|VAUGHN, JUSTIN - University Of Georgia
|BOERMA, ROGER - University Of Georgia
|LI, ZENGLU - University Of Georgia
Submitted to: Genes, Genomes, and Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/22/2014
Publication Date: 11/1/2014
Publication URL: http://handle.nal.usda.gov/10113/61838
Citation: Vaughn, J.N., Nelson, R.L., Song, Q., Cregan, P.B., Boerma, R., Li, Z. 2014. The genetic architecture of seed composition in soybean is refined by genome-wide association scans across multiple populations. Genes, Genomes, and Genomics. 4:2283-2294. doi: 10.1534/g3.114.013433.
Interpretive Summary: Soybean oil and meal are major contributors to world-wide food production. The genetic basis for soybean seed composition has been intensely studied but our knowledge is still very incomplete. The objective of this research was to combine existing data on soybean seed oil, protein, and amino acid concentrations and extensive DNA marker data for accessions in the USDA Soybean Germplam Collection with new statistical approaches to refine the location of existing quantitative trait loci (QTL) and identify new QTL that control these ecnomically important traits. We were able to locate a major QTL for protein concentration on chromosome 20 with a precision never before obtained. Although there is a negative correlation between oil and protein concentration, we were able to identify QTL that were only associated with oil concentration. We also identified DNA markers associated with methionine, cysteine, threonine and lysine concentrations, which have not been previously found. Chromosomes 1 and 8 contain strong candidate alleles for essential amino acid increases. Our results will assist soybean breeders in using DNA markers to selection for important changes in soybean seed composition.
Technical Abstract: Soybean oil and meal are major contributors to world-wide food production. Consequently, the genetic basis for soybean seed composition has been intensely studied using family-based mapping. Population-based mapping approaches, in the form of genome-wide association (GWA) scans, have been able to resolve loci controlling moderately complex quantitative traits (QTLs) in numerous crop species. Yet, it is still unclear how soybean’s unique population history will affect GWA. Using >30,000 single-nucleotide polymorphisms, we simulated a range of genetic architectures across multiple populations. We found that, with a heritability of 0.5, we were able to recover ~100% and ~33% of the 4 and 20 simulated QTLs, respectively, with a nominal false-positive rate. Additionally, we demonstrated that combining information from multi-locus mixed models and compressed linear-mixed models improves QTL identification and interpretation. We applied these insights to exploring seed composition in soybean, refining the Linkage Group I (chromosome 20) protein QTL and identifying additional oil QTL that may allow some decoupling of highly correlated oil and protein phenotypes. Because the value of protein meal is closely related to its essential amino acid profile, we attempted to identify QTL underlying methionine, threonine, cysteine, and lysine content. Multiple QTL were found that have not been observed in family-based mapping studies, and each trait exhibited associations across multiple populations. Chromosomes 1 and 8 contain strong candidate alleles for essential amino acid increases. Overall, we present these and additional data that will be useful in determining breeding strategies for the continued improvement of soybean's nutrient portfolio.