pop.predict2 {PopVar}

Predict genetic variance and genetic correlations in bi-parental populations using a deterministic model

Description

Generates predictions of the genetic variance and genetic correlation in bi-parental populations using a set of deterministic equations instead of simulations.

Usage

pop.predict2(
  G.in,
  y.in,
  map.in,
  crossing.table,
  parents,
  tail.p = 0.1,
  self.gen = Inf,
  DH = FALSE,
  models = c("rrBLUP", "BayesA", "BayesB", "BayesC", "BL", "BRR"),
  ...
)

pop_predict2(
  M,
  y.in,
  marker.effects,
  map.in,
  crossing.table,
  parents,
  tail.p = 0.1,
  self.gen = Inf,
  DH = FALSE,
  models = c("rrBLUP", "BayesA", "BayesB", "BayesC", "BL", "BRR"),
  ...
)

Arguments

G.in

See G.in in pop.predict.

y.in

See y.in in pop.predict.

map.in

See map.in in pop.predict.

crossing.table

See crossing.table in pop.predict.

parents

See parents in pop.predict.

tail.p

See tail.p in pop.predict.

self.gen

The number of selfing generations in the potential cross. Can be an integer or Inf for recombinant inbreds. Note: self.gen = 1 corresponds to an F2 population.

DH

Indicator if doubled-haploids are to be induced after the number of selfing generations indicated by self.gen. For example, if self.gen = 0 and DH = TRUE, then doubled-haploids are assumed to be induced using gametes from F1 plants.

models

See models in pop.predict.

...

Additional arguments to pass depending on the choice of model.

M

A Matrix of marker genotypes of dimensions nLine x nMarker, coded as -1, 0, and 1.

marker.effects

A data frame of marker effects. The first column should include the marker name and subsequent columns should include the marker effects. Supercedes y.in if passed.

Details

Predictions are based on the deterministic equations specified by Zhong and Jannink (2007), Allier et al. (2019), and Neyhart et al. (2019).

If you select a model other than "rrBLUP", you must specify the following additional arguments:

• nIter: See pop.predict.

• burnIn: See pop.predict.

Value

A data.frame containing predictions of \mu, V_G, and \mu_{sp} for each trait for each potential bi-parental cross. When multiple traits are provided, the correlated responses and correlation between all pairs of traits is also returned.

Functions

• pop_predict2():

References

Zhong, S., and J.-L. Jannink, 2007 Using quantitative trait loci results to discriminate among crosses on the basis of their progeny mean and variance. Genetics 177: 567–576. https://doi.org/10.1534/ genetics.107.075358

Allier, A., L. Moreau, A. Charcosset, S. Teyssèdre, and C. Lehermeier, 2019 Usefulness Criterion and Post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression. G3 9: 1469–1479. doi: 10.1534/g3.119.400129

Neyhart, J.L., A.J. Lorenz, and K.P. Smith, 2019 Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses. G3 9: 3153-3165. doi: 10.1534/g3.119.400406

Examples

# Load data
data("think_barley")

# Use example data to make predictions
out <- pop.predict2(G.in = G.in_ex_imputed, y.in = y.in_ex, map.in = map.in_ex, 
                    crossing.table = cross.tab_ex)
                    
# Provide a vector of parents to predict all possible crosses (some parents
# have missing phenotypic data)
out <- pop.predict2(G.in = G.in_ex_imputed, y.in = y.in_ex, map.in = map.in_ex, 
                    parents = y.in_ex$Entry[1:5])
                    
# Make predictions for 5 crosses with various levels of inbreeding
out_list <- lapply(X = 1:10, FUN = function(self.gen) {
  out <- pop.predict2(G.in = G.in_ex_imputed, y.in = y.in_ex, map.in = map.in_ex, 
                      crossing.table = cross.tab_ex[1:5,], self.gen = self.gen)
  out$self.gen <- self.gen
  out })
               
# Plot predictions of grain yield genetic variance over levels of inbreeding
dat <- do.call("rbind", lapply(out_list, subset, trait == "Yield"))
plot(pred_varG ~ self.gen, data = dat, type = "b", 
     subset = parent1 == parent1[1] & parent2 == parent2[1])






# Load data
data("think_barley")

# Use example data to make predictions
out <- pop_predict2(M = G.in_ex_mat, y.in = y.in_ex, map.in = map.in_ex, 
                    crossing.table = cross.tab_ex)
                    
# Provide a vector of parents to predict all possible crosses (some parents
# have missing phenotypic data)
out <- pop_predict2(M = G.in_ex_mat, y.in = y.in_ex, map.in = map.in_ex, 
                    parents = y.in_ex$Entry[1:10])