Present and future of quantitative trait locus analysis in plant breeding

Asíns, M. J.

doi:10.1046/j.1439-0523.2002.730285.x

Cited by 239 publications

(124 citation statements)

References 134 publications

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“…Interestingly, Verma et al (2004) mapped several QTL for grain yield and flag leaf senescence near the markers Xgwm30 and Xgwm539 on chromosome 2D in winter wheat, whereas in this study, three additive QTL for SWSCF and TGWM and eight loci associated with epistasis for SWSCF, TGWM, RESWC, GFEE, and GFEL were adjacent to markers Xgwm30 and Xgwm539 on chromosome 2D. In addition, phenotypic correlation analyses among these traits were found to be similar in our work and previous studies (Yang et al 2000;2002;Yang and Zhang 2006). It was hypothetical that the genetics of SWSC content, SWSC remobilization, grain filling, grain yield, and leaf senescence could be related.…”

Section: Discussionsupporting

confidence: 85%

“…Phenotypic plasticity of quantitative traits arises in nature from interactions between QTL and environments at molecular levels. Several examples of QEIs for agronomic traits showed that the expression of particular chromosome regions differs across environments (Crossa et al 1999;Hemamalini et al 2000;Asíns 2002;Campbell et al 2003). In this study, in addition to identifying the additive and epistatic QTL, A-QEIs and E-QEIs for all nine traits were also detected.…”

Section: Discussionsupporting

confidence: 60%

“…Galiba et al (1997) located a gene responsible for SWSC during cold acclimation on chromosome 5A of wheat. Although SWSC proved to be quantitative, no study has been undertaken to investigate QTL 3 environment interaction (QEI) variability and effects on agronomic traits of crops (Crossa et al 1999;Hemamalini et al 2000;Asíns 2002;Campbell et al 2003).…”

mentioning

confidence: 99%

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Identification of Quantitative Trait loci and Environmental Interactions for Accumulation and Remobilization of Water-Soluble Carbohydrates in Wheat (Triticum aestivum L.) Stems

et al. 2007

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Genetic analyses of nine traits associated with stem water-soluble carbohydrate (SWSC) accumulation and remobilization at grain-filling period under drought stress (DS) and well-watered (WW) conditions were undertaken using doubled haploid lines (DHLs) derived from two Chinese common wheat cultivars. Some significantly and very significantly positive correlation was observed among nine traits associated with SWSC. Higher phenotypic values for most traits were detected under DS. Broad sense heritabilities (h 2 B ) of the traits showed wide fluctuations between two water treatments. A total of 48 additive and 62 pairs of epistatic QTL for nine traits were identified as distributing on all 21 chromosomes. A majority of QTL involved significant additive and epistatic effects with interactions of QTL and environments (QEIs). Two additive and two pairs of epistatic loci involved only QEIs without corresponding significant additive or epistatic effects. The contributions of the additive QEIs were two-to fourfolds higher than those of their corresponding additive QTL. Most of the additive QEIs for traits associated with SWSC interacted with DS. In addition, some QTL for the grain-filling efficiencies and thousand-grain weight were colocated in the same or adjacent chromosome intervals with QTL for accumulation and remobilization efficiency of SWSC before 14 days after flowering.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionsupporting

confidence: 60%

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Identification of Quantitative Trait loci and Environmental Interactions for Accumulation and Remobilization of Water-Soluble Carbohydrates in Wheat (Triticum aestivum L.) Stems

et al. 2007

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“…Several factors have an important influence on the accuracy of QTL analysis (Asins 2002), including population size and experimental design. In the present study, 7 populations with over1000 lines were used to map the QTLs controlling KD.…”

Section: Penamentioning

confidence: 99%

Quantitative trait loci controlling kernel discoloration in barley (Hordeum vulgare L.)

Li¹,

Lance²,

Collins³

et al. 2003

Aust. J. Agric. Res.

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Abstract. Barley kernel discoloration (KD) leads to substantial annual loss in value through downgrading and discounting of malting barley. KD is a difficult trait to introgress into elite varieties as it is controlled by multiple genes and strongly influenced by environment and maturity. As the first step towards marker assisted selection for KD tolerance, we mapped quantitative trait loci (QTLs) controlling KD measured by grain brightness [Minolta L; (Min L)], redness (Min a), and yellowness (Min b) in 7 barley populations. One to 3 QTLs were detected for grain brightness in various populations, and one QTL could account for 5-31% of the phenotypic variation. The QTL located around the centromere region of chromosome 2H was consistently detected in 6 of the 7 populations, explaining up to 28% of the phenotypic variation. In addition, QTLs for grain brightness were most frequently identified on chromosomes 3H and 7H in various populations. Australian varieties Galleon, Chebec, and Sloop contribute an allele to increase grain brightness on chromosome 7H in 3 different populations. A major gene effect was detected for grain redness. One QTL on chromosome 4H explained 54% of the phenotypic variation in the Sloop/Halcyon population, and was associated with the blue aleurone trait. A second QTL was detected on the long arm of chromosome 2H in 3 populations, accounting for 23-47% of the phenotypic variation. The major QTLs for grain yellowness were mapped on chromosomes 2H and 5H. There were strong associations between the QTLs for heading date, grain brightness, and yellowness. The molecular markers linked with the major QTLs should be useful for marker assisted selection for KD.

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“…O VER the past decade, marker-assisted selection has become a standard tool in breeding programs (Young 1999;Asins 2002;Dekkers and Hospital 2002;Collard et al 2005). The selection of genetically superior individuals using molecular marker alleles linked to quantitative trait loci (QTL) affecting trait variation instead of using phenotypic trait values can speed up genetic improvement considerably (Hospital et al 1997).…”

mentioning

confidence: 99%

A Mixed-Model Approach to Association Mapping Using Pedigree Information With an Illustration of Resistance to Phytophthora infestans in Potato

Malosetti

Linden²,

Vosman³

et al. 2007

Genetics

219

187

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Association or linkage disequilibrium (LD)-based mapping strategies are receiving increased attention for the identification of quantitative trait loci (QTL) in plants as an alternative to more traditional, purely linkage-based approaches. An attractive property of association approaches is that they do not require specially designed crosses between inbred parents, but can be applied to collections of genotypes with arbitrary and often unknown relationships between the genotypes. A less obvious additional attractive property is that association approaches offer possibilities for QTL identification in crops with hard to model segregation patterns. The availability of candidate genes and targeted marker systems facilitates association approaches, as will appropriate methods of analysis. We propose an association mapping approach based on mixed models with attention to the incorporation of the relationships between genotypes, whether induced by pedigree, population substructure, or otherwise. Furthermore, we emphasize the need to pay attention to the environmental features of the data as well, i.e., adequate representation of the relations among multiple observations on the same genotypes. We illustrate our modeling approach using 25 years of Dutch national variety list data on late blight resistance in the genetically complex crop of potato. As markers, we used nucleotide binding-site markers, a specific type of marker that targets resistance or resistance-analog genes. To assess the consistency of QTL identified by our mixed-model approach, a second independent data set was analyzed. Two markers were identified that are potentially useful in selection for late blight resistance in potato.

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Present and future of quantitative trait locus analysis in plant breeding

Cited by 239 publications

References 134 publications

Identification of Quantitative Trait loci and Environmental Interactions for Accumulation and Remobilization of Water-Soluble Carbohydrates in Wheat (Triticum aestivum L.) Stems

Identification of Quantitative Trait loci and Environmental Interactions for Accumulation and Remobilization of Water-Soluble Carbohydrates in Wheat (Triticum aestivum L.) Stems

Quantitative trait loci controlling kernel discoloration in barley (Hordeum vulgare L.)

A Mixed-Model Approach to Association Mapping Using Pedigree Information With an Illustration of Resistance to Phytophthora infestans in Potato

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