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Breeding Value Estimation Protocols#

Class Family Overview#

The BreedingValueProtocol family of classes is used to estimate breeding values of individuals from phenotypes.

Summary of Breeding Value Protocol Classes#

Breeding value protocols can be found in the pybrops.breed.prot.bv module. PyBrOpS provides several simple breeding value estimation protocols, but for more complex scenarios, the user may implement his or her own custom breeding value estimation protocols using the BreedingValueProtocol interface. Breeding value estimation protocol classes are summarized in the table below.

Summary of classes in the pybrops.breed.prot.bv module#

Class Name

Class Type

Class Description

BreedingValueProtocol

Abstract

Interface for all breeding value estimation protocol classes.

TrueBreedingValue

Concrete

Class representing true breeding value calculation.

MeanPhenotypicBreedingValue

Concrete

Class representing breeding value estimation using simple means calculation.

Breeding Value Protocol Properties#

Breeding values protocols do not have any required properties defined in their interface.

Loading Class Modules#

Breeding value protocols can be imported using the following statements:

# import the BreedingValueProtocol class (an abstract interface class)
from pybrops.breed.prot.bv.BreedingValueProtocol import BreedingValueProtocol

# import the TrueBreedingValue class (a concrete implemented class)
from pybrops.breed.prot.bv.TrueBreedingValue import TrueBreedingValue

# import the MeanPhenotypicBreedingValue class (a concrete implemented class)
from pybrops.breed.prot.bv.MeanPhenotypicBreedingValue import MeanPhenotypicBreedingValue

Creating Breeding Value Estimation Protocols#

Breeding value estimation protocol class construction is entirely implementation dependent. Below is an example of how to construct a MeanPhenotypicBreedingValue object, which takes taxa and trait column names as its arguments.

bvprot = MeanPhenotypicBreedingValue(
    taxa_col = "taxa",
    taxa_grp_col = "taxa_grp",
    trait_cols = ["Trait01","Trait02"]
)

Estimating Breeding Values#

Breeding values may be estimated using the estimate method. The code below demonstrates its use.

#
# Creating a true genomic model
#

# model parameters
nfixed = 1      # number of fixed effects
ntrait = 2      # number of traits
nmisc = 0       # number of miscellaneous random effects
nadditive = 50  # number of additive marker effects

# create dummy values
beta = numpy.random.random((nfixed,ntrait))
u_misc = numpy.random.random((nmisc,ntrait))
u_a = numpy.random.random((nadditive,ntrait))
trait = numpy.array(["Trait"+str(i+1).zfill(2) for i in range(ntrait)], dtype = object)

# create additive linear genomic model
algmod = DenseAdditiveLinearGenomicModel(
    beta = beta,
    u_misc = u_misc,
    u_a = u_a,
    trait = trait,
    model_name = "example",
    params = None
)

#
# Construct random genomes
#

# shape parameters for random genomes
ntaxa = 100
nvrnt = nadditive
ngroup = 20
nchrom = 10
nphase = 2

# create random genotypes
mat = numpy.random.randint(0, 2, size = (nphase,ntaxa,nvrnt)).astype("int8")

# create taxa names
taxa = numpy.array(["Taxon"+str(i+1).zfill(3) for i in range(ntaxa)], dtype = object)

# create taxa groups
taxa_grp = numpy.random.randint(1, ngroup+1, ntaxa)
taxa_grp.sort()

# create marker variant chromsome assignments
vrnt_chrgrp = numpy.random.randint(1, nchrom+1, nvrnt)
vrnt_chrgrp.sort()

# create marker physical positions
vrnt_phypos = numpy.random.choice(1000000, size = nvrnt, replace = False)
vrnt_phypos.sort()

# create marker variant names
vrnt_name = numpy.array(["SNP"+str(i+1).zfill(4) for i in range(nvrnt)], dtype = object)

# create a phased genotype matrix from scratch using NumPy arrays
pgmat = DensePhasedGenotypeMatrix(
    mat = mat,
    taxa = taxa,
    taxa_grp = taxa_grp,
    vrnt_chrgrp = vrnt_chrgrp,
    vrnt_phypos = vrnt_phypos,
    vrnt_name = vrnt_name,
    ploidy = nphase
)

#
# Creating a phenotyping object
#

# phenotyping parameters
nenv = 3    # number of environments
nrep = 2    # number of replicates within each environment

# construct phenotyping object
ptprot = G_E_Phenotyping(
    gpmod = algmod,
    nenv = nenv,
    nrep = nrep
)

# set the narrow sense heritability
ptprot.set_h2(
    h2 = numpy.array([0.4, 0.7]),
    pgmat = pgmat
)

#
# Creating phenotypes for mean estimation
#

# phenotype individuals
pheno_df = ptprot.phenotype(pgmat)

#
# Calculating the mean values
#

# without a reference genotype matrix for alignment
bvmat1 = bvprot.estimate(pheno_df)

# with a reference genotype matrix for alignment
bvmat2 = bvprot.estimate(pheno_df, pgmat)
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