Consensus under Misaligned Orientations

This paper presents a consensus algorithm under misaligned orientations, which is defined as (i) misalignment to global coordinate frame of local coordinate frames, (ii) biases in control direction or sensing direction, or (iii) misaligned virtual global coordinate frames. After providing a mathematical formulation, we provide some sufficient conditions for consensus or for divergence. Besides the stability analysis, we also conduct some analysis for convergence characteristics in terms of locations of eigenvalues. Through a number of numerical simulations, we would attempt to understand the behaviors of misaligned consensus dynamics.Comment: 23 pages, 9 figure

Genome-wide association analysis for drought tolerance-associated traits in common bean

Conclusions from GWAS • Population structure analysis confirmed empirical knowledge • Novel information on levels of admixture • Many promising associations, overlapping signals in rainfed and irrigated conditions • High coefficients of determination (~29%), high h2 in some cases (~50%) • Significant SNPs can be converted into other marker types or assays for MA

Genome-wide association analysis for drought tolerance and associated traits in common bean

Common bean is an important food legume and its production is frequently threatened by recurring drought events worldwide. The detection of genetic signals associated with drought tolerance has great potential in breeding for drought tolerance in common bean and gaining insight into the genetic control drought responses in plants. The objectives of this study were to explore the genetic variation present within a 96-entry diversity panel grown under irrigated and rainfed conditions and use genome-wide association (GWAS) analysis to identify candidate regions associated with drought tolerance traits and agronomic performance in common bean genotypes from the Middle American gene pool. We used single nucleotide polymorphism (SNP) data to explore genetic diversity and ancestry of the diversity panel and discovered varying levels of admixture and purity as well as distinctly divergent individuals. We report in this significant study marker-trait associations for shoot biomass at harvest (irrigated and rainfed), shoot biomass at flowering (irrigated), seed size (irrigated and rainfed), lodging score (irrigated), leaf elongation rate and wilting score

DataSheet_1_Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max).zip

Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.</p

Table_1_Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max).docx

Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.</p

Supplemental Material for Stewart-Brown et al., 2019

Table S1 displays the effect of training set size on prediction ability when performing cross-validation across the entire genomic selection dataset. Table S2 displays the effect of marker density on prediction ability when performing cross-validation across the entire genomic selection dataset. Table S3 displays the effect of training set size on prediction ability when performing cross-validation to predict individual bi-parental families using the within population method versus across population method (Prediction ability averaged across Pop1-4). Table S4 displays the effect of training set size on prediction ability when performing cross-validation to predict individual bi-parental families using the within population method versus the across population method (Prediction ability displayed for each individual validation population). Figure S1 contains kernel density plots of BLUP values for phenotypic traits of interest. Figure S2 contains boxplots of observed BLUP values for traits of interest, broken out by populations and pedigrees used for genomic prediction. Figure S3 displays the effects of training set size on predictive ability when contrasting the within population method vs. the across population method. All data and code required to replicate the analyses are available in Files S1-5. File S1 contains the raw phenotypic data for calculation of BLUP values. File S2 contains the phenotypic BLUP values used for each method of GS. Worksheet 1 provides additional information for each genotype while Worksheets 2 and 3 contain information used for GS. Supplemental Data File S3 and S4 contains the genotypic data files used for prediction of seed yield and protein/oil content for each method of GS. Worksheet 1 of each file provides additional information for each genotype while Worksheets 1-10 contain genotypic data for the following marker densities: all SNPs, tag SNPs, half tag SNPs, 4th tag SNPs, and 8th tag SNPs. Odd sheets contain extra information while even sheets contain data used for GS. File S5 provides the r code used for calculation of predictive abilities which can be adapted to test all methods

Action, agency and responsibility

The list of 342 soybean landraces and 1062 improved lines sampled in this study. (XLSX 78 kb

Additional file 8: of Genomic consequences of selection and genome-wide association mapping in soybean

The descriptive summary of GWAS for 9 agronomic traits in soybean. (XLSX 44 kb

Additional file 2: of Genomic consequences of selection and genome-wide association mapping in soybean

Distribution of accessions in each subgroup based on genetic distance in landraces and improved lines. (DOCX 19 kb

Additional file 10: of Genomic consequences of selection and genome-wide association mapping in soybean

The heat map showing kinship value between individual accessions among the landraces and the improved lines. Pairwise kinship values are shown as color-index heat map. (DOCX 963 kb