Download Module 2: Introduction to Conventional Tree - Dendrome

Conifer Translational Genomics Network
Annual Meeting
Raleigh, NC
June 2008
Educational Workshop
Genomics in Tree Breeding and Forest Ecosystems
Dave Harry
Nick Wheeler
CTGN Workshop (Draft), 2008 Annual Meeting. Page 1 of 21
Conifer Translational Genomics Network
Workshop Syllabus
Genomics in Tree Breeding and Forest Ecosystems
Goals and Organization: This workshop is an intensive 5-day (approx 40 hr) shortcourse, with topics organized into 10 modules, two modules presented each day. The
workshop will provide a detailed overview of genomics-based (e.g. MAS and MAB)
breeding strategies in forestry, while maintaining sufficient breadth to be attractive to
students and researchers in the more general arena of forest ecosystems. Each module
(see topics below) will include both a lecture (PowerPoint slides) as well as an
opportunity for interactive learning (e.g. computer-based laboratory exercise or other
activity). All sessions will be held in a computer lab on the UC Davis campus.
Audience: Graduate students, post-docs, and advanced undergraduates will be
recruited from across the U.S. and internationally. There are no formal prerequisites, but
prospective students will be encouraged to have a general familiarity with basic concepts
in population / evolutionary genetics, molecular genetics, and quantitative genetics. For
each module, basic concepts will be reviewed briefly prior to introducing more advanced
content. All resource / reference materials will be provided, using a combination of
printed and online resources. We anticipate attendees will represent diverse academic
backgrounds, so the laboratory exercises will be designed to accommodate different
styles and paces of learning. All institutional and individual participants are committed to
encouraging ethnic, cultural, and gender diversity in the learning environment.
Outcomes: Upon completion of this 5-day workshop, students will have learned to:
 calculate and evaluate estimates of variation using standard population and
quantitative genetic metrics such as heterozygosity and heritability
 predict how historical or prospective natural or management activities are likely to
affect population structure
 anticipate likely impacts (genetic gain, inbreeding, etc) of standard treeimprovement activities
 outline how typical tree improvement operations are organized, and how they are
jointly influenced by institutional goals and biological constraints
 quantify and evaluate population dynamics based on information supplied by
various genetic markers
 trace the history of how various technologies have influenced the development of
different types of genetic markers
 use public computer programs to evaluate population parameters using genetic
markers
 distinguish among alternative strategies for genetic mapping of markers and QTL
 explain background concepts by which genetic markers are first developed, and
then applied, in marker-breeding applications
 predict and evaluate alternative breeding strategies based on quantitative
genetics, molecular breeding, and a combination of approaches
 locate, read, and evaluate original literature published in well-known scientific
journals, including inter-disciplinary studies (e.g. diagnostics and forensic
applications)
 attain an enhanced awareness and facility with the many rich online resources
encompassing a breadth of genomics tools and applications
CTGN Workshop (Draft), 2008 Annual Meeting. Page 2 of 21
Course Outline
I)
Day 1. Introduction and Basic Principles
A) Module 1. Basic Principles in Population and Quantitative Genetics
B) Module 2. Introduction to Conventional Tree Breeding
II) Day 2. Genetic Polymorphisms and Analyses
A) Module 3. Genetic Markers
B) Module 4. Measuring and Interpreting Marker Variation
III) Day 3. Complex Trait Dissection
A) Module 5. Genetic Maps and QTL mapping
B) Module 6. Association Testing and Mapping
IV) Day 4. Marker Assisted Breeding
A) Module 7. MAS in Tree Improvement—Concepts and Experimental Results
B) Module 8. Implementation Strategies and Economics of MAS in Tree
Improvement
V) Day 5. Gene Resource Management and Forest Ecosystem Applications
A) Module 9. Genomic Applications in Genetic Resource Management: CrossDisciplinary Studies
B) Module 10. On-line Tools and Resources
Lead Instructors will include:
 David Harry, Oregon State University
 Nicholas Wheeler, Oregon State University
 David Neale, University of California, Davis
 Jill Wegrzyn, University of California, Davis
CTGN Workshop (Draft), 2008 Annual Meeting. Page 3 of 21
Module 1: Basic Principles in Population and Quantitative
Genetics
Introduction
This module serves two purposes. The first is to introduce the goals, organization, and
general content of this week-long workshop. The second goal is to introduce basic
principles in population and quantitative genetics. Population genetics describes
relationships between allele and genotype frequencies illustrates frequency shifts due to
evolutionary forces such as migration, mutation, drift, and selection. Quantitative
genetics describes the connection between genotype and phenotype and provides tools
to illustrate how phenotypic selection changes allele frequencies. Molecular breeding is
based in both of these disciplines, so materials introduced in this module are the
foundation for all subsequent modules.
Key Messages
 Allele and genotype frequencies are easily related one to the other when populations
are unaffected by evolutionary forces, sampling errors are inconsequential, and
mating is at random. Such an idealized population is said to be in Hardy-Weinberg
equilibrium (HWE).
 Evolutionary forces affecting allele frequencies include mutation, migration, selection,
and drift.
 Shifts away from HWE conditions result in predictable shifts in genotype and/or allele
frequencies.
 Heritability measures the association of genotype and phenotype, estimated using a
statistical parameter known as variance.
 Variance estimates can be obtained in a variety of ways, e.g. from allele or genotype
frequencies, or by measuring phenotypes.
 Phenotypic variance can be partitioned into various types of causal components, such
as genetic and environmental, and each of these can be further subdivided.
Outcomes
Course attendees will:
 have a better understanding of the historical context of population and quantitative
genetics as related to other areas of genetics
 learn how population and quantitative genetics, as basic sciences, are useful in
applied fields such as plant and animal breeding, conservation genetics, and human
genetics
 have the ability to analyze, manipulate, and interpret, a variety of allelic, genotypic,
and phenotypic data
 will gain familiarity with several analytical software packages
 develop a framework of theoretical concepts upon which subsequent applications
(e.g. breeding, gene resource management) can be built
CTGN Workshop (Draft), 2008 Annual Meeting. Page 4 of 21
Module 1: Basic Principles in Population and Quantitative
Genetics
Outline
I)
Workshop Introduction and Overview
A) Overall goals and structure for the week
B) Introduce participants and instructors (group exercise).
C) Workshop philosophy, approach, and content
II) Overview of genetic approaches
A) Historical perspective and key developments
B) Interplay of various genetic approaches / pursuits, e.g.:
1) Classical and transmission genetics
2) Quantitative genetics
3) Population genetics
4) Molecular / evolutionary genetics
5) Applied: Human genetics, Conservation genetics, Applied breeding
6) Other fields: statistics, computational biology, computer science
C) Forest genetics and sources of variation
III) Overview of population genetics
A) How is pop gen useful?
B) Theoretical vs. empirical approaches and data
C) Predicting behavior of individual genes in populations.
1) Gene/allele frequencies vs. genotype frequencies
2) How allele frequencies change: selection, migration, mutation, drift
IV) Overview of quantitative genetics
A) How is QGen useful?
B) Historical context: Mendelians vs. biometricians
C) Quantitative phenotypes: multiple (to many) genes affecting a trait. How?
1) Measuring quantitative traits—statistical approaches
2) Mean and variance
3) Partitioning variance P = G + E
4) Variance components and their estimation
5) Interpreting and using genetic variation
V) Lab (Several options, so will likely have several concurrently).
A) Packages: Arlequin, Populus (UMinn)
B) Topical spreadsheets (custom-built??), illustrating: Wahlund effect; LD
calculations (crosses vs. random mating); ANOVA and estimating variance
components
C) Tutorial assistance (with instructors)
CTGN Workshop (Draft), 2008 Annual Meeting. Page 5 of 21
Module 2: Introduction to Conventional Tree Breeding
Introduction
Module 2 is designed to show how basic concepts of population and quantitative
genetics (Module 1) are used to guide elements of a tree breeding program. It explores
the fundamentals of the tree breeding cycle (breeding, testing and selection) while
conveying the distinction between various types of populations of interest: base,
breeding, selection and propagation. Module 2 develops the foundation needed to
introduce the concept of MAS applications in forest trees (Modules 7&8).
Key Messages
 Genetic improvement in forest trees typically means the application of quantitative
genetic principles to population improvement for a finite number of traits related to
wood quantity and quality.
 Tree improvement is based on the tree breeding cycle which, though conceptually
straight-forward, can be time-consuming, costly, and quite complex in its application.
 Key concepts associated with crop improvement include genetic gain, heritability,
genetic correlation, selection intensity, variance components, multi-trait selection
methods and indirect selection.
 Genetic gain is ultimately captured and deployed in plantation forests through
improved seedlings or vegetative propagules.
 The temporal, spatial and logistical complexities of tree improvement are sufficiently
described to permit later discussion of how, when and where MAS may be integrated
into a tree breeding program.
Outcomes
Course attendees will:
 Have a better understanding of the nature of crop and tree breeding practices
 Become conversant in basic concepts of population and quantitative genetics
 Develop a foundation of knowledge upon which the concepts of MAS / MAB can be
introduced
CTGN Workshop (Draft), 2008 Annual Meeting. Page 6 of 21
Module 2: Introduction to Conventional Tree Breeding
Outline
I.
Tree Improvement (definitions)
A. Objective
B. Strategy
C. Tree Breeding Objective
II. The Tree Breeding Cycle
A. Processes
1. Breeding
2. Testing
3. Selection
B. Populations
1. Base
2. Breeding
3. Selection
4. Propagation
III. Phenotypic Mass Selection
A. Selection (methods for mass selection)
B. Genetic Gain
C. Selection Intensity
D. Indirect selection
IV. Breeding and Testing
A. Objectives/Functions
B. Mating Designs
C. Field Designs
V. Data Analyses
A. Mixed Models
B. BLUP
C. Selecting Traits
D. Multiple trait selection in advanced generations
VI. Deployment
A. Seedlings - Seed Orchards
1. OP
2. Family
B. Clonal Forestry
1. Propagation techniques
2. Pros and Cons
C. Genetic Diversity
CTGN Workshop (Draft), 2008 Annual Meeting. Page 7 of 21
Module 3: Genetic Markers
Introduction
This module introduces genetic markers: what they are, how marker technologies have
changed through time, and how they are used as descriptive tools for genetic
applications. The emphasis will be on the markers themselves, rather than on analytical
approaches of marker data. Analytical methods are introduced in Module 4. Module 3
builds upon introductory materials (from Mod 1 & 2) and provides the technical
background of marker development and detection, serving as a basis for understanding
marker analyses and applications to be introduced in Modules 4 onward. Given the
current prevalence of markers derived from DNA polymorphisms, Module 3 also
describes the role of bioinformatics in processing, assembling and assessing sequence
information from a variety of sources.
Key Messages
 Genetic markers provide a means to track the inheritance of genes across
generations and their behavior within populations.
 Genetic markers are diverse, including: Mendelian morphological markers,
biochemical markers, and a variety of DNA-based markers. Markers can be either
dominant or co-dominant, and can be used to track nuclear or organellar genes
 The type and availability of genetic markers has largely been driven by detection
methods, including PCR and sequence polymorphisms
 Bioinformatics tools are essential for processing and analyzing sequence data. These
tools include generic applications as well as custom-built platforms.
Outcomes
Course attendees will:
 learn how marker technologies have evolved from distinct Mendelian visual traits to
modern DNA sequence polymorphisms
 understand how detection methods have gone hand-in-hand with the development of
new marker technologies
 be able to assess trade-offs among types of marker technologies in terms of ease of
application, marker numbers and throughput, and marker inheritance (dominant vs.
co-dominant)
 gain familiarity with standard analytical tools for assessing DNA sequence variation
CTGN Workshop (Draft), 2008 Annual Meeting. Page 8 of 21
Module 3: Genetic Markers
Outline
I)
Introduction to genetic markers
A) What is a genetic marker?
B) How are genetic markers useful?
C) Historical developments and utility
II) Marker overview: types, historical applications, examples, limitations
A) Morphological markers
B) Cytological markers
C) Biochemical markers
1) Diverse types: Blood typing, Secondary compounds, Pigments,
Monoterpenes
2) Allozymes
D) Nuclear vs. organellar markers
III) DNA markers (emphasis on SNPs, below)
A) General discussion strategy to include (for each marker type)
1) What they are and how they work
2) What’s needed to develop and implement marker detection system
3) What technology(‘s) allowed their development??
4) Advantages and disadvantages...
5) Historical context (Where we’ve come in 20-30 years)
B) RFLPs (pre-PCR)
C) PCR-based: random targets (RAPDs, AFLP, etc)
D) PCR-based: targeted amplification (SSRs, PCR-RFLPs, SCARs, ESTPs)
IV) Genomic-scale sequence-based methods (SNPs)
A) Marker Development
1) Sequence resources, reagents and approaches
2) Genes, genomes, and genome architecture
3) Processing sequence data: bioinformatics
4) SNP-calling and validation
B) Genotyping platforms and strategies
V) Lab (concurrent sessions)
A) Continue Day 1 exercises
B) Examples of DNA chromatograms, base-calling, etc
1) Generation and analysis of individual sequences
2) Phred/Phrap algorithms
C) Assembler and alignment algorithms
CTGN Workshop (Draft), 2008 Annual Meeting. Page 9 of 21
Module 4: Measuring and Interpreting Marker Variation
Introduction
This module moves from the technical side of marker detection (Module 3) to the
analytical side of marker data. Now that we have seen how marker data can be
generated, what do the data tell us? This module includes a collection of widely used
diversity statistics as well as divergence and distance metrics to assess how allelic
variation is distributed within and among populations. Theoretical expectations based on
the absence of evolutionary forces (i.e. neutrality theory) form baseline comparators
against which observations are tested. In this way, historical population behavior (such
as admixture or selection) can be inferred from contemporary observations. We also
shift gears to include analyses of data from multiple loci simultaneously, which brings
into play the role of genetic recombination and linkage disequilibrium (LD). LD plays a
crucial role in the ability of markers to predict behavior of linked functional genes which
might otherwise remain undetected. LD is the basis for mapping QTL using either
genetic linkage maps (Module 5) or association mapping (Module 6).
Key Messages
 Measures of genetic variation within populations include counts of polymorphic alleles,
heterozygosity, and proportions of polymorphic loci. These measures (or their
analogs) can be estimated for a variety of marker types, including DNA sequence
polymorphisms
 Variation among populations can be measured in terms of variance (Fst, Gst) or
distance metrics.
 Neutral theory provides a means of formulating “null hypotheses” against which
observed data can be compared
 Linkage disequilibrium measures the correlation among alleles at different loci (linked
or unlinked) and can be influenced by population history
 Haplotypes represent clusters of linked genes that tend to be inherited together, and
their size is highly variable among organisms
Outcomes
Course attendees will:
 learn how genetic variation within and among populations is measured in a variety of
ways, including using DNA polymorphisms
 learn how to estimate genetic variation using several types of marker data
 understand how population history affects measures of genetic variation
 realize that linkage disequilibrium can affect both linked and unlinked loci, and how LD
behaves over generations
 gain hands-on familiarity with several types of diversity estimators and data types
CTGN Workshop (Draft), 2008 Annual Meeting. Page 10 of 21
Module 4: Measuring and Interpreting Marker Variation
Outline
I)
Historical Perspective and Background
A) Early observations from allozymes
B) Background on neutrality
II)
“Classical” metrics
A) Levels of polymorphism: polymorphic alleles (A), proportion of polymorphic
alleles (P), heterozygosity (H)
B) Organization of variation: hierarchical populations
C) Measures of Genetic Divergence
III) What happens with multiple loci?
A) Linkage disequilibrium (LD, or D)
B) Alternative metrics
C) Other background
IV) DNA Sequence Polymorphisms
A) New dimensions and challenges
1) Evolutionary and functional considerations: homologs, orthologs, paralogs
2) SNPs vs. “indels” (defined in Mod 3)
3) Synonymous vs. non-synonymous substitutions
B) Nucleotide diversity and metrics
C) Neutrality Tests: Tajima’s D, others
D) HKA Test
E) MK Test
F) Haplotypes, haploblocks, and LD decay
1) Visualizing LD
2) Human HapMap Project
V) Examples and case studies
A) Human
B) Maize
C) Conifers
D) Others??
VI) Lab (several concurrent)
A) FASTA files and DNAsp
B) Hands-on calculations (custom spreadsheet examples) of
1) Estimates of LD, given
(a) recombination
(b) admixture
2) Nucleotide diversity estimators
CTGN Workshop (Draft), 2008 Annual Meeting. Page 11 of 21
Module 5: Genetic Maps and QTL Mapping
Introduction
Now that genetic markers have been introduced, and measures of genetic polymorphism
explored, we begin the process of applying genetic markers to dissect quantitative traits,
that is to identify QTL. In this module, we introduce the process of creating a linkage
map and show how it is used for QTL mapping. Association mapping of QTL is
introduced in Module 6. Both strategies are used for marker breeding (Modules 7&8).
Just as different types of geographic maps are used for different purposes, so do
different types of genome maps have different purposes. Genetic maps are built around
the inheritance of linked genes. This module describes how segregating populations are
used in creating genetic maps. When phenotypic data are combined with segregation
data, correlations of marker genotypes and phenotypes are combined to infer the
location of unseen QTL.
Key Messages
 Genome maps include cytological maps, physical maps, genetic maps, and in some
cases, a whole genome sequence
 Genetic maps are built on the observation that alleles of linked genes tend to be
inherited together.
 Genes affecting quantitative traits are called QTL, and their location can be inferred
from segregation data, even if QTL are not observed directly
 QTL mapping requires that both markers and phenotypes be polymorphic, and
measured in related individuals
 QTL mapping can be done without a genetic map, but using a genetic map is
advantageous because the data are used more efficiently
Outcomes
Course attendees will:
 learn about different types of genome maps and how they are useful in different ways
 learn how to test for genetic linkage, and how genetic maps are constructed
 understand how different types of mapping populations are constructed, and how
these populations can be useful
 see how mapping efficiency can be affected by availability of maps and markers, or by
controlling phenotypic variation (experimentally or through clonal replicates)
 gain hands-on familiarity with mapping QTL given segregating marker and phenotypic
data
CTGN Workshop (Draft), 2008 Annual Meeting. Page 12 of 21
Module 5: Genetic Maps and QTL Mapping
Outline
I)
Background and introduction to mapping and QTLs
II) Types of maps
A) Cytological maps
B) Physical map: research applications and genome sequencing
C) Genetic (linkage) maps
III) Creating linkage maps
A) Essential resources
1) Genetic markers
2) Mapping population: e.g. full-sib, half-sib, grand-daughter
B) Data acquisition and analysis
1) Collecting and assaying marker genotypes
2) Mapping steps
3) Mapping software
IV) Dissecting complex traits
A) By definition: complex traits are affected by many genes. How is this done?
1) Terminology (with examples): QTL, ETL, ATL, etc; & QTN
2) These are broad categories related to the type of trait being mapped
B) Key challenge: none of these phenotypes are observed directly as Mendelian
traits. Presence/absence of alternative QTL alleles is inferred from
measurements of quantitative phenotypes.
C) Experimental designs
1) Field designs
2) Trait measurement and reduction
V) Approaches for QTL Mapping
A) Single marker analysis (without a map)
B) Interval mapping (with a map)
C) Interpretation and application
1) Interpreting marker segregation and unseen QTL
2) Verifying QTL and comparative maps of QTL
3) How can QTL info be used in breeding
VI) Lab: Genetic and QTL Mapping
A) Mapping Software (e.g. Joinmap, Mapmaker, Cri-map)
B) QTL Analysis (QTL Express)
CTGN Workshop (Draft), 2008 Annual Meeting. Page 13 of 21
Module 6: Association Testing and Mapping
Introduction
Genetic maps have proven to be useful in a variety of ways, particularly for
demonstrating the feasibility of dissecting quantitative traits. For marker breeding,
however, the utility of such maps is limited by their low resolution—caused by lack of
genetic recombination in a limited number of generations. Association mapping, on the
other hand, takes advantage of genetic recombination over 10’s to 1000’s of generations
and if done properly, can thereby be accomplished with greater accuracy. In this
module, we move beyond the use of genetic maps to locate QTL (Module 5), and
introduce association mapping. In addition to phenotypic data, association mapping
requires critical analyses of multiple markers, statistical controls to account for false
positives, and an understanding possible confounding factors that might wrongly suggest
causal associations between genetic markers and phenotypes. Association mapping
provides the scientific underpinnings for MAS and MAB applications introduced in
Modules 7 & 8.
Key Messages
 Association mapping has only recently become feasible given abundant sequence
data and high throughput genotyping platforms
 Association mapping can be done at the level of whole genomes, or for selected
genomic targets, e.g. for candidate genes
 Association mapping depends on detecting non-random associations between
markers and phenotypes, typically maintained by LD
 Significant LD does not necessarily indicate causal associations since LD can also be
influenced by population history. Therefore, LD needs to be interpreted cautiously,
using appropriate experimental controls
 Methods for association mapping are developing rapidly in humans, model organisms,
and in selected agricultural plants and animals, as well as forest trees
Outcomes
Course attendees will:
 learn how LD in segregating families limits resolution in genetic maps, while enabling
the creation of fine-scale maps using association approaches
 learn how various kinds of potential confounding factors need to be accounted for in
order to properly interpret results from association studies
 understand how association mapping approaches have been strongly influenced by
studies of genetic epidemiology in humans
 understand how neutrality theory is used to formulate null hypotheses against which
to test the statistical significance of LD and marker-phenotype associations
CTGN Workshop (Draft), 2008 Annual Meeting. Page 14 of 21
Module 6: Association Testing and Mapping
Outline
I)
Background and introduction
A) Genetic and QTL mapping require pedigreed families
B) Association testing/mapping: can be done with or without families
II) General Testing/Mapping Strategies
A) Where to look?
1) Genome-wide? (requires more sequence info)
2) Or a subset—candidate genes (can be done based on ESTs)
B) What to look for
C) What type of mapping population?
1) Unrelated individuals
2) Family-based studies
III) Association testing depends on LD. How do we evaluate LD?
A) Quick review of LD statistics and properties
B) HapMap Project (and human diversity)
C) Other examples in plants and animals
IV) Association Tests: Additional background details
A) Early studies: Examples and caveats in humans
B) Testing and experimental considerations
C) Factors affecting success
V) Case studies
A) Maize I: Dwarf8 and flowering time (Thornsberry et al)
B) Maize II: TASSEL
C) Maize III:
D) Loblolly I: Gonzalez-Martinez 2007
E) Loblolly II: Gonzalez-Martinez et al 2008 Family assn testing
VI) Lab: Assn mapping
A) TASSEL
B) STRUCTURE
C) Excoffier and Heckel 2006 (NatRevGenet: Software Survival Guide)
CTGN Workshop (Draft), 2008 Annual Meeting. Page 15 of 21
Module 7: MAS in Tree Improvement – Concepts and
Experimental Results
Outline
Introduction
The purpose of Module 7 is to build upon the approaches of QTL and Association
genetic mapping (Modules 5 & 6) as tools for Marker Assisted Selection (MAS) and
Marked Assisted Breeding (MAB). Alternative approaches to MAS are introduced, their
advantages and disadvantages discussed, and results of case studies in forest trees
investigated. This module completes the foundation needed to discuss implementation
strategies for MAS and other marker applications in forest tree improvement programs
(Module 8) and natural resource management and ecosystem health (Module 9).
Key Messages
 Marker assisted selection and breeding are conceptually compelling yet applications
in crop and forest tree species are few.
 The allure to MAS in forestry is driven by the prospects of significantly reducing
generation times, decreasing field test costs, or increasing genetic gain per unit of
effort.
 Alternative approaches to MAS (LE, LD, Gene) are differentiated by the proximity of
the marker polymorphism to the functional mutation.
 Experimental results with forest trees show promise for MAS but commercial
application will require appropriate populations, large-scale gene and SNP discovery,
high throughput phenotyping and novel analytical approaches.
Outcomes
Course attendees will:
 Gain an understanding of the goals and objectives of MAS, the allure of the
technology for forest tree species, and the current status of application in industry.
 Learn to differentiate between alternative approaches to MAS, what is required to
apply each approach, and the potential utility of each.
 Acquire the guidelines for expanding the current body of knowledge on MAS in forest
trees to include all known genes and how to associate polymorphisms with phenotypic
variation.
CTGN Workshop (Draft), 2008 Annual Meeting. Page 16 of 21
Module 7: MAS in Tree Improvement – Concepts and
Experimental Results
Outline
I)
Introduction to MAS
A) Goals and Objectives
B) Allure of MAS
C) Current Status
II) Alternative Approaches to MAS
A) LE MAS, LD MAS, GENE MAS
B) MAS and the Tree Improvement Process
C) Traits Suited to MAS
III) LE MAS
A) QTL Mapping – Concept
B) Experimental Case Study: Loblolly Pine Wood Properties
C) Take Home Lessons
D) Applied Case Study: CAD null allele
1) Limitations of LE MAS
IV) LD MAS / GENE MAS
A) Association Genetics
B) Experimental Case Study: Loblolly pine Wood Properties
C) Association Genetics Components
1) Populations
2) Nucleotide Diversity / Neutrality Tests
3) LD
4) Associations
D) Experimental Case Study: Douglas-fir Adaptation Traits
V) Review
CTGN Workshop (Draft), 2008 Annual Meeting. Page 17 of 21
Module 8: Implementation Strategies and Economics of MAS in
Tree Improvement
Introduction
The primary purpose of Module 8 is to demonstrate implementation strategies for
MAS in tree improvement by building upon information developed in the previous
modules (with emphasis on Modules 2 and 6). Additionally, alternative
applications of genetic markers for managing genetic variation in breeding
programs are described or illustrated. The module concludes with an economic
analysis of MAS applications based on modeled gains and simulated
econometric models for probabilistic forecasting and risk analysis (Simetar)
developed for agricultural crops.
Key Messages
 Genetic markers have many applications in the management of genetic
variation in tree and crop breeding programs. The most common application of
markers today is in backcross breeding (introgression).
 Neutral markers can be used to enhance breeding designs.
 Strategies for use of MAS in forest trees include forward and backward
selection, early culling, skipping generations, and mate selection, though not
all programs will be amenable to all options.
 The economics of MAS in forestry may be attractive but adoption of the
technology will require convincing evidence of success.
Outcomes
Course attendees will:
 Gain an understanding of how genetic markers can play important roles in
managing crops today.
 Gain insights into how, when and where MAS may be applied in traditional tree
improvement programs.
 Learn how to evaluate the economic merits of logistically feasible but complex
and expensive genetic applications such as MAS.
Future Outcomes
We anticipate that in future years, we will expand this module to include specifics
of how to apply association genetics (LD and GENE MAS) detailing index
selection and BLUP protocols.
CTGN Workshop (Draft), 2008 Annual Meeting. Page 18 of 21
Module 8: Implementation Strategies and Economics of MAS in
Tree Improvement
Outline
I)
Marker Assisted Management of Genetic Variation
A) Quality Control
B) Characterizing Propagation Populations
C) Hybrid Breeding and Introgression
1) Mate Selection
2) Backcross Breeding
D) Enhanced Breeding Designs
1) Complementary Breeding
2) Index Breeding
3) PMX/WPA
II) Implementation Strategies for MAS in Tree Improvement
A) Scenario 1: Standard Complementary Breeding with Mainline and
Populations
1) Case 1b: Selecting Individual trees
2) Case 5: Culling Orchards
3) Cases 3/7: Early Culling
B) Scenario 2: Polymix Breeding with Paternity Analysis – No
pedigree crosses
C) Scenario 3: Complementary Breeding with Skipped Generations of
Field Testing for Elite Crosses
1) Case 4: Mate Selection
III) Modeling Gain and Econometrics
CTGN Workshop (Draft), 2008 Annual Meeting. Page 19 of 21
Elite
Module 9: Genomic Applications in Genetic Resource
Management: Cross-Disciplinary (e.g. Diagnostics, Forensics,
Ecological Genomics, & Case Studies)
Introduction
Previous modules introduced genetic principles geared primarily towards using genetic
markers and genomic tools for breeding applications. This module introduces other
tools and approaches geared toward broader questions involving genetic resource
management. For example, allozymes have long been used to describe population
structure and gene flow in forest tree populations. Similar questions can now be
addressed using DNA-based marker systems can may offer greater precision.
Examples of other applications include DNA profiling for tracing population origins, DNA
fingerprinting for forensic analyses, disease diagnostics, forest health assessments, or
possibly predicting health declines from anticipated climate change. Many of these
applications could play important roles in managing gene resources at the landscape
scale, or for related disciplines such as conservation biology and wildlife or fisheries
management.
Key Messages
 Genetic markers and genomic tools can be applied to a diverse collection of genetic
resource management issues
 Approaches encompass diagnostic tools, forensic analyses, and descriptive methods,
among others
 Timescales include contemporary populations, historical interpretations, and
predictions of future situations.
Outcomes
Course attendees will:
 have a better understanding of how genetic markers and genomic applications can be
used as genetic resource management tools in ways other than applied breeding
 become familiar with several case studies illustrating how these diverse tools have
been applied
 be better prepared to plan, implement, or coordinate such activities in the course of
their own research or professional responsibilities
CTGN Workshop (Draft), 2008 Annual Meeting. Page 20 of 21
Module 10: Online Tools and Resources
Introduction
Previous modules introduced selected online resources representing a small fraction of
the broad collection of materials available. Genome scientists have amassed a
particularly rich collection of online resources for both exploration and use. This module
will introduce an assortment of these resources, focusing on particularly useful portals
and offering commentary on effective ways of accessing these diverse collections.
Key Messages
 There is a rich collection of online resources and tools available to genomic scientists
 These collections include, among others, information sources, tools, software,
tutorials, and protocols
Outcomes
Course attendees will:
 become more aware of the availability of various online resources
 increase their proficiency in using a subset of these resources
 learn ways to efficiently, locate, evaluate, and use such resources
 acquire a list of recommended resources useful for a variety of applications
CTGN Workshop (Draft), 2008 Annual Meeting. Page 21 of 21