attachEpiNet: Attach epistatic network to population.

In epinetr: Epistatic Network Modelling with Forward-Time Simulation

Attach epistatic network to population.

Description

Constructs and attaches a new epistatic network between QTLs to a given Population object.

Usage

attachEpiNet(pop, scaleFree = FALSE, k = 2, m = 1, additive = 0, incmat = NULL)

Arguments

pop	the Population object to which the epistatic network will be attached
scaleFree	an optional logical value indicating whether to construct a network using the Barabasi-Albert model for generating scale-free networks
k	an optional vector listing orders of interaction to include
m	an optional integer indicating the minimum number of interactions for each QTL; see details below
additive	an optional integer indicating the number of QTLs which will remain purely additive
incmat	an optional incidence matrix specifying a user-defined network

Details

attachEpiNet() can be used to construct a new epistatic network based either on stochastic processes or by adapting a user-supplied incidence matrix.

If scaleFree is FALSE, a network is constructed at random; if it is TRUE, however, a scale-free network is constructed between QTLs using the Barabasi-Albert model. (The random network is constructed using the same algorithm but without preferential attachment.)

A minimal initial network of randomly selected QTLs is first constructed, before then growing the network using randomly selected QTLs, preferentially attaching each QTL to at least m other QTLs (in the scale-free case). For increasing orders of interaction, degrees from lower orders of interaction are used when determining preferential attachment.

If a user-supplied incidence matrix is given via the incmat argument, the scaleFree, k and m arguments are ignored, and the network structure is instead derived from the incidence matrix. The matrix should be such that the rows represent QTLs and the columns represent interactions between QTLs: a 1 at both incmat[i1,j] and incmat[i2,j] means there is an interaction between the QTLs i1 and i2.

The contributions that interactions make towards the phenotypic value are generated randomly when this function is called, based on a normal distribution. By default, each order of interaction has the same variance; however, using the varfun argument, a function can be supplied to alter the variance per order of interaction.

Internally, each QTL is assigned a randomly generated value per interaction per genotype (i.e. the heterozygous genotype and the two homozygous genotypes), and these values are summed according to the value of the genotype.

Value

A copy of the supplied Population is returned, with the new epistatic network attached.

Author(s)

Dion Detterer, Paul Kwan, Cedric Gondro

References

Barabasi AL, Albert R, 'Emergence of scaling in random networks,' Science 286(5439): 509-12, 15 October 1999.

See Also

Population, getEpiNet, plot.EpiNet, addEffects

Examples

# Generate a population and attach additive effects
pop <- Population(
  popSize = 200, map = map100snp, QTL = 20,
  alleleFrequencies = runif(100),
  broadH2 = 0.9, narrowh2 = 0.6, traitVar = 40
)
pop <- addEffects(pop)

# Attach a random epistatic network with two- to four-way
# interactions between QTLs
popRnd <- attachEpiNet(pop, k = 2:4)

# Plot random network
plot(getEpiNet(popRnd))

# Attach a scale-free epistatic network with two-way interactions
# between QTLs and a minimum of three interactions per QTL
popSF <- attachEpiNet(pop, scaleFree = TRUE, m = 3)

# Plot scale-free network
plot(getEpiNet(popSF))

# Attach user-defined epistatic network
popUser <- attachEpiNet(pop, incmat = rincmat100snp)

# Plot user-defined network
plot(getEpiNet(popUser))

# Attach a random epistatic network with two- to ten-way
# interactions between QTLs and decaying variance
popDecay <- attachEpiNet(pop, k = 2:10)

epinetr documentation built on March 18, 2022, 7:01 p.m.