Last updated: 2024-09-16
Checks: 7 0
Knit directory: rss/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200623) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 941a146. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (rmd/recombination.Rmd) and HTML
(docs/recombination.html) files. If you’ve configured a
remote Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | bab3f58 | Xiang Zhu | 2020-06-24 | Build site. |
| html | baa426d | Xiang Zhu | 2020-06-23 | Build site. |
| Rmd | 7ba0e4f | Xiang Zhu | 2020-06-23 | wflow_publish("rmd/recombination.Rmd") |
The following example illustrates how to compute the “scaled population recombination rate” \(\rho_{ij}\) between SNPs \(i\) and \(j\) using HapMap genetic map.
We take six SNPs in chromosome 22 from HapMap CEU Phase 2, with the
genetic map shown below. We also get the effective diploid population
size \(N_e=11418\) from IMPUTE
software document.
position COMBINED_rate(cM/Mb) Genetic_Map(cM)
1: 14431347 8.096992 0.00000000
2: 14432618 8.131520 0.01029128
3: 14433624 8.131967 0.01847159
4: 14433659 8.132625 0.01875620
5: 14433758 8.129606 0.01956133
6: 14434713 8.024772 0.02732511To compute recombination rates from two SNPs, we use the following formula from Li and Stephens (2003):
\[ \rho = 4 \times \text{effective diploid population size} \times \text{genetic distance}. \]
For example,
As a validation, we compare our calculations above with the results
from BLIMP
(Wen and
Stephens, 2010).
The file rmb.ceu.ch22 lists recombination rate between
all adjacent markers in the panel.
library(data.table)
genetic.map.chr22 <- data.table::fread("genetic_map_chr22.txt")
legend.ceu.chr22 <- data.table::fread("legend.ceu.ch22", header = T)
rmb.ceu.chr22 <- data.table::fread("rmb.ceu.ch22")
# locate all the snps specified by the legend file
selected.pos <- intersect(genetic.map.chr22$position, legend.ceu.chr22$position)
map.index <- which(genetic.map.chr22$position %in% selected.pos)
leg.index <- which(legend.ceu.chr22$position %in% selected.pos)
# check the location is correct
location.check <- prod(genetic.map.chr22$position[map.index] == legend.ceu.chr22$position[leg.index])
if (location.check != 1) stop('locate the snps wrongly')
# calculate the shrinking coefficient: exp(-rho_{ij}/(2*m)) for adjacent (i,j)
# general formula: rho = 4 * effective population size * genetic distance
# hapmap phase 2 ceu: effective population size (Ne)=11418, haplotype size (m)=120
num.snp <- length(map.index);
ro.coef <- rep(0, num.snp);
ro.coef[1] <- 1;
map.selected <- genetic.map.chr22[map.index, ]
rcb.selected <- map.selected[[3]]
for (i in 2:num.snp){
ro.coef[i] <- exp(-4*11418*(rcb.selected[i]/100-rcb.selected[i-1]/100)/120)
}
# compare the result with the one provided in BLIMP (Wen and Stephens, 2010)
# make sure rss compute rho in the same way as BLIMP
results <- matrix(0, nrow=num.snp, ncol=2)
results[, 1] <- unlist(rmb.ceu.chr22)
results[, 2] <- ro.coef> head(results)
[,1] [,2]
[1,] 1.0000000 1.0000000
[2,] 0.9615886 0.9615886
[3,] 0.9693454 0.9693454
[4,] 0.9989173 0.9989173
[5,] 0.9969404 0.9969404
[6,] 0.9708834 0.9708834
> tail(results)
[,1] [,2]
[34021,] 0.9963701 0.9963701
[34022,] 0.9909937 0.9909937
[34023,] 0.9986032 0.9986032
[34024,] 0.9879030 0.9879030
[34025,] 0.9962124 0.9962124
[34026,] 0.9847357 0.9847357
> max(abs(results[,1]-results[, 2]))
[1] 4.999806e-08
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14)
os macOS Sonoma 14.6.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Los_Angeles
date 2024-09-16
pandoc 3.1.11 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
bslib 0.8.0 2024-07-29 [1] CRAN (R 4.4.0)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
callr 3.7.6 2024-03-25 [1] CRAN (R 4.4.0)
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.0)
devtools 2.4.5 2022-10-11 [1] CRAN (R 4.4.0)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.0)
evaluate 0.24.0 2024-06-10 [1] CRAN (R 4.4.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.4.0)
getPass 0.2-4 2023-12-10 [1] CRAN (R 4.4.0)
git2r 0.33.0 2023-11-26 [1] CRAN (R 4.4.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.4.0)
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
knitr 1.48 2024-07-07 [1] CRAN (R 4.4.0)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.0)
processx 3.8.4 2024-03-16 [1] CRAN (R 4.4.0)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
ps 1.8.0 2024-09-12 [1] CRAN (R 4.4.1)
purrr 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.0)
remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.0)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
rmarkdown 2.28 2024-08-17 [1] CRAN (R 4.4.0)
rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
sass 0.4.9.9000 2024-07-11 [1] Github (rstudio/sass@9228fcf)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
shiny 1.9.1 2024-08-01 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
tibble 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.0)
usethis 3.0.0 2024-07-29 [1] CRAN (R 4.4.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
whisker 0.4.1 2022-12-05 [1] CRAN (R 4.4.0)
workflowr * 1.7.1 2023-08-23 [1] CRAN (R 4.4.0)
xfun 0.47 2024-08-17 [1] CRAN (R 4.4.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.0)
[1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
──────────────────────────────────────────────────────────────────────────────