Genetic diversity assessment of east African finger millet and cost-effective

development of new SSR markers

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

First Bio-Innovate Regional Scientific ConferenceUnited Nations Conference Centre (UNCC-ECA)

Addis Ababa, Ethiopia, 25-27 February 2013

ICRISAT: Santie de Villiers, Vincent Njunge, Annis Saiyiorri, Santosh Deshpande, Davis Gimode, Damaris Odeny

AAU: Kassahun Tesfaye, Dagnachew Lule

MARI: Emmarold Mneney, Ismail Mohamed

Makerere: Patrick Okori, Isaac Dramadri

Maseno: Mathews Dida

UGA: Katrien Devos

Project Partners

Combined abstracts

‘Genetic diversity assessment of east African finger millet germplasm using SSR genotyping’

and‘Employing next generation sequencing technology for

cost-effective development of new SSR markers for finger millet’

Outline

• Microsatellite genotyping for genetic diversity assessment

• Generation of new molecular tools for marker assisted breeding

• Capacity building• Challenges and opportunities

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

Finger milletMicrosatellite genotyping for to determine

genetic relatedness or diversityOptimised 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

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

Optimised SSR markers for genotypingMarker 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 GCP21 UGEP15 AAGGCAATCTCGAATGCAAC AAGCCATGGATCCTTCCTTC 6 0.98 0.6 GCP22 UGEP56 CTCCGATACAGGCGTAAAGG ACCATAATAGGGCCGCTTG 4 0.98 0.54 GCP23 UGEP107 TCATGCTCCATGAAGAGTGTG TGTCAAAAACCGGATCCAAG 9 0.57 0.52 GCP24 UGEP65 AGTGCTAGCTTCCCATCAGC ACCGAAACCCTTGTCAGTTC 5 0.31 0.46 GCP25 UGEP31 ATGTTGATAGCCGGAAATGG CCGTGAGCCTCGAGTTTTAG 4 0.97 0.45 GCP26 UGEP3 CCACGAGGCCATACTGAATAG GATGGCCACTAGGGATGTTG 3 0.94 0.4 GCP27 UGEP68 CGGTCAGCATATAACGAATGG TCATTGATGAATCCGACGTG 3 0.93 0.38 GCP28 UGEP81 AAGGGCCATACCAACACTCC CACTCGAGAACCGACCTTTG 3 0.97 0.35 GCP29 UGEP18 TTGCATGTGTTGCTTTTTGC TGTTCTTGATTGCAAACTGATG 3 0.88 0.31 GCP30 UGEP102 ATGCAGCCTTTGTCATCTCC GATGCCTTCCTTCCCTTCTC 4 0.8 0.19 GCP

Finger millet genotypingAssembly and evaluation of FM resources

Country received from Cultivated WildEthiopia 287 72Uganda 105Tanzania 198 5Kenya 150Wild (all countries) 29Total 740 106HOPE (ICRISAT) 337Egerton University 225Total 1302

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 finalised

Post-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

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 strategyCultivated for each country Wild germplasm across countries

5/6 from BioInnovate, 2 from HOPE

Genetic diversity resultsEthiopia Kenya

Tanzania Uganda

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

SSR marker developmentFrom Roche 454 data, 178 new SSRs identified–Validated in laboratory (MSc student, KU)–63 polymorphic (12 did not work well), 15 monomorphic, 100 did not work/work well

–PIC ranged from 0.12 - 0.77; mean 0.67

Total NGS data assembled and run through MISA to identify SSRs:–KNE796 - 1552 SSRs; KNE755 - 1845 SSRs–SNP markers to be identified from NGS data

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

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