Tales of Genetic Mapping:From Transcripts to

Complex Traits

Todd Vision

Department of Biology

University of North Carolina at Chapel Hill

Genetic mapping

• To dissect the genetic basis of phenotypic variation

• Particularly important for– Naturally occuring alleles– Complex traits (eg polygenes)– Non-model systems

Experimental Populations

• Most powerful design for measuring the effects of Quantitative Trait Loci (QTL)

• Cross two inbred lines• In segregating progeny

– Measure the phenotype – Genotype markers throughout the genome

• Model the relationship between genotype and phenotype along the genome

Limitations of QTL mapping• Simplistic statistical models• Variation is cross-dependent• Minor loci do not surpass genome-wide

significance thresholds• Low resolution (10-20 cM)• To go from QTL to gene, additional resources

are needed– Transcript maps– Markers cross-referenced to a model genome– Large-insert clones– Transgenics

Outline

• Design of transcript mapping projects

• QTL mapping of complex plant physiological traits– Aluminum tolerance in Arabidopsis– Carbon-water balance in tomato and rice

Outline

• Designing a transcript mapping project

• QTL mapping of physiological variation in plants– Aluminum tolerance– Carbon-water balance

Transcript mapping

• Uses Expressed Sequence Tags (ESTs) as markers

• Can provide candidate genes for QTL-regions

• Allows comparative mapping between model and non-model species– Anchoring markers can be matched in

silico

Experimental design for transcript mapping

• With Dan Brown (U of Waterloo)• Size of mapping population and number of

markers dictate the number of genotypes one needs to score

• But there are a finite number of crossovers in a population – scoring more markers cannot fix this

• How can we maximize the resolution of our map with minimal genotyping?

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Bins are population-specific

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Bins are population-specific

Facility location

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Selective Mapping

• Generate a sparse framework map for a large population

• Use adjacent markers to identify crossover-containing intervals

• Computationally select a set of lines that optimize bin length– Maximum– Expected (sum of squares)

Simulated data100 doubled haploids

1000 cM genomepe

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0

24

6

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Marker Spacing (cM)

Bin

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MBL perceived

MBL actual

EBL perceived

EBL actual

Expected bin size is robust to sparse framework marker

density

Test case:maize recombinant inbreds

(184 markers, 4140 cM)Bin

LengthWhole

N=976

Optimized

N=90

Random

N=90

Maximum 1.8 7.5 12.7

Expected 0.3 1.7 2.6

Accurate marker placement:barley IGRI x Franka chr. VII

n = 30

Selective Mapping: a summary• Transcript maps require many genotypes• MapPop algorithm optimizes map

resolution for a fixed number of genotypes

• MapPop locates new markers on a framework map more quickly and accurately than traditional methods

• Selective Mapping has been applied to several large, commercially important populations

Outline

• Design of transcript mapping projects

• QTL mapping of physiological variation in plants– Aluminum tolerance– Carbon-water balance

Why are physiological traits so hard to map?

• Difficult to measure– Low penetrance– Variable expressivity– Assays are often low-throughput

• Sensitive to environmental conditions• A common perception is that they are

more genetically complex than other traits

Aluminum toxicity

• In acid soils (pH < 5.0) Al3+ is solubilized

• Al3+ is a potent inhibitor of root growth

• Al toxicity limits productivity on 30% of all cultivated land on Earth

• Tolerance is a major target of breeders– Most work has focused on cereal grains– No genes have been cloned – yet

Mapping Al tolerance in Arabidopsis

• With Owen Hoekenga (USDA)• Mapping population

– 100 recombinant inbred lines (RIL) from Col x Ler– Col more tolerant– Lines genotyped for 113 markers by AGI

• Phenotypic measurements– Seedling root length in hydroponic culture– With and without added aluminum– Two timepoints (6 and 8 days)– Care to avoid anoxia and to maintain stable pH

QTLs on chrs. 1 and 5

Day 6, w/ Al Day 8, w/ Al

Day 6, Al-control

Mechanism of QTL action

• Tolerance can occur via – Exclusion– Sequestration– Insensitivity

• Organic acids can act as ligands to exclude or sequester metal ions– Citrate– Malate

Organic acid release

• Measured organic acids released into growth medium– malate, citrate, phosphate– Separation via capillary electrophoresis– Followed by spectrophotometry

• Used 10 RIL from each of four genotypic classes at the two QTL (CC/CC, CC/LL, LL/CC and LL/LL)

Both QTL increase malate release in Col

Genetic complexity, physiological simplicity

• Two QTL, explaining only 40% of the variance • Synergistic epistasis (p<0.05) between the two major QTL• Strong correlation between malate release and tolerance• Mechanisms

– Perception (no)– Synthesis (possibly)– Transport (likely)

• The other 60% of the variance may well be malate release

Where things stand now…

• Fine mapping– Isolating new recombinants near QTL

• Nearly isogenic lines– Backcrossing with marker assisted selection– To test QTL in isolation

• The goal… positional cloning

Outline

• Design of transcript mapping projects

• QTL mapping of physiological variation in plants– Aluminum tolerance– Carbon-water balance

Genomic analysis of water use efficiency

Boyce Thompson Institute for Plant Science

Cornell University

Oklahoma State University

University of North Carolina at Chapel Hill

http://isotope.bti.cornell.edu/

Water use efficiency• A fundamental trade-off

– open stomates allow photosynthesis– but also result in water loss through transpiration

• WUE is the ratio of carbon fixed to water lost– Somewhat related to drought tolerance– More closely to yield potential under irrigation

• Water is the most limiting resource to global agricultural production

• In some crops, and under some conditions, greater WUE would be desirable and in others less

WUE considered at 3 levels

• Whole-field (under agronomic control)

• Whole-plant (driven by respiration)

• Single-leaf (of interest here)

Wind

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WUE AE

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Photosynthesis:

Transpiration:

CO2 and H2O and diffusion gradients

Stable carbon isotopes

• Naturally occuring– Atmospheric CO2 is 99 12C : 1 13C

• Rubisco, the key enzyme in carbon fixation, discriminates against 13C

• Easily measured by mass spectrometry

Isotope measurements

• Isotopic ratioR = 13C/12C

• Discrimination index = (Rair/Rplant) – 1

and WUE

• WUE is very difficult to measure directly• Both ∆ & WUE depend on the CO2 diffusion

gradient• In C3 plants, variation in this gradient is the

primary determinant of and leaf-level WUE.• provides a high-throughput proxy for internal

[CO2] – Values of are typically negative– Values closer to zero represent greater WUE (more

carbon fixed per unit of water)

Goals• To dissect natural variation in WUE• Discovery and characterization of WUE QTL

under well-watered conditions – Rice– Tomato

• Lay ground-work for positional cloning– Fine mapping– Develop congenics

The ideal mapping population

• Genetically compatible parents• Phenotypic difference between parents• Transgressive segregation in progeny• Permanent (can be replicated)• Large• Readily available markers• One or both parents have useful genetic

background

Tomato introgression lines

• Each line contains a single introgression from a wild related species on a cultivated genetic background

• Excellent starting point to Mendelize complex traits

QTL in pennellii population

QTL in hirsutum population

Physiological basis for WUE

• Several of the candidate QTL lines have– High nitrogen content– Low specific leaf area (m2/g)

• These correlates suggest that increased carboxylation capacity is responsible for greater WUE in these QTL

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13C Content

Kasalath x Nipponbare

Lemont x Teqing

Oryza wild species

O.sativa

Rufipogon x Jefferson

IR64 x Nipponbare

Variation among rice lines

Rice - Nip x Kas BIL population

Mapping physiological traits ain’t so tough as long

as you…• Select the mapping population with care

– Variation can be hidden– Different population types have different strengths

• Pay heed to environmental conditions– Do systematic experimentation– Control variation as much as possible– Find good physiologist to collaborate with!

• Have a good, cheap, fast assay

Acknowledgements

• Transcript mapping– Dan Brown– Steve Tanksley– David Shmoys– Rick Durrett

• QTL mapping– Owen Hoekenga– Leon Kochian– Jonathan Comstock– Susan McCouch– Bjorn Martin– Chuck Tauer

Funding from NSF and USDA