Diversity analysis of maize inbred lines using SSR markers
2004
Sales, E.K. | Magulama, E.E. | Butardo, N.G. | Cadungog, R.C. | Manceras, A.,University of Southern Mindanao, Kabacan, Cotabato (Philippines) | Regalado, E. | George, M.L.C.
With the assistance of the Asian Maize Biotechnology Network (AMBIONET) and DA-BAR, a molecular biology laboratory was established at the University of Southern Mindanao in 2002. National Staff was trained to strengthen the university's capability in the application of molecular techniques for crop improvement. The authors initiated the DNA fingerprinting of Philippine publicly derived maize inbred lines for local as well as region-wide diversity analyses. Initially, 87 Philippine inbred lines were fingerprinted using SSR markers and standard protocols from AMBIONET. Analysis at 21 SSR loci, however, revealed that most of the lines had 20% heterozygosity. These lines were recommended for further purification and are presently in the second selfing generation. In another study aimed at the introgression of the opaque 2 allele to elite local lines and development of quality protein maize (QPM) cultivars, 21 QPM lines (7 yellow and 14 white maize) were obtained from CIMMYT and analyzed using 40 SSR markers. SSR data of 8 QPM lines, remaining after 13 white lines 20% heterozygosity were excluded, were merged with a data set of 16 QPM lines from AMBIONET-Vietnam and with a regional dataset of 102 inbred lines from the AMBIONET Service Lab that included 10 lines from the Philippines as well as lines from various tropical and temperate countries. Error rates (ER) were calculated between laboratories and marker data having less than 10% ER were combined. By cluster analysis, the 24 QPM lines were seen in at least 7 different groups of the regional dataset, which suggests that this set of QPM lines are genetically diverse. These results are useful as a guide for selecting crosses with potential superior combining ability. The authors continue to analyze the diversity of Philippine inbred lines and eventually combine it with data sets of other lines that are relevant to local breeders.
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