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Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat

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Abstract

Yield is known to be a complex trait, the expression of which interacts strongly with environmental conditions. Understanding the genetic basis of these genotype × environment interactions, particularly under limited input levels, is a key objective when selecting wheat genotypes adapted to specific environments. Our principal objectives were thus: (1) to identify genomic regions [quantitative trait loci (QTL)] involving QTL × environment interactions (QEI) and (2) to develop a strategy to understand the specificity of these regions to certain environments. The two main components of yield were studied: kernel number (KN) and thousand-kernel weight (TKW). The Arche × Récital doubled-haploid population of 222 lines was grown in replicated field trials during 2000 and 2001 at three locations in France, under two nitrogen levels. The 12 environments were characterized in terms of water deficit, radiation, temperature and nitrogen stress based on measurements conducted on the four-probe genotypes: Arche, Récital, Ritmo and Soissons. A four-step strategy was developed to explain QTL specificity to some environments: (1) the detection of QTL for KN and TKW in each environment; (2) the estimation of genotypic sensitivities as the factorial regression slope of KN and TKW to environmental covariates and the detection of QTL for these genotypic sensitivities; (3) study of the co-locations of QTL for KN and TKW and of the QTL for sensitivities; in the event of a co-location partitioning the QEI, appropriate covariates were employed; (4) a description of the environments where QTL were detected for KN and TKW using the environmental covariates. A total of 131 QTL were found to be associated with KN, TKW and their sensitivity to environmental covariates across the 12 environments. Four of these QTL, for both KN and TKW, were located on linkage groups 1B, 2D1, 4B and 5A1, and displayed pleiotropic effects. Factorial regression explained from 15.1 to 83.2% of the QEI for KN and involved three major environmental covariates: cumulative radiation-days ±3 days at meiosis, cumulative degree-days >25°C ±3 days at meiosis and nitrogen stress at flowering. For TKW, 13.5–81.8% of the effect of the QEI was partitioned and involved three major environmental covariates: water deficit from flowering to the milk stage, cumulative degree-days >0°C from the milk stage to maturity and soil water deficit at maturity. A comparative analysis was then performed on the QTL detected during this and previous studies published on QEI and some interacting QTL may be common to different genetic backgrounds. Focusing on these QTL common to different genetic backgrounds would give some guidance to understand genotype × environment interaction.

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Acknowledgments

This work was supported from the Génoplante French Genomics project. We would like to thank P. Pluchard, C. Quandalle and Dr. P. Brabant for the development of the population, and G. Charmet, P. Dufour and F. Dedryver for the development of molecular map. The authors also want to thank the staff at Estrées-Mons experimental unit (INRA), namely D. Bouthors, D. Brasseur and J.-P. Noclercq for their helpful technical assistance. We also wish to thank the staff at Le Moulon experimental unit (INRA), as well as P. Bérard and the staff at the Clermont-Ferrand experimental unit (INRA). We also wish to thank the reviewers for their helpful comments and proposals on the manuscript.

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Authors and Affiliations

  1. INRA/USTL UMR 1281 SADV, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France

    Bing Song Zheng, Martine Leflon, Wen Ying Rong, Anne Laperche & Maryse Brancourt-Hulmel

  2. School of Forestry and Biotechnology, Zhejiang A&F University, Lin’an, 311300, China

    Bing Song Zheng

  3. INRA/UBP UMR 1095 GDEC, Domaine de Crouël, 234 avenue du Brézet, 63100, Clermont-Ferrand, France

    Jacques Le Gouis

  4. INRA/Agrocampus Ouest/Université de Rennes 1 UMR 118 APBV, 35653, Le Rheu, France

    Anne Laperche

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  1. Bing Song Zheng
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  2. Jacques Le Gouis
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  3. Martine Leflon
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  4. Wen Ying Rong
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  5. Anne Laperche
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  6. Maryse Brancourt-Hulmel
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Correspondence to Maryse Brancourt-Hulmel.

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Communicated by E. Carbonell.

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Supplementary Tables (DOC 268 kb)

122_2010_1406_MOESM2_ESM.psd

Differences between Arche and Récital allele classes. Mean KN values at markers gwm268, gpw4085, wmc238, gpw1108, rht1 and gwm540b as a function of environmental covariates. Covariates are coded according to Table S1 (PSD 219 kb)

122_2010_1406_MOESM3_ESM.psd

Differences between Arche and Récital allele classes. Mean TKW values at markers SPA, gwm102, gpw4085 and Fdgo-d1 as a function of environmental covariates. Covariates are coded according to Table S1 (PSD 132 kb)

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Zheng, B.S., Le Gouis, J., Leflon, M. et al. Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat. Theor Appl Genet 121, 1501–1517 (2010). https://doi.org/10.1007/s00122-010-1406-6

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  • DOI: https://doi.org/10.1007/s00122-010-1406-6

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