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carefully before genotyping by screening each sample on a 0.8% (w/v) agarose gel. Once the DNA quality passed the quality control, the DNA samples were used for SNP genotyping by a commercially available Sequenom MassARRAY platform following the standard protocols described by Gabriel et al. (2009) at DNA Land Marks Inc., Quebec, Canada. The protocol for this assay recommended using 2.5 ng μl-1 DNA per sample.
5.2.4 Statistical analysis
SNP data was scored on the basis of presence or absence of marker alleles and this data was used to estimate the genetic similarity (GS) between any pair of lines based on the Jaccard coefficient using the NTSYSpc v2.1 software package (Exeter Software Setauket, NY, USA).The dendrogram showing the genetic relatedness among the lines was constructed using the Unweighted paired group method using arithmetic averages (UPGMA) method.
For each SNP, number of alleles, allele frequency, number of genotypes, genotype frequency, observed heterozygosity, gene diversity, and polymorphic information content (PIC) were computed using PowerMarker version 3.25. Observed heterozygosity was calculated by dividing the number of heterozygous individuals by the number of individuals scored.
Polymorphism information content (PIC) for the SSR markers in the sample DNA was calculated as:
PIC = 1- Σp 2
i where p
i is the frequency of the ith allele in a locus for individual p.
138 Table 5.2: Average (minimum-maximum) of polymorphism for all lines assayed with 400 SNPs.
Parameter Mean and Range
No. of alleles per locus 1.88 (1-2)
PIC 0.18 (0.00-0.389)
Gene diversity 0.22 (0.00-0.509)
Heterozygosity 0.11 (0.00-1.00)
The SNP call rate was 98.3%. Estimates of genetic similarities based on the SNP markers among the 50 maize lines are presented in Appendix 2. The genetic similarity coefficients among the lines ranged from 52.45% to 87.52%. The lowest similarity value of 52.45% was between the standard line M162W and the common parent for progeny lines, LP23. Table 5.2 gives the genetic similarity coefficients for each of the 35 DMSR lines against the three parental lines (LP23, CML505 and CML509) used in the breeding programme.
The similarity percentages among all the DMSR progeny lines ranged between 71% and 87%
(Table 5.3). Wider genetic distances were observed between all the DMSR progeny lines and the MSV resistant donor parent CML505, as similarity of the lines to CML505 ranged from 60% to 83%. Similarity values of the DMSR progenies with the LP23 and CML509 ranged from 71% to 86% and 61% to 68% respectively. On the other hand, the DMSR lines with CML509 background are 60% to 65% similar to CML505 and DMSR lines with the CML505 background also displayed almost the same level of similarity (61-68%) with parent CML509. It is clearly shown that the progeny lines were more distantly related with the CML509 than with the other parental lines.
The highest similarity of 88% was observed between DMSR55 and DMSR47 (Appendix 2).
The similarity values of lines DMSR47 and DMSR55 are 0.80 and 0.81 against LP23, 0.60 for both against CML505 and 0.65 and 0.66 against CML509, respectively. The progeny line DMSR69 had the highest similarity coefficient of 86% with LP23, DMSR21 with CML505 at 83% and DMSR23 with CML509 at 68%. All progeny had ≥70% similarity percentage to LP23. Overall results indicate that the DMSR lines were more closely related to the elite LP23 Mozambican line than their respective CML parents.
139 Table 5.3: Similarity percentage index of the progeny lines (DMSR lines) against the parental lines (LP23, CML505 and CML509)
Progeny lines LP23 (common parent)
CML505 (MSV donor)
CML509 (MSV donor)
DMSR1 0.83 0.73 0.64
DMSR2 0.73 0.81 0.61
DMSR4 0.75 0.78 0.65
DMSR8 0.80 0.75 0.64
DMSR10 0.77 0.77 0.62
DMSR12 0.71 0.82 0.64
DMSR13 0.72 0.80 0.62
DMSR16 0.78 0.75 0.62
DMSR18 0.82 0.74 0.65
DMSR21 0.73 0.83 0.62
DMSR23 0.84 0.70 0.68
DMSR26 0.83 0.70 0.62
DMSR30 0.80 0.73 0.63
DMSR34 0.82 0.71 0.62
DMSR35 0.75 0.78 0.65
DMSR39 0.81 0.62 0.64
DMSR40 0.75 0.64 0.63
DMSR43 0.84 0.63 0.64
DMSR46 0.82 0.61 0.64
DMSR47 0.80 0.60 0.65
DMSR51 0.83 0.61 0.64
DMSR55 0.81 0.60 0.66
DMSR56 0.80 0.61 0.63
DMSR57 0.73 0.62 0.67
DMSR60 0.81 0.61 0.64
DMSR62 0.80 0.65 0.61
DMSR64 0.79 0.61 0.65
DMSR65 0.80 0.60 0.64
DMSR66 0.80 0.63 0.64
DMSR69 0.86 0.61 0.61
DMSR71 0.85 0.61 0.62
DMSR73 0.79 0.64 0.66
DMSR74 0.79 0.60 0.63
DMSR75 0.71 0.64 0.66
DMSR77 0.77 0.61 0.64
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5.3.2 Patterns of genetic diversity
A dendrogram (Fig. 5.1) was generated to further assess the genetic diversity of the maize inbred lines. The dendrogram analysed all 50 maize lines, 35 being DMSR lines, three parental lines and the 12 maize control lines. The dendrogram based on UPGMA cluster analysis of genetic similarities showed that all maize lines were grouped into 13 major clusters at 72% similarity coefficient (Fig. 5.1).
Most of the clusters were consistent with the origin and the pedigree information of the inbred lines. For example, Cluster 6 contained all the CML505/LP23 progeny lines and the parental line CML505. The LP23 parental line was in Cluster 7 with all the CML509/LP23 progeny lines. The parental line CML509 was not placed in the same cluster with any of its progenies, but was in its own cluster 8. At a 60% similarity coefficient the temperate (B73WX and MO17WX derivatives) from the USA and subtropical control lines (DXL37, 8CED67 and PA2) from South Africa (Lai et al., 2010) clustered together. There were four clusters. B73WX to PA2 – Cluster 1; CML 202 to PA1 – Cluster 2; DXL59 and M162W – Cluster 3; I137TN – Cluster 4. The controls M162W and DXL59 at a 65% similarity coefficient were grouped in one cluster and so were lines LP19 and CML509. I137TN and PA1 stood each in their own cluster each. Overall, the clusters corresponded to pedigree breeding groups. Line DMSR55 and DMSR47 were the most genetically related with an 88%
similarity value which was also confirmed in the matrix (Appendix 2) as 0.88.
141 Figure 5.1: UPGMA dendrogram deciphering the genetic relatedness of maize streak virus and downy mildew resistant progeny lines based on Jaccard distances calculated using 400 SNP markers.
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
Cluster 6
Cluster 7
Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12 Cluster 13
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