Category Archives: ORIGINAL ARTICLE

Identification of stable restorer lines developed through inter‐sub‐specific hybridization in rice (Oryza sativa) using multi‐trait stability index

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Abstract

Inter-sub-specific hybridization between indica and tropical japonica rice germplasm is the most efficient approach for broadening the genetic base of hybrid rice parental lines and enhancing the heterotic potential of hybrids in a tropical country like India. In the present study, 106 indica/tropical–japonica derived lines were developed through inter-sub-specific hybridization and screened using functional markers with respect to the major fertility restorer genes, Rf3 and Rf4, and the wide compatibility gene, S5n. The fertility restoration ability of newly developed restorers was validated through test cross nursery performance. The functional markers were observed to accurately predict the trait of fertility restoration. From the present study, the decreasing order of efficiency of different gene combinations on fertility restoration ability are as follows: Rf4/S5n > Rf4/Rf3/S5n > Rf4 > Rf4/Rf3 > Rf3/S5n > Rf3. Based on multi-trait stability index (MTSI) analysis, the four newly identified restorers, that is, RP6388-90, RP6382-49, RP6375-81 and RP6368-38, were selected as the top best performing genotypes with high stability for multiple traits, and these genotypes will be useful for the development of superior rice hybrids in India.

Genetic control of grain amino acid composition in a UK soft wheat mapping population

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Abstract

Wheat (Triticum aestivum L.) is a major source of nutrients for populations across the globe, but the amino acid composition of wheat grain does not provide optimal nutrition. The nutritional value of wheat grain is limited by low concentrations of lysine (the most limiting essential amino acid) and high concentrations of free asparagine (precursor to the processing contaminant acrylamide). There are currently few available solutions for asparagine reduction and lysine biofortification through breeding. In this study, we investigated the genetic architecture controlling grain free amino acid composition and its relationship to other traits in a Robigus × Claire doubled haploid population. Multivariate analysis of amino acids and other traits showed that the two groups are largely independent of one another, with the largest effect on amino acids being from the environment. Linkage analysis of the population allowed identification of quantitative trait loci (QTL) controlling free amino acids and other traits, and this was compared against genomic prediction methods. Following identification of a QTL controlling free lysine content, wheat pangenome resources facilitated analysis of candidate genes in this region of the genome. These findings can be used to select appropriate strategies for lysine biofortification and free asparagine reduction in wheat breeding programs.

Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.)

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Abstract

In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013–2014 from Powerkheda (Madhya Pradesh) and for 2014–2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.

Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic‐assisted selection

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Abstract

Improved selection of wheat varieties with high end-use quality contributes to sustainable food systems by ensuring productive crops are suitable for human consumption end-uses. Here, we investigated the genetic control and genomic prediction of milling and baking quality traits in a panel of 379 historic and elite, high-quality UK bread wheat (Triticum eastivum L.) varieties and breeding lines. Analysis of the panel showed that genetic diversity has not declined over recent decades of selective breeding while phenotypic analysis found a clear trend of increased loaf baking quality of modern milling wheats despite declining grain protein content. Genome-wide association analysis identified 24 quantitative trait loci (QTL) across all quality traits, many of which had pleiotropic effects. Changes in the frequency of positive alleles of QTL over recent decades reflected trends in trait variation and reveal where progress has historically been made for improved baking quality traits. It also demonstrates opportunities for marker-assisted selection for traits such as Hagberg falling number and specific weight that do not appear to have been improved by recent decades of phenotypic selection. We demonstrate that applying genomic prediction in a commercial wheat breeding program for expensive late-stage loaf baking quality traits outperforms phenotypic selection based on early-stage predictive quality traits. Finally, trait-assisted genomic prediction combining both phenotypic and genomic selection enabled slightly higher prediction accuracy, but genomic prediction alone was the most cost-effective selection strategy considering genotyping and phenotyping costs per sample.

Meta‐QTL s and haplotypes for efficient zinc biofortification of rice

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Abstract

Biofortification of rice with improved grain zinc (Zn) content is the most sustainable and cost-effective approach to address Zn malnutrition in Asia. Genomics-assisted breeding using precise and consistent Zn quantitative trait loci (QTLs), genes, and haplotypes can fast-track the development of Zn biofortified rice varieties. We conducted the meta-analysis of 155 Zn QTLs reported from 26 different studies. Results revealed 57 meta-QTLs with a significant reduction of 63.2% and 80% in the number and confidence interval of the Zn QTLs, respectively. Meta-quantitative trait loci (MQTLs) regions were found to be enriched with diverse metal homeostasis genes; at least 11 MQTLs were colocated with 20 known major genes involved in the production of root exudates, metal uptake, transport, partitioning, and loading into grains in rice. These genes were differentially expressed in vegetative and reproductive tissues, and a complex web of interactions were observed among them. We identified superior haplotypes and their combinations for nine candidate genes (CGs), and the frequency and allelic effects of superior haplotypes varied in different subgroups. The precise MQTLs with high phenotypic variance, CGs, and superior haplotypes identified in our study are useful for an efficient Zn biofortification of rice and to ensure Zn as an essential component of all the future rice varieties through mainstreaming of Zn breeding.

Genetic dissection of monosaccharides contents in rice whole grain using genome‐wide association study

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The simplest form of carbohydrates are monosaccharides which are the building blocks for the synthesis of polymers or complex carbohydrates. Monosaccharide contents of 197 rice accessions were quantified by HPAEC-PAD in rice (Oryza sativa L.) whole grain (RWG). A genome-wide association study (GWAS) was carried out using 33,812 single nucleotide polymorphisms (SNPs) to identify corresponding genomic regions influencing neutral monosaccharides contents. In total, 49 GWAS signals contained in 17 genomic regions (quantitative trait loci [QTLs]) on seven chromosomes of rice were determined to be associated with monosaccharides contents of whole grain. The QTLs were found for fucose (1), mannose (1), xylose (2), arabinose (2), galactose (4), and rhamnose (7) contents, all of which are novel. Based on co-location of annotated rice genes in the vicinity of GWAS signals, the constituents of the whole grain were associated with the following candidate genes: arabinose content with α-N-arabinofuranosidase, pectinesterase inhibitor, and glucosamine-fructose-6-phosphate aminotransferase 1; xylose content with ZOS1-10 (a C2H2 zinc finger transcription factor [TF]); mannose content with aldose 1-epimerase-like protein and a MYB family TF; galactose content with a GT8 family member (galacturonosyltransferase-like 3), a GRAS family TF, and a GH16 family member (xyloglucan endotransglucosylase/hydrolase xyloglucan 23); fucose content with gibberellin 20 oxidase and a lysine-rich arabinogalactan protein 19, and finally rhamnose content with myo-inositol-1-phosphate synthase, UDP-arabinopyranose mutase, and COBRA-like protein precursor. The results of this study should improve our understanding of the genetic basis of the factors that might be involved in the biosynthesis, regulation, and turnover of monosaccharides in RWG, aiming to enhance the nutritional value of rice grain and impact the related industries.

Genomic prediction of tocochromanols in exotic‐derived maize

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Abstract

Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.

Whole‐genome sequencing based discovery of candidate genes and diagnostic markers for seed weight in groundnut

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Abstract

Seed weight in groundnut (Arachis hypogaea L.) has direct impact on yield as well as market price because of preference for bold seeds by consumers and industry, thereby making seed-size improvement as one of the most important objectives of groundnut breeding programs globally. Marker-based early generation selection can accelerate the process of breeding for developing large-seeded varieties. In this context, we deployed the quantitative trait locus-sequencing (QTL-seq) approach on a biparental mapping population (Chico × ICGV 02251) to identify candidate genes and develop markers for seed weight in groundnut. A total of 289.4–389.4 million reads sequencing data were generated from three libraries (ICGV 02251 and two extreme bulks) achieving 93.9–95.1% genome coverage and 8.34–9.29× average read depth. The analysis of sequencing data using QTL-seq pipeline identified five genomic regions (three on chromosome B06 and one each on chromosomes B08 and B09) for seed weight. Detailed analysis of above associated genomic regions detected 182 single-nucleotide polymorphisms (SNPs) in genic and intergenic regions, and 11 of these SNPs were nonsynonymous in the genomic regions of 10 candidate genes including Ulp proteases and BIG SEED locus genes. Kompetitive allele specific polymerase chain reaction (KASP) markers for 14 SNPs were developed, and four of these markers (snpAH0031, snpAH0033, snpAH0037, and snpAH0038) were successfully validated for deployment in breeding for large-seeded groundnut varieties.

Susceptibility of UK oat (Avena sativa) varieties to infection by Fusarium species and subsequent HT‐2 and T‐2 toxin contamination

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The aim of this study was to compare the susceptibility of oats to Fusarium langsethiae infection, as measured by combined HT-2 and T-2 mycotoxin concentration (HT2 + T2) in harvested oat grain samples. Over 10 years (2004–2013), samples from single replicates of each UK Recommended List oat trial were analyzed for HT-2 and T-2. For spring oats, there were small but statistically significant differences between varieties, whereas for winter oats, they had a broader range and higher mean of HT2 + T2 concentration compared with spring oats. For winter oats, the short-strawed varieties had consistently high HT2 + T2 levels compared with other varieties, whereas naked varieties were at the lower end of the range, and short, naked varieties had intermediate levels. A separate set of harvested oat grain samples of eight common varieties from 17 field experiments were analyzed by modified joint regression analysis. Results showed that environment had the strongest impact on HT-2 and T-2 concentrations but that the varietal susceptibility to HT-2 and T-2 contamination was highly stable across environments. This methodology can be used to calculate a Fusarium (HT2 + T2) resistance score for oats to aid grower selection of suitable varieties, as is available for Fusarium (DON) resistance for wheat varieties in many countries.