Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "Soybean yield"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Genome wide association mapping and genomic selection for agronomic and disease traits in soybean
    (2014-09) Bao, Yong
    Genome-wide association mapping and genomic selection are two emerging genomic approaches for investigating genetic architecture and improving breeding efficiency for complex traits in crop species. The objectives of our study were to: 1) dissect the genetic basis of resistance to soybean cyst nematode (SCN) and sudden death syndrome (SDS) through association mapping (AM) and 2) evaluate genomic selection (GS) as an improved marker-based selection tool for predicting agronomic and disease traits in a public soybean breeding program. For AM, we genotyped 282 common breeding parents from the University of Minnesota soybean breeding program using a genome-wide panel of 1,536 single nucleotide polymorphism (SNP) markers and evaluated plant responses to SCN and SDS in the greenhouse. AM rediscovered reported resistance genes (rhg1 and FGAM1 for SCN resistance; cqSDS001, cqRfs4, and SDS11-2 for SDS resistance) and also identified novel loci. For GS, average prediction accuracy through cross-validation studies was 0.67 for SCN resistance and 0.64 for root lesion severity associated with SDS resistance. We also empirically assessed the prediction accuracy and responses to GS for agronomic traits. Soybean lines in the AM panel were used as a training set and a validation set consisting of 273 breeding lines were selected from the ongoing breeding program. Existing historical trial data were used to train the GS model. GS was then conducted to select the top 20% individuals from the validation set based on a comprehensive consideration including genomic estimated breeding values. Our GS model predicted yield with a significant positive accuracy in only two MN x MN crosses, while the prediction accuracy was near to zero or negative for protein and oil, and for the rest of crosses. Moreover, one generation of GS didn't significantly change the population mean of yield, seed protein and oil content. Overall, our study suggested AM holds promise to be used as an alternative approach for mapping QTL in soybean breeding germplasm, and GS deserves further investigation prior to implementation in genetic improvement in existing soybean breeding programs.