Basics of 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:

Markers are dominant (i.e. heterozygotes are scores as homozygotes)

Can be tedious to score

Size homoplasy

Reproducibility?

STEP 1: Restriction-Ligation

EcoRI PRE-SELECTIVE PRIMER

MseI PRE-SELECTIVE PRIMER

GTAGACTGCGTACC AATT CA

CA AT GAGTCCTGAGTA

STEP 2: Pre-selective PCR

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

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

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

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

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

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

A very fine example

Too many peaks

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)

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

Normalization

Genemapper

Freeware for scoring AFLPfrom ABI runs:

Genographer v 1.6

GenoProfiler 2.0

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

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

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

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

Microsatellites

* Di- or tri-nuleotide repeats

* Ubiquitous

* High mutation rate (102-106)

High level of variability

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

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

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

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

Some scoring issues

Great looking heterozygote

Some scoring issues

Extra peak because of partial A overhang addition of Taq

Stutter bands of the two high peaks due to slippage

Some scoring issues

Heterozygote

Some scoring issues

A single large allele with many repeatsLots of slippage

35 repeats

Some scoring issues

Increase in slippage with increase in repeat number

Some scoring issues

How many alleles?

Some scoring issues

Find a heterozygote that clearlyshows the shape of a single allele

Some scoring issues

The alleles

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

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

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

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.

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

Thanks to

Chaz, Kristen, Deborah and Bob Marra