W675
Molecular Characterization Of Genomic Locations Underlying Resistance To Multiple Nematode Species In Soybean
Molecular Characterization Of Genomic Locations Underlying Resistance To Multiple Nematode Species In Soybean
Date: Tuesday, January 15, 2013
Time: 5:10 PM
Time: 5:10 PM
Room: Pacific Salon 4-5 (2nd Floor)
Soybean [Glycine max (L.) Merr.] is an important leguminous crop that produces the major sources of vegetable fats, oils, and protein meal. However, like many other crop plants, soybean can become infested with and severely damaged by common plant-parasitic nematode species: soybean cyst (H. glycines), southern root-knot (M. incognita), and reniform (R. reniformis). For many years, remarkable advances in genetic marker technologies, such as the Universal Soybean Linkage Panel 1.0 (USLP 1.0) and the iSelect SoySNP50K chip, have been successfully utilized for quantitative trait loci (QTL) mapping and genetic analysis in soybean. Our efforts in the present study were to identify and molecularly characterize the genomic regions associated with broad-based resistance to these nematode species. An F7 recombinant inbred line (RIL) mapping population was developed from a cross between Magellan and PI 438489B, a susceptible cultivar and a resistant plant introduction (PI), respectively. These RILs and the two parents were genotyped using USLP 1.0 arrays and were phenotyped for their responses to the three parasitic nematode species. A high-resolution linkage map was successfully constructed. The detection and mapping of QTL were subsequently performed using the program MapQTL 5.0. Results showed several QTL were significantly associated with resistance to either single or multiple nematode species and were consistently mapped to the previously reported genomic regions. The present study indicates that new single nucleotide polymorphism (SNP) marker-based genotyping platform provided an efficient tool for QTL mapping, resulting in the enhancement of QTL mapping accuracy. In addition, markers closely associated with QTL regions can be useful for map-based cloning and marker-assisted selection.
