Molecular analysis of genetic variation in trees

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Molecular analysis of genetic variation in trees
Caroline Agufa Tree Domestication Course, 17 to
22 November 2003 World Agroforestry Centre
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Molecular analysis of genetic variation in trees

• Introduction Why molecular analysis?
• Techniques for molecular genetic analysis
• What does molecular analysis reveal about genetic
  variation in trees?
• Molecular genetic variation and the impact of
  tree cultivation
• Case Studies of molecular analysis
• Limitations of molecular analysis

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Introduction Why molecular analysis?

• Utility of traditional techniques is limited
  because
• Influenced by environmental factors
• The number of characters available are few
• Long time for evaluation (trees)
• Therefore, molecular genetic markers are now
  often used
• These provide information on the underlying
  diversity and genetic constitution of trees and
  allow more optimal genetic management strategies
  to be developed

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Techniques for molecular genetic analysis

• The most commonly applied are isozyme and
  PCR-based approaches
• Isozyme analysis
• Detection of different allelic forms of the same
  enzyme by electrophoresis and staining
• Inherited in a Mendelian and codominant manner
• Disadvantage Need fresh material because relies
  on enzyme activity

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Techniques for molecular genetic analysis II

• Polymerase Chain Reaction (PCR) analysis
• amplified fragment length polymorphism
• random amplified polymorphic DNA (RAPD)
• restriction fragment length polymorphism-PCR
• simple sequence repeats
• Disadvantage Expensive

RAPD profile Arrows indicate polymorphisms
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What does molecular analysis reveal
about genetic variation in trees?

• Genetic variation within tree populations is high
  
• Molecular genetic differentiation among
  populations is generally low (but statistically
  significant). However, there are exceptions, and
  under-differentiation of some tropical taxa

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Prunus africana
Clustering of genetic distances (48 RAPD markers)
Genetic distance
0.0
0.1
0.2
0.3
0.4
Ethiopia
Kenya
Mount Kilum
Ntingue
Mendankwe
Mount Cameroon
Uganda
Manakambahiny


Antsevabe

Mantadia
Cameroon ? Madagascar
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Prunus africana
Clustering of genetic distances (41 RAPD markers)
Mt Kilum 1 (planted)
ONADEF (nursery)
Mendankwe (planted)
Ntingue 2 (natural)
Mt Cameroon (natural)
Cameroon
Mt Kilum 2 (natural)
Sop (natural)
Ntingue 1 (planted)
MESG (nursery)
Bwindi (Uganda)
Kobujoi (natural)
Western Kenya
Muguga (planted)
Maseno (nursery)
Lepsi-Arsi (Ethiopia)
Nyeri 1 (natural)
Nyeri 2 (planted)
Eastern Kenya
Chuka 2 (natural)
Meru (natural)
populations established using seeds from
Kobujoi area
Chuka 1 (nursery)
Tigoni (natural)
0.6
0.3
0
Genetic distance
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Sclerocarya birrea
Clustering of genetic distances (80 RAPD markers)

0.12
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Sclerocarya birrea
Principal component analysis for populations of
Sclerocarya birrea based on 80 RAPD markers
Magamba
4
Country
Kenya
Swaziland
Mali
Namibia
Malawi
2
Zambia
Tanzania
Botswana
0
Second principal component (7 of variation)
-2
-4
Makadaga, Mialo, Mandimu
-6
-2
2
10
6
-4
0
4
8
First principal component (11 of variation)
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Uapaca kirkiana
Principal component analysis for populations of
Uapaca kirkiana based on 132 RAPD markers
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Molecular genetic variation and the impact of
tree cultivation

• Levels of genetic variation in cultivated
  material are generally lower than in wild
  populations
• A narrow genetic base in cultivated material can
  have serious negative implications for
  sustainable utilisation
• With the trend to tree populations on-farm, more
  focus is required on assessing genetic variation
  in cultivated trees, to devise sustainable
  on-farm management strategies

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Limitations of molecular analysis

• Molecular markers are by nature neutral
  indicators of underlying genetic variation,
  rather than linked to any one character trait
• Molecular markers ought to be used in combination
  with field evaluation techniques