Download B - Dendrome

Conifer Translational Genomics Network
Coordinated Agricultural Project
www.pinegenome.org/ctgn
AN INTEGRATED RESEARCH, EDUCATION AND EXTENSION PROJECT
AIMED AT MAINTAINING AND ENHANCING HEALTHY FORESTS AND
ECOSYSTEMS BY BRINGING GENOMIC ASSISTED BREEDING TO
APPLICATION IN THE UNITED STATES.
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Workshop Introduction
Genomics in tree breeding
and forest ecosystems
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Genomics in tree breeding and forest
ecosystems
 Overall goals of the Conifer Translational Genomics Network
– What is a Coordinated Agricultural Project (CAP)?
– What’s the objective of the Conifer Translational Genomics Network
(CTGN) CAP, and how does this workshop fit in?
– How will the CTGN help forestry?
 Overall goals of this workshop
 What we‘ll be covering throughout the week
– Modules one through ten
 Meet and greet
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What is a Coordinated Agricultural Project?
 Coordinated Agricultural Project (CAP) awards are large-scale,
multimillion dollar national research and extension initiatives funded
by the Cooperative State Research, Education, and Extension
Service (CSREES), an agency within the US Department of
Agriculture (USDA)
 CAPs promote collaboration and information exchange; coordinate
activities among individuals, institutions, states, and regions; and
reduce the duplication of effort. They promote extension and
education functions.
 CAP awards encourage maximum flexibility in applied plant
genomics research
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What is the objective of the CTGN CAP?
 The Conifer Translational Genomics Network CAP seeks to deliver
genomic assisted breeding to tree improvement cooperatives by
developing genomic resources and linking laboratory and field
research with education and extension
 Comprehensive outreach education and extension programs are an
integral part of the CTGN CAP, providing diverse training
opportunities for post-doctoral researchers, graduate and
undergraduate students, tree breeders, managers, stakeholders,
and the public
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How will the CTGN help forestry?
 Developments in biotechnology have allowed plant breeders to
complement traditional phenotypic-based selection systems with
molecular markers. Selection may now include the presence or
absence of a specific alleles at known genes, either alone or in
combination with traditional phenotypes
 The CTGN‘s goal is to develop marker-based breeding applications
for tree breeding cooperatives, which provide over 1.3 billion
seedlings annually in the US
 The CTGN team will leverage decades of tree breeding experience,
institutional resources, and genomics research to enhance and
complement ongoing tree breeding activities by:
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providing new tools for tree breeding
increasing field selection efficiency and genetic gains
improving the growth potential of planted seedlings
enhancing forest health and vigor
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CTGN informs non-industrial forestry
 The genomic resources developed by CTGN have utility for tree
breeders as well as gene resource managers (Module 9)
 Application of genomics tools and resources range from
characterizing a species‘ genetic diversity to identifying specific
genes and alleles that may favor a population‘s survival under
changing climatic conditions
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Workshop goals
Upon completion the workshop, students will have learned to:
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Estimate variation using population and quantitative genetic metrics
Predict how management activities will affect population structure
Anticipate likely impacts of tree-improvement
Assess the roles of institutional goals and biological constraints in tree
improvement operations
Quantify and evaluate population dynamics
Use software to calculate population parameters based on genetic markers
Distinguish strategies for mapping markers and quantitative trait loci
Understand marker development and marker-assisted applications
Predict and evaluate alternative breeding strategies
Locate, read, and assess relevant scientific literature
Gain facility with web-based genomics tools and resources
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Workshop syllabus
Course Outline

Day 1. Introduction and Basic Principles
– Module 1. Basic Principles in Population and Quantitative Genetics
– Module 2. Introduction to Conventional Tree Breeding

Day 2. Genetic Polymorphisms and Analyses
– Module 3. Genetic Markers
– Module 4. Molecular Population Genetics

Day 3. Complex Trait Dissection
– Module 5. Genetic Maps and QTL Mapping
– Module 6. Association Genetics
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Day 4. Marker Assisted Breeding
– Module 7. Marker Informed Breeding: Concepts and Discovery
– Module 8. Marker Informed Breeding: Application and Economics (Day 5 in 2009)
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Day 5. Gene Resource Management and Forest Ecosystem Applications
– Module 9. Linear Models and Molecular Markers (Day 4 in 2009)
– Module 10. Genomic Applications in Ecosystems and Genetic Resource
Management
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Short course structure
Topics delivered in a modular format
– Two modules a day, roughly 4 hours per module
– Opportunities for evening presentations, discussions, or computer use
Modules will consist of…
– Lectures with PowerPoint slides (2-3 hours)
– Computer lab demonstrations or exercises (1-2 hours)
– Plenty of breaks
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Our Team
PROJECT DIRECTOR
David Neale - University of California, Davis
[email protected]
CO-INVESTIGATORS AND COOPERATORS
Tom Byram - Texas Forest Service
Jeff Dean - University of Georgia
David Harry - Oregon State University
Glenn Howe - Oregon State University
Dudley Huber - University of Florida
Fikret Isik - North Carolina State University
Steve McKeand - North Carolina State University
Dana Nelson - USDA Forest Service, SIFG
Brad St. Clair - USDA Forest Service, PNWRS
Jill Wegrzyn - University of California, Davis
Nick Wheeler - Oregon State University
Ross Whetten - North Carolina State University
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Introductions
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Module 1
Basic principles in
population and quantitative
genetics
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Genes and Genomes
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Quick review: Genes and genomes
In eukaryotes, DNA is found in the...
 Nucleus
Plant Cell
 Mitochondria
 Chloroplasts (plants)
 Organelle inheritance is often
uniparental, making it powerful for
certain types of applications
 For this workshop, we‘ll focus almost
exclusively on nuclear genes
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Chromosomes
Linear strands of DNA and associated
proteins in the nucleus of eukaryotic cells
 Chromosomes carry the genes and function in
the transmission of hereditary information
 Diploid cells have two copies of each
chromosome
 One copy comes from each parent
 Paternal and maternal chromosomes may have
different alleles
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Alleles
Alternative forms of a gene
 Alleles arise through mutation
 A diploid cell has two copies of each gene (i.e. two
alleles) at each locus
 Alleles on homologous chromosomes may be the
same or different (homozygous vs. heterozygous)
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Genes
Units of information on heritable traits
 In eukaryotes, genes are distributed along
chromosomes
 Each gene has a particular physical
location: a locus
 Genes encompass regulatory switches
and include both coding and non-coding
regions
 Genes are separated by intergenic
regions whose function is not understood
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The genome
An individual‘s complete genetic complement
 For eukaryotes, a haploid set of chromosomes
 For bacteria, often a single chromosome
 For viruses, one or a few DNA or RNA molecules
 Genome size is typically reported as the number of base pairs in
one genome complement (i.e. haploid for eukaryotes)
 Until recently, we studied genes and alleles one or a few at a time
(genetics)
 Aided by high throughput technologies we can now study 100‘s to
1000‘s of genes simultaneously (genomics).
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Genome size
Lambda phage
4.8 x 103 bp
E. coli
4.6 x 106 bp
Arabidopsis
1.6 x 108 bp
Cottonwood
4.8 x 108 bp
Chestnut
9.6 x 108 bp
Humans
3.0 x 109 bp
Pines
~3 x 1010 bp
Fritillaria
1.3 x 1011 bp
Amoeba dubia
6.7 x 1011 bp
www.pinegenome.org/ctgn
Douglas-fir, Pseudotsuga menziesii, has a chromosome
number of 26. It’s diploid, so that means that n=13. Most
Conifers have n=12.
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Genes and
genomes
Using what
we've learned
In this workshop, we will convey many ways
that our knowledge of genetics and
genomics can be used by breeders and land
managers
Our emphasis will be on the study of
methods that can be used to characterize or
dissect complex (quantitatively inherited)
traits and associate phenotype with
genotype, leading to marker informed
applications
To do this, we will need to review several
sub-disciplines of the science of genetics
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Genetics
To understand marker-informed breeding, we will first set
the stage by briefly reviewing…
 Mendelian genetics describes inheritance from parents to offspring
– discrete qualitative traits (including genetic markers)
– predicts frequencies of offspring given specific matings
 Population genetics describes allele and genotype frequencies over
space and time, including
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–
–
–
changes in allele frequencies between generations
environmental factors contributing to fitness
models are limited to a small number of genes
analyzes variation within and among populations
 Quantitative genetics describes variation in traits influenced by
multiple genes (continuous rather than discrete attributes)
– relies on statistical tools describing correlations among relatives
– each of many genes may have little influence on a specific trait
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Mendelian Genetics
(and the basis of genetic markers)
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Mendelian inheritance
 We begin with family resemblance: ―Like begets like.‖
 How do we explain it?
www.madeyoulaugh.com
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Traits tend to run in families
Sib 1
Family 1
Sib 2
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Sib 3
Family 2
Sib 4
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Genotype and phenotype
 Genotype refers to the particular gene or genes an individual carries
 Phenotype refers to an individual‘s observable traits
 Only rarely can we determine genotype by observing phenotype
 Genomics offers tools to better understand the relationship between
genotype and phenotype
 Individual genetic markers behave as Mendelian traits, so
understanding Mendelian traits is key to understanding markers
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Single-gene traits in trees are rare…
Here‘s one in alder (f. pinnatisecta)
P
x
F1
F1 x F1
(crossed or
selfed)
F2
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3:1
Mendel’s
predictions
hold up
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Mendel‘s insights were amazing, and yet...
 Knowledge of biological processes was rudimentary, including…
– cell division (mitosis or meiosis)
– chromosomes were not yet known
 With the discovery of chromosomes, we realized
– That genes are packaged on chromosomes
– That genes on the same chromosome are associated (genetic linkage).
Very important! We will explore this a great deal in future modules
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Markers reflect genetic polymorphisms that
are inherited in a Mendelian fashion
 DNA markers 'mark' locations where DNA varies (sequence or size)
– Such polymorphisms can vary within and among individuals (e.g.
heterozygotes vs. homozygotes) and populations
 Markers may be located in genes or elsewhere in the genome
– Historically, we've had too few markers to inform breeding
 Genomics tools provide an almost unlimited supply of markers
 Today‘s marker applications were only imagined a few years ago
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DNA markers reflect sequence variation
Marker “1”
Marker “2”
A
T
C
A
A
T
C
G
A
C
G
A
T
G
A
T
T
A
C
T
A
C
G
G
T
C
G
C
T
G<>C
T
C
C
G<>T
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Markers track inheritance
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Trait dissection using markers
Hypothetical genes (QTLs) affecting economic
traits, mapped using genetic markers a-m
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Single Nucleotide Polymorphisms
(SNPs) embedded within a DNA sequence
 DNA sequences are aligned
 Polymorphic sites are identified
 Haplotypes (closely linked markers of a specific configuration) are
deduced by direct observation or statistical inference
*
*
*
atggctacctgaactggtcaactcatcgaaagctaa
atggctacctgaactggtcaactcatcgaaagctaa
atgcctacctgaactggtcaactcatcgaaagctaa
atgcctacctgaactggtcaactcatcgaaggctaa
atgcctacctgaactggtcaacacatcgaaggctaa
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1
1
2
3
4
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Genetic linkage and recombination
 Genes on different chromosomes are inherited independently
 Genes located on the same chromosome tend to be inherited
together because they are physically linked—except that widely
separated genes behave as if they are unlinked.
 Recombination during gametogenesis breaks up parental
configurations into new (recombinant) classes
 The relative frequency of parental and recombinant gametes
reflects the degree of genetic linkage
 Genetic mapping is the process of determining the order and
relative distance between genes or markers (to be discussed in
Module 3)
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Genetic linkage and recombination
A
B
a
b
A
B
a
b
A
B
a
b
a
b
A
B
A
a
B
b
Parental gametes
(Non-recombinant)
freq = 1-r
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A
b
a
B
Grandparents
(2N)
Parents
(2N)
Gametes
(1N)
Recombinant
gametes
freq = r
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Population Genetics
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Population genetics
 Population genetics is the study of genetic differences within and
among populations of individuals, and how these differences
change across generations
 It extends Mendelian genetics to include population dynamics and
chance events such as…
–
–
–
–
survival
frequency of specific matings
random sampling from populations, and
mutation
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Population genetics
 Over time, changes among populations can lead to genetic isolation
and speciation
 Population genetics describes the mechanics of how evolution
takes place
 As we discuss genetic differences, keep in mind that
–
–
polymorphisms reflect differences among individuals within a species
divergence reflects differences between species
 We‘ll discuss more specifics on these processes later on...
– (D. L. Hartl 2000. A Primer of Population Genetics)
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What do population geneticists measure?
 Studies limited to simply-inherited traits
–
–
–
–
–
historically, this involved morphological or biochemical markers
shifted to allozymes in 1960’s to 1980’s
DNA markers became more common beginning in later 1980’s
many types of DNA markers have been developed
we’ll re-visit markers in Module 3
 Key points
– population geneticists measure discrete (Mendelian) traits
– quantitative geneticists measure continuous traits controlled by multiple
genes (we’ll talk about quantitative genetics later in this Module)
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Why study genes in populations?
 In natural populations:
– Adaptation, or the ability to survive and exploit an environmental niche,
involves the response of populations, not individuals.
 In breeding populations:
– Genetic gain—improving the average performance of populations for
desired breeding objectives—depends on selecting and breeding
parents with the best genetic potential
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Population genetics: Key questions
Population genetics provides empirical models to predict genetic
behavior for these and other situations
 What genotypes are present in a population and at
what frequency?
 Are all genotypes equally likely to survive and
reproduce?
 Are mating frequencies independent of genotype?
 Is the population stratified in some way, e.g. by
proximity, size, or the timing of natural events?
 To what extent does mating occur with individuals
outside the immediate area?
outside
 To what extent are environmental conditions stable across generations?
 Etc…
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Population genetics
Provides empirical models to predict genetic behavior
 What genotypes are present in a population and at what frequencies?
 Are all genotypes equally likely to survive and
reproduce?
 Are mating frequencies independent of genotype?
 Is the population stratified in some way, e.g. by
proximity, size, or the timing of natural events?
 To what extent does mating occur with individuals
outside the immediate area?
 To what extent are environmental conditions stable across generations?
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Population genetics
 How genetically diverse is a species or population?
– contrast diversity in populations that differ in life-history traits, pop size,
breeding structure, etc
 Are different populations closely related to one another?
– monitor diversity for conservation purposes
 What is the potential for inbreeding depression?
– what is the minimum viable population size from a genetic standpoint?
 How is genetic variation maintained?
 Identify genes/alleles responsible for phenotypic variation
 Phylogenetic and biogeographic questions
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Populations are groups of individuals whose
relatedness is usually unknown
Typical descriptive statistics
Locus ‘X’ in pop #1
Allele
A1
A2
A3
Frequency
0.2
0.5
0.3
Total = 1.0
Genotype
A1 A1
A1 A2
A1 A3
A2 A2
A2 A3
A3 A3
Frequency
0.1
0.1
0.1
0.3
0.3
0.1
Sum = 1.0
A (# alleles) = 3
Ho (observed heterozygosity) = 0.5
With data from more loci, you an also calculate,
P (% polymorphic loci) = % of loci with >1 allele
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The Hardy-Weinberg Principle
 The frequencies of alleles and genotypes in a population will
remain constant over time (given certain assumptions): describing
a static, or non-evolving population
 The frequencies of alleles and genotypes can be described
mathematically, where p and q are the frequencies of the alleles A1
and A2
Freq. A1A1 homozygote
2
Freq. A2A2 homozygote
2
p + 2pq + q = 1.0
Freq. A1A2 heterozygote
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HW proportions…
 Predict frequencies of all genotypes based on allele frequencies
 Provide a quantitative measure of variation among populations
differing in allele frequencies
 Provide a measure of within-population, heterozygosity
 Expected heterozygosity (He) is the combined frequency of all
heterozygotes calculated from allele frequencies
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Random mating restores HW proportions
each generation
White et al. 2007
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HW and random sampling
 Strictly speaking, Hardy-Weinberg proportions require certain
assumptions, such as
– an infinitely large population (translation = sampling with replacement)
– mating is at random (translation = all possible pairings of mates is
equally likely)
– no selection (which biases genotype frequencies)
– no migration (since all alleles must be sampled from the same pool)
– no mutation (which introduces new variants)
 These conditions represent an ―ideal‖ population that is rarely (if
ever) never fully realized
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HW and random sampling
 Minor violations of assumptions usually have little impact
 In practice…
– HW proportions apply for many natural populations
– breeding populations are different:
– population sizes can be small
– individuals chosen for breeding may represent a subset of relatives
– matings are often non-random
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HW : Non-random mating
When individual genotypes do not mate randomly, then HW
proportions are not observed among the offspring
 We‘ll look at two kinds of non-random mating
– population substructure/admixture
– inbreeding (mating among related individuals)
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HW : Population admixture
 Consider mixing individuals
from non-interbreeding
subpopulations (e.g. Offshore
salmon from different runs)
 Even if each subpopulation is
in HW, the admixed group is
not (p1 ≠ p2)
 The admixed group will
appear to have too many
homozygotes
 This situation is called
Wahlund‘s effect
Hartl, 2000, Fig. 2.6
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Population structure: Wahlund‘s effect
 Larger populations may be subdivided into smaller groups, which
may be difficult to delineate
– sub-population can have different allele frequencies
– each sub-population may show HW proportions
 A biologist may unknowingly sample individuals from different
subpopulations and group them together. What would you
observe?
– HW proportions in the entire sample, or
– more heterozygous individuals than predicted from HW expectations, or
– more homozygous individuals than predicted from HW expectations?
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Population structure: Wahlund‘s effect
 Wahlund’s effect: As long as allele frequencies vary among
subpopulations, even if each subpopulation exhibits HW
proportions, then more homozygotes will be observed than would
be expected based on the allele frequency of the metapopulation
 The relative increase in homozygosity is proportional to the
variance in allele frequencies among subpopulations, as measured
by F (where 0 ≤ F ≤ 1).
 There are many versions of F, formulated in different ways. Each is
a measure of increased genetic relatedness
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Inbreeding
 Inbreeding (mating among relatives) increases homozygosity
relative to HW
– rate is proportional to degree of relationship
– distant cousin < first cousin < half-sib < full-sib < self
 Recurrent inbreeding leads to a build-up of homozygosity, and a
corresponding reduction in heterozygosity
 Inbreeding affects genotype frequencies, but not allele frequencies
 How does inbreeding affect deleterious recessive alleles?
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Inbreeding and homozygosity
White et al. 2007, Fig. 5.6
 F reflects a proportional reduction in heterozygosity, and a build-up of
genetic relatedness. HW implies F= 0. With recurrent selfing, F goes to 1
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Inbreeding depression
 Inbreeding often leads to
reduced vitality (growth,
fitness)
 Deleterious recessive alleles
are made homozygous
 Outcrossing species are more
likely to suffer higher
inbreeding depression
White et al. 2007, Fig. 5.7
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Evolutionary forces change allele frequencies
 Mutation  a random heritable change in the genetic material
(DNA) - ultimate source of all new alleles
 Migration (gene flow)  the introduction of new alleles into a
population via seeds, pollen, or vegetative propagules
 Random genetic drift  the random process whereby some alleles
are not included in the next generation by chance alone
 Natural selection  the differential, non-random reproductive
success of individuals that differ in hereditary characteristics
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Mutation
 Heritable changes in DNA sequence alter allele frequencies as
new alleles are formed
 Mutations at any one locus are rare, but with sufficient time,
cumulative effects can be large
 Mutations are the ultimate source of genetic variation on which
other evolutionary forces act (e.g., natural selection)
 Effects on populations – Mutations promote differentiation (but
effects are gradual in the absence of other evolutionary forces)
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Gene flow: Migration of alleles
 Gene Flow – the movement of
alleles among populations
 Movement may occur by
individuals (via seed) or
gametes (via pollen)
 Effects on populations –
gene flow hinders
differentiation. It is a
cohesive force tends to bind
populations together
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Seed
(low gene flow)
Pollen
(high gene flow)
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Genetic drift
 Drift reflects sampling in small
populations
 Subgroups follow independent
paths
 Allele frequencies vary among
subgroups
 Frequencies in the
metapopulation remain
relatively stable
 How does F behave?
Hartl & Jones, 2004.
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Random genetic drift
 Genetic bottleneck: An
extreme form of genetic drift
that occurs when a population
is severely reduced in size
such that the surviving
population is no longer
genetically representative of
the original population
 Effects on populations –
Drift promotes differentiation
Large proportion of white beads
Some yellow beads
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Small proportion of white beads
No yellow beads
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Natural selection
 Natural selection  First proposed by Charles Darwin in mid1800‘s. The differential reproductive success of individuals that
differ in hereditary characteristics
– not all offspring survive and reproduce
– some individuals produce more offspring than others (mortality, disease,
bad luck, etc)
– offspring differ in hereditary characteristics affecting their survival
(genotype and reproduction are correlated)
– individuals that reproduce pass along their hereditary characteristics to
the next generation
– favorable characteristics become more frequent in successive
generations
 Effects on populations:
– Promotes differentiation between populations that inhabit dissimilar
environments
– Hinders differentiation between populations that inhabit similar
environments
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Selection: Numerical example
White et al. 2007, Table 5.3
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Selection: Equations
White et al. 2007, Table 5.4
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Relative fitness: Key considerations
 Which genotype has the largest relative fitness?
– determines the direction in which allele frequencies will change
 Are fitness differences large or small?
– determines rate of change over generations—fast or slow
 What is the fitness of the heterozygote compared to either
homozygote?
– reflects dominance
– complete (heterozygote identical to either homozygote)
– no dominance (additive, heterozygote is intermediate)
– partial (heterozygote more closely resembles one homozygote)
– dominance influences how selection ―sees‖ heterozygotes
– affects rate of change across generations
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Natural selection
 Fitness: the relative contribution an individual makes to the gene
pool of the next generation
Directional
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Diversifying
Stabilizing
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Gene action: Additive vs. dominance
A2A2
A1A2
A1A1
1-s
1-(1/2)s
1
additive
A2A2
A1A2
A1A1
1-s
1-hs
1
partial dominance
complete dominance
A2A2
A1A2
A1A1
1-s
1
A2A2
A1A1
A1A2
1-s2
1-s1
1
overdominance
phenotype
Jennifer Kling, OSU
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Dominance and rate of change
Hartl, 2000
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What if selection is weak or absent?
 We‘ve already seen that mutation can supply new variation that
selection may act upon
 Most mutations are deleterious and are lost, but rarely,
advantageous mutations can occur
 What about mutations that cause no effect either way?
 Neutrality theory pertains to alleles that confer no difference in
relative fitness—as if selection is oblivious to them
 We‘ll revisit the behavior of neutral alleles later on
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Measuring population structure
 Generically speaking, population structure measures the degree to which
allele frequencies vary among subpopulations
 This can be thought of in several ways
– variance among subpopulations
– heterozygosity among pairs of alleles drawn at random
 Recall, expected heterozygosity measures
– the frequency of heterozygous genotypes in a HW population
– which equals the frequency of random pairs of haploid gametes with different
alleles
 Whenever allele frequencies vary among subpopulations (regardless of the
cause), the variance in allele frequencies can be measured by F
F = (He – Ho)/He
 We‘ll revisit this in Module 4
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Population genetics: A final concept
Linkage disequilibrium (LD, also called gametic phase disequilibrium)
 Conceptually—LD is a correlation in allelic state among loci
 Numerically
– expected haplotype (gamete) frequency is the product of the two allele
frequencies, i.e. f(AB) = f(A) x f (B)
– if f(AB) = f(A) x f (B), then LD = 0
– if f(AB) ≠ f(A) x f (B), then LD ≠ 0
 LD may arise from factors such as
– recent mutations
– historical selection (hitchhiking effect)
– population admixture
 Recombination causes LD to decay over generations
 LD plays a major role in association genetics. We will revisit!
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A numeric example of LD
•
•
•
•
determine allele frequencies
ask whether f(A) x f(B) = f(AB)
repeat for f(Ab), f(aB), and f(ab)
linkage disequilibrium (LD) reflects this difference
Gamete Type
(linked)
A
B
A
b
a
B
a
b
f(A) = 0.7 f(B) = 0.6
f(a) = 0.3 f(b) = 0.4
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Gamete Frequency
No LD
0.42
0.28
0.18
0.12
Gamete Type
(unlinked)
Higher LD Lower LD
0.60
0.55
0.10
0.15
-0.05
0.30
0.25
Allele Frequencies
A
B
A
b
a
B
a
b
f(A) = 0.7 f(B) = 0.6
f(a) = 0.3 f(b) = 0.4
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Summary: Population Genetics
 Population genetics extends Mendelian genetics to describe how
allele and genotype frequencies can be predicted given certain
dynamic population processes
 For populations in Hardy-Weinberg (HW) proportions, genotype
frequencies are easily calculated given allele frequencies
 HW proportions are used as a comparative baseline
 Population genetics questions include
– How much genetic diversity (heterozygosity) are in populations?
– How is genetic diversity distributed?
– What mechanisms have shaped the diversity we observe?
 Our challenge: How can we measure, interpret, and utilize genetic
diversity?
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Quantitative Genetics
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Quantitative genetics
 Metric traits
– show continuous variation – cannot be grouped into discrete categories
– are affected by environmental influence (to a larger extent)
 Traits such as:
–
–
–
–
growth, Survival, Reproductive ability
cold hardiness, Drought hardiness
wood quality, Disease resistance
economic Traits! Adaptive Traits! Applied & Evolutionary
 Genetic Principles:
– builds upon both Mendelian and population genetics
– not limited to traits influenced by only one or a few genes
– analysis encompasses traits affected by many genes
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Quantitative genetics
 Extends analysis of Mendelian traits (with discrete phenotypes) to
metric traits (continuously distributed, often influenced by many
genes)
 Describes genetic variation based on phenotypic resemblance
among relatives
 Is usually the primary genetic tool for plant and animal breeding
 Provides the basis for evaluating the relative genetic merit of
potential parents
 Provides tools for predicting response to selection (genetic gain)
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Distinctions
 Mendelian vs metrical
– lies in the magnitude of effect
– recognizable discontinuity – Mendelian (major)
– non-recognizable discontinuity – metrical (minor)
 How can Mendelian and metrical inheritance be reconciled?
 How can we explain the continuous variation of metrical traits in
terms of the discontinuous categories of Mendelian inheritance?
– simultaneous segregation of many genes
– non-genetic or environmental variation (truly continuous effects)
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Additive trait with two genes
Consider crossing
AABB x aabb,
where phenotypically:
AABB, with 4 doses
aabb, with 0 doses
Then crossing F1:
AaBb x AaBb
Genotype/
(Dose)
AB
Ab
aB
ab
(2)
(1)
(1)
(0)
AB
(2)
AABB
(4)
AABb
(3)
AaBB
(3)
AaBb
(2)
4 doses, 1
Ab
AABb
AAbb
AaBb
Aabb
3 doses, 4
(1)
(3)
(2)
(2)
(1)
aB
(1)
AaBB
(3)
AaBb
(2)
aaBB
(2)
aaBb
(1)
ab
AaBb
Aabb
aaBb
aabb
(0)
(2)
(1)
(1)
(0)
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Doses, Color
& Number
2 doses, 6
1 doses, 4
0 doses, 1
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Now consider a trait influenced by 3 genes
 Similar to previous example
with 2 genes
 ‗Upper-case' alleles (black
dots) behave as unit doses.
 Genotypes with comparable
doses are grouped together in
colored boxes
 Colors depict groups with
identical phenotypes
 Gene effects are additive
Hartl & Jones, 2004.
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Phenotypes
 Phenotypic categories from
the previous slide are
represented here in the
histogram, with bar heights
showing the relative frequency
of each category
 Quantitative genetics
describes populations using
trait means variances, as well
as co variances among traits
and relatives
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How to describe a population?
 Mean ≈ average
 Variance is dispersion around the mean
– individual observations (usually) differ from the mean
– deviation is distance from mean
– variance is average squared deviation
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Height in humans is a quantitative trait
Students from the University of Connecticut line up by height: 5‘0‖ to
6‘5‖ in 1‖ increments. Women are in white, men are in blue
Crow 1997. Genetics 147:1-6
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Population properties for metrical traits
Means, variances, covariances
 Measuring variation within and among families allows estimating
genetic and environmental variance components
 Phenotypic resemblance among relatives allows estimating
heritability, breeding values, genetic correlations and so forth
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We also measure properties of genes
 Dominance – allelic interactions at a locus
 Epistasis - non-allelic interactions
 Pleiotropy – allelic affects on multiple traits
 Linkage – genes on the same chromosome tend to be inherited
together
 Fitness – how genes affect the likelihood that an individual survives
and reproduces (may be natural or artificial)
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Phenotypic expression of a metrical trait
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Partitioning phenotypic variance
 As we‘ve seen, an individual‘s phenotype reflects both genetic and
environmental influences, modeled as
P=G+E
 With variances shown as
Var (P) = Var (G) + Var (E)
or
σ2p = σ2G +σ2E
 Where
– P = Phenotypic variance
– G = Genetic variance
– E = Environmental variance
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Partitioning phenotypic variance
 Genetic variance includes a combination of additive and nonadditive (mostly dominance, but also epistasis and other)
G=A+ I
 With variances (and expanding from the previous slide)
Var (P) = Var (A) + Var (I) + Var (E)
Or
σ2p = σ2A +σ2I + σ2E
 Where
– A = Additive genetic variance (breeding value)
– I = Interaction, or non-additive
– E = Environmental Variance
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Breeding value (additive genetic value)
 The sum of all average allelic effects at each locus influencing the
trait of interest
– alleles, not genotypes are passed on to the next generation
 Breeding value is a concept associated with parents in a sexually
breeding population
 Historically, average allelic effects could not be measured…now
they can
– how?
– what is effect of population gene frequencies on average effect?
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Non-additive genetic variance
 Non-additive effects depend on the interactions of specific alleles
 Specific combinations of allelic effects cannot be predicted in a
general way, for example
 Dominance
– dominant (vs. recessive) gene action reflects allelic interactions for one
gene
– multiple genes can be involved simultaneously
– dominance variance summarizes all of these interactions
 Epistasis
– allelic interactions involving specific combinations of alleles of different
genes
– may involve two or more genes
– epistatic variance summarizes all of these interactions
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Genetics and the environment
 Among trees, phenotypic variation for most traits represents
more environmental variation, rather than genetic
 It‘s hard to judge the genetic value of a tree just by looking at it
 Heritability (h2) – the percentage of variation among trees that is
genetic
– h2 ranges from 0 to 100% (0 to 1)
– Heritability for growth is often only 10-30% (0.10 – 0.30)
– Low heritabilities make genetic improvement difficult
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Heritability (h2)
P= G+E
h2 = σ2G /σ2P
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E
P
G
92
92
Heritability
A measure of the degree to which the variance in the
distribution of a phenotype is due to genetic causes
 In the broad sense, heritability is measured by the total genetic
variance divided by the total phenotypic variance
 In the narrow sense, it is measured by the genetic variance due to
additive genes divided by the total phenotypic variance
 Heritability is mathematically defined in terms of population variance
components. It can only be estimated from experiments that have a
genetic structure: sexually produced offspring in this case
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Narrow-sense heritability, h2
 Thus, narrow sense heritability can be written as
h2 = σ2A/ σ2P
= σ2A/ (σ2A + σ2I + σ2E)
 Where
– σ2P is the phenotypic variance, which can be partitioned as
– σ2A is the additive genetic variance (variance among breeding values in
a reference population)
– σ2I is the interaction or non-additive genetic variance (which includes
both dominance variance and epistatic variance)
– σ2E is the variance associated with environment
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Broad sense heritability (H2, or h2b)
 Broad sense heritability is used when we deal with clones! Clones
can capture all of genetic variance due to both the additive breeding
value and the non-additive interaction effects. Thus,
H2 = σ2G / (σ2A + σ2I + σ2E)
= (σ2A + σ2I) / (σ2A + σ2I + σ2E)
 Consequently, broad sense heritability is typically larger than
narrow sense heritability and progress in achieving genetic gain can
be faster when clonal selection is possible. What might be a
drawback to clonal based programs?
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Genetic gain (G)
Level of improvement in one or more measured traits as
compared to natural or unimproved populations
 Usually expressed as a percentage
 Improvement is determined by two factors:
– Heritability of the trait (h2)
– Selection Differential (S)
 Genetic Gain = Heritability x Selection Differential
G = h2 x S
 Gain can be expressed in other ways as well
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Another expression of genetic gain
G = h2 i σp
 A related measure of genetic gain is based on three
parameters…for this measure, S = i x σp, where
– Heritability (h2 or H2)
– Measure of the degree to which the variance in the distribution
of a phenotype is due to genetic causes
– Selection intensity (i)
– Difference between the mean selection criterion of those
individuals selected to be parents and the average selection
criterion of all potential parents, expressed in standard
deviation units
– The proportion of trees selected from the population of trees
measured for the trait
– Phenotypic standard deviation of a trait (σ p)
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Predicting genetic gain
Gain = h2  (selection differential)
selection differential = i  σP
Gain = h2  i  σP
Get more gain by controlling the
environmental variation and
increasing h2
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Get more gain by selecting a
smaller proportion of the
population
98
98
Calculating the selection differential
S = 1.6
S = 2.8
S = 1.4
(from Falconer and Mackay 1996)
 Selection differential is a function of the selection intensity (i), which
is related to the proportion of the population selected, and the
variability of the population (σP):
Selection Differential = i x σp
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A little more on selection intensity
 The factor most under
breeders control
 i increases as the fraction of
trees selected decreases
 Law of diminishing returns
takes hold
 Intensity drops rapidly with
increasing number of traits
selected simultaneously (See
White et al. 2007 p. 342)
White et al 2007
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Additional complications: more traits and how
they are evaluated
 Selecting on one or a few traits is complicated enough
 As more traits are evaluated, then we must also consider
–
–
–
–
indirect selection
genetic correlations
correlated selection response
methods for multi-trait selection
 Including a few to (perhaps) thousands of genetic markers to
facilitate selection is a non-trivial matter
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Genetic correlations
 Correlations in phenotype
– may be due to genetic or environmental causes
– may be positive or negative
 Genetic causes may be due to
– pleiotropy
– linkage
– gametic phase disequilibrium
 The additive genetic correlation (correlation of breeding values) is
of greatest interest to plant breeders
– genetic correlation usually refers to the additive genetic correlation (r G
is usually rA )
 We typically measure phenotypic correlations
Falconer and Mackay, Chapt. 19; Bernardo, Chapt. 12
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Indirect selection
 Can we make greater progress from indirect selection than from
direct selection?
 Molecular markers are strongly inherited (h2~1), and yet their
correlation with a desired trait can be indirect
 Need to consider other factors in using markers (e.g. time & cost)
 Is there a benefit to practicing both direct and indirect selection at
the same time?
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Strategies for multiple trait selection
 We usually wish to improve more than one trait in a breeding
program
 Traits may be correlated or independent from each other
 Options…
– independent culling
– tandem selection
– index selection
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Independent culling
 Minimum levels of performance are set for each trait
10
9
8
7
Trait Y
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Trait X
Jennifer Kling, OSU
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Tandem selection
 Conduct one or more cycles of selection for one trait, and then
select for another trait
10
9
8
Select for trait
X
in the next
cycle
7
Trait Y
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Trait X
Jennifer Kling, OSU
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Selection indices
 Values for multiple traits are incorporated into a single index value
for selection
10
9
8
7
Trait Y
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Trait X
Jennifer Kling, OSU
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Effects of multiple trait selection
 Selection for n traits reduces selection intensity for any one trait
 Reduction in selection intensity per trait is greatest for tandem
selection, and least for index selection
 Expected response to selection
index selection ≥ independent culling ≥ tandem selection
 To this discussion we will ultimately add the approaches of
BLUP and genome wide selection
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How to estimate the genotype of a tree?
Traditionally, by measuring…
 The average performance of many ―copies‖ of the same tree (i.e.,
the same genotype)
– Clones can be produced via rooted cuttings or tissue culture
 The average performance of its offspring
 The average performance of its siblings
– (i.e., 'brothers and sisters')
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Genetic evaluation trials
 Common-garden experiments can be used to separate genetic from
environmental effects
Plantation #1
Block
#1
Block
#2
Plantation #2
Block
#1
Block
#2
Family 8
Family 6
Family 3
Family 8
Family 7
Family 2
Family 7
Family 5
Family 3
Family 9
Family 9
Family 1
Family 4
Family 8
Family 8
Family 9
Family 9
Family 5
Family 4
Family 4
Family 6
Family 1
Family 1
Family 6
Family 2
Family 7
Family 2
Family 2
Family 1
Family 4
Family 5
Family 3
Family 5
Family 3
Family 6
Family 7
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Estimating a tree‘s genotype
 Until recently, this has been accomplished using evaluation trials
 As genomics tools and platforms have developed, we are more
seriously evaluating the potential of genetic markers to augment
phenotypic assessments
– QTL mapping in pedigreed populations
– association genetics
 How might marker data be incorporated in breeding?
–
–
–
–
selection index
correlated traits
BLUP
genomic selection
 We will revisit this question over the next few days
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Summary: Quantitative genetics
 Quantitative genetics deals with metrical traits
– two or more loci, their interactions with each other and their environment
 Properties of populations and genes
 Crop improvement programs use basic parameters of means,
variances, covariances to calculate relevant heritabilities, gain, etc
 Characterizing genotypes require breeding and evaluation of
offspring or other relatives
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Overall summary:
Population and Quantitative genetics
 These disciplines provide the theoretical framework for applied
breeding
 Both disciplines rely on mathematical models and probabilities
 Models are based on approximations of how genes function
 End result…both provide predictions of genetic behavior, involving
– allele and genotype frequencies (population genetics)
– population-level phenotypes and genetic gain (quantitative genetics)
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