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AFLP and microsatellite analysis. 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:

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slide1

AFLP and

microsatellite analysis

amplified fragment length polymorphism
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?

slide4

STEP 2: Pre-selective PCR

EcoRI PRE-SELECTIVE PRIMER

GTAGACTGCGTACC AATT CA

CA AT GAGTCCTGAGTA

MseI PRE-SELECTIVE PRIMER

slide5

GTAGACTGCGTACC AATT CACT

STEP 3: Selective PCR

SELECTIVE PRIMER

FAM

EcoRI SELECTIVE PRIMER (labeled)

GTAGACTGCGTACC AATT CA

CA AT GAGTCCTGAGTA

GACA AT GAGTCCTGAGTA

MseI SELECTIVE PRIMER

slide6

MseI

EcoRI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

EcoRI

EcoRI

EcoRI

MseI

MseI

EcoRI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

EcoRI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

MseI

EcoRI: 6bp cutter --> one cut every 4096 bp

MseI: 4bp cutter --> one cut every 256 bp

Selective PCR product contains many unlabeled

fragments that will not be visible on ABI

slide7

Number of bands in AFLP profile

is 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

slide8

Choosing selective primer combinations

Use many of these to

get enough markers (cheap)

And use these to optimize

number of bands

EcoR1-AGT MseI-CGT

EcoR1-AGC MseI-CGA

MseI-CGC

MseI-CGG

etc.

MseI-CGTG

MseI-CG

Use few of these

(expensive),

but allows use of multiple colors

(multiplex run on ABI)

An additional nucleotide reduces number of peaks 4-fold

One less nucleotide

increases number

of peaks 4-fold

slide9

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 by

repeating several samples from scratch

i.e. starting with DNA extraction

ideal aflp profile

1 Well separated peaks

2 Right number of peaks

2 Little noise

3 Peaks are distributed across size range

4 High level of Polymorphism

Note: Genome size is correlated with noise level

Around 20% of primer combinations provide profiles that are

suitable for high throughput genotyping.

Ideal AFLP profile
optimizing aflp reactions
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)

slide14

Scoring AFLP profiles

Normalize samples: Arbitrary cut-off peak height has to be

used 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 allows

you 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

genemapper
Genemapper

Freeware for scoring AFLP from ABI runs:

Genographer v 1.6

GenoProfiler 2.0

a few population genetic programs for aflp analyses
A few population genetic programs for AFLP analyses

RAPDFst: Fst(Lynch and Milligam, 1994)

MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)

Arlequin, TFPGA: Amova

Genalex: st, analog of Fst, Amova

Structure, BAPS: inference of population structure.

Hickory: Bayesian estimation of F statistics for dominant markers

a few population genetic programs for aflp analyses18
A few population genetic programs for AFLP analyses

RAPDFst: Fst(Lynch and Milligam, 1994)

MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)

Arlequin, TFPGA: Amova

Genalex: st, analog of Fst, Amova

Structure, BAPS: inference of population structure.

Hickory: Bayesian estimation of F statistics for dominant markers

Assumes H-W equilibrium

a few population genetic programs for aflp analyses19
A few population genetic programs for AFLP analyses

RAPDFst: Fst(Lynch and Milligam, 1994)

MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)

Arlequin, TFPGA: Amova

Genalex: st, 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

a few population genetic programs for aflp analyses20
A few population genetic programs for AFLP analyses

RAPDFst: Fst(Lynch and Milligam, 1994)

MVSP, NTSYS: Jaccard coeficient, Nei and Li (1979)

Arlequin, TFPGA: Amova

Genalex: st, 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

Low information content

No assumption of H-W equilibrium

microsatellites
Microsatellites
  • * Di- or tri-nuleotide repeats
  • * Ubiquitous
  • * High mutation rate (102-106)

High level of variability

mutational mechanism
Mutational mechanism

Slippage during replication

(also happens during PCR)

ACCGAGTCGATCGTGTGTGTGTGTGTGTGTACGCTA

TGGCTCAGCTAGCACACA

C

A

C

A

C

A

C

ACCGAGTCGATCGTGTGTG TGTGTGTGTGTACGCTA

TGGCTCAGCTAGCACACAC ACACACACACATGCGAT

C

A

Reduces or decreases number of repeats

Slippage increases with number of repeats

obtaining microsatellites

This paper is particularly useful. It comes from a

Lab that has isolated microsatellites from 125+ species

Obtaining Microsatellites
  • Screening sequenced genomes
  • Screening enriched genomic library

Glenn and Schable (2005) Methods in Enzymology 395: 202-222.

selecting loci

Choosing loci:

  • 8 - 20 repeats
  • uninterrupted repeats
SELECTING LOCI

Too few repeats Low variability

Too many repeats Difficult to score, Homoplasy

  • Screening of loci:
  • Number of alleles Cloning pool of PCR amplicons, followed by labeled PCR
  • Heterozygosity,
  • allelic richness

M13 labeled primers

m13 tailed primer

Forward primer

Forward primer

Reverse primer

M13 primer

FAM

M13 tailed primer

Boutin-Ganache et al (2001) Biotechniques 31, 26-28

Forward primer

Reverse primer

(Low concentration)

M13-tail

slide26

Some scoring issues

Great looking heterozygote

slide27

Some scoring issues

Extra peak because of partial A overhang addition of Taq

Stutter bands of the two

high peaks due to slippage

slide29

Some scoring issues

A single large allele with many repeats

Lots of slippage

slide30

Some scoring issues

35 repeats

Increase in slippage with increase in repeat number

slide31

Some scoring issues

How many alleles?

slide32

Some scoring issues

Find a heterozygote that clearly shows the shape of a single allele

slide34

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 same

PCR products loaded at different concentration

slide35

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

optimizing pcr
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

analysis issues
Analysis Issues

Population subdivision

causes both. Null alleles

only cause HW disequilibrium.

Microsats biggest problem

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 by temporarily removing affected loci.

Adjust allele and genotype frequencies (Microchecker)

some population genetics software
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