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 Conference
United Nations Conference Centre (UNCC-ECA)
Addis Ababa, Ethiopia, 25-27 February 2013

Project Partners
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

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 millet
Microsatellite 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

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 represented
Ease 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 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

Finger millet genotyping
Assembly and evaluation of FM resources
Country received from Cultivated Wild
Ethiopia 287 72
Uganda 105
Tanzania 198 5
Kenya 150
Wild (all countries) 29
Total 740 106
HOPE (ICRISAT) 337
Egerton University 225
Total 1302

Diversity assessment - genotyping
SSR/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 continued
Results
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

Genetic diversity results
Ethiopia 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 development
From 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