinct(reg, regcolor) %>% tibble::deframe()
ggplot() + 
  geom_polygon(data=map_data("world"), 
               aes(long, lat, group=group), 
               col="gray30", fill="gray95", lwd=.2, alpha=.5) + 
  geom_point(data=kgpmeta, aes(lng, lat, col=reg), size=4) + 
  scale_colour_manual(values=pal) +
  theme_minimal() + 
  theme(axis.ticks = element_blank(), 
        axis.text = element_blank(), 
        axis.title = element_blank(), 
        legend.title = element_blank(),
        panel.grid = element_blank(),
        legend.position = "bottom")

Map showing locations of the 1000 Genomes Phase 3 populations.

The table below shows a selection of samples from kgpe showing pedigree information for each sample. This pedigree information could be used in downstream analysis to filter out related individuals, select only trios, or to visualize family structure.

kgpe %>% 
  filter(pid!="0" & mid!="0") %>% 
  group_by(pop) %>% 
  slice(1) %>% 
  head(12) %>% 
  arrange(reg, pop) %>% 
  select(fid:reg) %>% 
  select(-sexf) %>% 
  knitr::kable()
fid id pid mid sex pop reg
BB01 HG01881 HG01879 HG01880 2 ACB AFR
2367 NA19702 NA19700 NA19701 1 ASW AFR
NG06 HG02924 HG02923 HG02922 1 ESN AFR
GB15 HG02463 HG02461 HG02462 1 GWD AFR
SL02 HG03056 HG03054 HG03055 1 MSL AFR
CLM03 HG01114 HG01112 HG01113 2 CLM AMR
SH001 HG00405 HG00403 HG00404 2 CHS EAS
VN046 HG02015 HG02017 HG02016 1 KHV EAS
1341 NA06991 NA06993 NA06985 2 CEU EUR
IBS001 HG01502 HG01500 HG01501 1 IBS EUR
BD01 HG03008 HG03006 HG03007 1 BEB SAS
IT002 HG03719 HG03725 HG03722 2 ITU SAS

The figure below shows an example of a pedigree plot made by parsing the pedigree information using skater and plotting using kinship2. The skater package provides documentation, examples, and a vignette demonstrating how to iteratively plot all pedigrees in a given data set.

kgpe %>% 
  filter(fid=="13291") %>% 
  transmute(fid, id, dadid=pid, momid=mid, sex, affected=1) %>% 
  skater::fam2ped() %>% 
  pull(ped) %>% 
  purrr::pluck(1) %>% 
  kinship2::plot.pedigree(mar=c(4,2,4,2), cex=.8)

Trios in 1000 Genomes Project family 13291.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.