hypredTruePerformance: Determine the true genetic performance (genotypic value) of...
In timflutre/hypred: Simulation of Genomic Data in Applied Genetics

Description Usage Arguments Details Value Author(s) See Also Examples

A generic function to determine the true genetic performance (genotypic value) of individuals according to their genome.

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hypredTruePerformance(object, ...)
## S4 method for signature 'hypredGenome'
hypredTruePerformance(object, genotypes, DH)

`object`	an object of a class that holds information on genome parameters necessary to determine the true genetic performance of individuals, typically an `"hypredGenome"` object.
`genotypes`	Integer matrix giving the genotypes of the individuals (as a series of 1s and 0s). If `DH = FALSE`, always two adjacent rows stand for one individual (the two chromosome sets). If `DH = TRUE`, always one row stands for one individual, since then the chromosome sets are identical copies of each other.
`DH`	logical argument indicating if one row in the matrix given to `genotypes` belongs to one DH line, i.e. completely homozygous line (`TRUE`) or if two adjacent rows belong to one, DH or not, individual (`FALSE`).
`...`	Methods may require further arguments.

The additive genotypic value is determined by multiplying the effects of the QTL with the number of 1 alleles of the given QTL in an individual and then summing over the QTL. The part of the genotypic value due to dominance is determined by summing the dominance effects of all dominance QTL that are heterozygous.

Example: Assume that there is only one QTL, with both additive and dominance effect. The additive effect is 1, the dominance effect is 0.5. An individual with genotype 0-1 at this QTL would have a performance of 1.5, a individual with 0-0 a performance of 0 and an individual with 1-1 a performance of 2.

Making use of DH = TRUE, when the individuals under consideration are indeed DHs, has two advantages, one is that the comupations run a littly faster, because dominance can be ignored. The main advantage, however, is that only half the amount of data needs to be carried around because one row in the martrix is sufficient to represent the genotype. When two adjacent (identical) rows represent the genotypes of the DH individuals, using DH = FALSE gives identical results to DH = TRUE when one row stands for one DH individual.

When the individuals are not DH (or not all of them), using DH = TRUE would create nonsense results!

A one column matrix with as many elements as there were individuals, giving their true genetic performances in the order in which they appeard in the the matrix.

Frank Technow

The function hypredRecombine which is used to create progeny genomes and the function hypredNewQTL which allows to assign new QTLs to the "hypredGenome" object.

## one chromosome of length 1 M and 5 SNP
genomeDef <- hypredGenome(1, 1.0, 5)

## assign one QTL with and additive effect of 1 to the first two loci,
## with the first loci also having a dominance effect of 0.5 . Firts QTL
## is perfect marker.

genomeDef <- hypredNewQTL(genomeDef,
                          new.id.add = 1:2,
                          new.id.dom = 1,
                          new.id.per.mar = 1,
                          new.eff.add = c(1,1),
                          new.eff.dom = 0.5)

summary(genomeDef)

## QTL genotype individual 1 = 1-1; 0-1 value must be 3
## QTL genotype individual 2 = 1-0; 0-1 value must be 2.5

individuals <- matrix(c(1,0,1,0,1,
                        1,1,0,1,0,
                        1,0,1,0,1,
                        0,1,0,1,0), nrow = 4, byrow = TRUE)

gvalues <- hypredTruePerformance(genomeDef,
                                genotypes = individuals,
                                DH = FALSE)

print(gvalues)

## test
gvalues <- as.vector(gvalues)

stopifnot(all.equal(gvalues, c(3,2.5)))