Journal articles: 'QTL'

1

Takahashi, Hidekazu. "QTL analysis using the Windows QTL Cartographer." Breeding Research 10, no. 1 (2008): 11–14. http://dx.doi.org/10.1270/jsbbr.10.11.

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Broman, K. W., H. Wu, S. Sen, and G. A. Churchill. "R/qtl: QTL mapping in experimental crosses." Bioinformatics 19, no. 7 (May 1, 2003): 889–90. http://dx.doi.org/10.1093/bioinformatics/btg112.

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Kang, Yiwei, Miao Zhang, Yue Zhang, Weixun Wu, Pao Xue, Xiaodeng Zhan, Liyong Cao, Shihua Cheng, and Yingxin Zhang. "Genetic Mapping of Grain Shape Associated QTL Utilizing Recombinant Inbred Sister Lines in High Yielding Rice (Oryza sativa L.)." Agronomy 11, no. 4 (April 7, 2021): 705. http://dx.doi.org/10.3390/agronomy11040705.

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Grain shape is a key factor for yield and quality in rice. To investigate the genetic basis of grain shape in the high-yielding hybrid rice variety Nei2You No.6, a set of recombinant inbred sister lines (RISLs) were used to map quantitative trait loci (QTLs) determining grain length (GL), grain width (GW), and length-width ratio (LWR) in four environments. A total of 91 medium/minor-effect QTL were detected using a high-density genetic map consisting of 3203 Bin markers composed of single nucleotide polymorphisms, among which 64 QTL formed 15 clusters. Twelve of 15 clusters co-localized with QTL previously reported for grain shape/weight. Three new QTL were detected: qGL-7a, qGL-8, and qGL-11a. A QTL cluster, qLWR-12c/qGW-12, was detected across all four environments with phenotypic variation explained (PVE) ranging from 3.67% to 11.93%, which was subsequently validated in paired lines of F17 progeny and tightly linked marker assay in F10 generation. Subsequently, 17 candidate genes for qLWR-12c/qGW-12 were detected in the 431 Kb interval utilizing bulk segregant analysis (BSA). Among these, OsR498G1222170400, OsR498G1222171900, OsR498G1222185100, OsR498G1222173400, and OsR498G1222170500 were the best candidates, which lays the foundation for further cloning and will facilitate high-yield breeding in rice.

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Zhang, Hongwei, Xi Wang, Qingchun Pan, Pei Li, Yunjun Liu, Xiaoduo Lu, Wanshun Zhong, et al. "QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples." Molecular Plant 12, no. 3 (March 2019): 426–37. http://dx.doi.org/10.1016/j.molp.2018.12.018.

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Georges, Michel. "QTL Mapping to QTL Cloning: Mice to the Rescue." Genome Research 7, no. 7 (July 1, 1997): 663–65. http://dx.doi.org/10.1101/gr.7.7.663.

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Arends, Danny, Pjotr Prins, Ritsert C. Jansen, and Karl W. Broman. "R/qtl: high-throughput multiple QTL mapping: Fig. 1." Bioinformatics 26, no. 23 (October 21, 2010): 2990–92. http://dx.doi.org/10.1093/bioinformatics/btq565.

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Liu, Jingxian, Danfeng Wang, Mingyu Liu, Meijin Jin, Xuecheng Sun, Yunlong Pang, Qiang Yan, Cunzhen Liu, and Shubing Liu. "QTL Mapping for Agronomic Important Traits in Well-Adapted Wheat Cultivars." Agronomy 14, no. 5 (April 30, 2024): 940. http://dx.doi.org/10.3390/agronomy14050940.

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Wheat (Triticum aestivum L.) is one of the most important food crops worldwide and provides the staple food for 40% of the world’s population. Increasing wheat production has become an important goal to ensure global food security. The grain yield of wheat is a complex trait that is usually influenced by multiple agronomically important traits. Thus, the genetic dissection and discovery of quantitative trait loci (QTL) of wheat-yield-related traits are very important to develop high-yield cultivars to improve wheat production. To analyze the genetic basis and discover genes controlling important agronomic traits in wheat, a recombinant inbred lines (RILs) population consisting of 180 RILs derived from a cross between Xinong822 (XN822) and Yannong999 (YN999), two well-adapted cultivars, was used to map QTL for plant height (PH), spike number per spike (SNS), spike length (SL), grain number per spike (GNS), spike number per plant (SN), 1000- grain weight (TGW), grain length (GL), grain width (GW), length/width of grain (GL/GW), perimeter of grain (Peri), and surface area of grains (Sur) in three environments. A total of 64 QTL were detected and distributed on all wheat chromosomes except 3A and 5A. The identified QTL individually explained 2.24–38.24% of the phenotypic variation, with LOD scores ranging from 2.5 to 29. Nine of these QTL were detected in multiple environments, and seven QTL were associated with more than one trait. Additionally, Kompetitive Allele Specific PCR (KASP) assays for five major QTL QSns-1A.2 (PVE = 6.82), QPh-2D.1 (PVE = 37.81), QSl-2D (PVE = 38.24), QTgw-4B (PVE = 8.78), and QGns-4D (PVE = 13.54) were developed and validated in the population. The identified QTL and linked markers are highly valuable in improving wheat yield through marker-assisted breeding, and the large-effect QTL can be fine-mapped for further QTL cloning of yield-related traits in wheat.

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Mangin, B., P. Thoquet, and N. Grimsley. "Pleiotropic QTL Analysis." Biometrics 54, no. 1 (March 1998): 88. http://dx.doi.org/10.2307/2533998.

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Anderson, Jill T., and Thomas Mitchell-Olds. "Beyond QTL Cloning." PLoS Genetics 6, no. 11 (November 11, 2010): e1001197. http://dx.doi.org/10.1371/journal.pgen.1001197.

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Niemitz, Emily. "RNA decay QTL." Nature Genetics 44, no. 12 (November 28, 2012): 1293. http://dx.doi.org/10.1038/ng.2488.

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Hina, Aiman, Yongce Cao, Shiyu Song, Shuguang Li, Ripa Akter Sharmin, Mahmoud A. Elattar, Javaid Akhter Bhat, and Tuanjie Zhao. "High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)." International Journal of Molecular Sciences 21, no. 3 (February 4, 2020): 1040. http://dx.doi.org/10.3390/ijms21031040.

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Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

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Liu, Xiaolu, Xiangyuan Wan, Xiaodong Ma, and Jianmin Wan. "Dissecting the genetic basis for the effect of rice chalkiness, amylose content, protein content, and rapid viscosity analyzer profile characteristics on the eating quality of cooked rice using the chromosome segment substitution line population across eight environments." Genome 54, no. 1 (January 2011): 64–80. http://dx.doi.org/10.1139/g10-070.

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Quantitative trait locus (QTL) mapping and stability analysis were carried out for 16 rice ( Oryza sativa L.) quality traits across eight environments, by using a set of chromosome segment substitution lines with ‘Asominori’ as genetic background. The 16 quality traits include percentage of grain with chalkiness (PGWC), area of chalky endosperm (ACE), amylose content (AC), protein content (PC), peak viscosity, hot paste viscosity, cool paste viscosity, breakdown viscosity (BDV), setback viscosity (SBV), consistency viscosity, cooked-rice luster (LT), scent, tenderness (TD), viscosity, elasticity, and the integrated values of organleptic evaluation (IVOE). A total of 132 additive effect QTLs are detected for the 16 quality straits in the eight environments. Among these QTLs, 56 loci were detected repeatedly in at least three environments. Interestingly, several QTL clusters were observed for multiple quality traits. Especially, one QTL cluster near the G1149 marker on chromosome 8 includes nine QTLs: qPGWC-8, qACE-8, qAC-8, qPC-8a, qBDV-8a, qSBV-8b, qLT-8a, qTD-8a, and qIVOE-8a, which control PGWC, ACE, AC, PC, BDV, SBV, LT, TD, and IVOE, respectively. Moreover, this QTL cluster shows high stability and repeatability in all eight environments. In addition, one QTL cluster was located near the C2340 marker on chromosome 1 and another was detected near the XNpb67 marker on chromosome 2; each cluster contained five loci. Near the C563 marker on chromosome 3, one QTL cluster with four loci was found. Also, there were nine QTL clusters that each had two or three loci; however, their repeatability in different environments was relatively lower, and the genetic contribution rate was relatively smaller. Considering the correlations among all of the 16 quality traits with QTL cluster distributions, we can conclude that the stable and major QTL cluster on chromosome 8 is the main genetic basis for the effect of rice chalkiness, AC, PC, and rapid viscosity analyzer profile characteristics on the eating quality of cooked rice. Consequently, this QTL cluster is a novel gene resource for controlling rice high-quality traits and should be of great significance for research on formation mechanism and molecule improvement of rice quality.

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Verdugo, Ricardo A., Charles R. Farber, Craig H. Warden, and Juan F. Medrano. "Serious limitations of the QTL/Microarray approach for QTL gene discovery." BMC Biology 8, no. 1 (2010): 96. http://dx.doi.org/10.1186/1741-7007-8-96.

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Wang, Shibo, Fangjie Xie, and Shizhong Xu. "Estimating genetic variance contributed by a quantitative trait locus: A random model approach." PLOS Computational Biology 18, no. 3 (March 11, 2022): e1009923. http://dx.doi.org/10.1371/journal.pcbi.1009923.

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Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F2 mouse population.

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Cyplik, Adrian, and Jan Bocianowski. "A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies." International Journal of Molecular Sciences 24, no. 12 (June 12, 2023): 10043. http://dx.doi.org/10.3390/ijms241210043.

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The goal of the breeding process is to obtain new genotypes with traits improved over the parental forms. Parameters related to the additive effect of genes as well as their interactions (such as epistasis of gene–by–gene interaction effect and additive–by–additive–by–additive of gene–by–gene–by–gene interaction effect) can influence decisions on the suitability of breeding material for this purpose. Understanding the genetic architecture of complex traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL–by–QTL interactions and QTL–by–QTL–by–QTL interactions. With regards to the comparing methods for estimating additive–by–additive–by–additive of QTL×QTL×QTL interaction effects by Monte Carlo simulation studies, there are no publications in the open literature. The parameter combinations assumed in the presented simulation studies represented 84 different experimental situations. The use of weighted regression may be the preferred method for estimating additive–by–additive–by–additive of QTL–QTL–QTL triples interaction effects, as it provides results closer to the true values of total additive–by–additive–by–additive interaction effects than using unweighted regression. This is also indicated by the obtained values of the determination coefficients of the proposed models.

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Star, K. V. "QTL MatchMaker: a multi-species quantitative trait loci (QTL) database and query system for annotation of genes and QTL." Nucleic Acids Research 34, no. 90001 (January 1, 2006): D586—D589. http://dx.doi.org/10.1093/nar/gkj027.

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KIM, JONG-JOO, HONGHUA ZHAO, HAUKE THOMSEN, MAX F. ROTHSCHILD, and JACK C. M. DEKKERS. "Combined line-cross and half-sib QTL analysis of crosses between outbred lines." Genetical Research 85, no. 3 (June 2005): 235–48. http://dx.doi.org/10.1017/s0016672305007597.

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Data from an F2 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.

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Uimari, Pekka, and Ina Hoeschele. "Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms." Genetics 146, no. 2 (June 1, 1997): 735–43. http://dx.doi.org/10.1093/genetics/146.2.735.

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A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL and no additional or an unlinked QTL.

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de Koning, Dirk-Jan, Henk Bovenhuis, and Johan A. M. van Arendonk. "On the Detection of Imprinted Quantitative Trait Loci in Experimental Crosses of Outbred Species." Genetics 161, no. 2 (June 1, 2002): 931–38. http://dx.doi.org/10.1093/genetics/161.2.931.

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Abstract In this article, the quantitative genetic aspects of imprinted genes and statistical properties of methods to detect imprinted QTL are studied. Different models to detect imprinted QTL and to distinguish between imprinted and Mendelian QTL were compared in a simulation study. Mendelian and imprinted QTL were simulated in an F2 design and analyzed under Mendelian and imprinting models. Mode of expression was evaluated against the H0 of a Mendelian QTL as well as the H0 of an imprinted QTL. It was shown that imprinted QTL might remain undetected when analyzing the genome with Mendelian models only. Compared to testing against a Mendelian QTL, using the H0 of an imprinted QTL gave a higher proportion of correctly identified imprinted QTL, but also gave a higher proportion of false inference of imprinting for Mendelian QTL. When QTL were segregating in the founder lines, spurious detection of imprinting became more prominent under both tests, especially for designs with a small number of F1 sires.

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SAAD, YASSER, MICHAEL R. GARRETT, and JOHN P. RAPP. "Multiple blood pressure QTL on rat chromosome 1 defined by Dahl rat congenic strains." Physiological Genomics 4, no. 3 (January 19, 2001): 201–14. http://dx.doi.org/10.1152/physiolgenomics.2001.4.3.201.

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A series of congenic strains were constructed in which segments of chromosome (chr) 1 from Lewis (LEW) rats were introgressed into the Dahl salt-sensitive (S) strain. Three blood pressure quantitative trait loci (QTL) were defined. Two of these (QTL 1a and QTL 1b) were closely linked in the region between 1q31 and 1q35. The third blood pressure QTL (QTL region 2) was close to the centromere between 1p11 and 1q12, which includes the candidate gene Slc9a3 for sodium/hydrogen exchange. The blood pressure QTL 1a and QTL 1b defined here overlap significantly with QTL for disease phenotypes of renal failure, stroke, ventricular mass, and salt susceptibility defined in other rat strains, implying that these disease phenotypes and our blood pressure phenotype have causes in common. QTL 1b also corresponded approximately with a blood pressure QTL described on human chr 15. The QTL region 2 corresponded approximately with blood pressure QTL described on mouse chr 10 and human chr 6.

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Kao, Chen-Hung. "On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci." Genetics 156, no. 2 (October 1, 2000): 855–65. http://dx.doi.org/10.1093/genetics/156.2.855.

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AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

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Chung, Ill-Min, Tae-Ho Ham, Gi-Won Cho, Soon-Wook Kwon, Yoonjung Lee, Jeonghwan Seo, Yeon-Ju An, So-Yeon Kim, Seung-Hyun Kim, and Joohyun Lee. "Study of Quantitative Trait Loci (QTLs) Associated with Allelopathic Trait in Rice." Genes 11, no. 5 (April 26, 2020): 470. http://dx.doi.org/10.3390/genes11050470.

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In rice there are few genetic studies reported for allelopathy traits, which signify the ability of plants to inhibit or stimulate growth of other plants in the environment, by exuding chemicals. QTL analysis for allelopathic traits were conducted with 98 F8 RILs developed from the cross between the high allelopathic parents of ‘Sathi’ and non-allelopathic parents of ‘Nong-an’. The performance of allelopathic traits were evaluated with inhibition rate on root length, shoot length, total length, root weight, shoot weight, and total weight of lettuce as a receiver plant. With 785 polymorphic DNA markers, we constructed a linkage map showing a total of 2489.75 cM genetic length and 3.17 cM of average genetic distance between each adjacent marker. QTL analysis detected on QTL regions on chromosome 8 responsible for the inhibition of shoot length and inhibition of total length. The qISL-8 explained 20.38% of the phenotypic variation for the inhibition on the shoot length. The qITL-8 explained 14.93% of the phenotypic variation for the inhibition on total length. The physical distance of the detected QTL region was 194 Kbp where 31 genes are located.

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Szalma, Stephen J., Maurice E. Snook, Bradley S. Bushman, Katherine E. Houchins, and Michael D. McMullen. "Duplicate Loci as QTL." Crop Science 42, no. 5 (September 2002): 1679–87. http://dx.doi.org/10.2135/cropsci2002.1679.

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Basrak, Bojan, Chris A. J. Klaassen, Marian Beekman, Nick G. Martin, and Dorret I. Boomsma. "Copulas in QTL Mapping." Behavior Genetics 34, no. 2 (March 2004): 161–71. http://dx.doi.org/10.1023/b:bege.0000013730.63991.ba.

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McCough, Susan R., and Rebecca W. Doerge. "QTL mapping in rice." Trends in Genetics 11, no. 12 (December 1995): 482–87. http://dx.doi.org/10.1016/s0168-9525(00)89157-x.

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Almasy, Laura, and John Blangero. "Human QTL linkage mapping." Genetica 136, no. 2 (July 31, 2008): 333–40. http://dx.doi.org/10.1007/s10709-008-9305-3.

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Ukai, Yasuo. "Theory of QTL analysis." Breeding Research 1, no. 1 (1999): 25–31. http://dx.doi.org/10.1270/jsbbr.1.25.

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Peng, J., and D. Siegmund. "QTL Mapping Under Ascertainment." Annals of Human Genetics 70, no. 6 (November 2006): 867–81. http://dx.doi.org/10.1111/j.1469-1809.2006.00286.x.

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Curtsinger, James W. "Peeking under QTL peaks." Nature Genetics 34, no. 4 (August 2003): 358–59. http://dx.doi.org/10.1038/ng0803-358.

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Soriano, Jose Miguel, and Conxita Royo. "Dissecting the Genetic Architecture of Leaf Rust Resistance in Wheat by QTL Meta-Analysis." Phytopathology® 105, no. 12 (December 2015): 1585–93. http://dx.doi.org/10.1094/phyto-05-15-0130-r.

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Leaf rust is an important disease that causes significant yield losses in wheat. Many studies have reported the identification of quantitative trait loci (QTL) controlling leaf rust resistance; therefore, QTL meta-analysis has become a useful tool for identifying consensus QTL and refining QTL positions among them. In this study, QTL meta-analysis was conducted using reported results on the number, position, and effects of QTL for leaf rust resistance in bread and durum wheat. Investigation of 14 leaf rust resistance traits from 19 studies involving 20 mapping populations and 33 different parental lines provided information for 144 unique QTL that were projected onto the Wheat Composite 2004 reference map. In total, 35 meta-QTL for leaf rust resistance traits were identified in 17 wheat chromosomes and 13 QTL remained as unique QTL. The results will facilitate further work on the cloning of QTL for pyramiding minor- and partial-effect resistance genes to develop varieties with durable resistance to leaf rust.

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Xie, Chongqing, Damian D. G. Gessler, and Shizhong Xu. "Combining Different Line Crosses for Mapping Quantitative Trait Loci Using the Identical by Descent-Based Variance Component Method." Genetics 149, no. 2 (June 1, 1998): 1139–46. http://dx.doi.org/10.1093/genetics/149.2.1139.

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Abstract Mapping quantitative trait loci (QTLs) is usually conducted with a single line cross. The power of such QTL mapping depends highly on the two parental lines. If the two lines are fixed for the same allele at a putative QTL, the QTL is undetectable. On the other hand, if a QTL is segregating in the line cross and is detected, the estimated variance of the QTL cannot be extrapolated beyond the statistical inference space of the two parental lines. To reduce the likelihood of missing a QTL and to increase the statistical inference space of the estimated QTL variance, we present a consensus QTL mapping strategy. We adopt the identical by descent (IBD)-based variance component method originally applied to human linkage analysis by combining multiple line crosses as independent families. We explore the properties of consensus QTL mapping and demonstrate the method with F2, backcross (BC), and full-sib (FS) families. In addition, we examine the effects of the QTL heritability, marker informativeness, QTL position, the number of families, and family size. We show that F2 families notably outperform BC and FS families in detecting a QTL. There is a substantial reduction in the standard deviation of the estimated QTL position and the separation of the QTL and polygenic variance. Finally, we show that the power to detect a QTL is greater when using a small number of large families than a large number of small families.

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MALAU-ADULI, Aduli Enoch Othniel, Tomomi NIIBAYASHI, Takatoshi KOJIMA, Kazunaga OSHIMA, and Masanori KOMATSU. "Genome-wide scanning for QTL: Mapping methodology and detected QTL in cattle." Journal of animal genetics 30, no. 2 (2003): 3–16. http://dx.doi.org/10.5924/abgri2000.30.2_3.

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Charmet, Gilles. "Power and accuracy of QTL detection: simulation studies of one-QTL models." Agronomie 20, no. 3 (April 2000): 309–23. http://dx.doi.org/10.1051/agro:2000129.

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Wu, Sanling, Jie Qiu, and Qikang Gao. "QTL-BSA: A Bulked Segregant Analysis and Visualization Pipeline for QTL-seq." Interdisciplinary Sciences: Computational Life Sciences 11, no. 4 (August 6, 2019): 730–37. http://dx.doi.org/10.1007/s12539-019-00344-9.

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TOGASHI, Kenji, Naoyuki YAMAMOTO, Osamu SASAKI, JEO Rege, and Hisato TAKEDA. "Marker-QTL-Association Analysis Incorporating Diversification of QTL Variance and its Application." Nihon Chikusan Gakkaiho 67, no. 11 (1996): 923–29. http://dx.doi.org/10.2508/chikusan.67.923.

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Han, Tae-Ho, Herman J. van Eck, Marjo J. De Jeu, and Evert Jacobsen. "Mapping of Quantitative Trait Loci Involved in Ornamental Traits in Alstroemeria." HortScience 37, no. 3 (June 2002): 585–92. http://dx.doi.org/10.21273/hortsci.37.3.585.

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An F1 population, derived from an intraspecific cross between two Alstroemeria aurea accessions, was used to map quantitative trait loci (QTL) involved in ornamental and morphological characteristics. One QTL for leaf length was mapped on linkage group three of both parents near marker E+ACCT/M+CGCA-I165 explaining 20% and 14.8% phenotypic variation. Two putative QTL were detected on leaf width on A002-3 and A002-6. One QTL and three putative QTL, involved in the leaf length/width ratio were identified accounting for 46.7% of the phenotypic variance in total. Significant interaction was observed between two QTL, S+AC/M+ACT-I162 and S+AC/M+AGA-I465 in a two-way analysis of variance (ANOVA). For the main color of the flower one QTL and putative QTL accounted for up to 60% of phenotypic variance suggesting simple genetic control of flower color. A two-way ANOVA of these QTL suggested an epistatic interaction. A QTL was detected for color of the inner side of outer lateral tepal with 26.5% of the phenotypic variance explained. This QTL was also associated with main color of the flower just below the 95% threshold value. Two QTL were detected with the Kruskal-Wallis test for the tip color of inner lateral tepal near QTL for other flower color traits. Consequently flower color traits were significantly correlated. A QTL and a putative QTL for the flower size was mapped near marker E+ACCG/M+CGCT-I193 and E+ACCG/M+CGCG-197, respectively. One putative QTL was detected for the stripe width of the inner lateral tepal.

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Melchinger, Albrecht E., H. Friedrich Utz, and Chris C. Schön. "Quantitative Trait Locus (QTL) Mapping Using Different Testers and Independent Population Samples in Maize Reveals Low Power of QTL Detection and Large Bias in Estimates of QTL Effects." Genetics 149, no. 1 (May 1, 1998): 383–403. http://dx.doi.org/10.1093/genetics/149.1.383.

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Abstract The efficiency of marker-assisted selection (MAS) depends on the power of quantitative trait locus (QTL) detection and unbiased estimation of QTL effects. Two independent samples (N = 344 and 107) of F2 plants were genotyped for 89 RFLP markers. For each sample, testcross (TC) progenies of the corresponding F3 lines with two testers were evaluated in four environments. QTL for grain yield and other agronomically important traits were mapped in both samples. QTL effects were estimated from the same data as used for detection and mapping of QTL (calibration) and, based on QTL positions from calibration, from the second, independent sample (validation). For all traits and both testers we detected a total of 107 QTL with N = 344, and 39 QTL with N = 107, of which only 20 were in common. Consistency of QTL effects across testers was in agreement with corresponding genotypic correlations between the two TC series. Most QTL displayed no significant QTL × environment nor epistatic interactions. Estimates of the proportion of the phenotypic and genetic variance explained by QTL were considerably reduced when derived from the independent validation sample as opposed to estimates from the calibration sample. We conclude that, unless QTL effects are estimated from an independent sample, they can be inflated, resulting in an overly optimistic assessment of the efficiency of MAS.

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Lin, Fan, Elena Z. Lazarus, and Seung Y. Rhee. "QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants." G3: Genes|Genomes|Genetics 10, no. 7 (May 19, 2020): 2411–21. http://dx.doi.org/10.1534/g3.120.401122.

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Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

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Babu, B. M. Showkath, H. C. Lohithaswa, M. G. Mallikarjuna, and N. Mallikarjuna. "Mapping of QTL for resistance to fusarium stalk rot (FSR) in tropical maize (Zea mays L.)." Indian Journal of Genetics and Plant Breeding (The) 84, no. 01 (April 10, 2024): 81–91. http://dx.doi.org/10.31742/isgpb.84.1.7.

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Fusarium stalk rot disease (FSR) caused by Fusarium verticilloides is emerging as the major production constraint in maize across theworld.As a prelude to developing maize hybrids resistant to FSR, an attempt was made to identify QTL as the genetics of resistancewas found to be quantitative in nature.Two doubled haploid (DH) mapping populations induced from F2 of crosses VL1043 × CM212and VL121096 × CM202 were challenged with FSR during two seasons. The FSR response was influenced by significant DHs × seasoninteraction. The DH populations were genotyped employing 199 and 193 polymorphic SNP markers in the DHs induced from the crosses VL1043 × CM212 and VL121096 × CM202, respectively. Inclusive composite interval mapping was performed to detect significant QTL, QTL × QTL, QTL × season interaction effects. Two and one QTL were identified in the rainy season of 2019 and winter 2019-20, respectively. The QTL identified in the linkage group 10 (qFSR_10_1) was common across two seasons in DHs derived from the cross VL1043 × CM212. Similarly, two QTL were identified for FSR resistance in DHs derived from the cross VL121096 × CM202 and one QTL (qFSR_6_2) was common. The QTL qFSR_10_1 was common in both the crosses. The position and effect of the QTL varied with the seasons. Seven di-QTL interactions were detected for FSR resistance in both DH populations.

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Hu, Bo, Yuqiu Li, Hongyan Wu, Hong Zhai, Kun Xu, Yi Gao, Jinlong Zhu, Yuzhuo Li, and Zhengjun Xia. "Identification of quantitative trait loci underlying five major agronomic traits of soybean in three biparental populations by specific length amplified fragment sequencing (SLAF-seq)." PeerJ 9 (December 14, 2021): e12416. http://dx.doi.org/10.7717/peerj.12416.

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Flowering time, plant height, branch number, node numbers of main stem and pods per plant are important agronomic traits related to photoperiodic sensitivity, plant type and yield of soybean, which are controlled by multiple genes or quantitative trait loci (QTL). The main purpose of this study is to identify new QTL for five major agronomic traits, especially for flowering time. Three biparental populations were developed by crossing cultivars from northern and central China. Specific loci amplified fragment sequencing (SLAF-seq) was used to construct linkage map and QTL mapping was carried out. A total of 10 QTL for flowering time were identified in three populations, some of which were related to E1 and E2 genes or the other reported QTL listed in Soybase. In the Y159 population (Xudou No.9 × Kenfeng No.16), QTL for flowering time on chromosome 4, qFT4_1 and qFT4_2 were new. Compared with the QTL reported in Soybase, 1 QTL for plant height (PH), 3 QTL for branch number (BR), 5 QTL for node numbers of main stem, and 3 QTL for pods per plant were new QTL. Major E genes were frequently detected in different populations indicating that major the E loci had a great effect on flowering time and adaptation of soybean. Therefore, in order to further clone minor genes or QTL, it may be of great significance to carefully select the genotypes of known loci. These results may lay a foundation for fine mapping and clone of QTL/genes related to plant-type, provided a basis for high yield breeding of soybean.

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Brown, Garth R., Daniel L. Bassoni, Geoffrey P. Gill, Joseph R. Fontana, Nicholas C. Wheeler, Robert A. Megraw, Mark F. Davis, Mitchell M. Sewell, Gerald A. Tuskan, and David B. Neale. "Identification of Quantitative Trait Loci Influencing Wood Property Traits in Loblolly Pine (Pinus taedaL.). III. QTL Verification and Candidate Gene Mapping." Genetics 164, no. 4 (August 1, 2003): 1537–46. http://dx.doi.org/10.1093/genetics/164.4.1537.

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AbstractA long-term series of experiments to map QTL influencing wood property traits in loblolly pine has been completed. These experiments were designed to identify and subsequently verify QTL in multiple genetic backgrounds, environments, and growing seasons. Verification of QTL is necessary to substantiate a biological basis for observed marker-trait associations, to provide precise estimates of the magnitude of QTL effects, and to predict QTL expression at a given age or in a particular environment. Verification was based on the repeated detection of QTL among populations, as well as among multiple growing seasons for each population. Temporal stability of QTL was moderate, with approximately half being detected in multiple seasons. Fewer QTL were common to different populations, but the results are nonetheless encouraging for restricted applications of marker-assisted selection. QTL from larger populations accounted for less phenotypic variation than QTL detected in smaller populations, emphasizing the need for experiments employing much larger families. Additionally, 18 candidate genes related to lignin biosynthesis and cell wall structure were mapped genetically. Several candidate genes colocated with wood property QTL; however, these relationships must be verified in future experiments.

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Srinivasachary, N. Gosman, A. Steed, S. Faure, R. Bayles, P. Jennings, and P. Nicholson. "Mapping of QTL associated with Fusarium head blight in spring wheat RL4137." Czech Journal of Genetics and Plant Breeding 44, No. 4 (January 22, 2009): 147–59. http://dx.doi.org/10.17221/70/2008-cjgpb.

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Fusarium head blight (FHB) is a destructive disease of wheat worldwide. We aimed to map QTL for FHB resistance in RL4137, a FHB resistant line derived from Frontana using 90 recombinant inbred lines (RIL) from a cross between RL4137 and the moderately FHB resistant variety Timgalen. A total of seven putative FHB resistance QTL (1B, 2B, 3A, 6A, 6B, 7A and 7D) were identified and in all but one instance, the alleles from RL4137 had a positive effect on FHB resistance. The QTL, Qfhs.jic-2band Qfhs.jic-6b contributed by the alleles from RL4137 and Timgalen, respectively were detected in multiple trials. Our study also identified three QTL for plant height (2B, 4A and 5B), two QTL for weight of infected spikelets from infected ears (2B and 6A) and one QTL for “awns” (2B). The QTL mapped on 2B for PH, WIS and awns co-localized with Qfhs.jic-2b. The FHB QTL on 1B and 6B were not associated with PH QTL and that the minor PH QTL on 4A and 5B, did not co-localise with any other FHB resistance QTL.

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Knott, S. A., and C. S. Haley. "Aspects of maximum likelihood methods for the mapping of quantitative trait loci in line crosses." Genetical Research 60, no. 2 (October 1992): 139–51. http://dx.doi.org/10.1017/s0016672300030822.

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SummaryMaximum likelihood methods for the mapping of quantitative trait loci (QTL) have been investigated in an F2 population using simulated data. The use of adjacent (flanking) marker pairs gave improved power for the detection of QTL over the use of single markers when markers were widely spaced and the QTL effect large. The use of flanking marker loci also always gave moreaccurate and less biassed estimates for the effect and position of the QTL and made the method less sensitive to violations of assumptions, for example non-normality of the distribution. Testing the hypothesis of a linked QTL against that of no QTL is not biassed by the presence of unlinked QTL. This test is more robust and easier to obtain than the comparison of a linked with an unlinked QTL. Fixing the recombination fraction between the markers at an incorrect value in the analyses with flanking markers does not generally bias the test for QTL or estimates of their effect. The presence of multiple linked QTL bias both tests and estimates of effect with interval mapping, leading to inflated values when QTL are in association in the lines crossed and deflated values when they are in dispersion.

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Brensha, Williams, Stella K. Kantartzi, Khalid Meksem, Robert L. Grier IV, Abdelali Barakat, David A. Lightfoot, and My Abdelmajid Kassem. "Genetic Analysis of Root and Shoot Traits in the ‘Essex’ By ‘Forrest’ Recombinant Inbred Line (RIL) Population of Soybean [Glycine max (L.) Merr.]." Plant Genetics, Genomics, and Biotechnology 1, no. 1 (June 15, 2017): 1–9. http://dx.doi.org/10.5147/pggb.v1i1.146.

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Crop productivity is severely reduced by water deficit and drought in many plant species including soybean. Improved root and shoot traits can contribute to drought tolerance ability of the plant. This research was conducted to identify QTL that underlie several root and shoot traits in the ‘Essex’ by ‘Forrest’ (ExF RILs, n=94) recombinant inbred line (RIL) soybean population. Field collected samples were used for gathering phenotypic data of basal root thickness (BRT), lateral root number (LRN), maximum root length (MRL), root fresh weight (RFW), root dry weight (RDW), shoot fresh weight (SFW), shoot dry weight (SDW), and calculating RFW/SFW, and RDW/SDW ratios. All traits and ratios were compared against DNA markers using the composite interval mapping (CIM). A total of 12 QTL: 3 for MRL, 1 QTL for LRN, 1 QTL for BRT, 2 QTL for RFW, 2 QTL for RDW, 4 QTL for SFW, 3 QTL for SDW, and 3 QTL for SFW/SDW were identified and mapped on different linkage groups (LGs) A2, B2, C2, D1a, F, G, and N. The LOD scores of these QTL ranged from 2.5 to 6.0. No QTL were associated with RFW/RDW. The root and shoot trait QTL of this study may benefit breeding programs for producing cultivars tolerant to water deficit and high yield. Preliminary analyses of genes the QTL regions using GO annotation gave insight into genes that may underlie some of these QTLs.

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Jermstad, Kathleen D., Daniel L. Bassoni, Keith S. Jech, Gary A. Ritchie, Nicholas C. Wheeler, and David B. Neale. "Mapping of Quantitative Trait Loci Controlling Adaptive Traits in Coastal Douglas Fir. III. Quantitative Trait Loci-by-Environment Interactions." Genetics 165, no. 3 (November 1, 2003): 1489–506. http://dx.doi.org/10.1093/genetics/165.3.1489.

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Abstract Quantitative trait loci (QTL) were mapped in the woody perennial Douglas fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) for complex traits controlling the timing of growth initiation and growth cessation. QTL were estimated under controlled environmental conditions to identify QTL interactions with photoperiod, moisture stress, winter chilling, and spring temperatures. A three-generation mapping population of 460 cloned progeny was used for genetic mapping and phenotypic evaluations. An all-marker interval mapping method was used for scanning the genome for the presence of QTL and single-factor ANOVA was used for estimating QTL-by-environment interactions. A modest number of QTL were detected per trait, with individual QTL explaining up to 9.5% of the phenotypic variation. Two QTL-by-treatment interactions were found for growth initiation, whereas several QTL-by-treatment interactions were detected among growth cessation traits. This is the first report of QTL interactions with specific environmental signals in forest trees and will assist in the identification of candidate genes controlling these important adaptive traits in perennial plants.

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Uleberg, Eivind, and Theo HE Meuwissen. "Fine mapping of multiple QTL using combined linkage and linkage disequilibrium mapping – A comparison of single QTL and multi QTL methods." Genetics Selection Evolution 39, no. 3 (2007): 285. http://dx.doi.org/10.1186/1297-9686-39-3-285.

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Somorjai, Ildiko M. L., Roy G. Danzmann, and Moira M. Ferguson. "Distribution of Temperature Tolerance Quantitative Trait Loci in Arctic Charr (Salvelinus alpinus) and Inferred Homologies in Rainbow Trout (Oncorhynchus mykiss)." Genetics 165, no. 3 (November 1, 2003): 1443–56. http://dx.doi.org/10.1093/genetics/165.3.1443.

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Abstract We searched for quantitative trait loci (QTL) affecting upper temperature tolerance (UTT) in crosses between the Nauyuk Lake and Fraser River strains of Arctic charr (Salvelinus alpinus) using survival analysis. Two QTL were detected by using two microsatellite markers after correcting for experiment-wide error. A comparative mapping approach localized these two QTL to homologous linkage groups containing UTT QTL in rainbow trout (Oncorhynchus mykiss). Additional marginal associations were detected in several families in regions homologous to those with QTL in rainbow trout. Thus, the genes underlying UTT QTL may antedate the divergence of these two species, which occurred by ∼16 MYA. The data also indicate that one pair of homeologs (ancestrally duplicated chromosomal segments) have contained QTL in Arctic charr since the evolution of salmonids from a tetraploid ancestor 25-100 MYA. This study represents one of the first examples of comparative QTL mapping in an animal polyploid group and illustrates the fate of QTL after genome duplication and reorganization.

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Suto, Jun-ichi, and Misaki Kojima. "Identification of Quantitative Trait Loci That Determine Plasma Total-Cholesterol and Triglyceride Concentrations in DDD/Sgn and C57BL/6J Inbred Mice." Cholesterol 2017 (May 31, 2017): 1–10. http://dx.doi.org/10.1155/2017/3178204.

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DDD/Sgn mice have significantly higher plasma lipid concentrations than C57BL/6J mice. In the present study, we performed quantitative trait loci (QTL) mapping for plasma total-cholesterol (CHO) and triglyceride (TG) concentrations in reciprocal F2 male intercross populations between the two strains. By single-QTL scans, we identified four significant QTL on chromosomes (Chrs) 1, 5, 17, and 19 for CHO and two significant QTL on Chrs 1 and 12 for TG. By including cross direction as an interactive covariate, we identified separate significant QTL on Chr 17 for CHO but none for TG. When the large phenotypic effect of QTL on Chr 1 was controlled by composite interval mapping, we identified three additional significant QTL on Chrs 3, 4, and 9 for CHO but none for TG. QTL on Chr 19 was a novel QTL for CHO and the allelic effect of this QTL significantly differed between males and females. Whole-exome sequence analysis in DDD/Sgn mice suggested that Apoa2 and Acads were the plausible candidate genes underlying CHO QTL on Chrs 1 and 5, respectively. Thus, we identified a multifactorial basis for plasma lipid concentrations in male mice. These findings will provide insight into the genetic mechanisms of plasma lipid metabolism.

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Liu, L., Y. D. Zhang, H. Y. Li, Y. Q. Bi, L. J. Yu, X. M. Fan, J. Tan, D. P. Jeffers, and M. S. Kang. "QTL Mapping for Gray Leaf Spot Resistance in a Tropical Maize Population." Plant Disease 100, no. 2 (February 2016): 304–12. http://dx.doi.org/10.1094/pdis-08-14-0825-re.

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A tropical gray leaf spot (GLS)-resistant line, YML 32, was crossed to a temperate GLS-susceptible line, Ye 478, to produce an F2:3 population for the identification of quantitative trait loci (QTL) associated with resistance to GLS. The population was evaluated for GLS disease resistance and flowering time at two locations in Yunnan province. Seven QTL using GLS disease scores and six QTL using flowering time were identified on chromosomes 2, 3, 4, 5, and 8 in the YML 32 × Ye 478 maize population. All QTL, except one identified on chromosome 2 using flowering time, were overlapped with the QTL for GLS disease scores. The results indicated that QTL for flowering time in this population strongly corresponded to QTL for GLS resistance. Among the QTL, qRgls.yaas-8-1/qFt.yaas-8 with the largest genetic effect accounted for 17.9 to 18.1 and 11.0 to 21.42% of variations for GLS disease scores and flowering time, respectively, and these should be very useful for improving resistance to GLS, especially in subtropical maize breeding programs. The QTL effects for resistance to GLS were predominantly additive in nature, with a dominance effect having been found for two QTL on the basis of joint segregation genetic analysis and QTL analysis.

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Esvelt Klos, K., T. Gordon, P. Bregitzer, P. Hayes, X. M. Chen, I. A. del Blanco, S. Fisk, and J. M. Bonman. "Barley Stripe Rust Resistance QTL: Development and Validation of SNP Markers for Resistance to Puccinia striiformis f. sp. hordei." Phytopathology® 106, no. 11 (November 2016): 1344–51. http://dx.doi.org/10.1094/phyto-09-15-0225-r.

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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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Journal articles on the topic 'QTL'

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