sldest: Sliding window LD estimation

In ldsep: Linkage Disequilibrium Shrinkage Estimation for Polyploids

Sliding window LD estimation

Description

This function is a wrapper for ldest() for estimating LD along a sliding window of a fixed size. Support is provided for parallelization through the foreach and doParallel packages.

Usage

sldest(
  geno,
  K,
  win = 50,
  nc = 1,
  type = c("hap", "comp"),
  model = c("norm", "flex"),
  pen = ifelse(type == "hap", 2, 1),
  se = TRUE
)

Arguments

geno	One of two possible inputs: A matrix of genotypes (allele dosages). The rows index the SNPs and the columns index the individuals. That is, genomat[i, j] is the allele dosage for individual j in SNP i. When type = "comp", the dosages are allowed to be continuous (e.g. posterior mean genotypes). A three-way array of genotype log-likelihoods. The first dimension indexes the SNPs, the second dimension indexes the individuals, and the third dimension indexes the genotypes. That is, genolike_array[i, j, k] is the genotype log-likelihood at SNP i for individual j and dosage k - 1.
K	The ploidy of the species. Assumed to be the same for all individuals.
win	The window size. Pairwise LD will be estimated plus or minus these many positions. Larger sizes significantly increase the computational load.
nc	The number of computing cores to use. This should never be more than the number of cores available in your computing environment. You can determine the maximum number of available cores by running parallel::detectCores() in R. This is probably fine for a personal computer, but some environments are only able to use fewer. Ask your admins if you are unsure.
type	The type of LD to calculate. The available types are haplotypic LD (type = "hap") or composite LD (type = "comp"). Haplotypic LD is only appropriate for autopolyploids when the individuals are in Hardy-Weinberg equilibrium (HWE). The composite measures of LD are always applicable, and consistently estimate the usual measures of LD when HWE is fulfilled in autopolyploids. However, when HWE is not fulfilled, interpreting the composite measures of LD could be a little tricky.
model	When type = "comp" and using genotype likelihoods, should we use the proportional bivariate normal model to estimate the genotype distribution (model = "norm"), or the general categorical distribution (model = "flex")? Defaults to "norm".
pen	The penalty to be applied to the likelihood. You can think about this as the prior sample size. Should be greater than 1. Does not apply if model = "norm", type = "comp", and using genotype likelihoods. Also does not apply when type = "comp" and using genotypes.
se	A logical. Should we calculate standard errors (TRUE) or not (FALSE). Calculating standard errors can be really slow when type = "comp", model = "flex", and when using genotype likelihoods. Otherwise, standard error calculations should be pretty fast.

Details

See ldest() for details on the different types of LD estimators supported.

Value

A data frame of class c("lddf", "data.frame") with some or all of the following elements:

i

The index of the first SNP.

j

The index of the second SNP.

snpi

The row name corresponding to SNP i, if row names are provided.

snpj

The row name corresponding to SNP j, if row names are provided.

D

The estimate of the LD coefficient.

D_se

The standard error of the estimate of the LD coefficient.

r2

The estimate of the squared Pearson correlation.

r2_se

The standard error of the estimate of the squared Pearson correlation.

r

The estimate of the Pearson correlation.

r_se

The standard error of the estimate of the Pearson correlation.

Dprime

The estimate of the standardized LD coefficient. When type = "comp", this corresponds to the standardization where we fix allele frequencies.

Dprime_se

The standard error of Dprime.

Dprimeg

The estimate of the standardized LD coefficient. This corresponds to the standardization where we fix genotype frequencies.

Dprimeg_se

The standard error of Dprimeg.

z

The Fisher-z transformation of r.

z_se

The standard error of the Fisher-z transformation of r.

p_ab

The estimated haplotype frequency of ab. Only returned if estimating the haplotypic LD.

p_Ab

The estimated haplotype frequency of Ab. Only returned if estimating the haplotypic LD.

p_aB

The estimated haplotype frequency of aB. Only returned if estimating the haplotypic LD.

p_AB

The estimated haplotype frequency of AB. Only returned if estimating the haplotypic LD.

q_ij

The estimated frequency of genotype i at locus 1 and genotype j at locus 2. Only returned if estimating the composite LD.

n

The number of individuals used to estimate pairwise LD.

Author(s)

David Gerard

See Also

ldest()

For the base function that estimates pairwise LD.

mldest()

For estimating pairwise LD between all provided SNPs.

ldfast()

Fast, moment-based approach to LD estimation that also accounts for genotype uncertainty.

format_lddf()

For formatting the output of sldest() as a matrix.

plot.lddf()

For plotting the output of sldest().

Examples

set.seed(1)

## Simulate genotypes when true correlation is 0
nloci <- 100
nind  <- 100
win <- 5
K <- 6
nc <- 1
genomat <- matrix(sample(0:K, nind * nloci, TRUE), nrow = nloci)

## Composite LD estimates
lddf <- sldest(geno = genomat,
               K = K,
               win = win,
               nc = nc,
               type = "comp")
plot(lddf, element = "z")

ldsep documentation built on Oct. 19, 2022, 1:08 a.m.