Construction of a high-density genetic map using specific-length amplified fragment markers and identification of QTLs for branching angle in poplar
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Candidate gene
Branching (polymer chemistry)
Trait
Abstract Objectives : To investigate possible obesity candidate genes in regions of porcine quantitative trait loci (QTL) for fat deposition and obesity‐related phenotypes. Research Methods and Procedures : Chromosome mapping and QTL analyses of obesity candidate genes were performed using DNA panels from a reference pig family. Statistical association analyses of these genes were performed for fat deposition phenotypes in several other commercial pig populations. Results : Eight candidate genes were mapped to QTL regions of pig chromosomes in this study. These candidate genes also served as anchor loci to determine homologous human chromosomal locations of pig fat deposition QTL. Preliminary analyses of relationships among polymorphisms of individual candidate genes and a variety of phenotypic measurements in a large number of pigs were performed. On the basis of available data, gene‐gene interactions were also studied. Discussion : Comparative analysis of obesity‐related genes in the pig is not only important for development of marker‐assisted selection on growth and fat deposition traits in the pig but also provides for an understanding of their genetic roles in the development of human obesity.
Candidate gene
Family-based QTL mapping
Polygene
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Phytophthora infestans, the causal agent of late blight, threatens potato production worldwide. Many quantitative trait loci (QTL) for late blight resistance have been mapped in several potato populations. At the same time, numerous expressed sequences tags (EST) related to late blight resistance have been deposited in databases. In order to screen for putative candidate genes associated with late blight resistance, 65 candidate genes were selected for mapping and investigation of their relationship with QTL in three diploid potato populations PCC1, BCT, and PD. In total, 26 primers from the 65 selected genes that showed PCR length polymorphism were mapped on the linkage groups of three populations. Further comparison between map location of QTL and candidate gene loci indicated that three candidate gene markers were placed in a QTL region. The locus of a putative receptor-like protein kinase b co-localized with an important QTL region on chromosome XI of PCC1. In the PD population, the Lox gene was in a QTL with moderate effect on chromosome III and two protein phosphatase loci were localized in a QTL with the largest effect on chromosome XII. These mapped candidate gene markers could be used as a bridge to other genetic maps of potato. The association of candidate genes with QTL forms the basis for further studies on the contributions of these candidate genes to natural variation for potato late blight resistance. Key words: Candidate gene, quantitative resistance loci, late blight, potato
Candidate gene
Phytophthora infestans
Genetic linkage
Family-based QTL mapping
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Almost all crops have been studied on QTL(quantitative trait loci) mapping with many QTL mapping methods,such as IM(interval mapping),CIM(composite interval mapping),MCIM(mixed-model based composite interval mapping) and Bayesian QTL mapping having been developed.However,these methods have had shortcomings,namey,the genomic region of the QTL detected,within which there were probably hundreds of candidate genes was still too large.In this paper,a better understanding of the molecular functions of QTLs was obtained by first briefly reviewing methods of analyzing the candidate gene within QTL intervals based on bioinformatics.Then,in order to provide a new analytical method for better use of QTLs in the future,the genetics,genome organization,gene expression and function of candidate genes located on QTL region were analyzed.
Family-based QTL mapping
Candidate gene
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Poor lodging resistance could limit increases in soybean yield. Previously, a considerable number of observations of quantitative trait loci (QTL) for lodging resistance have been reported by independent studies. The integration of these QTL into a consensus map will provide further evidence of their usefulness in soybean improvement. To improve informative QTL in soybean, a mapping population from a cross between the Harosoy and Clark cultivars, which inherit major U.S. soybean genetic backgrounds, was used along with previous mapping populations to identify QTL for lodging resistance. Together with 78 QTL for lodging collected from eighteen independent studies, a total of 88 QTL were projected onto the soybean consensus map. A total of 16 significant QTL clusters were observed; fourteen of them were confirmed in either two or more mapping populations or a single population subjected to different environmental conditions. Four QTL (one on chromosome 7 and three on 10) were newly identified in the present study. Further, meta-analysis was used to integrate QTL across different studies, resulting in two significant meta-QTL each on chromosomes 6 and 19. Our results provide deeper knowledge of valuable lodging resistance QTL in soybean, and these QTL could be used to increase lodging resistance.
Family-based QTL mapping
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Imprinting (psychology)
Genomic Imprinting
Intramuscular fat
Family-based QTL mapping
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A systematic study has been conducted of all available reports in PubMed and OMIM (Online Mendelian Inheritance in Man) to examine the genetic and molecular basis of quantitative genetic loci (QTL) of diabetes with the main focus on genes and polymorphisms. The major question is, What can the QTL tell us? Specifically, we want to know whether those genome regions differ from other regions in terms of genes relevant to diabetes. Which genes are within those QTL regions, and, among them, which genes have already been linked to diabetes? whether more polymorphisms have been associated with diabetes in the QTL regions than in the non-QTL regions.Our search revealed a total of 9038 genes from 26 type 1 diabetes QTL, which cover 667,096,006 bp of the mouse genomic sequence. On one hand, a large number of candidate genes are in each of these QTL; on the other hand, we found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may provide unexpected benefit for identifying QTL genes for diabetes.
Candidate gene
Family-based QTL mapping
Mendelian inheritance
Genetic linkage
Polygene
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Abstract Background Meta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. The combination of meta-QTL intervals, significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants. Results In our study, 884 QTL associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1, as a result, 74 meta-QTL were identified, including 19 meta-QTL for fiber length (FL), 18 meta-QTL for fiber strength (FS), 11 meta-QTL for fiber uniformity (FU), 11 meta-QTL for fiber elongation (FE), and 15 meta-QTL for micronaire (MIC). Combined with 8589 significant SNPs associated with fiber quality traits collected from 15 studies, 297 candidate genes were identified in the meta-QTL intervals, 20 of which showed high expression specifically in the developing fibers. According to the function annotations, some of the 20 key candidate genes are associated with the fiber development. Conclusions This study provides not only stable QTLs used for marker-assisted selection (MAS), but also candidate genes to uncover the molecular mechanisms for cotton fiber development.
Candidate gene
Family-based QTL mapping
SNP
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Data from an F 2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele frequencies (FD) between parental breeds were simulated. Use of all models increased power to detect QTL. The line-cross model was the most powerful model to detect QTL for FD>0·6. The combined and half-sib models had similar power for FD<0·4. The proportion of detected QTL declared as LC QTL decreased with FD. The opposite was observed for HS QTL. The proportion of CB QTL decreased as FD deviated from 0·5. Accuracy of map position tended to be greatest for CB QTL. Models were applied to a cross of Berkshire and Yorkshire pig breeds and revealed 160 (40) QTL at the 5% chromosome (genome)-wise level for the 39 growth, carcass composition and quality traits, of which 72, 54, and 34 were declared as LC, HS and CB QTL. Fourteen CB QTL were detected only by the combined model. Thus, the combined model can increase power to detect QTL and mapping accuracy and enable characterization of QTL that segregate within breeds.
Family-based QTL mapping
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Quantitative trait loci(QTL) analysis for some important agronomic traits(plant height,ear height and leaf angle) were carried out,using SSR markers and three F2:3 populations(H21×Mo17,Zi330×K36 and B73×L050) in order to understand the genetic and molecular mechanisms of these traits.Eighteen QTL were detected for plant height,twelve QTL for ear height and nine QTL for leaf angle.Some of these QTL were located within the same chromosome regions,and the peaks of some QTL regions overlaid.In addition,it was found that some QTL were located in the same regions with known qualitative genetic loci affecting these traits.
Trait
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Congenic
Candidate gene
Family-based QTL mapping
Positional cloning
Genetic linkage
Chromosomal region
Genetic architecture
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