ldest_hap {ldsep}

Estimate haplotypic pair-wise LD using either genotypes or genotype likelihoods.

Description

Given genotype (allele dosage) or genotype likelihood data for each individual at a pair of loci, this function will calculate the maximum likelihood estimates and their corresponding asymptotic standard errors of some measures of linkage disequilibrium (LD): D, D', the Pearson correlation, the squared Pearson correlation, and the Fisher-z transformation of the Pearson correlation. This function can be used for both diploids and polyploids.

Usage

ldest_hap(
  ga,
  gb,
  K,
  reltol = 10^-8,
  nboot = 100,
  useboot = FALSE,
  pen = 2,
  grid_init = FALSE,
  se = TRUE
)

Arguments

ga

One of two possible inputs:

1. A vector of counts, containing the genotypes for each individual at the first locus. When type = "comp", the vector of genotypes may be continuous (e.g. the posterior mean genotype).

2. A matrix of genotype log-likelihoods at the first locus. The rows index the individuals and the columns index the genotypes. That is ga[i, j] is the genotype likelihood of individual i for genotype j-1.

gb

One of two possible inputs:

1. A vector of counts, containing the genotypes for each individual at the second locus. When type = "comp", the vector of genotypes may be continuous (e.g. the posterior mean genotype).

2. A matrix of genotype log-likelihoods at the second locus. The rows index the individuals and the columns index the genotypes. That is gb[i, j] is the genotype likelihood of individual i for genotype j-1.

K

The ploidy of the species. Assumed to be the same for all individuals.

reltol

The relative tolerance for the stopping criterion.

nboot

Sometimes, the MLE standard errors don't exist. So we use the bootstrap as a backup. nboot specifies the number of bootstrap iterations.

useboot

A logical. Optionally, you may always use the bootstrap to estimate the standard errors (TRUE). These will be more accurate but also much slower, so this defaults to FALSE. Only applicable if using genotype likelihoods.

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.

grid_init

A logical. Should we initialize the gradient ascent at a grid of initial values (TRUE) or just initialize at one value corresponding to the simplex point rep(0.25, 4) (FALSE)?

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

Let A and a be the reference and alternative alleles, respectively, at locus 1. Let B and b be the reference and alternative alleles, respectively, at locus 2. Let paa, pAb, paB, and pAB be the frequencies of haplotypes ab, Ab, aB, and AB, respectively. Let pA = pAb + pAB and let pB = paB + pAB The ldest returns estimates of the following measures of LD.

• D: pAB - pA pB

• D': D / Dmax, where Dmax = min(pA pB, (1 - pA) (1 - pB)) if D < 0 and Dmax = min(pA (1 - pB), pA (1 - pB)) if D > 0

• r-squared: The squared Pearson correlation, r^2 = D^2 / (pA (1 - pA) pB (1 - pB))

• r: The Pearson correlation, r = D / sqrt(pA (1 - pA) pB (1 - pB))

Estimates are obtained via maximum likelihood under the assumption of Hardy-Weinberg equilibrium. The likelihood is calculated by integrating over the possible haplotypes for each pair of genotypes.

The resulting standard errors are based on the square roots of the inverse of the negative Fisher-information. This is from standard maximum likelihood theory. The Fisher-information is known to be biased low, so the actual standard errors are probably a little bigger for small n (n < 20). In some cases the Fisher-information matrix is singular, and so we in these cases we return a bootstrap estimate of the standard error.

The standard error estimate of the squared Pearson correlation is not valid when r^2 = 0.

In cases where either SNP is estimated to be monoallelic (pA %in% c(0, 1) or pB %in% c(0, 1)), this function will return LD estimates of NA.

Value

A vector with some or all of the following elements:

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

Examples

set.seed(1)
n <- 100 # sample size
K <- 6 # ploidy

## generate some fake genotypes when LD = 0.
ga <- stats::rbinom(n = n, size = K, prob = 0.5)
gb <- stats::rbinom(n = n, size = K, prob = 0.5)
head(ga)
head(gb)

## generate some fake genotype likelihoods when LD = 0.
gamat <- t(sapply(ga, stats::dnorm, x = 0:K, sd = 1, log = TRUE))
gbmat <- t(sapply(gb, stats::dnorm, x = 0:K, sd = 1, log = TRUE))
head(gamat)
head(gbmat)

## Haplotypic LD with genotypes
ldout1 <- ldest_hap(ga = ga,
                    gb = gb,
                    K = K)
head(ldout1)

## Haplotypic LD with genotype likelihoods
ldout2 <- ldest_hap(ga = gamat,
                    gb = gbmat,
                    K = K)
head(ldout2)