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Starch Metabolism in Wheat: Gene Variation and Association Analysis Reveal Additive Effects on Kernel Weight.

ABSTRACT: Kernel weight is a key determinant of yield in wheat (Triticum aestivum L.). Starch consists of amylose and amylopectin and is the major constituent of mature grain. Therefore, starch metabolism in the endosperm during grain filling can influence kernel weight. In this study, we sequenced 87 genes involved in starch metabolism from 300 wheat accessions and detected 8,141 polymorphic sites. We also characterized yield-related traits across different years in these accessions. Although the starch contents fluctuated, thousand kernel weight (TKW) showed little variation. Polymorphisms in six genes were significantly associated with TKW. These genes were located on chromosomes 2A, 2B, 4A, and 7A; none were associated with starch content or amylose content. Variations of 15 genes on chromosomes 1A and 7A formed haplotype blocks in 26 accessions. Notably, accessions with higher TKWs had more of the favorable haplotypes. We thus conclude that these haplotypes contribute additive effects to TKW.

SUBMITTER: Hou J

PROVIDER: S-EPMC7573188 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Starch Metabolism in Wheat: Gene Variation and Association Analysis Reveal Additive Effects on Kernel Weight.

Hou Jian J Liu Yunchuan Y Hao Chenyang C Li Tian T Liu Hongxia H Zhang Xueyong X

Frontiers in plant science 20201006

Kernel weight is a key determinant of yield in wheat (<i>Triticum aestivum</i> L.). Starch consists of amylose and amylopectin and is the major constituent of mature grain. Therefore, starch metabolism in the endosperm during grain filling can influence kernel weight. In this study, we sequenced 87 genes involved in starch metabolism from 300 wheat accessions and detected 8,141 polymorphic sites. We also characterized yield-related traits across different years in these accessions. Although the ...[more]

PMID: 33123177

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Project description:BackgroundInduction of mutation through chemical mutagenesis is a novel approach for preparing diverse germplasm. Introduction of functional alleles in the starch biosynthetic genes help in the improvement of the quality and yield of cereals.ResultsIn the present study, a set of 350 stable mutant lines were used to evaluate dynamic variation of the total starch contents. A megazyme kits were used for measuring the total starch content, resistant starch, amylose, and amylopectin content. Analysis of variance showed significant variation (p < 0.05) in starch content within the population. Furthermore, two high starch mutants (JE0173 and JE0218) and two low starch mutants (JE0089 and JE0418) were selected for studying different traits. A multiple comparison test showed that significant variation in all physiological and morphological traits, with respect to the parent variety (J411) in 2019-2020 and 2020-2021. The quantitative expression of starch metabolic genes revealed that eleven genes of JE0173 and twelve genes of JE0218 had consistent expression in high starch mutant lines. Similarly, in low starch mutant lines, eleven genes of JE0089 and thirteen genes of JE0418 had consistent expression in all stages of seed development. An additional two candidate genes showed over-expression (PHO1, PUL) in the high starch mutant lines, indicating that other starch metabolic genes may also contribute to the starch biosynthesis. The overexpression of SSII, SSIII and SBEI in JE0173 may be due to presence of missense mutations in these genes and SSI also showed overexpression which may be due to 3-primer_UTR variant. These mutations can affect the other starch related genes and help to increase the starch content in this mutant line (JE0173).ConclusionsThis study screened a large scale of mutant population and identified mutants, could provide useful genetic resources for the study of starch biosynthesis and genetic improvement of wheat in the future. Further study will help to understand new genes which are responsible for the fluctuation of total starch.

Project description:IntroductionIn 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.MethodsTo 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.ResultsThe 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.DiscussionThe 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.

Dataset Information

Starch Metabolism in Wheat: Gene Variation and Association Analysis Reveal Additive Effects on Kernel Weight.

Publications

Starch Metabolism in Wheat: Gene Variation and Association Analysis Reveal Additive Effects on Kernel Weight.

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