Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions

Genomic prediction (GP) has been evaluated in durum wheat breeding programs for several years, but prediction accuracy (PA) remains insufficient for some traits. Recently, multivariate (MV) analysis has gained much attention due to its potential to significantly improve PA. In this study, PA was evaluated for several agronomic traits using a univariate (UV) model in durum wheat, subsequently, different multivariate genomic prediction models were performed to attempt to increase PA. The panel was phenotyped for 10 agronomic traits over two consecutive crop seasons and under two different field conditions: high nitrogen and well-watered (HNW), and low nitrogen and rainfed (LNR). Multivariate GP was implemented using two cross-validation (CV) schemes: MV-CV1, testing the model for each target trait using only the markers, and MV-CV2, testing the model for each target trait using additional phenotypic information. These two MV-CVs were applied in two different analyses: modelling the same trait under both HNW and LNR conditions, and modelling grain yield together with the five most genetically correlated traits. PA for all traits in HNW was higher than LNR for the same trait, except for the trait yellow index. Among all traits, PA ranged from 0.34 (NDVI in LNR) to 0.74 (test weight in HNW). In modelling the same traits in both HNW and LNR, MV-CV1 produced improvements in PA up to 12.45% (NDVI in LNR) compared to the univariate model. By contrast, MV-CV2 increased PA up to 56.72% (thousand kernel weight in LNR). The MV-CV1 scheme did not improve PA for grain yield when it was modelled with the five most genetically correlated traits, whereas MV-CV2 significantly improved PA by up to ~18%. This study demonstrated that increases in prediction accuracy for agronomic traits can be achieved by modelling the same traits in two different field conditions using MV-CV2. In addition, the effectiveness of MV-CV2 was established when grain yield was modelled with additional correlated traits.

Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale

This study aimed to determine whether using DNA-based markers assigned to individual chromosomes would detect the genetic structures of 446 winter triticale forms originating from two breeding companies more effectively than using the entire pool of markers. After filtering for quality control parameters, 6380 codominant single nucleotide polymorphisms (SNPs) markers and 17,490 dominant diversity array technology (silicoDArT) markers were considered for analysis. The mean polymorphic information content (PIC) values varied depending on the chromosomes and ranged from 0.30 (2R) to 0.43 (7A) for the SNPs and from 0.28 (2A) to 0.35 (6R) for the silicoDArTs. The highest correlation of genetic distance (GD) matrices based on SNP markers was observed among the 5B-5R (0.642), 5B-7B (0.626), and 5A-5R (0.605) chromosomes. When silicoDArTs were used for the analysis, the strongest correlations were found between 5B-5R (0.732) and 2B-5B (0.718). A Bayesian analysis showed that SNPs (total marker pool) allowed for the identification of a more complex structure (K = 4, ΔK = 2460.2) than the analysis based on silicoDArTs (K = 2, ΔK = 128). Triticale lines formed into groups, ranging from two (most of the chromosomes) to four (7A) groups depending on the analyzed chromosome when SNP markers were used for analysis. Linkage disequilibrium (LD) varied among individual chromosomes, ranging from 0.031 for 1A to 0.228 for 7R.

The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds

Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain - K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K - 0.67, Ca - 0.61, Mg - 0.4, Mn - 0.5, Fe - 0.38, Zn - 0.46, Cu - 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.

Genome-wide association study of agronomic traits related to nitrogen use efficiency in Henan wheat

BACKGROUND

Nitrogen use efficiency (NUE) is closely related to crop yield and nitrogen fertilizer application rate. Although NUE is susceptible to environments, quantitative trait nucleotides (QTNs) for NUE in wheat germplasm populations have been rarely reported in genome-wide associated study.

RESULTS

In this study, 244 wheat accessions were phenotyped by three NUE-related traits in three environments and genotyped by 203,224 SNPs. All the phenotypes for each trait were used to associate with all the genotypes of these SNP markers for identifying QTNs and QTN-by-environment interactions via 3VmrMLM. Among 279 QTNs and one QTN-by-environment interaction for low nitrogen tolerance, 33 were stably identified, especially, one large QTN (r2 > 10%), qPHR3A.2, was newly identified for plant height ratio in one environment and multi-environment joint analysis. Among 52 genes around qPHR3A.2, four genes (TraesCS3A01G101900, TraesCS3A01G102200, TraesCS3A01G104100, and TraesCS3A01G105400) were found to be differentially expressed in low-nitrogen-tolerant wheat genotypes, while TaCLH2 (TraesCS3A01G101900) was putatively involved in porphyrin metabolism in KEGG enrichment analyses.

CONCLUSIONS

This study identified valuable candidate gene for low-N-tolerant wheat breeding and provides new insights into the genetic basis of low N tolerance in wheat.

Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection

Lodging is one of the most important limiting environmental factors for achieving the maximum yield and quality of grains in cereals, including wheat. However, little is known about the genetic foundation underlying lodging resistance (LR) in wheat. In this study, 208 landraces and 90 cultivars were phenotyped in two cropping seasons (2018-2019 and 2019-2020) for 19 LR-related traits. A genome-wide association study (GWAS) and genomics prediction were carried out to dissect the genomic regions of LR. The number of significant marker pairs (MPs) was highest for genome B in both landraces (427,017) and cultivars (37,359). The strongest linkage disequilibrium (LD) between marker pairs was found on chromosome 4A (0.318). For stem lodging-related traits, 465, 497, and 478 marker-trait associations (MTAs) and 45 candidate genes were identified in year 1, year 2, and pooled. Gene ontology exhibited genomic region on Chr. 2B, 6B, and 7B control lodging. Most of these genes have key roles in defense response, calcium ion transmembrane transport, carbohydrate metabolic process, nitrogen compound metabolic process, and some genes harbor unknown functions that, all together may respond to lodging as a complex network. The module associated with starch and sucrose biosynthesis was highlighted. Regarding genomic prediction, the GBLUP model performed better than BRR and RRBLUP. This suggests that GBLUP would be a good tool for wheat genome selection. As a result of these findings, it has been possible to identify pivotal QTLs and genes that could be used to improve stem lodging resistance in Triticum aestivum L.

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert's method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.

Genome-wide association mapping of genomic regions associated with drought stress tolerance at seedling and reproductive stages in bread wheat

Understanding the genetic architecture of drought stress tolerance in bread wheat at seedling and reproductive stages is crucial for developing drought-tolerant varieties. In the present study, 192 diverse wheat genotypes, a subset from the Wheat Associated Mapping Initiative (WAMI) panel, were evaluated at the seedling stage in a hydroponics system for chlorophyll content (CL), shoot length (SLT), shoot weight (SWT), root length (RLT), and root weight (RWT) under both drought and optimum conditions. Following that, a genome-wide association study (GWAS) was carried out using the phenotypic data recorded during the hydroponics experiment as well as data available from previously conducted multi-location field trials under optimal and drought stress conditions. The panel had previously been genotyped using the Infinium iSelect 90K SNP array with 26,814 polymorphic markers. Using single as well as multi-locus models, GWAS identified 94 significant marker-trait associations (MTAs) or SNPs associated with traits recorded at the seedling stage and 451 for traits recorded at the reproductive stage. The significant SNPs included several novel, significant, and promising MTAs for different traits. The average LD decay distance for the whole genome was approximately 0.48 Mbp, ranging from 0.07 Mbp (chromosome 6D) to 4.14 Mbp (chromosome 2A). Furthermore, several promising SNPs revealed significant differences among haplotypes for traits such as RLT, RWT, SLT, SWT, and GY under drought stress. Functional annotation and in silico expression analysis revealed important putative candidate genes underlying the identified stable genomic regions such as protein kinases, O-methyltransferases, GroES-like superfamily proteins, NAD-dependent dehydratases, etc. The findings of the present study may be useful for improving yield potential, and stability under drought stress conditions.