Predicting genetic variances of biparental populations has been a long‐standing goal for plant breeders. The ability to discriminate among crosses with similarly predicted high means but different levels of genetic variance (VG) should improve the effectiveness of breeding. We developed a procedure that uses established progeny simulation and genomic prediction strategies to predict the population mean (μ) and VG, the mean of the desired 10% of the progeny (superior progeny mean [μsp]), and correlated responses of multiple traits for biparental populations. The proposed procedure, PopVar, is herein demonstrated using a training population (TP) composed of 383 breeding lines that have been genotyped and phenotyped for yield and deoxynivalenol (DON). Marker effects estimated from the TP were used to calculate genotypic estimated breeding values (GEBVs) of 200 simulated recombinant inbred lines (RILs) per cross. Values of μ, VG, and μsp were then calculated directly from the RIL GEBVs. We found that μ explained 82 and 88% of variation in μsp for yield and DON, respectively, and adding VG to the regression model increased those respective R2 values to 99.5 and 99.6%. The results of correlated response revealed that although yield and DON are unfavorably correlated, the correlation was near zero or slightly negative in some simulated crosses, indicating the potential to increase yield while decreasing DON. This work extends the current benefits of genomic selection to include the ability to design crosses that maximize genetic variance with more favorable correlations among traits. PopVar is available as an R package that researchers and breeders are encouraged to use for empirical evaluation of the methodology.

Two hundred one hexaploid wheat accessions, representing 200 years of selection and breeding history, were sampled from the National Small Grains Collection in Aberdeen, ID, and evaluated for five root traits at the seedling stage. A paper roll-supported hydroponic system was used for seedling growth. Replicated roots samples were analyzed by WinRHIZO. We observed accessions with nearly no branching and accessions with up to 132 cm of branching. Total seminal root length ranged from 70 to 248 cm, a 3.5-fold difference. Next-generation sequencing was used to produce single-nucleotide polymorphism (SNP) markers and genomic libraries that were aligned to the wheat reference genome IWGSCv1 and were called single-nucleotide polymorphism (SNP) markers. After filtering and imputation, a total of 20,881 polymorphic sites were used to perform association mapping in TASSEL. Gene annotations were conducted for identified marker-trait associations (MTAs) with - logP > 3.5 (p value < 0.003). In total, we identified 63 MTAs with seven for seminal axis root length (SAR), 24 for branching (BR), four for total seminal root length (TSR), eight for root dry matter (RDM), and 20 for root diameter (RD). Putative proteins of interest that we identified include chalcone synthase, aquaporin, and chymotrypsin inhibitor for SAR, MYB transcription factor and peroxidase for BR, zinc fingers and amino acid transporters for RDM, and cinnamoyl-CoA reductase for RD. We evaluated the effects of height-reducing Rht alleles and the 1B/1R translocation event on root traits and found presence of the Rht-B1b allele decreased RDM, while presence of the Rht-D1b allele increased TSR and decreased RD.

We report malt quality QTLs relevant to breeding with greater precision than previous mapping studies. The distribution of favorable alleles suggests strategies for marker-assisted breeding and germplasm exchange. This study leverages the breeding data of 1,862 barley breeding lines evaluated in 97 field trials for genome-wide association study of malting quality traits in barley. The mapping panel consisted of six-row and two-row advanced breeding lines from eight breeding populations established at six public breeding programs across the United States. A total of 4,976 grain samples were subjected to micro-malting analysis and mapping of nine quality traits was conducted with 3,072 SNP markers distributed throughout the genome. Association mapping was performed for individual breeding populations and for combined six-row and two-row populations. Only 16% of the QTL we report here had been detected in prior bi-parental mapping studies. Comparison of the analyses of the combined two-row and six-row panels identified only two QTL regions that were common to both. In total, 108 and 107 significant marker-trait associations were identified in all six-row and all two-row breeding programs, respectively. A total of 102 and 65 marker-trait associations were specific to individual six-row and two-row breeding programs, respectively indicating that most marker-trait associations were breeding population specific. Combining datasets from different breeding program resulted in both the loss of some QTL that were apparent in the analyses of individual programs and the discovery of new QTL not identified in individual programs. This suggests that simply increasing sample size by pooling samples with different breeding history does not necessarily increase the power to detect associations. The genetic architecture of malting quality and the distribution of favorable alleles suggest strategies for marker-assisted selection and germplasm exchange.

Populations continually incur new mutations with fitness effects ranging from lethal to adaptive. While the distribution of fitness effects of new mutations is not directly observable, many mutations likely either have no effect on organismal fitness or are deleterious. Historically, it has been hypothesized that a population may carry many mildly deleterious variants as segregating variation, which reduces the mean absolute fitness of the population. Recent advances in sequencing technology and sequence conservation-based metrics for inferring the functional effect of a variant permit examination of the persistence of deleterious variants in populations. The issue of segregating deleterious variation is particularly important for crop improvement, because the demographic history of domestication and breeding allows deleterious variants to persist and reach moderate frequency, potentially reducing crop productivity. In this study, we use exome resequencing of 15 barley accessions and genome resequencing of 8 soybean accessions to investigate the prevalence of deleterious single nucleotide polymorphisms (SNPs) in the protein-coding regions of the genomes of two crops. We conclude that individual cultivars carry hundreds of deleterious SNPs on average, and that nonsense variants make up a minority of deleterious SNPs. Our approach identifies known phenotype-altering variants as deleterious more frequently than the genome-wide average, suggesting that putatively deleterious variants are likely to affect phenotypic variation. We also report the implementation of a SNP annotation tool BAD_Mutations that makes use of a likelihood ratio test based on alignment of all currently publicly available Angiosperm genomes.

This investigation was done to study GE interaction over twelve environments for seed yield in 18 genetically diverse genotypes. Grain yield performances were evaluated for three years at four locations in Iran using a randomized complete block design. The first two principal components (IPC1 and IPC2) were used to create a two-dimensional GGE biplot that accounted percentages of 49% and 20% respectively of sums of squares of the GE interaction. The combined analysis of variance indicated that year and location were the most important sources affecting yield variation and these factors accounted for percentages of 50.0% and 33.3% respectively of total G+E+GE variation. The GGE biplot suggested the existence of three lentil mega-environments with wining genotypes G1, G11 and G14. According to the ideal-genotype biplot, genotype G1 was the better genotype demonstrating high mean yield and high stability of performance across test locations. The average tester coordinate view indicated that genotype G1 had the highest average yield, and genotypes G1 and G12 recorded the best stability. The study revealed that a GGE biplot graphically displays interrelationships between test locations as well as genotypes and facilitates visual comparisons.