LD.Measures: LD Measures

In LDcorSV: Linkage Disequilibrium Corrected by the Structure and the Relatedness

Description

This function estimates for a set of loci:

- the usual measure of linkage disequilibrium (r^2)

- the measure of linkage disequilibrium corrected by the structure of the sample (r^2_S).

- the measure of linkage disequilibrium corrected by the relatedness of genotyped individuals (r^2_V).

- the measure of linkage disequilibrium corrected by both, the relatedness of genotyped individuals and the structure of the sample (r^2_{VS}).

This function gives extra informations on the studied loci.

Usage

LD.Measures(donnees, V = NA, S = NA, data = "G", supinfo = FALSE, na.presence=TRUE)

Arguments

donnees	Numeric matrix (N x M), where N is the number of genotypes (or haplotypes) and M is the number of markers. Matrix values are the allelic doses: - (0,1,2) for genotypes. - (0,1) for haplotypes. Row names correspond to the ID of individuals. Column names correspond to the ID of markers. Missing values are allowed.
V	Numeric matrix (N x N), where N is the number of genotypes (or haplotypes). Matrix values are coefficients of genetic variance-covariance between every pair of individuals. Row and column names must correspond to the ID of individuals. No missing value.
S	Numeric matrix (N x (1-P)), where N is the number of genotypes (or haplotypes) and P the number of sub-populations. Matrix values are the probabilities (between 0 and 1) for each genotype (or haplotype) to belong to each sub-populations. Row names must correspond to the ID of individuals. Column names correspond to the ID of sub-populations. The matrix must be inversible, if the structure is with P sub-populations, only P-1 columns are expected. No missing value.
data	Value equal to "G" or "H" depending on the type of data (Genotype or Haplotype). Default value is "G".
supinfo	Boolean indicating whether you wish to get information about the loci. If supinfo=TRUE, for each locus, the Minor Allelic Frequency (MAF), the frequency of heterozygous genotypes (only if the data are genotypes) and the missing value frequency are computed. By default, supinfo=FALSE.
na.presence	Boolean indicating the presence of missing values in data. If na.presence=FALSE (no missing data), computation of r^2_V and r^2_{VS} is largely optimized. By default, na.presence=TRUE.

Value

The returned value is a dataframe of size (M(M-1))/2 rows and C columns, where M is the number of markers and C is a number between 3 and 12 depending on options chosen by user. The first three columns contain respectively the name of the first marker, the name of the second marker and the estimated value of the usual measure of linkage disequilibrium (r^2) between these two markers.

If only V is different from NA, the fourth column contains the estimated value of the measure of linkage disequilibrium corrected by the relatedness of genotyped individuals (r^2_V).

If only S is different from NA, the fourth column contains the estimated value of the measure of linkage disequilibrium corrected by relatedness corrected by the structure of the sample (r^2_S).

If V and S are simultaneously different from NA, the fourth, fifth and sixth columns respectively contain the estimated values of r^2_V, r^2_S and r^2_{VS} (r^2 corrected by both the relatedness of genotyped individuals and the structure of the sample).

If Supinfo=TRUE, then the last six columns contain information for both loci : the MAF, the frequency of heterozygous genotype (NA if haplotype data) and the missing value frequency.

Author(s)

David Desrousseaux, Florian Sandron, Aurélie Siberchicot, Christine Cierco-Ayrolles and Brigitte Mangin

References

Mangin, B., Siberchicot, A., Nicolas, S., Doligez, A., This, P., Cierco-Ayrolles, C. (2012). Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity, 108 (3), 285-291. DOI: 10.1038/hdy.2011.73

Examples

data(data.test)
Geno <- data.test[[1]]
V.WAIS <- data.test[[2]]
S.2POP <- data.test[[3]]

LD <- LD.Measures(Geno, V = V.WAIS, S = S.2POP, data = "G", supinfo = TRUE, na.presence = TRUE)
head(LD)

Example output

Warning messages:
1: In readChild(ch) : unable to terminate child process: Permission denied
2: In readChild(ch) : unable to terminate child process: Permission denied
3: In cleanup(mc.cleanup) : unable to terminate child: Permission denied
4: In cleanup(mc.cleanup) : unable to terminate child: Permission denied
           loc1          loc2           r2         r2v         r2s        r2vs
1 chr17_4275736 chr17_5227478 0.0793638380 0.082286377 0.088923563 0.085113365
2 chr17_4275736 chr17_5240924 0.0903251616 0.082206018 0.086776112 0.081418884
3 chr17_4275736 chr17_5241230 0.0006585004 0.001078544 0.001131242 0.001206726
4 chr17_4275736 chr17_5241341 0.0045849172 0.003840849 0.002572121 0.003275527
5 chr17_4275736 chr17_5376966 0.0678289290 0.058289810 0.064277683 0.057164797
6 chr17_4275736 chr17_5377031 0.0214050460 0.015051926 0.024188299 0.016301124
  MAF.loc1 heterofreq.loc1 NAfreq.loc1    MAF.loc2 heterofreq.loc2 NAfreq.loc2
1 0.456044       0.5604396           0 0.060439560      0.09890110  0.00000000
2 0.456044       0.5604396           0 0.093406593      0.16483516  0.00000000
3 0.456044       0.5604396           0 0.022222222      0.04444444  0.01098901
4 0.456044       0.5604396           0 0.049450549      0.09890110  0.00000000
5 0.456044       0.5604396           0 0.093406593      0.16483516  0.00000000
6 0.456044       0.5604396           0 0.005494505      0.01098901  0.00000000
Error while shutting down parallel: unable to terminate some child processes
sh: 1: rm: Permission denied
Warning messages:
1: In cleanup(kill = tools::SIGKILL, detach = TRUE, shutdown = TRUE) :
  unable to terminate child: Permission denied
2: In cleanup(kill = tools::SIGKILL, detach = TRUE, shutdown = TRUE) :
  unable to terminate child: Permission denied

LDcorSV documentation built on Aug. 26, 2020, 9:06 a.m.