Transcript Breeding strategies - Tree Improvement Program

Computer Simulation of Breeding
Strategies & Inbreeding Depression
Adam Festa
5/12/16
General Background
• The most costly part of running a tree breeding program is
maintaining a large base population and conducting progeny
testing
• A large base population is maintained in order to keep a wide
range of genetic diversity in order to continue making genetic
and phenotypic gain while not running into the dilemma of
inbreeding depression
• Consequences of inbreeding depression on economical traits
in loblolly can be severe, therefore proper management of
future breeding strategies is critical to the longevity of
economic and genetic gain in a tree breeding program
Overall Objective
• Evaluate the ability of different breeding strategies to
mitigate the risk of long-term inbreeding depression
• Identifying the most efficient breeding strategies to
maintain or increase genetic gain while keeping
inbreeding depression to a minimum can potentially
have a large beneficial impact in breeding programs
• Computer simulations can help to accomplish this by
providing a cost effective (“free”) way to probe the
genetic architecture of economical traits, study long term
impacts of breeding strategies and evaluate their
potential for inbreeding depression
Causes of inbreeding depression
• Generally speaking, inbreeding depression is defined as a reduction
in fitness traits or mean phenotype (ex. seed # or survival rate)
among progeny of a given cross
• There are multiple hypothesis on what can give rise to inbreeding
depression such as the dominance or over-dominance models
• All of these models may hold true for different traits, however we
chose to model the expression of major deleterious recessive genes
as this type of effect has been observed in previous loblolly pine
studies
• While inbreeding depression can occur among full sibs or other
related individuals, selfing is the fastest way to achieve homozygous
expression of deleterious alleles.
Inbreeding Depression: Example
• negative allele
• positive allele
outcross
self
dead
alive
• Ex: Hunting dogs which are inbred have a much higher susceptibility
of decreased litter size or to display diseases such as hip dysplasia
Inbreeding Depression Management
• After thousands of generations, natural selection
has worked to purge the majority of these harmful
deleterious alleles from loblolly pine populations
• However, some recessive alleles are still hiding out
within families
• If these alleles could be purged, then potentially
placing restrictions on relatedness when making
future crosses among selections may not be as
important
Supporting Evidence
Effects of Inbreeding on Loblolly pine traits
• Recently published work by Graham Ford assessed the effects of inbreeding
depression on growth and quality traits for 9 families from the Coastal &
Piedmont Region
• Reported results suggest that families may differ in the amount of “genetic
load” (harmful alleles) they carry
Effect of breeding strategies on deleterious allele fixation
• Work published by Wu et al 2016. simulated 6 different breeding strategies
over 20 generations using varying levels of dominance coefficients and
allele frequency distributions
• Results indicated that different breeding strategies lead to different rates at
which detrimental or harmful allele fixation may occur
Effects of Inbreeding on Growth and Quality Traits in
Loblolly Pine (Ford et al 2015)
•
Changes in family rankings
due to the various levels of
inbreeding
•
Magnitude of inbreeding
depression can be seen to be
dependent on the trait
•
Some families show relatively
little response to high
amounts of inbreeding,
suggesting that these
families may have less
deleterious alleles
Breeding strategies and their effect on fixation of
detrimental alleles
•
•
Fig. 5 from their
publication, shown on the
left, assessed 6 different
breeding strategies over
20 generations
•
Each strategy showed
different rates at which
deleterious alleles
responsible for inbreeding
depression may reach
fixation within the
population
However, the underlying assumptions of genetic architecture and breeding strategies utilized in
the Wu et al paper doesn’t reflect that of loblolly pine
Current Breeding Population Management
• NCSUTIP 4th-cycle breeding population is currently managed using the
differential evolution algorithm
• This algorithm uses a pedigree to put constraints on relatedness among
selections which are to be mated while maintaining genetic gain
• A potential consequence of this approach is that more crosses are made
with individuals which have superior breeding values, leading to a higher coancestry in future generations
• Our goal is to develop a simulator whose genetic assumptions and breeding
strategies more accurately reflect that of loblolly pine and can potentially be
used by other crop or animal breeding programs
Simulator Overview
• The foundation of the simulator was built by Ross Whetten and
included:
– creation of a genetic map with user defined map length and # of
chromosomes
– simulated genotypes
– creation of progeny with simulated recombination
– scaled phenotypes
• A multitude of user adjustable inputs were added; including but not
limited to:
– Dominance co-efficient, Deleterious SNP frequencies and effects, # of
Quantitative Trait Loci (QTL), # of Selections, and an array of built-in
breeding and selection strategies
• The final resulting simulator can be adjusted to have a genetic
architecture, population structure, and input of breeding strategies
designed to the users preference
Proof of Concept: Effects of Inbreeding Depression
on Loblolly Pine Growth & Quality Traits
• Our simulator can produce different outcomes depending on parameters set
• The overall objective is to better understand genetic architecture seen in real
populations
Total QTL
SNP QTL
Dominance Coefficient
Major Allele Value
Minor Allele Value
Straightness
Height
Volume
1000
900
0
1
0
1000
950
1
1
-1.5
1000
960
1
1
-0.1
Proof of Concept: Impact of breeding strategies
on genetic gain
•
Similarly, setting parameters to those found in Wu et al., (with the exception of linked loci) we can
reproduce the same rates of genetic gain for 4 out of 6 of their breeding strategies over 20
generations.
Breeding Strategies
•
While each breeding strategy results in different rates of genetic gain, rank of breeding strategy
doesn’t change given different assumptions of trait control (additive, partial dominance, complete
dominance)
•
Significant reduction in expected genetic gain from traits under complete dominance
•
Knowing the type of genetic control a trait is influenced by is important in understanding the expected
genetic gain
Summary of Population Structure for All Designs
• Initial founder population of 300 unrelated individuals
• First generation includes an input cross design of 150 single
pairwise crosses
• Parental phenotypes are simulated to have an individual-tree
heritability h2=0.3. The same variance is used for all following
simulated generations
• 3,000 progeny are simulated each generation with 300 selections
made
• All following generations include 150 crosses with 20 progeny each
Proposed Population Breeding and Selection
Strategies to assess
Breeding strategies
• Random Mating
• Mate-Select type approach:
Mating pairs are first determined by which selections pass a co-ancestry threshold and then the top
mating pair is selected based upon estimated mid-parent mean, which can be done using:
• Single pair mating OR..
• Multiple pair mating scheme with individuals who have top estimated breeding values
(EBV’s)
Selection strategies
• ABLUP based on Pedigree
• GBLUP based on Genomic relationship matrix
• Phenotypic
• Selfing strategy:
Approach that makes selections from each generation based on assessment of marker effects
using GBLUP from selfed parents
• Future Selections are made based on which progeny share the least amount of those
markers found in linkage with the deleterious allele
Summary of Genetic Architecture for
Founder Population
• Map size: 1800
cM
• 12 linkage groups
• 60,000 total loci
• 500 SNP QTLs
• 200 random
valued QTLs
• Dominance
coefficient = 1
Distribution of Harmful Alleles Among
Parents
• Each individual in the
population carries a different
frequency of recessive
deleterious alleles
•
The number each parent carries
can impact the probability of
making crosses which result in
harmful alleles being in the
homozygous state
•
Potentially purging or reducing
these recessive alleles in the
population could allow for the
reduction of inbreeding when
mating closely related individuals
Example Result: Comparison of Population
vs. Selections Inbreeding Levels
Example Result: Effects of Breeding
Strategies on Genetic and Phenotypic Gain
Work In Progress…
• Current version of simulator is computationally
challenging, working to lower run time to allow for
carrying out more generations
• Incorporation of multiple pair mating strategy
similar to that used in TIP
• Assessment of selfing strategy for ability to purge
harmful alleles from the population
– Is it possible?
– If so, how many generations may it take?
Take-home Message & Prospective Impact
• Breeding & testing is the most expensive part of maintaining a breeding
program, therefore numerous benefits exist in being able to potentially limit
population size while maintaining genetic gain
• Proper management of inbreeding is an important factor to long-term
economic and genetic gain in a loblolly pine breeding program
• Families may differ in their frequency of carrying recessive deleterious
alleles
• Identifying the most efficient breeding strategies to maintain or increase
genetic gain while keeping inbreeding depression to a minimum can have a
large positive benefit on loblolly pine breeding programs
• A potential outcome of this project is identifying a prospective breeding
strategy that can purge deleterious alleles from the population allowing for a
smaller test population while maintaining an increase in genetic gain.
Acknowledgements
• PINEMAP for supporting
the funding of this project
• All the members of NCSU
Cooperative Tree
Improvement Program for
their hard work and
ongoing contributions!