Download (QTL) mapping for adaptive traits of tree growth in forests

Quantitative Trait Loci (QTL) mapping for
adaptive traits of tree growth in forests
2 Quercus species in western Europe
Bill Rucker, Department of Environmental and Plant Biology
Ohio University, Porter Hall 315, Athens, OH 45701; [email protected]
Spring 2014
Presentation Outline
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Basic Problem
Past Research / Future Needs;
Genomic approaches to forest tree research;
Introduction to forest tree growth processes;
Why is this research important?
Introduction to Quantitative Trait Loci mapping;
Two papers addressing QTL of adaptive traits;
Conclusions – QTL mapping and forest tree research.
Outline of the Basic Problem
• Introduction:
Identify processes determining tree growth in
forests
Control by genetic and epigenetic factors
responding dynamically to environmental signals
Growth related traits are fundamental
components for
– survival
– productivity
– reproduction
Outline of the Basic Problem
• Introduction
Research in tree growth is important
– management of adaptive genetic
variation
– sustainable forests in changing
environments
– efficiency of selective breeding for
productivity / harvests
Quick Review of Research
• Prior research:
Arabadopsis thaliana - 1° apical meristem tissue
– hormone action
– transcriptional control
– other regulatory factors
• Future needs:
Genomics of 2° growth from the vascular cambium?
Genes controlling wood growth are not unique to
tree species
Apical meristem (1°) and vascular cambium (2°)
tissues share overlapping regulatory systems
Basic Research Approaches
• Recent advances in genomics of tree growth
5 complementary approaches:
o Forward genetics – trait-based; look for mutant
alleles/phenotypes; clone and analyze segments
1) Quantitative Trait Locus (QTL) mapping
o Reverse genetics – gene-based; cloned DNA fragment
introduces a mutation into a genome; explore function
2) Linkage disequilibrium (LD) mapping and
population genomics
Basic Research Approaches
• Recent advances in genomics of tree growth
5 complementary approaches:
o Both forward and reverse genetics
3) Gene discovery using microarrays
– integrated w/ QTL and LD mapping;
4) Transgenic-based approaches
– gene activation and/or suppression
5) Genomic Selection (GS) methods
– simultaneous selection of many 1000s of markers to
improve growth and adaptive traits.
Status of forest tree growth research
 Most prior tree growth research in Populus
trichocarpa and Eucalyptus sp.
(first tree genomes sequenced)
 Continuing research in Picea (Spruce), Pinus and
Pseudotsuga (Douglas fir)
 My Focus: 2 genomic studies in trees in Fagaceae
o1 Quercus sp. (Oak) and 1 Castenea sp. (Chestnut)
1) QTL mapping for genes controlling water use efficiency
2) Comparative QTL mapping for adaptive traits
Introduction:
• Processes determining tree growth in forests
cell division and expansion in the apical and
cambial meristem tissue;
developmental and seasonal growth transitions;
efficiency of photosynthesis;
nutrient and water uptake and transport; and
ability to respond to biotic and abiotic stress
Introduction:
• Processes controlled by genetic and epigenetic
factors in response to environmental signals
• Trees develop structures for:
growth
defense mechanisms
translocation
1) Water
2) Solutes
3) Signaling molecules
Why it’s important research
• Growth related traits are fundamental
components of survival and productivity
• Two examples:
Growth to dormancy transition
– crucial in temperate and boreal zones
Pioneer species
– rapid height growth for light
– root growth toward water
• Productivity - foremost target for tree breeding
– Need constant supply
– Optimal land use
– Efficiency of scale for timber operations
Why it’s important research
• Many unknowns about quantitative traits
 Additive variance
 Number loci involved
 Magnitude of effects
 Type of gene action (e.g. dominance, epistasis, pleiotropy)
 Interactions genotype x environment
Quantitative Trait Locus (QTL) Mapping
• First step to understand growth-related traits
 Commonly used to study the evolution of populations in
response to environmental variations;
 QTL genes have allelic variants that make small,
quantitative contributions to phenotype;
 Phenotype helps reveal the location of regulatory genes or
other genomic regions affecting growth;
Arabadopsis thaliana
Left: ~5,000 plants from an
F2 generation after 22 days under
long-day photoperiod at 16°C
Right: 3 individuals from
one population
(Adapted from Salome et al., 2011)
Quantitative Trait Locus (QTL) Mapping
• General model of QTL mapping procedure – e.g. plant dormancy
Taiz and Zeiger, Plant Physiology, 5th Edition - online
Case #1: QTL controlling water use efficiency
and related traits in Quercus robur L.
Brendel, O. et al. 2008
• Genetic variation for intrinsic water use efficiency (Wi)
• Wi is a determinant of transpiration efficiency
• Wi is a complex trait - many physiological processes involved
• Higher Wi
– lose less water and/or
– maintain higher assimilation
rates
• Differences in Wi among
genotypes
– impact competition for growth
rate and/or
– survival of trees in a stand
Case #1: Water use efficiency in Quercus robur L.
• Water use efficiency (Wi) defined as the
ratio of net CO2 assimiliation rate (A) to
stomatal conductance for water vapor (gw)
Wi = A/gw
• Gas exchange measurements used to
standardize estimates of A and gw
• Model sensitivity of gw to leaf-to-air vapor
pressure deficit (sgVDP)
or
• Wi estimated by carbon stable isotope
composition (δ13C) – proxy?
– Farquhar Model (Farquhar et al. 1982)
– Crop plants
– Genetic
Environment interactions
Case #1: Water use efficiency in Quercus robur L.
• Quercus robur L. (English Oak = Pedunculate Oak)
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widely distributed
moderately drought tolerant
large intraspecific variability detected within natural stands
how do ecotypes differentiate?
Kew Gardens
EUFORGEN, 2009
Case #1: Water use efficiency in Quercus robur L.
• 4 objectives:
1) Estimate genetic and environmental components of
the phenotypic variability for Wi ;
2) Estimate the number, location and effect of genes
involved in the complex;
3) Examine stability of trait Wi over time; and
4) Analyze co-localizations of the components involved
in Wi
Case #1: Water use efficiency in Quercus robur L.
Methods and Statistical Evaluation:
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Measure leaf δ13C for C discrimination and other phenotypic traits
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Test for distribution of phenotypic values
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Shapiro-Wilks normality test; and
evaluate visually
Generate male and female genetic maps
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full-sibling population of individuals over 3 years.
n=278 individual clones from a controlled cross of 2 parents
128 evenly distributed allelic markers established
12 linkage groups identified for both maps
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representing the 12 chromosomes of Quercus species
Case #1: Water use efficiency in Quercus robur L.
Modeling gas exchange - 2 models
• Gas exchange measured
– 120 specimen subset;
– 10 parameters / clone
– 24 measurements per clone
Fig. a – 1st model
• linear correlation between
A (assimilation rate) and gw
(stomatal conductance for water vapor)
• A tightly controlled by gw
Fig. b – 2nd model
• stomatal sensitivity to different VPD
(vapor pressure deficit) levels
• estimate by the slope of the linear
regression of gw vs VPD
Clone #288
Case #1: Water use efficiency in Quercus robur L.
Results for phenotypic variation:
(sorry, no good graphs from paper)
1) A dependence on gw ;
p<0.05 for 79.3% of clones;
1) gw responds to different VPD;
p<0.05 for 28% of clones;
http://www.panoramio.com/photo/37878613
2) gw shows a higher variability compared to other leaf traits;
3) Heritability = proportion of phenotypic variance due to genetics
– highest for δ13C
– lowest for %N and leaf mass (LM)
Case #1: Water use efficiency in Quercus robur L.
QTL Detection:
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4 regions located on linkage
groups included QTL for δ13C
and other components of Wi
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6 regions on other linkage
groups included QTL for δ13C
with weaker effects
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No significant QTL detected
for stomatal density or any
gas exchange parameters
measured, calculated or
modeled.
Case #1: Water use efficiency in Quercus robur L.
Conclusions:
• QTL results in agreement with the exponential model of
Orr (1998) - adaptive traits controlled by
– few QTL with strong effects;
– many with weak effects;
• Number of QTLs detected and the variety of
co-localizations reflect the complexity of the Wi trait;
Case #1: Water use efficiency in Quercus robur L.
Conclusions:
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δ13C influenced via
• leaf chlorophyll content,
• stomatal sensitivity to vapor pressure deficit (VDP),
• leaf morphology,
• leaf nitrogen content (Rubisco function?);
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Direct co-localizations of QTLs for δ13C and A/gw
validates for Quercus robur L. the Farquhar model .
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut Based on an
Expressed Sequence Tag Consensus Map
Casasoli, M. et al. 2006
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Comparative QTL mapping for 2 major tree spp. in
Fagaceae
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Phylogenetically closely related
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Quercus robur L. (English Oak)
Castenea sativa (Sweet Chestnut)
Genera diverged ~60 mya
Both widepread throughout
Europe
 Important ecologically and economically
 Castenea also cultivated
http://stories.rbge.org.uk/archives/9191
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Orr’s model of adaptive evolution (1998, 2000)
 L-shaped distribution of factors fixed during evolution
 Adaptation - mostly w/ mutations of intermediate effect
 Does mutation achieve appropriate trade-off between
o acceptable probability of fixation and
o acceptable probablility to be favorable
• QTL has corroborated some of Orr’s model
 L-shaped distributions of QTL effects
 Few individual genes w/ large effects on variation
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Other interactions determine quantitative variation
 several interacting polymorphisms in coding and non-coding
regions of a single gene
 Epistatic interactions among alleles at different loci
 QTL important to answer basic questions :
o Number
o Effect
o Stability
 Comparative QTL among populations and species
o Identify highly conserved genomic regions
o Non-conserved QTL – important for
– local adaptation
– differentiation
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Prior work established QTLs in both species for
 timing of bud burst
 carbon isotope discrimination
 growth height
• This work compares locations for these QTLs
 Linkage groups aligned to identify orthologs
 Use of expressed sequence tag (EST) sequences
o EST-derived markers are good ‘anchor’ points for comparisons
o ESTs are coding sequences easily transferable between species;
o ESTs can be expressional and/or functional candidate genes
• Colocations between ESTs and QTLs – indicate
putative genes expressing a trait?
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Objectives:
 Long-term
o provide molecular tools for monitoring adaptive variation in
natural populations of forest trees;
o Understand genetic mechanisms of adaptive differentiation
within and between populations and species.
 Short-term
 Align 12 linkage groups of Q. robur L. and C. sativa genetic maps
using EST-derived markers;
 Compare number, effect and position of QTL controlling 3 different
adaptive traits between both species.
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Materials and Methods:
 92 ESTs used to develop molecular markers (Sequence Tag Sites
- STS) for mapping both in oak and in chestnut;
 Amplification of STS markers;
 Construct a consensus map to merge marker and linkage
information from different mapping experiments for each
species;
 Use orthologous markers from both consensus maps to identify
common intervals between oak and chestnut consensus maps;
 Define unique QTLs for each species;
 Compare QTLs between species – conserved if they map in the
same common interval.
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Results:
 51 STS markers mapped in oak;
 45 STS markers mapped in chestnut;
 55 orthologous markers mapped in both species –
o from these, 12 linkage groups could be aligned;
 37 STS markers mapped in both species;
 Homeologous linkage groups
o 2-7 common ‘anchor points’
o marker order usually conserved
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Results:
Merged data from 3 prior QTL studies onto consensus map;
 Compare QTL positions (oak / chestnut)
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2)
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Timing of bud burst – 19 / 14
Carbon isotope discrimination – 7 / 8
Height growth – (mean) 3 / 1.5
 34 common intervals identified; unique QTLs declared
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2)
3)
Timing of bud burst - 10 / 13
Carbon isotope discrimination – 7 / 5
Height growth - 6 / 5
 Colocations between oak and chestnut
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Timing of bud burst – 9; (P = 0.0002)
Carbon isotope discrimination – 0
Height growth – 2; (P = 0.1937)
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Conclusions:
 EST-derived markers (STS) are a powerful tool;
 12 linkage groups between Q. robur L. and C. sativa are
homeologous;
o Caveat: density of common orthologous anchor markers between
oak and chestnut genomes is still very low;
 Genome sizes are conserved between Q. robur L. and C.
sativa (2n=24 chromosomes);
 Preliminary results suggest relatively stable genomes with
a low proportion of repeated sequences;
Case #2: Comparison of QTL for Adaptive Traits
Between Oak and Chestnut
• Conclusions:
– Comparative QTL mapping for adaptive traits for both
species only involved full-sib family – what about rest of
population?
– QTL of low effect remain undetected ;
– Large confidence intervals for QTL = imprecise locations;
 QTL number and contribution to phenotypic variance is
overall conserved for each species;
 Highly significant statistical value for QTL controlling the
timing of bud burst observed between oak and chestnut.