Nested association mapping-based GWAS for grain yield and related traits in wheat grown under diverse Australian environments

GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions

KEY MESSAGE

Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.

Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield stability

KEY MESSAGE

A comprehensive environmental characterization allowed identifying stable and interactive QTL for seed yield: QA09 and QC09a were detected across environments; whereas QA07a was specifically detected on the most stressed environments. A main challenge for rapeseed consists in maintaining seed yield while adapting to climate changes and contributing to environmental-friendly cropping systems. Breeding for cultivar adaptation is one of the keys to meet this challenge. Therefore, we propose to identify the genetic determinant of seed yield stability for winter oilseed rape using GWAS coupled with a multi-environmental trial and to interpret them in the light of environmental characteristics. Due to a comprehensive characterization of a multi-environmental trial using 79 indicators, four contrasting envirotypes were defined and used to identify interactive and stable seed yield QTL. A total of four QTLs were detected, among which, QA09 and QC09a, were stable (detected at the multi-environmental trial scale or for different envirotypes and environments); and one, QA07a, was specifically detected into the most stressed envirotype. The analysis of the molecular diversity at QA07a showed a lack of genetic diversity within modern lines compared to older cultivars bred before the selection for low glucosinolate content. The results were discussed in comparison with other studies and methods as well as in the context of breeding programs.

GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits-grain protein content (GPC) and thousand kernel weight (TKW)-across 179 Bulgarian contemporary and historic varieties and landraces across three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, and genotypic data from the 25K Infinium iSelect array. As a result, we detected 16 quantitative trait nucleotides (QTNs) associated with GPC and 15 associated with TKW, all of which passed the false discovery rate threshold. Seven loci favorably influenced GPC, resulting in an increase of 1.4% to 8.1%, while four loci had a positive impact on TKW with increases ranging from 1.9% to 8.4%. While some loci confirmed previously published associations, four QTNs linked to GPC on chromosomes 2A, 7A, and 7B, as well as two QTNs related to TKW on chromosomes 1B and 6A, may represent novel associations. Annotations for proteins involved in the senescence-associated nutrient remobilization and in the following buildup of resources required for seed germination have been found for selected putative candidate genes. These include genes coding for storage proteins, cysteine proteases, cellulose-synthase, alpha-amylase, transcriptional regulators, and F-box and RWP-RK family proteins. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Genome-wide association study and development of molecular markers for yield and quality traits in peanut (Arachis hypogaea L.)

BACKGROUND

This study aims to decipher the genetic basis governing yield components and quality attributes of peanuts, a critical aspect for advancing molecular breeding techniques. Integrating genotype re-sequencing and phenotypic evaluations of seven yield components and two grain quality traits across four distinct environments allowed for the execution of a genome-wide association study (GWAS).

RESULTS

The nine phenotypic traits were all continuous and followed a normal distribution. The broad heritability ranged from 88.09 to 98.08%, and the genotype-environment interaction effects were all significant. There was a highly significant negative correlation between protein content (PC) and oil content (OC). The 10× genome re-sequencing of 199 peanut accessions yielded a total of 631,988 high-quality single nucleotide polymorphisms (SNPs), with 374 significant SNP loci identified in association with the nine traits of interest. Notably, 66 of these pertinent SNPs were detected in multiple environments, and 48 of them were linked to multiple traits of interest. Five loci situated on chromosome 16 (Chr16) exhibited pleiotropic effects on yield traits, accounting for 17.64-32.61% of the observed phenotypic variation. Two loci on Chr08 were found to be strongly associated with protein and oil contents, accounting for 12.86% and 14.06% of their respective phenotypic variations, respectively. Linkage disequilibrium (LD) block analysis of these seven loci unraveled five nonsynonymous variants, leading to the identification of one yield-related candidate gene and two quality-related candidate genes. The correlation between phenotypic variation and SNP loci in these candidate genes was validated by Kompetitive allele-specific PCR (KASP) marker analysis.

CONCLUSIONS

Overall, molecular markers were developed for genetic loci associated with yield and quality traits through a GWAS investigation of 199 peanut accessions across four distinct environments. These molecular tools can aid in the development of desirable peanut germplasm with an equilibrium of yield and quality through marker-assisted breeding.

Genomic regions involved in the control of 1,000-kernel weight in wild relative-derived populations of durum wheat

Terminal drought is one of the most common and devastating climatic stress factors affecting durum wheat (Triticum durum Desf.) production worldwide. The wild relatives of this crop are deemed a vast potential source of useful alleles to adapt to this stress. A nested association mapping (NAM) panel was generated using as a recurrent parent the Moroccan variety 'Nachit' derived from Triticum dicoccoides and known for its large grain size. This was recombined to three top-performing lines derived from T. dicoccoides, T. araraticum, and Aegilops speltoides, for a total of 426 inbred progenies. This NAM was evaluated across eight environments (Syria, Lebanon, and Morocco) experiencing different degrees of terminal moisture stress over two crop seasons. Our results showed that drought stress caused on average 41% loss in yield and that 1,000-kernel weight (TKW) was the most important trait for adaptation to it. Genotyping with the 25K TraitGenetics array resulted in a consensus map of 1,678 polymorphic SNPs, spanning 1,723 cM aligned to the reference 'Svevo' genome assembly. Kinship distinguished the progenies in three clades matching the parent of origin. A total of 18 stable quantitative trait loci (QTLs) were identified as controlling various traits but independent from flowering time. The most significant genomic regions were named Q.ICD.NAM-04, Q.ICD.NAM-14, and Q.ICD.NAM-16. Allelic investigation in a second germplasm panel confirmed that carrying the positive allele at all three loci produced an average TKW advantage of 25.6% when field-tested under drought conditions. The underlying SNPs were converted to Kompetitive Allele-Specific PCR (KASP) markers and successfully validated in a third germplasm set, where they explained up to 19% of phenotypic variation for TKW under moisture stress. These findings confirm the identification of critical loci for drought adaptation derived from wild relatives that can now be readily exploited via molecular breeding.

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.