Genetic diversity assessment of eastAfrican finger millet and cost-effective  development of new SSR markersSantie de Vill...
Project PartnersICRISAT: Santie de Villiers, Vincent Njunge,Annis Saiyiorri, Santosh Deshpande, DavisGimode, Damaris Odeny...
Combined abstracts ‘Genetic diversity assessment of east Africanfinger millet germplasm using SSR genotyping’             ...
Outline• Microsatellite genotyping for  genetic diversity assessment• Generation of new molecular  tools for marker assist...
Molecular markers• Characters which inheritance can be followed at  morphological, biochemical or DNA level.• We use marke...
Finger milletMicrosatellite genotyping for to determine genetic relatedness or diversity  Optimised sampling strategy     ...
Genotyping markers optimization• Tested 83 available UGEP markers; 57 worked well• Identified set of 20 - 30 SSRs• Ranked ...
Optimised SSR markers for genotyping      Marker Name           Primer F                Primer R          Allele No   Avai...
Finger millet genotypingAssembly and evaluation of FM resourcesCountry received from   Cultivated   WildEthiopia          ...
Diversity assessment - genotypingSSR/microsatellite genotyping  – Analysed 1307 cultivated accessions (from Bio-Innovate  ...
Diversity assessment continuedResults  All data sets <10% missing data;  Analysed with genetic diversity software (PowerMa...
Genetic diversity resultsEthiopia                   KenyaTanzania                Uganda
Developing additional molecular tools for       marker-assisted breeding    SSR markers – status and potential– Only 82 pu...
SSR marker developmentFrom Roche 454 data, 178 new SSRs identified–Validated in laboratory (MSc student, KU)–63 polymorphi...
Outputs achieved• Optimized FM sampling, DNA extraction  and genotyping protocol, prepared  publication• Trained students ...
Challenges• Cost of validating SSRs  – $16 per primer pair, validation  – 1000s new primer pairs identified• Mapping of ne...
Genetic diversity assessment of east African finger millet and cost-effective development of new SSR markers
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Genetic diversity assessment of east African finger millet and cost-effective development of new SSR markers

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Presented by Santie de Villiers, Kassahun Tesfaye, Emmarold Mneney, Mathews Dida, Patrick Okori, Vincent Njunge, Annis Saiyiorri, Santosh Deshpande, Katrien Devos, Davis Gimode, Dagnachew Lule, Isaac Dramadri, Ismail Mohamed and Damaris Odeny at the First Bio-Innovate Regional Scientific Conference, Addis Ababa, Ethiopia, 25-27 February 2013

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  • This project encompass a number of regional institutes with the scientists indicated in bold and students in green. We trained 3 MSc and 1 PhD students
  • In order to save on time and maximise the number of presentations that can be accommodated, this presentation combines the two abstracts
  • How similar or different individual finger millet samples are
  • Genetic diversity assessment of east African finger millet and cost-effective development of new SSR markers

    1. 1. Genetic diversity assessment of eastAfrican finger millet and cost-effective development of new SSR markersSantie de Villiers, Kassahun Tesfaye, Emmarold Mneney, Mathews Dida, PatrickOkori, Vincent Njunge, Annis Saiyiorri, Santosh Deshpande, Katrien Devos, DavisGimode, Dagnachew Lule, Isaac Dramadri, Ismail Mohamed and Damaris Odeny First Bio-Innovate Regional Scientific Conference United Nations Conference Centre (UNCC-ECA) Addis Ababa, Ethiopia, 25-27 February 2013
    2. 2. Project PartnersICRISAT: Santie de Villiers, Vincent Njunge,Annis Saiyiorri, Santosh Deshpande, DavisGimode, Damaris OdenyAAU: Kassahun Tesfaye, Dagnachew LuleMARI: Emmarold Mneney, Ismail MohamedMakerere: Patrick Okori, Isaac DramadriMaseno: Mathews DidaUGA: Katrien Devos
    3. 3. Combined abstracts ‘Genetic diversity assessment of east Africanfinger millet germplasm using SSR genotyping’ and‘Employing next generation sequencing technology for cost-effective development of new SSR markers for finger millet’
    4. 4. Outline• Microsatellite genotyping for genetic diversity assessment• Generation of new molecular tools for marker assisted breeding• Capacity building• Challenges and opportunities
    5. 5. Molecular markers• Characters which inheritance can be followed at morphological, biochemical or DNA level.• We use markers to obtain information about the genetics of traits of interest• Examples – Morphological trait (such as seed or flower color) – Protein (storage or structural proteins and isozymes) – Identifiable piece of DNA sequence• Found at specific locations of the genome and transmitted by the standard laws of inheritance from one generation to the next• Can be used to identify and track particular genes in an experimental cross
    6. 6. Finger milletMicrosatellite genotyping for to determine genetic relatedness or diversity Optimised sampling strategy • Single sample per accession • Bulked samples of 3 to 5 individuals, leaves or DNA Finger millet is >95% inbred, very little variation expected at DNA level Single or bulked samples can be used
    7. 7. Genotyping markers optimization• Tested 83 available UGEP markers; 57 worked well• Identified set of 20 - 30 SSRs• Ranked according to:Polymorphic information content (PIC)No of alleles at each locus and how well representedEase to work with• 20 markers previously selected by GCP are not the best for African germplasm; only 2 fall within the 20 best markers• Publication
    8. 8. Optimised SSR markers for genotyping Marker Name Primer F Primer R Allele No Availability PIC GCP 1 UGEP24 GCCTTTTGATTGTTCAACTCG CGTGATCCCTCTCCTCTCTG 14 0.98 0.88   2 UGEP53 TGCCACAACTGTCAACAAAAG CCTCGATGGCCATTATCAAG 9 0.98 0.87   3 UGEP84 GGAACTTCCGTCAGTCCTTG TGGGGAAGGTGTTGAATCTC 8 0.98 0.82   4 UGEP27 TTGCTCTGAGGTTGTGTTGC TCAAGCATAGTGCCCTCCTC 8 0.98 0.81   5 UGEP98 GTCTTCCATTTGCAGCAACC ACGCGTACTGACGTGCTTG 9 0.93 0.81   6 UGEP95 AGGGGACGCTTGGTTATTTG GCCTCTACCTGTCTCCGTTG 8 0.98 0.79   7 UGEP64 GTCACGTCGATTGGAGTGTG TCTCACGTGCATTTAGTCATTG 11 0.94 0.78   8 UGEP33 TAGCCCGTTTGCTTGTTTTG AAGGCCCTAGAACGTCAAGC 7 0.93 0.76   9 UGEP67 CTCCTGATGCAAGCAAGGAC AGGTGCCGTAGTTTGTGCTC 8 0.96 0.76   10 UGEP106 AATTCCATTCTCTCGCATCG TGCTGTGCTCCTCTGTTGAC 7 0.89 0.75   11 UGEP110 AAATTCGCATCCTTGCTGAC TGACAAGAGCACACCGACTC 6 0.86 0.74   12 UGEP57 CCATGGGTTCATCAAACACC ACATGAGCTCGCGTATTGC 6 0.92 0.73   13 UGEP96 TAATGGGCCTAATGGCAATG CAAAATCCGAGCCAAGATTC 8 0.64 0.72   14 UGEP66 CAGATCTGGGTAGGGCTGTC GATGGTGGTTCATGCCAAC 9 0.93 0.72   15 UGEP46 CAAGTCAAAACATTCAGATGG CCACTCCATTGTAGCGAAAC 6 0.83 0.7   16 UGEP79 CCACTTTGCCGCTTGATTAG TGACATGAGAAGTGCCTTGC 8 0.97 0.7   17 UGEP20 GGGGAAGGCAATGATATGTG TTGGGGAGTGCCAACAATAC 5 1 0.69   18 UGEP12 ATCCCCACCTACGAGATGC TCAAAGTGATGCGTCAGGTC 7 0.97 0.69 GCP 19 UGEP73 GGTCAAAGAGCTGGCTATCG ACCAGAACCGAATCATGAGG 6 0.92 0.67   20 UGEP5 TGTACACAACACCACACTGATG TTGTTTGGACGTTGGATGTG 4 0.91 0.66 GCP 21 UGEP15 AAGGCAATCTCGAATGCAAC AAGCCATGGATCCTTCCTTC 6 0.98 0.6 GCP 22 UGEP56 CTCCGATACAGGCGTAAAGG ACCATAATAGGGCCGCTTG 4 0.98 0.54 GCP 23 UGEP107 TCATGCTCCATGAAGAGTGTG TGTCAAAAACCGGATCCAAG 9 0.57 0.52 GCP 24 UGEP65 AGTGCTAGCTTCCCATCAGC ACCGAAACCCTTGTCAGTTC 5 0.31 0.46 GCP 25 UGEP31 ATGTTGATAGCCGGAAATGG CCGTGAGCCTCGAGTTTTAG 4 0.97 0.45 GCP 26 UGEP3 CCACGAGGCCATACTGAATAG GATGGCCACTAGGGATGTTG 3 0.94 0.4 GCP 27 UGEP68 CGGTCAGCATATAACGAATGG TCATTGATGAATCCGACGTG 3 0.93 0.38 GCP 28 UGEP81 AAGGGCCATACCAACACTCC CACTCGAGAACCGACCTTTG 3 0.97 0.35 GCP 29 UGEP18 TTGCATGTGTTGCTTTTTGC TGTTCTTGATTGCAAACTGATG 3 0.88 0.31 GCP 30 UGEP102 ATGCAGCCTTTGTCATCTCC GATGCCTTCCTTCCCTTCTC 4 0.8 0.19 GCP
    9. 9. Finger millet genotypingAssembly and evaluation of FM resourcesCountry received from Cultivated WildEthiopia 287 72Uganda 105Tanzania 198 5Kenya 150Wild (all countries) 29Total 740 106HOPE (ICRISAT) 337Egerton University 225Total 1302
    10. 10. Diversity assessment - genotypingSSR/microsatellite genotyping – Analysed 1307 cultivated accessions (from Bio-Innovate and HOPE projects) – Used 20 SSR markers, generated > 26 000 data points – Wild germplasm (106 accessions) data being finalisedPost-graduate students at ICRISAT-Nairobi (3 months) Ethiopia (Dagnachew Lule), Uganda (Isaac Dramadri) and Tanzania (Ismail Mohamed) – DNA extraction – Genotyping – Data analysis – Training course (CAPACITATE) Publication write-up
    11. 11. Diversity assessment continuedResults All data sets <10% missing data; Analysed with genetic diversity software (PowerMarker, DARwin, Arlequin, STRUCTURE) Combined data: PIC ranged from 0.47 to 0.95 (mean 0.81)Publication strategy Cultivated for each country Wild germplasm across countries 5/6 from BioInnovate, 2 from HOPE
    12. 12. Genetic diversity resultsEthiopia KenyaTanzania Uganda
    13. 13. Developing additional molecular tools for marker-assisted breeding SSR markers – status and potential– Only 82 published SSR markers available– Basic genetic map with 32 SSRs (Dida et al, 2007)– Need more markers, mapped– Used next-generation sequencing (NGS) to identify more SSRs in finger millet • Ecogenics (Switzerland): Roche 454 seq after SSR enrichment (KNE 755, KNE 796 and E. indica) • UGA: Illumina MiSeq with Covaris random sheared and Pst1 digested libraries of KNE 755 and KNE796
    14. 14. SSR marker developmentFrom Roche 454 data, 178 new SSRs identified–Validated in laboratory (MSc student, KU)–63 polymorphic (12 did not work well), 15monomorphic, 100 did not work/work well–PIC ranged from 0.12 - 0.77; mean 0.67Total NGS data assembled and run through MISA toidentify SSRs:–KNE796 - 1552 SSRs; KNE755 - 1845 SSRs–SNP markers to be identified from NGS data
    15. 15. Outputs achieved• Optimized FM sampling, DNA extraction and genotyping protocol, prepared publication• Trained students (2 PhD, 2 MSc in molecular marker applications; 1 MSc in Bioinformatics• 1408 samples (cultivated and wild) genotyped and 6 publications under preparation• > 3000 new SSRs identified; 178 validated• 63 new SSR markers developed; potentially 1000s more
    16. 16. Challenges• Cost of validating SSRs – $16 per primer pair, validation – 1000s new primer pairs identified• Mapping of new and potential SSR markers• General lack of genomic resources for finger millet• Scarcity of genomics capacity, especially human

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