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Sebastian Gasparis - One of the best experts on this subject based on the ideXlab platform.
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Major Genes determining yield-related traits in wheat and barley
Theoretical and Applied Genetics, 2017Co-Authors: Anna Nadolska-orczyk, Izabela K. Rajchel, Wacław Orczyk, Sebastian GasparisAbstract:Key message Current development of advanced biotechnology tools allows us to characterize the role of key Genes in plant productivity. The implementation of this knowledge in breeding strategies might accelerate the progress in obtaining high-yielding cultivars. Abstract The achievements of the Green Revolution were based on a specific plant ideotype, determined by a single gene involved in gibberellin signaling or metabolism. Compared with the 1950s, an enormous increase in our knowledge about the biological basis of plant productivity has opened new avenues for novel breeding strategies. The large and complex genomes of diploid barley and hexaploid wheat represent a great challenge, but they also offer a large reservoir of Genes that can be targeted for breeding. We summarize examples of productivity-related Genes/mutants in wheat and barley, identified or characterized by means of modern biology. The Genes are classified functionally into several groups, including the following: (1) transcription factors, regulating spike development, which mainly affect grain number; (2) Genes involved in metabolism or signaling of growth regulators—cytokinins, gibberellins, and brassinosteroids—which control plant architecture and in consequence stem hardiness and grain yield; (3) Genes determining cell division and proliferation mainly impacting grain size; (4) floral regulators influencing inflorescence architecture and in consequence seed number; and (5) Genes involved in carbohydrate metabolism having an impact on plant architecture and grain yield. The implementation of selected Genes in breeding programs is discussed, considering specific genotypes, agronomic and climate conditions, and taking into account that many of the Genes are members of multigene families.
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Major Genes determining yield related traits in wheat and barley
Theoretical and Applied Genetics, 2017Co-Authors: Anna Nadolskaorczyk, Izabela K. Rajchel, Wacław Orczyk, Sebastian GasparisAbstract:Current development of advanced biotechnology tools allows us to characterize the role of key Genes in plant productivity. The implementation of this knowledge in breeding strategies might accelerate the progress in obtaining high-yielding cultivars. The achievements of the Green Revolution were based on a specific plant ideotype, determined by a single gene involved in gibberellin signaling or metabolism. Compared with the 1950s, an enormous increase in our knowledge about the biological basis of plant productivity has opened new avenues for novel breeding strategies. The large and complex genomes of diploid barley and hexaploid wheat represent a great challenge, but they also offer a large reservoir of Genes that can be targeted for breeding. We summarize examples of productivity-related Genes/mutants in wheat and barley, identified or characterized by means of modern biology. The Genes are classified functionally into several groups, including the following: (1) transcription factors, regulating spike development, which mainly affect grain number; (2) Genes involved in metabolism or signaling of growth regulators—cytokinins, gibberellins, and brassinosteroids—which control plant architecture and in consequence stem hardiness and grain yield; (3) Genes determining cell division and proliferation mainly impacting grain size; (4) floral regulators influencing inflorescence architecture and in consequence seed number; and (5) Genes involved in carbohydrate metabolism having an impact on plant architecture and grain yield. The implementation of selected Genes in breeding programs is discussed, considering specific genotypes, agronomic and climate conditions, and taking into account that many of the Genes are members of multigene families.
J W Snape - One of the best experts on this subject based on the ideXlab platform.
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rflp mapping of five Major Genes and eight quantitative trait loci controlling flowering time in a winter spring barley hordeum vulgare l cross
Genome, 1995Co-Authors: David A Laurie, Nicky Pratchett, Jeremy Bezant, J W SnapeAbstract:A genetic map of 92 RFLP loci and two storage protein loci was made using 94 doubled-haploid lines from a cross between the winter barley variety Igri and the spring variety Triumph. The markers were combined with data from two field experiments (one spring sown and one autumn (fall) sown) and a glasshouse experiment to locate a total of 13 Genes (five Major Genes and eight quantitative trait loci (QTL)) controlling flowering time. Two photoperiod response Genes were found; Ppd-H1 on chromosome 2(2H)S regulated flowering time under long days, while Ppd-H2 on chromosome 5(1H)L was detected only under short days. In the field experiments Ppd-H1 strongly affected flowering time from spring and autumn sowings, while Ppd-H2 was detected only in the autumn sowing. The glasshouse experiment also located two vernalization response Genes, probably Sh and Sh2, on chromosomes 4(4H)L and 7(5H)L, respectively. The vernalization response Genes had little effect on flowering time in the field. Variation in flowering tim...
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rflp mapping of five Major Genes and eight quantitative trait loci controlling flowering time in a winter spring barley hordeum vulgare l cross
Genome, 1995Co-Authors: David A Laurie, Nicky Pratchett, Jeremy Bezant, J W SnapeAbstract:A genetic map of 92 RFLP loci and two storage protein loci was made using 94 doubled-haploid lines from a cross between the winter barley variety Igri and the spring variety Triumph. The markers were combined with data from two field experiments (one spring sown and one autumn (fall) sown) and a glasshouse experiment to locate a total of 13 Genes (five Major Genes and eight quantitative trait loci (QTL)) controlling flowering time. Two photoperiod response Genes were found; Ppd-H1 on chromosome 2(2H)S regulated flowering time under long days, while Ppd-H2 on chromosome 5(1H)L was detected only under short days. In the field experiments Ppd-H1 strongly affected flowering time from spring and autumn sowings, while Ppd-H2 was detected only in the autumn sowing. The glasshouse experiment also located two vernalization response Genes, probably Sh and Sh2, on chromosomes 4(4H)L and 7(5H)L, respectively. The vernalization response Genes had little effect on flowering time in the field. Variation in flowering time was also affected by nine additional Genes, whose effects were not specifically dependent on photoperiod or vernalization. One was the denso dwarfing gene on chromosome 3(3H)L. The remaining eight were QTLs of smaller effect. One was located on chromosome 2(2H), one on 3(3H), one on 4(4H), one on 7(5H), two on 6(6H), and two on 1(7H). Model fitting showed that the 13 putative Genes, and their interactions, could account for all the observed genetical variation from both spring and autumn sowings, giving a complete model for the control of flowering time in this cross.
Thierry Pascal - One of the best experts on this subject based on the ideXlab platform.
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Identifying SNP markers tightly associated with six Major Genes in peach [Prunus persica (L.) Batsch] using a high-density SNP array with an objective of marker-assisted selection (MAS)
Tree Genetics and Genomes, 2016Co-Authors: Patrick Lambert, José Antonio Campoy-corbalan, Igor Pacheco, Jehan-baptiste Mauroux, Cassia Da Silva Linge, Diego Micheletti, Danièle Bassi, Laura Rossini, Elisabeth Dirlewanger, Thierry PascalAbstract:One of the applications of genomics is to identify genetic markers linked to loci responsible for variation in phenotypic traits, which could be used in breeding programs to select individuals with favorable alleles, particularly at the seedling stage. With this aim, in the framework of the European project FruitBreedomics, we selected five main peach fruit characters and a resistance trait, controlled by Major Genes with Mendelian inheritance: fruit flesh color Y, fruit skin pubescence G, fruit shape S, sub-acid fruit D, stone adhesion-flesh texture F-M, and resistance to green peach aphid Rm2. They were all previously mapped in Prunus. We then selected three F1 and three F2 progenies segregating for these characters and developed genetic maps of the linkage groups including the Major Genes, using the single nucleotide polymorphism (SNP) genome-wide scans obtained with the International Peach SNP Consortium (IPSC) 9K SNP array v1. We identified SNPs co-segregating with the characters in all cases. Their positions were in agreement with the known positions of the Major Genes. The number of SNPs linked to each of these, as well as the size of the physical regions encompassing them, varied depending on the maps. As a result, the number of useful SNPs for marker-assisted selection varied accordingly. As a whole, this study establishes a sound basis for further development of MAS on these characters. Additionally, we also discussed some limitations that were observed regarding the SNP array efficiency.
Zulma G Vitezica - One of the best experts on this subject based on the ideXlab platform.
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Genetic evaluation with Major Genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP
Genetics Selection Evolution, 2015Co-Authors: Andres Legarra, Zulma G VitezicaAbstract:Background In pedigreed populations with a Major gene segregating for a quantitative trait, it is not clear how to use pedigree, genotype and phenotype information when some individuals are not genotyped. We propose to consider gene content at the Major gene as a second trait correlated to the quantitative trait, in a gene content multiple-trait best linear unbiased prediction (GCMTBLUP) method.ResultsThe genetic covariance between the trait and gene content at the Major gene is a function of the substitution effect of the gene. This genetic covariance can be written in a multiple-trait form that accommodates any pattern of missing values for either genotype or phenotype data. Effects of Major gene alleles and the genetic covariance between genotype at the Major gene and the phenotype can be estimated using standard EM-REML or Gibbs sampling. Prediction of breeding values with genotypes at the Major gene can use multiple-trait BLUP software. Major Genes with more than two alleles can be considered by including negative covariances between gene contents at each different allele. We simulated two scenarios: a selected and an unselected trait with heritabilities of 0.05 and 0.5, respectively. In both cases, the Major gene explained half the genetic variation. Competing methods used imputed gene contents derived by the method of Gengler et al. or by iterative peeling. Imputed gene contents, in contrast to GCMTBLUP, do not consider information on the quantitative trait for genotype prediction. GCMTBLUP gave unbiased estimates of the gene effect, in contrast to the other methods, with less bias and better or equal accuracy of prediction. GCMTBLUP improved estimation of genotypes in non-genotyped individuals, in particular if these individuals had own phenotype records and the trait had a high heritability. Ignoring the Major gene in genetic evaluation led to serious biases and decreased prediction accuracy.ConclusionsCGMTBLUP is the best linear predictor of additive genetic merit including pedigree, phenotype, and genotype information at Major Genes, since it considers missing genotypes. Simulations confirm that it is a simple, efficient and theoretically sound method for genetic evaluation of traits influenced by polygenic inheritance and one or several Major Genes.
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Genetic evaluation with Major Genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP
Genetics Selection Evolution, 2015Co-Authors: Andres Legarra, Zulma G VitezicaAbstract:In pedigreed populations with a Major gene segregating for a quantitative trait, it is not clear how to use pedigree, genotype and phenotype information when some individuals are not genotyped. We propose to consider gene content at the Major gene as a second trait correlated to the quantitative trait, in a gene content multiple-trait best linear unbiased prediction (GCMTBLUP) method. The genetic covariance between the trait and gene content at the Major gene is a function of the substitution effect of the gene. This genetic covariance can be written in a multiple-trait form that accommodates any pattern of missing values for either genotype or phenotype data. Effects of Major gene alleles and the genetic covariance between genotype at the Major gene and the phenotype can be estimated using standard EM-REML or Gibbs sampling. Prediction of breeding values with genotypes at the Major gene can use multiple-trait BLUP software. Major Genes with more than two alleles can be considered by including negative covariances between gene contents at each different allele. We simulated two scenarios: a selected and an unselected trait with heritabilities of 0.05 and 0.5, respectively. In both cases, the Major gene explained half the genetic variation. Competing methods used imputed gene contents derived by the method of Gengler et al. or by iterative peeling. Imputed gene contents, in contrast to GCMTBLUP, do not consider information on the quantitative trait for genotype prediction. GCMTBLUP gave unbiased estimates of the gene effect, in contrast to the other methods, with less bias and better or equal accuracy of prediction. GCMTBLUP improved estimation of genotypes in non-genotyped individuals, in particular if these individuals had own phenotype records and the trait had a high heritability. Ignoring the Major gene in genetic evaluation led to serious biases and decreased prediction accuracy. CGMTBLUP is the best linear predictor of additive genetic merit including pedigree, phenotype, and genotype information at Major Genes, since it considers missing genotypes. Simulations confirm that it is a simple, efficient and theoretically sound method for genetic evaluation of traits influenced by polygenic inheritance and one or several Major Genes.
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genetic evaluation with Major Genes and polygenic inheritance when some animals are not genotyped using gene content multiple trait blup
Genetics Selection Evolution, 2015Co-Authors: Andres Legarra, Zulma G VitezicaAbstract:Background In pedigreed populations with a Major gene segregating for a quantitative trait, it is not clear how to use pedigree, genotype and phenotype information when some individuals are not genotyped. We propose to consider gene content at the Major gene as a second trait correlated to the quantitative trait, in a gene content multiple-trait best linear unbiased prediction (GCMTBLUP) method.
Ian Robert Franklin - One of the best experts on this subject based on the ideXlab platform.
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Major Genes and QTL influencing wool production and quality: a review
Genetics Selection Evolution, 2005Co-Authors: Ian William Purvis, Ian Robert FranklinAbstract:The opportunity exists to utilise our knowledge of Major Genes that influence the economically important traits in wool sheep. Genes with Mendelian inheritance have been identified for many important traits in wool sheep. Of particular importance are Genes influencing pigmentation, wool quality and the keratin proteins, the latter of which are important for the morphology of the wool fibre. Gene mapping studies have identified some chromosomal regions associated with variation in wool quality and production traits. The challenge now is to build on this knowledge base in a cost-effective way to deliver molecular tools that facilitate enhanced genetic improvement programs for wool sheep.
