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?

Bins are population-specific

Facility location POST OFFICE POST OFFICE POST OFFICE

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 data 100 doubled haploids 1000 cM genome performance ratio (maximum bin length)

Expected bin size is robust to sparse framework marker density

Test case: maize recombinant inbreds (184 markers, 4140 cM) Bin Length Whole N=976 Optimized N=90 Random N=90 Maximum Expected

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) Al 3+ is solubilized Al 3+ 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/ AlDay 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

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)

Photosynthesis: Transpiration: CO 2 and H 2 O and diffusion gradients

Stable carbon isotopes Naturally occuring –Atmospheric CO 2 is C : 1 13 C Rubisco, the key enzyme in carbon fixation, discriminates against 13 C Easily measured by mass spectrometry

Isotope measurements Isotopic ratio R = 13 C/ 12 C Discrimination index  = (R air /R plant ) – 1

 and WUE WUE is very difficult to measure directly Both ∆ & WUE depend on the CO 2 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 [CO 2 ] –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

Environmental controls Air temperature Relative humidity Wind velocity Soil moisture Soil volume Photosynthetically active radiation Carbon dioxide concentration Isotopic ratio of carbon dioxide

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 (m 2 /g) These correlates suggest that increased carboxylation capacity is responsible for greater WUE in these QTL

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