The effect of different sowing dates on dry matter and nitrogen dynamics for winter wheat: an experimental simulation study

Genome-wide association study unravels genomic regions associated with chlorophyll fluorescence parameters in wheat (Triticum aestivum L.) under different sowing conditions

KEY MESSAGE

Genome-wide association study identified 205 significant marker-trait associations for chlorophyll fluorescence parameters in wheat. Candidate gene mining, in silico expression, and promoter analyses revealed the potential candidate genes associated with the studied parameters. The present study investigated the effect of varied sowing conditions (viz., early, timely, and late) on different chlorophyll fluorescence parameters in diverse wheat germplasm set comprising of 198 lines over two cropping seasons (2020-2021 and 2021-2022). Further, a genome-wide association study was conducted to identify potential genomic regions associated with these parameters. The results revealed significant impacts of sowing conditions on all fluorescence parameters, with the maximum and minimum effects on FI (26.64%) and FV/FM (2.12%), respectively. Among the 205 marker-trait associations (MTAs) identified, 11 high-confidence MTAs were chosen, exhibiting substantial effects on multiple fluorescence parameters, and each explaining more than 10% of the phenotypic variation. Through gene mining of genomic regions encompassing high-confidence MTAs, we identified a total of 626 unique gene models. In silico expression analysis revealed 42 genes with an expression value exceeding 2 TPM. Among them, 10 genes were identified as potential candidate genes with functional relevance to enhanced photosynthetic efficiency. These genes mainly encoded for the following important proteins/products-ankyrin repeat protein, 2Fe-2S ferredoxin-type iron-sulfur-binding domain, NADH-ubiquinone reductase complex-1 MLRQ subunit, oxidoreductase FAD/NAD(P)-binding, photosystem-I PsaF, and protein kinases. Promoter analysis revealed the presence of light-responsive (viz., GT1-motif, TCCC-motif, I-box, GT1-motif, TCT-motif, and SP-1) and stress-responsive (viz., ABRE, AuxRR-core, GARE-motif, and ARE) cis-regulatory elements, which may be involved in the regulation of identified putative candidate genes. Findings from this study could directly help wheat breeders in selecting lines with favorable alleles for chlorophyll fluorescence, while the identified markers will facilitate marker-assisted selection of potential genomic regions for improved photosynthesis.

Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions

INTRODUCTION

In wheat, kernel weight (KW) is a key determinant of grain yield (GY). However, it is often overlooked when improving wheat productivity under climate warming. Moreover, little is known about the complex effects of genetic and climatic factors on KW. Here, we explored the responses of wheat KW to diverse allelic combinations under projected climate warming conditions.

METHODS

To focus on KW, we selected a subset of 81 out of 209 wheat varieties with similar GY, biomass, and kernel number (KN) and focused on their thousand-kernel weight (TKW). We genotyped them at eight kompetitive allele-specific polymerase chain reaction markers closely associated with TKW. Subsequently, we calibrated and evaluated the process-based model known as Agricultural Production Systems Simulator (APSIM-Wheat) based on a unique dataset including phenotyping, genotyping, climate, soil physicochemistry, and on-farm management information. We then used the calibrated APSIM-Wheat model to estimate TKW under eight allelic combinations (81 wheat varieties), seven sowing dates, and the shared socioeconomic pathways (SSPs) designated SSP2-4.5 and SSP5-8.5, driven by climate projections from five General Circulation Models (GCMs) BCC-CSM2-MR, CanESM5, EC-Earth3-Veg, MIROC-ES2L, and UKESM1-0-LL.

RESULTS

The APSIM-Wheat model reliably simulated wheat TKW with a root mean square error (RMSE) of < 3.076 g TK^-1 and R² of > 0.575 (P < 0.001). The analysis of variance based on the simulation output showed that allelic combination, climate scenario, and sowing date extremely significantly affected TKW (P < 0.001). The impact of the interaction allelic combination × climate scenario on TKW was also significant (P < 0.05). Meanwhile, the variety parameters and their relative importance in the APSIM-Wheat model accorded with the expression of the allelic combinations. Under the projected climate scenarios, the favorable allelic combinations (TaCKX-D1b + Hap-7A-1 + Hap-T + Hap-6A-G + Hap-6B-1 + H1g + A1b for SSP2-4.5 and SSP5-8.5) mitigated the negative effects of climate change on TKW.

DISCUSSION

The present study demonstrated that optimizing favorable allelic combinations can help achieve high wheat TKW. The findings of this study clarify the responses of wheat KW to diverse allelic combinations under projected climate change conditions. Additionally, the present study provides theoretical and practical reference for marker-assisted selection of high TKW in wheat breeding.