Genome‐wide quantitative trait loci reveal the genetic basis of cotton fibre quality and yield‐related traits in a Gossypium hirsutum recombinant inbred line population
Zhen ZhangJùnwén LǐMuhammad JamshedYùzhēn ShíÀiyīng LiúJǔwǔ GōngShufang WangJianhong ZhangFuding SunFei JiaQún GěLiqiang FanZhibin ZhangJìngtāo PānSenmiao FanYanling WangQuánwěi LúRuixian LiuXiǎoyīng DèngXianyan ZouXiao JiangPing LiuPengtao LiMuhammad Sajid IqbalChuanyun ZhangJuan ZouHong ChenQin TianXinhe JiaBaoqin WangNijiang AiGuoli FengYumei WangMei HongShilin LiWenming LianBo WuJinping HuaChaojun ZhangJinyong HuangAixia XuHǎihóng ShāngWànkuí GǒngYǒulù Yuán
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Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty-seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA-Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu-chr13-2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.Keywords:
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Candidate gene
Quantitative trait loci (QTL) for barley stripe rust resistance were mapped in recombinant inbred lines (RIL) from a 'Lenetah' × 'Grannelose Zweizeilige' (GZ) cross. GZ is known for a major seedling resistance QTL on chromosome 4H but linked markers suitable for marker-assisted selection have not been developed. This study identified the 4H QTL (log of the likelihood [LOD] = 15.94 at 97.19 centimorgans [cM]), and additional QTL on chromosomes 4H and 6H (LOD = 5.39 at 72.7 cM and 4.24 at 34.46 cM, respectively). A QTL on chromosome 7H (LOD = 2.04 at 81.07 cM) was suggested. All resistance alleles were derived from GZ. Evaluations of adult plant response in Corvallis, OR in 2013 and 2015 provided evidence of QTL at the same positions. However, the minor QTL on 4H was not statistically significant in either location/year, while the 7H QTL was significant in both. The single-nucleotide polymorphism markers flanking the resistance QTL were validated in RIL from a '95SR316A' × GZ cross for their ability to predict seedling resistance. In 95SR316A × GZ, 91 to 92% of RIL with GZ alleles at the major 4H QTL and at least one other were resistant to moderate in reaction. In these populations, at least two QTL were required to transfer the barley stripe rust resistance from GZ.
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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.
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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.
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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
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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.
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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.
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Leaf size is a crucial component of sesame ( Sesamum indicum L.) plant architecture and further influences yield potential. Despite that it is well known that leaf size traits are quantitative traits controlled by large numbers of genes, quantitative trait loci (QTL) and candidate genes for sesame leaf size remain poorly understood. In the present study, we combined the QTL-seq approach and SSR marker mapping to identify the candidate genomic regions harboring QTL controlling leaf size traits in an RIL population derived from a cross between sesame varieties Zhongzhi No. 13 (with big leaves) and ZZM2289 (with small leaves). The QTL mapping revealed 56 QTL with phenotypic variation explained (PVE) from 1.87 to 27.50% for the length and width of leaves at the 1/3 and 1/2 positions of plant height. qLS15-1 , a major and environmentally stable pleiotropic locus for both leaf length and width explaining 5.81 to 27.50% phenotypic variation, was located on LG15 within a 408-Kb physical genomic region flanked by the markers ZMM6185 and ZMM6206. In this region, a combination of transcriptome analysis with gene annotations revealed three candidate genes SIN_1004875 , SIN_1004882 , and SIN_1004883 associated with leaf growth and development in sesame. These findings provided insight into the genetic characteristics and variability for sesame leaf and set up the foundation for future genomic studies on sesame leaves and will serve as gene resources for improvement of sesame plant architecture.
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Leaf size
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Objective To localize quantitative trait loci (QTL) in an animal model that is potentially relevant to human hypertension. Design and methods Four congenic strains have been constructed by replacing various segments of the Dahl salt-sensitive (S) rat by those of the Lewis (LEW) rat. A marker-assisted approach was employed to facilitate this process. When these congenic strains were established, their blood pressures (BPs) were measured by telemetry and compared with that of the S rat. Moreover, a search was conducted to find possible intermediate phenotypes linking the BP effects of the QTL and other physiological traits. Results Two BP QTL, designated as QTL1 and QTL2, have been mapped to the regions of 4.2 centiMorgans (cM) and less than 12.1 cM respectively on rat chromosome 10. The effects of both QTL correlate with cardiac, left ventricular and aortic hypertrophy. The effect of QTL1 is also associated with renal hypertrophy. Conclusion The current study proved that multiple QTL exist in the region of Dahl rat chromosome 10. The identification of these QTL may help unravel the mechanisms underlying the pathogenesis of certain QTL in humans.
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Intermated Recombinant Inbred Lines (IRILs) in plants, or Advanced Recombinant Inbred Strains in animals, are constructed by carrying out generations of intermating between F2 individuals before starting recurrent inbreeding generations by selfing or sib-mating. IRILs are powerful for high-resolution genetic mapping because they have undergone more recombination than usual Recombinant Inbred Lines (RILs). However, there is no mapping software able to generate actual centiMorgan distances from the segregation data obtained with IRILs. IRILmap software converts genetic distances computed with any linkage mapping program designed for RILs, so that IRIL-derived data can be used to get actual centiMorgan distances, directly comparable to F2, backcross or RIL-derived maps.
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