IDENTIFICATION OF MOLECULAR MARKERS ASSOCIATED WITH SILICON ACCUMULATION AND YIELD TRAITS IN AEROBICALLY GROWN RICE {Oryza sativa L.)
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Date
2007-05-21
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UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BANGALORE
Abstract
Rice is life for almost half of the Earth's population. Silicon is the second most abundant
element in earth s crust. Silicon has been found to give resistance against various abiotic
and biotic stresses in rice. Eighty five diverse rice genotypes were used in the present
study and evaluated under aerobic conditions. Various yield related observations were
taken and a significant difference among the genotypes for various yield parameters was
observed. These diverse rice genotypes were assessed for silicon accumulation in
different plant parts sampled at different stages. Many RAPD markers and few SSR
markers were found associated with various yield traits. Single Marker Analysis (SMA)
established putative association of six RAPD markers with grain weight among which
OPD3500 showed 6.10% and OPB81000 showed 4.88% putative association and are having
positive parameter estimate (PE) values. SMA also established association of 8 RAPD
markers with number of panicles per plant among which OPA9io5o showed 7.43% and
OPE1i65o showed 5.19% putative association both having positive PE values. RAPD
marker OPC14i9oo contributed 3.89% for silicon accumulation in leaves at flowering
stage, OPD9i4oo contributed 5.71% for silieon accumulation in leaves at maturity stage
while OPE7i9oo, OPEI700 and OPB81200 contributed 6.50%, 7.02% and 5.27%
respectively and were found putatively associated with grain silicon content. By using
Stepwise Multiple Regression Analysis (SMRA) a good number of markers were found
out contributing for studied traits. For diversity studies NTYSIS programme was used,
mean value of Jaccard s coefficient of similarity for all the pairwise comparisons was
0.75 and the average polymorphic information content of 0.26 was observed. The results
showing putative association of various markers with different traits using SMA and
SMRA can be validated using various mapping populations like RILs, NlLs, DH etc. and
can be used as potential markers in marker assisted selection for the crop improvement
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