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Molecular analysis of genetic variation in trees

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

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Molecular analysis of genetic variation in trees

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  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) Genetic distance 0.0 0.1 0.2 0.3 0.4 Ethiopia Kenya * MountKilum * Ntingue * Mendankwe * Mount Cameroon Uganda Manakambahiny ¨ ¨ Antsevabe ¨ Mantadia * Cameroon  Madagascar

  8. Principal component analysis for populations of Prunus africana based on 41 RAPD markers 6 Cameroon 4 2 Eastern Kenya 0 Second principal component (7%) -2 Ethiopia -4 Western Kenya, Uganda -6 -8 -6 -4 -2 0 2 4 First principal component (24%)

  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) 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

  10. B Chyulu (Kenya) (Sbc) J Kalimbeza (Namibia) (Sbc) Chloroplast haplotypes M Pandamatenga (Botswana) (Sbc) R K Choma (Zambia) (Sbc) S I Oshikondilongo (Namibia) (Sbc) U L Siavonga (Zambia) (Sbc) T 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) 0.10 0.08 0.06 0.04 0.02 0.00 Genetic distance Sclerocarya birrea Clustering of genetic distances (80 RAPD markers) 0.12

  11. 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)

  12. 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) 0.05 0.04 0.03 0.07 0.06 0.02 0.01 0 Genetic distance

  13. Uapaca kirkiana 6 Country Malawi Tanzania Mozambique 4 Zambia Zimbabwe 2 0 Second principal component (3 % of variation) - 2 -4 - 6 2 -2 6 10 - 4 0 4 8 First principal component (6 % of variation) Principal component analysis for populations of Uapaca kirkiana based on 132 RAPD markers

  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

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