IBD
Tina Lasisi
2023-05-15 13:34:39
Last updated: 2023-05-15
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Knit directory: PODFRIDGE/
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# Load required packages
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(patchwork)
knitr::opts_knit$set(root.dir = "..")
knitr::opts_chunk$set(eval = TRUE, echo = FALSE, fig.width = 7, fig.height = 6)Load data and wrangle
Rows: 44737 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (4): ID1, POP1, ID2, POP2
dbl (1): IBD_CM_SUM
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
`summarise()` has grouped output by 'POP1'. You can override using the `.groups` argument.# A tibble: 3 × 3
# Groups: POP1 [2]
POP1 POP2 mean_ibd
<chr> <chr> <dbl>
1 ASW ASW 342.
2 CEU ASW 5.79
3 CEU CEU 17.3 
Here we see that there is much higher mean IBD in the ASW sample than expected. After checking for errors, it appears that this has to do with the nature of the HapMap sample. African Ancestry in SW USA (ASW) contains 20 parent-child duos, 13 trios, and 5 families of 2 or more with only 15 unrelated individuals (see link). Given the description of the sample, it seems really important to not use this population as a proxy for African Americans.
Below the filtered data is re-visualized as distributions for different kinship levels.
Welch Two Sample t-test
data: IBD_CM_SUM by POP1
t = 3.6655, df = 122.79, p-value = 0.0003659
alternative hypothesis: true difference in means between group ASW and group CEU is not equal to 0
95 percent confidence interval:
18.14282 60.74416
sample estimates:
mean in group ASW mean in group CEU
49.77737 10.33388 
R version 4.2.2 (2022-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.3.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.1.2 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[5] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[9] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.39 bslib_0.4.2 colorspace_2.1-0
[5] vctrs_0.6.2 generics_0.1.3 htmltools_0.5.5 yaml_2.3.7
[9] utf8_1.2.3 rlang_1.1.0 jquerylib_0.1.4 later_1.3.0
[13] pillar_1.9.0 glue_1.6.2 withr_2.5.0 RColorBrewer_1.1-3
[17] bit64_4.0.5 lifecycle_1.0.3 munsell_0.5.0 gtable_0.3.3
[21] evaluate_0.20 labeling_0.4.2 knitr_1.42 callr_3.7.3
[25] tzdb_0.3.0 fastmap_1.1.1 httpuv_1.6.9 ps_1.7.5
[29] parallel_4.2.2 fansi_1.0.4 highr_0.10 Rcpp_1.0.10
[33] promises_1.2.0.1 scales_1.2.1 cachem_1.0.7 vroom_1.6.1
[37] jsonlite_1.8.4 farver_2.1.1 bit_4.0.5 fs_1.6.1
[41] hms_1.1.3 digest_0.6.31 stringi_1.7.12 processx_3.8.1
[45] getPass_0.2-2 rprojroot_2.0.3 grid_4.2.2 cli_3.6.1
[49] tools_4.2.2 magrittr_2.0.3 sass_0.4.5 crayon_1.5.2
[53] whisker_0.4.1 pkgconfig_2.0.3 timechange_0.2.0 rmarkdown_2.21
[57] httr_1.4.5 rstudioapi_0.14 R6_2.5.1 git2r_0.32.0
[61] compiler_4.2.2