basics of aflp and usat - yale university of aflp and usat.pdf · amplified fragment length...
TRANSCRIPT
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Basics of AFLP and
microsatellite analysis
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Amplified Fragment Length Polymorphism
Pros:
Large number of markers with relatively little lab effort
No prior information about genome needed
Genome wide overage
Small amount of DNA needed
Cons:
Markers are dominant (i.e. heterozygotes are scores as homozygotes)
Can be tedious to score
Size homoplasy
Reproducibility?
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STEP 1: Restriction-Ligation
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EcoRI PRE-SELECTIVE PRIMER
MseI PRE-SELECTIVE PRIMER
GTAGACTGCGTACC AATT CA
CA AT GAGTCCTGAGTA
STEP 2: Pre-selective PCR
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SELECTIVE PRIMERSELECTIVE PRIMER
GTAGACTGCGTACC AATT CACT
GACA AT GAGTCCTGAGTA
GTAGACTGCGTACC AATT CA
CA AT GAGTCCTGAGTA
EcoRI SELECTIVE PRIMER (labeled)
MseI SELECTIVE PRIMER
STEP 3: Selective PCR
FAM
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MseI
MseI
MseI
MseI MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
MseI
EcoRI
EcoRI
EcoRI
MseI
EcoRI
MseI
EcoRI
MseI
EcoRI
MseI
EcoRI: 6bp cutter --> one cut every 4096 bp
MseI: 4bp cutter --> one cut every 256 bp
Selective PCR product contains many unlabeledfragments that will not be visible on ABI
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Number of bands in AFLP profileis determined by
1 Genome size: larger genome ---> more bands
2 Number of selective nucleotides in selective primers
3 Dilution of PCR product Low (noise) peaks get magnified
Why optimize number of bands?
1 Size homoplasy !!!!!
2 Difficult to score
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EcoR1-AGT MseI-CGTEcoR1-AGC MseI-CGA
MseI-CGCMseI-CGGetc.
MseI-CGTG
MseI-CG
Choosing selective primer combinations
An additionalnucleotide reducesnumber of peaks 4-fold
One less nucleotideincreases number of peaks 4-fold
Use few of these(expensive),
but allows use of multiple colors(multiplex run on ABI)
Use many of these to get enough markers (cheap)
And use these to optimize number of bands
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Reproducibility
High reproducibility has generally been reported
However, DNA quality is crucial component (use same DNA extraction protocol for all samples!)
Assess quality of data byrepeating several samples from scratch
i.e. starting with DNA extraction
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Note: Genome size is correlated with noise level
Around 20% of primer combinations provide profiles that are suitable for high throughput genotyping.
1 Well separated peaks
2 Right number of peaks
2 Little noise
3 Peaks are distributed across size range
4 High level of Polymorphism
Ideal AFLP profile
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A very fine example
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Too many peaks
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Optimizing AFLP reactions
1 DNA quality
2 DNA quality A successful AFLP analyses depends crucially on this
3 DNA quality
4 Increase restriction time to 2 hours
5 Increase ligation time to 16 hours
6 Use fresh T4 ligase
7 Increase amount of DNA (rest-lig) added to pre-selective PCR (15 ul DNA’ in 50ul reaction)
8 Reduce amount of DNA in Selective PCR
9 Increase amount of cycles in Selective PCR
10 Increase amount of TAQ in Selective PCR
11 Several people have reported better results with TaqI vs MseI
(but this requires different adaptors)
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Scoring AFLP profiles
Normalize samples: Arbitrary cut-off peak height has to beused and this needs to be relative since different samples have different intensity.
Set high cut-off for inclusion as marker (that is, at least one individual has to have this cut-off peak height), then reduce peak height for scoring the presence/absence for remainder of individuals.
In Genemapper do not use auto-bin option. Make your own bins
Analyze all samples for the same primer set in the same project. This allowsyou to assess the reliability of the marker by scrolling across samples. Also prevents you from including non-polymorphic markers. Also, normalization performed on all samples at the same time.
Do not include peaks that do not show clear presence or absence in most cases.
Score blindly to avoid bias.
Check for overflow from different dye
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Normalization
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Genemapper
Freeware for scoring AFLPfrom ABI runs:
Genographer v 1.6
GenoProfiler 2.0
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A few population genetic programs for AFLPanalyses
RAPDFst: Fst (Lynch and Milligam, 1994)
MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)
Arlequin, TFPGA: Amova
Genalex: Φst, analog of Fst, Amova
Structure, BAPS: inference of population structure.
Hickory: Bayesian estimation of F statistics for dominant markers
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A few population genetic programs for AFLPanalyses
RAPDFst: Fst (Lynch and Milligam, 1994)
MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)
Arlequin, TFPGA: Amova
Genalex: Φst, analog of Fst, Amova
Structure, BAPS: inference of population structure.
Hickory: Bayesian estimation of F statistics for dominant markers
Assumes H-W equilibrium
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A few population genetic programs for AFLPanalyses
RAPDFst: Fst (Lynch and Milligam, 1994)
MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)
Arlequin, TFPGA: Amova
Genalex: Φst, analog of Fst, Amova
Structure, BAPS: inference of population structure.
Hickory: Bayesian estimation of F statistics for dominant markers
Treats multilocus data as single haplotype
Assumes H-W equilibrium
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A few population genetic programs for AFLPanalyses
RAPDFst: Fst (Lynch and Milligam, 1994)
MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)
Arlequin, TFPGA: Amova
Genalex: Φst, analog of Fst, Amova
Structure, BAPS: inference of population structure.
Hickory: Bayesian estimation of F statistics for dominant markers
Assumes H-W equilibrium
Treats multilocus data as single haplotype
No assumption of H-W equilibrium
Low information content
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Microsatellites
* Di- or tri-nuleotide repeats
* Ubiquitous
* High mutation rate (102-106)
High level of variability
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Mutational mechanismSlippage during replication
(also happens during PCR)
ACCGAGTCGATCGTGTGTGTGTGTGTGTGTACGCTATGGCTCAGCTAGCACACACACACAC
ACCGAGTCGATCGTGTGTG TGTGTGTGTGTACGCTATGGCTCAGCTAGCACACAC ACACACACACATGCGAT
CA
Slippage increases with number of repeats
Reduces or decreases number of repeats
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Obtaining Microsatellites
• Screening sequenced genomes
• Screening enriched genomic library
Glenn and Schable (2005) Methods in Enzymology 395: 202-222.
This paper is particularly useful. It comes from a Lab that has isolated microsatellites from 125+ species
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SELECTING LOCI
Too few repeats Low variability
Too many repeats Difficult to score, Homoplasy
Choosing loci:• 8 - 20 repeats• uninterrupted repeats
Screening of loci:
•Number of alleles Cloning pool of PCR amplicons, followed bylabeled PCR
•Heterozygosity, allelic richness
M13 labeled primers
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M13 tailed primer
Forward primer
Reverse primer
M13-tail
Forward primer Reverse primer
M13 primer
Forward primer
FAM
(Low concentration)
Boutin-Ganache et al (2001) Biotechniques 31, 26-28
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Some scoring issues
Great looking heterozygote
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Some scoring issues
Extra peak because of partial A overhang addition of Taq
Stutter bands of the two high peaks due to slippage
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Some scoring issues
Heterozygote
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Some scoring issues
A single large allele with many repeatsLots of slippage
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35 repeats
Some scoring issues
Increase in slippage with increase in repeat number
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Some scoring issues
How many alleles?
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Some scoring issues
Find a heterozygote that clearlyshows the shape of a single allele
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Some scoring issues
The alleles
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Some scoring issues
Electrophoresis artifacts
(Fernando et al (2001) Mol. Ecol. Notes 1, 325-328)
The figures shows the difference in peak shape of the samePCR products loaded at different concentration
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Some scoring issues
Electrophoresis artifacts
(Fernando et al (2001) Mol. Ecol. Notes 1, 325-328)
Do not overload your gel !
Also keep in mind that in different PCR’s the left peak or the right peak may be dominant
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Optimizing PCR
Avoid Null Alleles (or try to)• Minimize annealing temp lowest temp that produces
clean bands• MgCl2 concentration increase reduces specificity• Different species design new primers (if possible)
(In my limited experience with cross species amplification null alleles can be big problem)
Reduce stutter:• Reduce number of cycles• Reduce amount of MgCl2• Touchdown PCR• 2/2/8 PCR (2 sec denat, 2 sec anneal, 8 sec extens.)• BSA, DMSO
Addition of A• Increase final extension time• Add Pigtail (GTTTCTT) on 5’end of reverse primer to
facilitate addition of A overhang
Seems to be most successfull
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Analysis Issues
Null alleles Are loci in HW equilibrium?
Linkage disequilibrium?
Possible solutions:
Remove loci from analysis (if enough loci are available)
Check if HW disequilibrium influences results bytemporarily removing affected loci.
Adjust allele and genotype frequencies (Microchecker)
Microsats biggest problem Population subdivision causes both. Null alleles only cause HW disequilibrium.
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Some population genetics software
Microsatellite toolkit: Excel plug-in for creating Arlequin, FSTAT and Genepop files.
Microchecker: Estimate null allele frequency. Adjust allele frequencies.
Arlequin: HW equilibrium, Linkage Disequilibrium, Fst, exact test of differentiation, Amova, Mantel test
FSTAT: Allelic richness, Fst per locus (to check contribution of each locus to observed pattern of differentiation)
Structure, BAPS: Population structuring, population assignment.
Migrate: Estimates of effective population size and migration rates
Bottleneck: Check for very recent population bottlenecks
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Thanks to
Chaz, Kristen, Deborah and Bob Marra