, Daniel Lawson [aut]
| Title: | Linkage Disequilibrium of Ancestry (LDA) and LDA Score (LDAS) |
|---|---|
| Description: | Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>. |
| Authors: | Yaoling Yang [aut, cre]
, Daniel Lawson [aut]
|
| Maintainer: | Yaoling Yang <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.3 |
| Built: | 2024-11-06 06:14:28 UTC |
| Source: | CRAN |
This is the software for Linkage disequilibrium of ancestry (LDA) and LDA score (LDAS) which is proposed by the papar Genetic risk for Multiple Sclerosis originated in Pastoralist Steppe populations, Barrie W, Yang Y, Attfield K E, et al (2022).
LDA quantifies the correlations between the ancestry of two SNPs, measuring the proportion of individuals who have experienced a recombination leading to a change in ancestry, relative to the genome-wide baseline.
LDA score is the total amount of genome in LDA with each SNP (measured in recombination map distance), which is useful for detecting the signal of “recombinant favouring selection”.
The codes for LDA and LDAS are hosted at https://github.com/YaolingYang/LDAandLDAscore.
Maintainer: Yaoling Yang [email protected] (ORCID)
Authors:
Daniel Lawson [email protected] (ORCID)
Barrie, W., Yang, Y., Irving-Pease, E.K. et al. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. Nature 625, 321–328 (2024).
Computation of the pairwise Linkage Disequilibrium of Ancestry (LDA) between a pair of single nucleotide polymorphisms (SNPs).
cal_lda(data_resample, data_base, data_experiment, n_ancestry)cal_lda(data_resample, data_base, data_experiment, n_ancestry)
data_resample |
a data frame of the first SNP's ancestry probabilities after resampling. Different ancestry probabilities are in different columns. |
data_base |
a data frame of the first SNP's ancestry probabilities. Different ancestry probabilities are in different columns. |
data_experiment |
a data frame of the second SNP's ancestry probabilities. Different ancestry probabilities are in different columns. |
n_ancestry |
a positive integer representing the number of different ancestries. |
This function computes the LDA between two SNPs.
Resampling of one of the SNPs' painting data is required prior to implementing this function.
To compute pairwise LDA between multiple pairs of SNPs, please use LDA.
a numeric number representing the pairwise LDA of the two SNPs.
Barrie, W., Yang, Y., Irving-Pease, E.K. et al. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. Nature 625, 321–328 (2024).
# compute the LDA between the 50th SNP and the 55th SNP # painting data for the 50th SNP (2 ancestries) data_base <- cbind(LDAandLDAS::example_painting_p1[,50], LDAandLDAS::example_painting_p2[,50]) # painting data for the 55th SNP (2 ancestries) data_experiment <- cbind(LDAandLDAS::example_painting_p1[,55], LDAandLDAS::example_painting_p2[,55]) # resample painting data for the 50th SNP data_resample <- data_base[sample(1:nrow(data_base)),] #compute their pairwise LDA LDA_value <- cal_lda(data_resample,data_base,data_experiment,2)# compute the LDA between the 50th SNP and the 55th SNP # painting data for the 50th SNP (2 ancestries) data_base <- cbind(LDAandLDAS::example_painting_p1[,50], LDAandLDAS::example_painting_p2[,50]) # painting data for the 55th SNP (2 ancestries) data_experiment <- cbind(LDAandLDAS::example_painting_p1[,55], LDAandLDAS::example_painting_p2[,55]) # resample painting data for the 50th SNP data_resample <- data_base[sample(1:nrow(data_base)),] #compute their pairwise LDA LDA_value <- cal_lda(data_resample,data_base,data_experiment,2)
Example genetic maps including the physical position and the genetic distance of single nucleotide polymorphisms (SNPs).
data("example_map")data("example_map")
A data frame with 2,000 haploid genomes of the physical position, named SNP, and the genetic distance of these SNP, named gd.
data(example_map)data(example_map)
Example painting data (average ancestry probabilities) of a chromosome for population 1, including 2000 genomes (observations) and 200 single nucleotide polymorphisms (SNPs).
data("example_painting_p1")data("example_painting_p1")
A data frame with 1500 haploid genomes (observations) on 200 SNPs (variables).
data(example_painting_p1)data(example_painting_p1)
Example painting data (average ancestry probabilities) of a chromosome for population 2, including 2000 genomes (observations) and 200 single nucleotide polymorphisms (SNPs).
data("example_painting_p2")data("example_painting_p2")
A data frame with 1500 haploid genomes (observations) on 200 SNPs (variables).
data(example_painting_p2)data(example_painting_p2)
Computation of the pairwise Linkage Disequilibrium of Ancestry (LDA) between all pairs of single nucleotide polymorphisms (SNPs).
LDA(paintings, SNPidx = NULL, SNPlimit = NULL, verbose = FALSE)LDA(paintings, SNPidx = NULL, SNPlimit = NULL, verbose = FALSE)
paintings |
a list of data frames of the N*k painting data (average ancestry probabilities) from different populations (N is the number of genomes, k is the number of SNPs). |
SNPidx |
a numeric vector representing the LDA of which SNPs (indices) are computed. By default, SNPidx=NULL which specifies the LDA of all the SNPs will be computed. |
SNPlimit |
a positive integer representing the maximum number of SNPs at each side of a SNP that is used to calculate the pairwise LDA for the SNP. The value shouldn't be larger than the total number of SNPs. We may set a limit if the LDAs between SNPs far in distance are not to be investigated. |
verbose |
logical. If verbose=TRUE, print the process of calculating the pairwise LDA for the i-th SNP. By default, verbose=FALSE |
Linkage Disequilibrium of Ancestry (LDA) quantifies the correlations between the ancestry of two SNPs, measuring the proportion of individuals who have experienced a recombination leading to a change in ancestry, relative to the genome-wide baseline.
a data frame of the pairwise LDA, with SNPs in the decreasing order of physical position on a chromosome.
Barrie, W., Yang, Y., Irving-Pease, E.K. et al. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. Nature 625, 321–328 (2024).
# visualize the painting data # Painting data are the average probabilities of different populations head(LDAandLDAS::example_painting_p1[1:5,],10) # combine the painting data for two populations as a list # to make to input data for function 'LDA'. paintings=list(LDAandLDAS::example_painting_p1, LDAandLDAS::example_painting_p2) # calculate the pairwise LDA of SNPs LDA_result <- LDA(paintings) # if we only want to calculate the LDA of the 76th-85th SNP in the map # based on the 31st-130th SNP, which aims at saving the memory paintings2=list(LDAandLDAS::example_painting_p1[,31:130], LDAandLDAS::example_painting_p2[,31:130]) # note that the 76th-85th SNP in the original dataset is only the # (76-30)th-(85-30)th SNP in the new dataset (paintings2) LDA_result2 <- LDA(paintings2,SNPidx=76:85-30)# visualize the painting data # Painting data are the average probabilities of different populations head(LDAandLDAS::example_painting_p1[1:5,],10) # combine the painting data for two populations as a list # to make to input data for function 'LDA'. paintings=list(LDAandLDAS::example_painting_p1, LDAandLDAS::example_painting_p2) # calculate the pairwise LDA of SNPs LDA_result <- LDA(paintings) # if we only want to calculate the LDA of the 76th-85th SNP in the map # based on the 31st-130th SNP, which aims at saving the memory paintings2=list(LDAandLDAS::example_painting_p1[,31:130], LDAandLDAS::example_painting_p2[,31:130]) # note that the 76th-85th SNP in the original dataset is only the # (76-30)th-(85-30)th SNP in the new dataset (paintings2) LDA_result2 <- LDA(paintings2,SNPidx=76:85-30)
Computation of the Linkage Disequilibrium of Ancestry Score (LDAS) of each single nucleotide polymorphism (SNP).
LDAS( LDA_data, SNPidx = NULL, map, window = 5, runparallel = FALSE, mc.cores = 8, verbose = TRUE )LDAS( LDA_data, SNPidx = NULL, map, window = 5, runparallel = FALSE, mc.cores = 8, verbose = TRUE )
LDA_data |
a data frame of LDA between all pairs of SNPs that are within the 'window'.
SNPs should be in the decreasing order of physical position on a chromosome for both rows and columns.
This is the output from |
SNPidx |
a numeric vector denoting the LDAS of which SNPs (located in which rows of the map) are computed. All the SNPs' indices in the LDA_data should be included in SNPidx. By default, SNPidx=NULL which specifies the LDAS of all the SNPs in the map will be computed. |
map |
a data frame of the physical position and genetic distance of all the SNPs contained in 'LDA_data'. 'map' contains two columns. The first column is the physical distance (unit: b) of SNPs in the decreasing order. The second column is the genetic distance (unit: cM) of SNPs. |
window |
a positive number specifying the genetic distance that the LDA score of each SNP is computed within. By default, window=5. |
runparallel |
logical. Parallel programming or not (note: unavailable for Windows system). |
mc.cores |
a positive number specifying the number of cores used for parallel programming. By default, mc.cores=8. |
verbose |
logical. Print the process of calculating the LDA score for the i-th SNP. |
LDA score is the total amount of genome in LDA with each SNP (measured in recombination map distance). A low LDA score is the signal of “recombinant favouring selection”.
a data frame of the LDA score and its upper and lower bound at the physical position of each SNP.
Barrie, W., Yang, Y., Irving-Pease, E.K. et al. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. Nature 625, 321–328 (2024).
# visualize the painting data # Painting data are the average probabilities of different populations head(LDAandLDAS::example_painting_p1[1:5,],10) # combine the painting data for two ancestries as a list # to make to input data for function 'LDA'. paintings=list(LDAandLDAS::example_painting_p1, LDAandLDAS::example_painting_p2) # calculate the pairwise LDA of SNPs LDA_result <- LDA(paintings) # map is the data containing two columns # The first column is the physical position (unit: b) (decreasing order) # The second column is the recombination distance (unit: cM) of the SNPs head(LDAandLDAS::example_map,10) # calculate the LDA score for the SNPs LDA_score <- LDAS(LDA_result,map=LDAandLDAS::example_map,window=10) #visualize the LDA scores plot(x=LDA_score$SNP,y=LDA_score$LDAS) #' # if we only want to calculate the LDA of the 76th-85th SNP in the map # based on the 31st-130th SNP, which aims at saving the memory paintings2=list(LDAandLDAS::example_painting_p1[,31:130], LDAandLDAS::example_painting_p2[,31:130]) # note that the 76th-85th SNP in the original dataset is only the # (76-30)th-(85-30)th SNP in the new dataset (paintings2) LDA_result2 <- LDA(paintings2,SNPidx=76:85-30) # calculate the LDA score for the SNPs LDA_score2 <- LDAS(LDA_result2,SNPidx=76:85-30, map=LDAandLDAS::example_map[31:130,],window=5)# visualize the painting data # Painting data are the average probabilities of different populations head(LDAandLDAS::example_painting_p1[1:5,],10) # combine the painting data for two ancestries as a list # to make to input data for function 'LDA'. paintings=list(LDAandLDAS::example_painting_p1, LDAandLDAS::example_painting_p2) # calculate the pairwise LDA of SNPs LDA_result <- LDA(paintings) # map is the data containing two columns # The first column is the physical position (unit: b) (decreasing order) # The second column is the recombination distance (unit: cM) of the SNPs head(LDAandLDAS::example_map,10) # calculate the LDA score for the SNPs LDA_score <- LDAS(LDA_result,map=LDAandLDAS::example_map,window=10) #visualize the LDA scores plot(x=LDA_score$SNP,y=LDA_score$LDAS) #' # if we only want to calculate the LDA of the 76th-85th SNP in the map # based on the 31st-130th SNP, which aims at saving the memory paintings2=list(LDAandLDAS::example_painting_p1[,31:130], LDAandLDAS::example_painting_p2[,31:130]) # note that the 76th-85th SNP in the original dataset is only the # (76-30)th-(85-30)th SNP in the new dataset (paintings2) LDA_result2 <- LDA(paintings2,SNPidx=76:85-30) # calculate the LDA score for the SNPs LDA_score2 <- LDAS(LDA_result2,SNPidx=76:85-30, map=LDAandLDAS::example_map[31:130,],window=5)