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

2. 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

3. 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

4. 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

5. 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

6. 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

7. Prunus africana Clustering of genetic distances (48 RAPD markers)
0.0
0.1
0.2
0.3
0.4
Ethiopia
Kenya
* Mount Kilum
* Ntingue
* Mendankwe
* Mount Cameroon
Uganda
Manakambahiny
Antsevabe
Mantadia
* Cameroon  Madagascar

8. -8 -6 -4 -2 2 4 6
First principal component (24%)
Second principal component (7%)
Cameroon
Eastern Kenya
Western Kenya, Uganda
Ethiopia
Principal component analysis for populations of Prunus africana based on 41 RAPD markers

9. 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)
Muguga (planted) ‡
Western Kenya
Maseno (nursery) ‡
Lepsi-Arsi (Ethiopia)
Nyeri 1 (natural)
Nyeri 2 (planted)
Chuka 2 (natural)
Eastern Kenya
Meru (natural)
Chuka 1 (nursery)
‡ populations established using
seeds from Kobujoi area
Tigoni (natural)
0.6
0.3
Genetic distance

10. Sclerocarya birrea Clustering of genetic distances (80 RAPD markers)
B Chyulu (Kenya) (Sbc)
J Kalimbeza (Namibia) (Sbc)
M Pandamatenga (Botswana) (Sbc)
K Choma (Zambia) (Sbc)
I Oshikondilongo (Namibia) (Sbc)
L Siavonga (Zambia) (Sbc)
G Mangochi (Malawi) (Sbc)
H Ntcheu (Malawi) (Sbc)
P Kalanga (Swaziland) (Sbc)
R Manyonyaneni (Swaziland) (Sbc)
N Tutume (Botswana) (Sbc)
E Magamba (Tanzania) (Sbc)
F Makadaga (Tanzania) (Sbm)
D Mialo (Tanzania) (Sbb)
C Mandimu (Tanzania) (Sbb)
A Missira (Mali) (Sbb)
Genetic distance R S U T
Chloroplast
haplotypes
0.02
0.04
0.06
0.08
0.10
0.00
0.12

11. Sclerocarya birrea
Principal component analysis for populations of Sclerocarya birrea based on 80 RAPD markers 4
Magamba
Country
Kenya
Swaziland
Mali
Namibia 2
Malawi
Zambia
Tanzania
Botswana
Second principal component (7% of variation) -2 -4
Makadaga, Mialo, Mandimu -6 -2 2 10 -4 4 6 8
First principal component (11% of variation)

12. Uapaca kirkiana Clustering of genetic distances (132 RAPD markers)
B Sumbawamga (Tanzania)
L Kapelula (Malawi)
D Mbeya (Tanzania)
K Luwawa (Malawi)
J Litende (Malawi)
E Songea (Tanzania)
A Mpwapwa (Tanzania)
C Iringa (Tanzania)
M Furacungo (Mozambique)
I Chipata (Zambia)
H Kanona (Zambia)
F Kasama (Zambia)
G Kitwe (Zambia)
S Mpanzure (Zimbabwe)
R Murewa (Zimbabwe)
P Musana (Zimbabwe)
N Domboshewa (Zimbabwe)
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Genetic distance

13. Uapaca kirkiana
Principal component analysis for populations of Uapaca kirkiana based on 132 RAPD markers - 4 6 8 2
First principal component (6 % of variation)
Second principal component (3 % of variation) -4 -2 10
Country
Malawi
Tanzania
Mozambique
Zambia
Zimbabwe

14. 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

15. 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