Systematic prediction of EMS-induced mutations in a sorghum mutant population.

The precise detection of causal DNA mutations (deoxyribonucleic acid) is very crucial for forward genetic studies. Several sources of errors contribute to false-positive detections by current variant-calling algorithms, which impact associating phenotypes with genotypes. To improve the accuracy of mutation detection, we implemented a binning method for the accurate detection of likely ethyl methanesulfonate (EMS)-induced mutations in a sequenced mutant population. We also implemented a clustering algorithm for detecting likely false negatives with high accuracy. Sorghum bicolor is a very valuable crop species with tremendous potential for uncovering novel gene functions associated with highly desirable agronomical traits. We demonstrate the precision of the described approach in the detection of likely EMS-induced mutations from the publicly available low-cost sequencing of the M3 generation from 600 sorghum BTx623 mutants. The approach detected 3,274,606 single nucleotide polymorphisms (SNPs), of which 96% (3,141,908) were G/C to A/T DNA substitutions, as expected by EMS-mutagenesis mode of action. We demonstrated the general applicability of the described method and showed a high concordance, 94% (3,074,759) SNPs overlap between SAMtools-based and GATK-based variant-calling algorithms. Our clustering algorithm uncovered evidence for an additional 223,048 likely false-negative shared EMS-induced mutations. The final 3,497,654 SNPs represent an 87% increase in SNPs detected from the previous analysis of the mutant population, with an average of one SNP per 125 kb in the sorghum genome. Annotation of the final SNPs revealed 10,263 high-impact and 136,639 moderate-impact SNPs, including 7217 stop-gained mutations, which averages 12 stop-gained mutations per mutant, and four high- or medium-impact SNPs per sorghum gene. We have implemented a public search database for this new genetic resource of 30,285 distinct sorghum genes containing medium- or high-impact EMS-induced mutations. Seedstock for a select 486 of the 600 described mutants are publicly available in the Germplasm Resources Information Network (GRIN) database.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Whole-Genome Sequence Accuracy Is Improved by Replication in a Population of Mutagenized Sorghum.

The accurate detection of induced mutations is critical for both forward and reverse genetics studies. Experimental chemical mutagenesis induces relatively few single base changes per individual. In a complex eukaryotic genome, false positive detection of mutations can occur at or above this mutagenesis rate. We demonstrate here, using a population of ethyl methanesulfonate (EMS)-treated Sorghum bicolor BTx623 individuals, that using replication to detect false positive-induced variants in next-generation sequencing (NGS) data permits higher throughput variant detection with greater accuracy. We used a lower sequence coverage depth (average of 7×) from 586 independently mutagenized individuals and detected 5,399,493 homozygous single nucleotide polymorphisms (SNPs). Of these, 76% originated from only 57,872 genomic positions prone to false positive variant calling. These positions are characterized by high copy number paralogs where the error-prone SNP positions are at copies containing a variant at the SNP position. The ability of short stretches of homology to generate these error-prone positions suggests that incompletely assembled or poorly mapped repeated sequences are one driver of these error-prone positions. Removal of these false positives left 1,275,872 homozygous and 477,531 heterozygous EMS-induced SNPs, which, congruent with the mutagenic mechanism of EMS, were >98% G:C to A:T transitions. Through this analysis, we generated a collection of sequence indexed mutants of sorghum. This collection contains 4035 high-impact homozygous mutations in 3637 genes and 56,514 homozygous missense mutations in 23,227 genes. Each line contains, on average, 2177 annotated homozygous SNPs per genome, including seven likely gene knockouts and 96 missense mutations. The number of mutations in a transcript was linearly correlated with the transcript length and also the G+C count, but not with the GC/AT ratio. Analysis of the detected mutagenized positions identified CG-rich patches, and flanking sequences strongly influenced EMS-induced mutation rates. This method for detecting false positive-induced mutations is generally applicable to any organism, is independent of the choice of in silico variant-calling algorithm, and is most valuable when the true mutation rate is likely to be low, such as in laboratory-induced mutations or somatic mutation detection in medicine.

Increased Power To Dissect Adaptive Traits in Global Sorghum Diversity Using a Nested Association Mapping Population.

Adaptation of domesticated species to diverse agroclimatic regions has led to abundant trait diversity. However, the resulting population structure and genetic heterogeneity confounds association mapping of adaptive traits. To address this challenge in sorghum [Sorghum bicolor (L.) Moench]-a widely adapted cereal crop-we developed a nested association mapping (NAM) population using 10 diverse global lines crossed with an elite reference line RTx430. We characterized the population of 2214 recombinant inbred lines at 90,000 SNPs using genotyping-by-sequencing. The population captures ∼70% of known global SNP variation in sorghum, and 57,411 recombination events. Notably, recombination events were four- to fivefold enriched in coding sequences and 5' untranslated regions of genes. To test the power of the NAM population for trait dissection, we conducted joint linkage mapping for two major adaptive traits, flowering time and plant height. We precisely mapped several known genes for these two traits, and identified several additional QTL. Considering all SNPs simultaneously, genetic variation accounted for 65% of flowering time variance and 75% of plant height variance. Further, we directly compared NAM to genome-wide association mapping (using panels of the same size) and found that flowering time and plant height QTL were more consistently identified with the NAM population. Finally, for simulated QTL under strong selection in diversity panels, the power of QTL detection was up to three times greater for NAM vs. association mapping with a diverse panel. These findings validate the NAM resource for trait mapping in sorghum, and demonstrate the value of NAM for dissection of adaptive traits.

Sorghum, a healthy and gluten-free food for celiac patients as demonstrated by genome, biochemical, and immunochemical analyses.

Wheat (Triticum spp. L.), rye (Secale cereal L.), and barley (Hordeum vulgare L.) seeds contain peptides toxic to celiac patients. Maize (Zea mays L.) and rice (Oryza sativa L.) are distant relatives of wheat as well as sorghum (Sorghum bicolor (L.) Moench) and are known to be safe for celiacs. Both immunochemical studies and in vitro and in vivo challenge of wheat-free sorghum food products support this conclusion, although molecular evidence is missing. The goal of the present study was to provide biochemical and genetic evidence that sorghum is safe for celiac patients. In silico analysis of the recently published sorghum genome predicts that sorghum does not contain peptides that are toxic for celiac patients. Aqueous/alcohol-soluble prolamins (kafirins) from different sorghum varieties, including pure lines and hybrids, were evaluated by SDS-PAGE and HPLC analyses as well as an established enzyme-linked immunosorbent assay (ELISA) based on the R5 antibody. These analyses provide molecular evidence for the absence of toxic gliadin-like peptides in sorghum, confirming that sorghum can be definitively considered safe for consumption by people with celiac disease.

Variability in tannin content, chemistry and activity in a diverse group of tannin containing sorghum cultivars.

BACKGROUND: Tannins are large polyphenolic polymers and are known to bind proteins, limiting their digestibility, but are also excellent antioxidants. Numerous studies investigating the functional properties of sorghum tannin have been conducted by comparing grain samples from different sorghum lines without considering the other intrinsic characteristics of the grain. The purpose of this study was to remove the confounding intrinsic factors present in the endosperm so the effect of the tannins could be evaluated utilizing a unique decortication/reconstitution procedure. RESULTS: The tannin content of the 14 cultivars tested ranged from 2.3 to 67.2 catechin equivalents. The bran fractions were studied for their impact on protein binding and antioxidant capacity. Protein digestibility by pepsin ranged from 8% to 58% at the highest tannin level addition. Protein binding ranged from 3.11 to 16.33 g blue bovine serum albumin kg⁻¹ bran. Antioxidant capacity ranged from 81.33 to 1122.54 µmol Trolox equivalents g⁻¹ bran. High-performance size-exclusion chromatography detailed molecular size distributions of the tannin polymers and relationship to tannin functionality. CONCLUSION: The tannin content and composition play a significant role in determining tannin functionality. These differences will allow for selections of high-tannin sorghums with consideration of the biological activities of the tannins.