GeneticsPed (version 1.34.0)

gpi: Genotype probability index

Description

gpi calculates Genotype Probability Index (GPI), which indicates the information content of genotype probabilities derived from segregation analysis.

Usage

gpi(gp, hwp)

Arguments

gp

numeric vector or matrix, individual genotype probabilities

hwp

numeric vector or matrix, Hard-Weinberg genotype probabilities

Value

• Vector of $N$ genotype probability indices, where $N$ is number of individuals

concept

• segregation
• genotype probability
• information

Details

Genotype Probability Index (GPI; Kinghorn, 1997; Percy and Kinghorn, 2005) indicates information that is contained in multi-allele genotype probabilities for diploids derived from segregation analysis, say Thallman et. al (2001a, 2001b). GPI can be used as one of the criteria to help identify which ungenotyped individuals or loci should be genotyped in order to maximise the benefit of genotyping in the population (e.g. Kinghorn, 1999).

gp and hwp arguments accept genotype probabilities for multi-allele loci. If there are two alleles (1 and 2), you should pass vector of probabilities for genotypes (11 and 12) i.e. one value for heterozygotes (12 and 21) and always skipping last homozygote. With three alleles this vector should hold probabilities for genotypes (11, 12, 13, 22, 23) as also shown bellow and in examples. hwp and gpLong2Wide functions can be used to ease the setup for gp and hwp arguments.

2 alleles: 1 and 2 11 12 --> no. dimensions = 2

3 alleles: 1, 2, and 3 11 12 13 22 23 --> no. dimensions = 5

...

5 alleles: 1, 2, 3, 4, and 5 11 12 13 14 15 22 23 24 25 33 34 35 44 45 --> no. dimensions = 14

In general, number of dimensions ($k$) for $n$ alleles is equal to:

$$k = (n * (n + 1) / 2) - 1.$$

If you have genotype probabilities for more than one individual, you can pass them to gp in a matrix form, where each row represents genotype probabilities of an individual. In case of passing matrix to gp, hwp can still accept a vector of Hardy-Weinberg genotype probabilities, which will be used for all individuals due to recycling. If hwp also gets a matrix, then it must be of the same dimension as that one passed to gp.

References

Kinghorn, B. P. (1997) An index of information content for genotype probabilities derived from segregation analysis. Genetics 145(2):479-483 http://www.genetics.org/cgi/content/abstract/145/2/479

Kinghorn, B. P. (1999) Use of segregation analysis to reduce genotyping costs. Journal of Animal Breeding and Genetics 116(3):175-180 http://dx.doi.org/10.1046/j.1439-0388.1999.00192.x

Percy, A. and Kinghorn, B. P. (2005) A genotype probability index for multiple alleles and haplotypes. Journal of Animal Breeding and Genetics 122(6):387-392 http://dx.doi.org/10.1111/j.1439-0388.2005.00553.x

Thallman, R. M. and Bennet, G. L. and Keele, J. W. and Kappes, S. M. (2001a) Efficient computation of genotype probabilities for loci with many alleles: I. Allelic peeling. Journal of Animal Science 79(1):26-33 http://jas.fass.org/cgi/reprint/79/1/34

Thallman, R. M. and Bennet, G. L. and Keele, J. W. and Kappes, S. M. (2001b) Efficient computation of genotype probabilities for loci with many alleles: II. Iterative method for large, complex pedigrees. Journal of Animal Science 79(1):34-44 http://jas.fass.org/cgi/reprint/79/1/34

See Also

hwp and gpLong2Wide

Examples

## --- Example 1 from Percy and Kinghorn (2005) ---
  ## No. alleles: 2
  ## No. individuals: 1
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 22) = (.1, .5, .4)
  ##
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2)   = (.75, .25)
  ##   Pr(11, 12,   (.75^2, 2*.75*.25,
  ##           22) =             .25^2)
  ##               = (.5625, .3750,
  ##                         .0625)

  gp <- c(.1, .5)
  hwp <- c(.5625, .3750)
  gpi(gp=gp, hwp=hwp)

  ## --- Example 1 from Percy and Kinghorn (2005) extended ---
  ## No. alleles: 2
  ## No. individuals: 2
  ## Individual genotype probabilities:
  ##   Pr_1(11, 12, 22) = (.1, .5, .4)
  ##   Pr_2(11, 12, 22) = (.2, .5, .3)

  (gp <- matrix(c(.1, .5, .2, .5), nrow=2, ncol=2, byrow=TRUE))
  gpi(gp=gp, hwp=hwp)

  ## --- Example 2 from Percy and Kinghorn (2005) ---
  ## No. alleles: 3
  ## No. individuals: 1
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 13,   (.1, .5, .0,
  ##          22, 23  =      .4, .0,
  ##              33)            .0)
  ##
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2, 3)    = (.75, .25, .0)
  ##   Pr(11, 12, 13,   (.75^2, 2*.75*.25, .0,
  ##          22, 23, =            0.25^2, .0,
  ##              33)                      .0)
  ##                  = (.5625, .3750, .0
  ##                            .0625, .0,
  ##                                   .0)

  gp <- c(.1, .5, .0, .4, .0)
  hwp <- c(.5625, .3750, .0, .0625, .0)
  gpi(gp=gp, hwp=hwp)

  ## --- Example 3 from Percy and Kinghorn (2005) ---
  ## No. alleles: 5
  ## No. individuals: 1
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2, 3, 4, 5)   = (.2, .2, .2, .2, .2)
  ##   Pr(11, 12, 13, ...) = (Pr(1)^2, 2*Pr(1)+Pr(2), 2*Pr(1)*Pr(3), ...)
  ##
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 13, ...) = gp / 2
  ##   Pr(12) = Pr(12) + .5

  (hwp <- rep(.2, times=5) %*% t(rep(.2, times=5)))
  hwp <- c(hwp[upper.tri(hwp, diag=TRUE)])
  (hwp <- hwp[1:(length(hwp) - 1)])
  gp <- hwp / 2
  gp[2] <- gp[2] + .5
  gp

  gpi(gp=gp, hwp=hwp)

  ## --- Simulate gp for n alleles and i individuals ---
  n <- 3
  i <- 10

  kAll <- (n*(n+1)/2) # without -1 here!
  k <- kAll - 1
  if(require("gtools")) {
    gp <- rdirichlet(n=i, alpha=rep(x=1, times=kAll))[, 1:k]
    hwp <- as.vector(rdirichlet(n=1, alpha=rep(x=1, times=kAll)))[1:k]
    gpi(gp=gp, hwp=hwp)
  }