Genetic analysis of grain yield conditioned on its component traits in rice (Oryza sativa L.)

Liu, G. F.; Yang, Jian; Xu, Hongwei; Hayat, Yousaf; Zhu, Jun

doi:10.1071/ar07163

Cited by 41 publications

(28 citation statements)

References 36 publications

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“…The remaining variation excluding the contributions of causal traits is defined as conditional variation. This method is also used to analyze the influence of different agro-technologies or agronomic practices on crop growth and development to discover QTLs specifically expressed in stress environments (Jiang et al 2008;Zhang et al 2014), to identify QTLs expressed at certain developmental stages of plants (Zhu 1995;Atchley and Zhu 1997;Yan et al 1998;Cao et al 2001), or to analyze contributions of component traits to a complex trait (Guo et al 2005;Liu et al 2008;Cui et al 2011). QTL analysis of conventional P-deficient tolerance is generally aimed at analysing the expression of phenotypic traits at different P levels (Guo et al 2012;Su et al 2006;Cao et al 2001).…”

Section: Conditional Qtl and Unconditional Qtlmentioning

confidence: 99%

QTL mapping for traits related to P-deficient tolerance using three related RIL populations in wheat

Zhang

Wang

2014

Euphytica

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Phosphorus (P) deficiency is one of the major constraints to wheat production worldwide. In this study, conditional and unconditional quantitative trait locus (QTL) analyses for 12 traits of wheat seedlings under P-normal and P-deficient conditions were conducted using three F 9 recombinant inbred line (RIL) populations with a common female parent in wheat. Of the total 110 QTLs identified by unconditional QTL analysis, 29 were detected in multiple populations or under different P treatment, with 28 major QTLs. Notably, QRPU-4B on chromosome 4B in interval wPt-3608-wPt-667817 was detected in two populations and at two P levels, accounting for 44.08 % average value of phenotypic variance. Of 49 specific QTLs related to P-deficient tolerance detected by conditional QTL analysis, only nine were detected by unconditional QTL mapping under P-deficient condition, indicating that most QTLs directly concerned with P-deficient tolerance could be detected only by conditional QTL analysis. Eighteen important QTL clusters were mapped on 12 chromosomes 1D, 2A, 2B, 3A, 3B, 4B, 4D, 5A, 5D, 6A, 6B, and 7B, involving 85 QTLs that accounted for 53.4 % of total numbers of QTLs detected by unconditional and conditional mapping. Of these 18 QTL clusters, 14 were detected in multiple populations and 11 were detected by both unconditional and conditional analyses. Thus, these chromosome intervals were important areas controlling the expression of seedling traits related to P-deficient tolerance.Keywords Triticum aestivum Á QTL mapping Á Traits related to P-deficient tolerance Á Unconditional and conditional QTL analyses Abbreviations PH Plant height RLThe maximal root length RN

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Section: Conditional Qtl and Unconditional Qtlmentioning

confidence: 99%

QTL mapping for traits related to P-deficient tolerance using three related RIL populations in wheat

Zhang

Wang

2014

Euphytica

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“…More recently, a method for multivariable conditional analysis was proposed for analyzing the contributions of individual component traits to a complex trait (Wen and Zhu, 2005). By combining the conditional genetic analysis method with QTL mapping (unconditional QTL analysis), the method was extended to map conditional QTL for the molecular dissection of complex traits (Guo et al, 2005;Zhao et al, 2006;Liu et al, 2008;Cui et al, 2011). Therefore, conditional QTL mapping analysis is an efficient tool for determining relationships between complex traits and their components.…”

Section: Qtl Analysis: Corroborations and New Findingsmentioning

confidence: 99%

“…This method could be used to determine genetic interrelationships between traits at the level of individual QTL, and to find additional QTL that are undetectable with unconditional mapping (Li et al, 2008). This method has been successfully used to analyze QTL by Guo et al (2005), Zhao et al (2006), Liu et al (2008), andCui et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Genetic relationship between lodging and lodging components in barley (Hordeum vulgare) based on unconditional and conditional quantitative trait locus analyses

Chen¹,

Liu²,

Deng³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. Lodging (LD) is a major constraint limiting the yield and forage quality of barley. Detailed analyses of LD component (LDC) traits were conducted using 246 F 2 plants generated from a cross between cultivars ZQ320 and 1277. Genetic relationships between LD and LDC were evaluated by unconditional and conditional quantitative trait locus (QTL) mapping with 117 simple sequence repeat markers. Ultimately, 53 unconditional QTL related to LD were identified on seven barley chromosomes. Up to 15 QTL accounted for over 10% of the phenotypic variation, and up to 20 QTL for culm strength were detected. Six QTL with pleiotropic effects showing significant negative correlations with LD were found between markers Bmag353 and GBM1482 on chromosome 4H. These alleles and alleles of QTL for wall thickness, culm strength, plant height, and plant weight originated from ZQ320. Conditional mapping identified 96 additional QTL for LD. Conditional QTL analysis demonstrated that plant height, plant height center of gravity, and length of the sixth internode had the greatest contribution to LD, whereas culm strength and length of the fourth internode, and culm strength of the second internode were the key factors for LD-resistant. Therefore, lodging resistance in barley can be improved based on selection of alleles affecting culm strength, wall thickness, plant height, and plant weight. The conditional QTL mapping method can be used to evaluate possible genetic relationships between LD and LDC while efficiently and precisely determining counteracting QTL, which will help in understanding the genetic basis of LD in barley.

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“…Such an approach can be used to estimate the genetic contribution of one component trait (cause-trait) to the resultant trait of interest. For example, Guo et al [65] studied rice yield and its component traits using this kind of approach and a similar study has been reported by Liu et al [66]. Zhao et al [67] analyzed the relationship between oil content in rapeseed with protein content and other traits related to plant development.…”

Section: Qtl Dissection Of Genetic Correlation Between Traitsmentioning

confidence: 99%

Statistical approaches in QTL mapping and molecular breeding for complex traits

Zhu

2012

Chin. Sci. Bull.

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Most of the important agronomic traits in crops, such as yield and quality, are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction. Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs. Conventionally, the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects. With recent advances in molecular genotyping and high-throughput technology, the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible. The improvement of complex traits has also been achieved by pyramiding individual QTL. In this review, we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction, and discuss their applications in crop breeding for complex traits. The most important economic traits in crops are quantitative in nature. The genetic variation of quantitative traits is usually controlled by minor-effect genes and the environments as well as by epistatic effects between different genes and by interactions between genes and environments. Because of their complicated genetic architecture, quantitative traits are usually referred to as complex trait. The genetic architecture includes knowledge about the numbers and positions of the genes in the chromosomes, the magnitude of their effects, and the contributions of additive, dominance and epistatic effects [1].Most plant breeders and quantitative geneticists have long been interested in uncovering the genetic architecture of yield and other complex traits [2]. Understanding genetic architecture can provide insights into how the architecture is translated into genetic variation and selection response [3]. Based on diallele cross experiments, the genetic architecture of a trait has been analyzed by decomposing the trait variation into additive, dominance, and epistasis variances and studying their interaction variances with the environments [4][5][6][7]. The total genetic covariance between two traits can also be dissected into genetic covariate components. With the increasing availability of molecular markers, the quantitative genetics theory of complex traits and marker-assisted selection has undergone major advances. It is now possible to connect marker variations with phenotypic variations, and to dissect genetic architecture of traits into individual genetic variants. The selection of traits can also be achieved using individual quantitative trait loci (QTLs). In this review, we discuss some of the statistical methods used for QTL mapping of experimental populations and examine their applications in marker-assisted selection (MAS) for improving complex traits.

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Genetic analysis of grain yield conditioned on its component traits in rice (Oryza sativa L.)

Cited by 41 publications

References 36 publications

QTL mapping for traits related to P-deficient tolerance using three related RIL populations in wheat

QTL mapping for traits related to P-deficient tolerance using three related RIL populations in wheat

Genetic relationship between lodging and lodging components in barley (Hordeum vulgare) based on unconditional and conditional quantitative trait locus analyses

Statistical approaches in QTL mapping and molecular breeding for complex traits

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