Molecular analysis of genetic
variation in trees
Caroline Agufa
Tree Domestication Course,
17 to 22 November 2003
World Agroforestry Centre
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
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
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
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
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
Prunus africana
Clustering of genetic distances (48 RAPD markers)
Genetic distance
0.0
0.1
Ethiopia
Kenya
*
Mount Kilum
* Ntingue
* Mendankwe
* Mount Cameroon
Uganda
 Manakambahiny
 Antsevabe
 Mantadia
* Cameroon  Madagascar
0.2
0.3
0.4
Principal component analysis for populations of Prunus africana based
on 41 RAPD markers
6
Cameroon
Second principal component (7%)
4
2
Eastern Kenya
0
-2
Ethiopia
-4
Western Kenya, Uganda
-6
-8
-6
-4
-2
0
2
First principal component (24%)
4
Prunus africana
Clustering of genetic distances (41 RAPD markers)
Mt Kilum 1 (planted)
ONADEF (nursery)
Mendankwe (planted)
Ntingue 2 (natural)
Mt Cameroon (natural)
Mt Kilum 2 (natural)
Cameroon
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)
Eastern Kenya
Chuka 2 (natural)
Meru (natural)
Chuka 1 (nursery)
Tigoni (natural)
0.6
0.3
Genetic distance
0
‡ populations established using
seeds from Kobujoi area
Sclerocarya birrea
Clustering of genetic distances (80 RAPD markers)
B Chyulu (Kenya) (Sbc)
J Kalimbeza (Namibia) (Sbc)
Chloroplast
haplotypes
M Pandamatenga (Botswana) (Sbc)
R
S
U
T
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)
0.12
0.10
0.08
0.06
0.04
0.02
Genetic distance
0.00
Sclerocarya birrea
Principal component analysis for populations of Sclerocarya birrea
based on 80 RAPD markers
Magamba
Second principal component (7% of variation)
4
Country
Kenya
Swaziland
Mali
Namibia
Malawi
Zambia
Tanzania
Botswana
2
0
-2
-4
Makadaga, Mialo, Mandimu
-6
-4
-2
2
0
4
First principal component (11% of variation)
6
8
10
Uapaca kirkiana
Clustering of genetic distances (132 RAPD markers)
L Kapelula (Malawi)
D Mbeya (Tanzania)
K Luwawa (Malawi)
J Litende (Malawi)
B Sumbawamga (Tanzania)
E Songea (Tanzania)
A Mpwapwa (Tanzania)
C Iringa (Tanzania)
M Furacungo (Mozambique)
I Chipata (Zambia)
H Kanona (Zambia)
F Kasama (Zambia)
G Kitwe (Zambia)
N Domboshewa (Zimbabwe)
S Mpanzure (Zimbabwe)
R Murewa (Zimbabwe)
P Musana (Zimbabwe)
0.07
0.06
0.05
0.04
0.03
Genetic distance
0.02
0.01
0
Uapaca kirkiana
Principal component analysis for populations of Uapaca
kirkiana based on 132 RAPD markers
Second principal component (3 % of variation)
6
Country
Malawi
Tanzania
Mozambique
Zambia
Zimbabwe
4
2
0
-2
-4
-6
-4
-2
0
2
4
6
First principal component (6 % of variation)
8
10
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
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