Mapping QTLs with digenic epistasis under multiple environments and predicting heterosis based on QTL effects
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Keywords:
Epistasis
Plant biochemistry
Family-based QTL mapping
Most of the important agronomic traits of rice are quantitative in nature, which are controlled by polygenes. In conventional quantitative genetics, QTL analysis was only executed as a whole. In recent years, the construction of highly dense molecular linkage map and the development of effective biostatistical methods have revolutionized the genetic studies of quantitative traits. The joint analysis of molecular marker genotype and phenotype values of individuals or lines in different populations enabled the detection and location of quantitative trait loci (QTL). In the present paper, the principles and methods of QTL mapping were introduced, recent progresses in the studies of QTL analysis in rice were reviewed, including QTL numbers and effects, epistasis effect, interaction between QTL and environment, QTLs of related characters, developmental QTL and so on. The rice genome sequencing project has been finished, therefore in this paper, the strategy for fine mapping and cloning of QTLs in the genome era was also discussed. Application prospect of QTL analysis in rice breeding was forecasted.
Family-based QTL mapping
Polygene
Epistasis
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Epistasis
Family-based QTL mapping
Genetic architecture
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Family-based QTL mapping
Candidate gene
Identification
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The interaction between segregation distortion loci (SDL) has been often observed in all kinds of mapping populations. However, little has been known about the effect of epistatic SDL on quantitative trait locus (QTL) mapping. Here we proposed a multi-QTL mapping approach using epistatic distorted markers. Using the corrected linkage groups, epistatic SDL was identified. Then, these SDL parameters were used to correct the conditional probabilities of QTL genotypes, and these corrections were further incorporated into the new QTL mapping approach. Finally, a set of simulated datasets and a real data in 304 mouse F2 individuals were used to validate the new method. As compared with the old method, the new one corrects genetic distance between distorted markers, and considers epistasis between two linked SDL. As a result, the power in the detection of QTL is higher for the new method than for the old one, and significant differences for estimates of QTL parameters between the two methods were observed, except for QTL position. Among two QTL for mouse weight, one significant difference for QTL additive effect between the above two methods was observed, because epistatic SDL between markers C66 and T93 exists (P = 2.94e-4).
Epistasis
Family-based QTL mapping
Linkage (software)
Genetic linkage
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Family-based QTL mapping
Germ plasm
Inheritance
Trait
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Advances on methods for mapping quantitative trait loci (QTL) are firstly summarized. Then, some new methods, including mapping multiple QTL, fine mapping of QTL, and mapping QTL for dynamic traits, are mainly described. Finally, some future prospects are proposed, including how to dig novel genes in the germplasm resource, map expression QTL (eQTL) by the use of all markers, phenotypes and micro-array data, identify QTL using genetic mating designs and detect viability loci. The purpose is to direct plant geneticists to choose a suitable method in the inheritance analysis of quantitative trait and in search of novel genes in germplasm resource so that more potential genetic information can be uncovered.
Family-based QTL mapping
Germ plasm
Inheritance
Trait
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Most quantitative trait loci (QTL) studies have focussed on the identification of individual QTL effects (additive, dominance and imprinting) in the absence of interactions (epistasis). There are numerous reports in the literature for QTL associated with growth and body composition of pigs, however much less effort has focussed around the identification of epistatic QTL for these traits. Growth and body composition of pigs are probably influenced by numerous QTL located throughout the genome as well as interactions between QTL. Therefore the objective of this study was to investigate the contribution of epistasis to the genomic regulation of growth and body composition of pigs.
Epistasis
Family-based QTL mapping
Dominance (genetics)
Genetic architecture
Trait
Imprinting (psychology)
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Epistasis
Family-based QTL mapping
Genetic linkage
Linkage (software)
Genome Scan
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Isoflavone, protein and oil are the most important quality traits in soybean. Since these quality traits are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify the complex and unclear genetic background of them. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and high-density linkage map based on whole genome resequencing to map novel QTL; and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with high logarithm of odd scores were identified through composite interval mapping (CIM) including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1034 QTL collected from previous studies. For the first-time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL respectively related to oil and protein were obtained as well. The results promote the understandings on the biosynthesis and regulation of isoflavones, protein and oil at molecular level, and facilitate the construction of molecular modular for great quality traits in soybean.
Family-based QTL mapping
Candidate gene
Genetic linkage
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Epistasis
Family-based QTL mapping
Trait
Genetic architecture
Genome Scan
Quantitative Genetics
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