Friday, January 20, 2012

Identification and Validation of Quantitative Trait Loci for Agronomic Traits in Advanced Backcross Breeding Lines Derived from Oryza rufipogon × Oryza sativa Cultivar MR219

Ratnam Wickneswari, M. A. R. Bhuiyan, Sabu Kalluvettankuzhy K., Li Sze Lim, Michael J. Thomson, Md. Kairudin Narimah and Md. Zain Abdullah

A backcross breeding strategy was used to identify quantitative trait loci (QTLs) associated with 14 traits in a BC2F2 population derived from a cross between MR219, an indica rice cultivar and an accession of Oryza rufipogon (IRGC 105491). A total of 261 lines were genotyped with 96 microsatellite markers and evaluated for plant morphology, yield components and growth period. The genetic linkage map generated for this population with an average interval size of 16.2 cM, spanning 1,553.4 cM (Kosambi) of the rice genome. Thirty-eight QTLs were identified with composite interval mapping (CIM), whereas simple interval mapping (SIM) resulted in 47 QTLs (LOD >3.0). The O. rufipogon allele was favourable for 59% of QTLs detected through CIM. Of 261 BC2F2 families, 26 advanced backcross breeding lines (BC2F5) were used for QTL validation. These lines were selected on the basis of the yield traits potentiality in BC2F3 and BC2F4 generations. The field trial was conducted at three different locations in Malaysia using randomized complete block design with three replications. Trait based marker analysis was done for QTL determination. Twenty-five QTLs were detected in BC2F5 generation whereas 29 QTLs were detected in BC2F2 generation of the same population. Two QTLs (qPL-1 and qSPL-7) were not considered for validation due to their low R 2 values and two QTLs (qPSS-3-2 and qGW-3-2) were not detected in the BC2F5 population. Fifteen QTLs showed the beneficial effect to enhance the trait value of the breeding lines. QTL validation aided to select the promising lines for further utilization. [More details]

Backcross breeding – Composite interval mapping – Oryza rufipogon – Oryza sativa – QTL identification – QTL validation – Trait-based marker analysis

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