The notion that many nutrients and beneficial phytochemicals in maize are lost due to food product processing is common, but this has not been studied in detail for the phenolic acids. Information regarding changes in phenolic acid content throughout processing is highly valuable because some phenolic acids are chemopreventive agents of aging-related diseases. It is unknown when and why these changes in phenolic acid content might occur during processing, whether some maize genotypes might be more resistant to processing induced changes in phenolic acid content than other genotypes, or if processing affects the bioavailability of phenolic acids in maize-based food products. For this study, a laboratory-scale processing protocol was developed and used to process whole maize kernels into toasted cornflakes. High-throughput microscale wet-lab analyses were applied to determine the concentrations of soluble and insoluble-bound phenolic acids in samples of grain, three intermediate processing stages, and toasted cornflakes obtained from 12 ex-PVP maize inbreds and seven hybrids. In the grain, insoluble-bound ferulic acid was the most common phenolic acid, followed by insoluble-bound p-coumaric acid and soluble cinnamic acid, a precursor to the phenolic acids. Notably, the ferulic acid content was approximately 1950 μg/g, more than ten-times the concentration of many fruits and vegetables. Processing reduced the content of the phenolic acids regardless of the genotype. Most changes occurred during dry milling due to the removal of the bran. The concentration of bioavailable soluble ferulic and p-coumaric acid increased negligibly due to thermal stresses. Therefore, the current dry milling based processing techniques used to manufacture many maize-based foods, including breakfast cereals, are not conducive for increasing the content of bioavailable phenolics in processed maize food products. This suggests that while maize is an excellent source of phenolics, alternative or complementary processing methods must be developed before this nutritional resource can be utilized.

Obtaining genome-wide genotype information for millions of SNPs in soybean [ Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK’s Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean.

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage.Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI short read achieve. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK's Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean.

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype’s range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield “stability” values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies (GWAS) has not been well defined. In this study, the GWAS results for yield and yield stability were compared in 213 soybean lines across 11 environments to determine their utility and potential intersection. Both univariate and multivariate conventional stability estimates were considered alongside a mixed model for yield that fit marker by environment interactions as a random effect. One-hundred and six total QTL were discovered across all mapping results, however, genetic loci that were significant in the mixed model for grain yield that fit marker by environment interactions were completely distinct from those that were significant when mapping using traditional stability measures as a phenotype. Furthermore, 73.21% of QTL discovered in the mixed model were determined to cause a crossover interaction effect which cause genotype rank changes between environments. Overall, the QTL discovered via explicitly mapping GxE interactions also explained more yield variance that those QTL associated with differences in traditional stability estimates making their theoretical impact on selection greater. A lack of intersecting results between mapping approaches highlights the importance of examining stability in multiple contexts when attempting to manipulate GxE interactions in soybean.

Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome‐wide single‐nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) are a targeted genotyping‐by‐sequencing (GBS) method that could be used for soybean [Glycine max (L.) Merr.] that is both cost‐effective, high‐throughput, and provides high data quality to screen breeder's germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the northern‐central and middle‐southern regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line (RIL) population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. The MIP genotyping accuracy was 93% overall, whereas homozygous call accuracy was 98% with <10% missing data. The accuracy of MIPs combined with its low per‐sample cost makes it a powerful tool to enable genomic selection within soybean breeding programs.