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

Content Provider	Springer Nature Link
Author	Zheng, Bing Song Gouis, Jacques Leflon, Martine Rong, Wen Ying Laperche, Anne Brancourt Hulmel, Maryse
Copyright Year	2010
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.
Starting Page	1501
Ending Page	1517
Page Count	17
File Format	PDF
ISSN	00405752
Journal	Theoretical and Applied Genetics
Volume Number	121
Issue Number	8
e-ISSN	14322242
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-08-10
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biotechnology Biochemistry Plant Biochemistry Agriculture Plant Genetics & Genomics Plant Breeding/Biotechnology
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine Biotechnology Agronomy and Crop Science

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