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Journal articles on the topic 'QTL detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Freyer, G., and N. Vukasinovic. "Comparison of granddaughter design and general pedigree design analysis of QTL in dairy cattle: a simulation study." Czech Journal of Animal Science 50, No. 12 (2011): 545–52. http://dx.doi.org/10.17221/4260-cjas.

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Traditional methods for detection and mapping of quantitative trait loci (QTL) in dairy cattle populations are usually based on daughter design (DD) or granddaughter design (GDD). Although these designs are well established and usually successful in detecting QTL, they consider sire families independently of each other, thereby ignoring relationships among other animals in the population and consequently, reducing the power of QTL detection. In this study we compared a traditional GDD with a general pedigree design (GPD) and assessed the precision and power of both methods for detecting and lo

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Santos, Francisco Herbeth Costa dos, José Jaime Vasconcelos Cavalcanti, and Fanuel Pereira da Silva. "QTL detection for physicochemical characteristics of cashew apple." Crop Breeding and Applied Biotechnology 11, no. 1 (2011): 17–26. http://dx.doi.org/10.1590/s1984-70332011000100003.

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The identification of quantitative trait loci (QTL) and marker-assisted selection have aroused great interest in breeding programs aiming at fruit quality. The objective of this study was to detect QTL related to the quality of the cashew apple. The physicochemical characteristics oligomeric phenolics, total soluble solids, total titrable acidity and vitamin C contents were analyzed in the mapped cashew population. QTL were detected by QTL interval and multiple QTL mapping. The results showed high phenotypic variation in the segregating F1 generation for all traits. Eighteen QTL associated wit

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Cavalcanti, José Jaime Vasconcelos, Francisco Herbeth Costa dos Santos, Fanuel Pereira da Silva, and Cássia Renata Pinheiro. "QTL detection of yield-related traits of cashew." Crop Breeding and Applied Biotechnology 12, no. 1 (2012): 60–66. http://dx.doi.org/10.1590/s1984-70332012000100008.

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The identification of quantitative trait loci (QTL) and marker-assisted selection with a view to breeding programs have aroused great interest, including for cashew improvement. This study identified QTL for yield-related traits: nut weight, male and hermaphrodite flowers. The traits were evaluated in 71 F1 genotypes of the cross CCP 1001 x CP 96. The methods of interval mapping and multiple QTL mapping were applied to identify QTL. Eleven QTL were detected: three for nut weight, four for male flowers and four for hermaphrodite flowers. The QTL accounted for 3.79 to 12.98 % of the total phenot

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Blaker, K. M., J. X. Chaparro, and T. G. Beckman. "DETECTION OF DORMANCY QTL IN PEACH." Acta Horticulturae, no. 929 (March 2012): 103–6. http://dx.doi.org/10.17660/actahortic.2012.929.13.

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5

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

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6

Knott, Sara A., and Chris S. Haley. "Multitrait Least Squares for Quantitative Trait Loci Detection." Genetics 156, no. 2 (2000): 899–911. http://dx.doi.org/10.1093/genetics/156.2.899.

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Abstract A multiple-trait QTL mapping method using least squares is described. It is presented as an extension of a single-trait method for use with three-generation, outbred pedigrees. The multiple-trait framework allows formal testing of whether the same QTL affects more than one trait (i.e., a pleiotropic QTL) or whether more than one linked QTL are segregating. Several approaches to the testing procedure are presented and their suitability discussed. The performance of the method is investigated by simulation. As previously found, multitrait analyses increase the power to detect a pleiotro

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7

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 (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

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8

Nakamichi, Reiichiro, Yasuo Ukai, and Hirohisa Kishino. "Detection of Closely Linked Multiple Quantitative Trait Loci Using a Genetic Algorithm." Genetics 158, no. 1 (2001): 463–75. http://dx.doi.org/10.1093/genetics/158.1.463.

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AbstractThe existence of a quantitative trait locus (QTL) is usually tested using the likelihood of the quantitative trait on the basis of phenotypic character data plus the recombination fraction between QTL and flanking markers. When doing this, the likelihood is calculated for all possible locations on the linkage map. When multiple QTL are suspected close by, it is impractical to calculate the likelihood for all possible combinations of numbers and locations of QTL. Here, we propose a genetic algorithm (GA) for the heuristic solution of this problem. GA can globally search the optimum by i

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9

ROWE, SUZANNE J., PONG-WONG RICARDO, CHRISTOPHER S. HALEY, SARA A. KNOTT, and DIRK-JAN DE KONING. "Detecting dominant QTL with variance component analysis in simulated pedigrees." Genetics Research 90, no. 4 (2008): 363–74. http://dx.doi.org/10.1017/s0016672308009336.

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SummaryDominance is an important source of variation in complex traits. Here, we have carried out the first thorough investigation of quantitative trait locus (QTL) detection using variance component (VC) models extended to incorporate both additive and dominant QTL effects. Simulation results showed that the empirical distribution of the test statistic when testing for dominant QTL effects did not behave in accordance with existing theoretical expectations and varied with pedigree structure. Extensive simulations were carried out to assess accuracy of estimates, type 1 error and statistical p

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10

Jannink, Jean-Luc, and Ritsert Jansen. "Mapping Epistatic Quantitative Trait Loci With One-Dimensional Genome Searches." Genetics 157, no. 1 (2001): 445–54. http://dx.doi.org/10.1093/genetics/157.1.445.

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AbstractThe discovery of epistatically interacting QTL is hampered by the intractability and low power to detect QTL in multidimensional genome searches. We describe a new method that maps epistatic QTL by identifying loci of high QTL by genetic background interaction. This approach allows detection of QTL involved not only in pairwise but also higher-order interaction, and does so with one-dimensional genome searches. The approach requires large populations derived from multiple related inbred-line crosses as is more typically available for plants. Using maximum likelihood, the method contras

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11

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 (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

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12

Akond, Masum, Jiazheng Yuan, Shiming Liu, et al. "Detection of QTL underlying seed quality components in soybean [Glycine max (L.) Merr.]." Canadian Journal of Plant Science 98, no. 4 (2018): 881–88. http://dx.doi.org/10.1139/cjps-2017-0204.

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Improving seed composition and quality, including protein, oil, fatty acid, and amino acid contents, is an important goal of soybean farmers and breeders. The aim of this study was to map the quantitative trait loci (QTL) underlying the contents of protein, oil, fatty acids, and amino acids with 1510 single nucleotide polymorphism (SNP) markers using the ‘Hamilton’ × ‘Spencer’ recombinant inbred line population (H × S; n = 93). A total of 13 QTL for the traits studied have been mapped on 3 chromosomes (Chr.) of the soybean genome. Three major QTL have been mapped to a 7–13 cM region on Chr. 6.

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CHATZIPLIS, DIMITRIOS G., HENNING HAMANN, and CHRIS S. HALEY. "Selection and subsequent analysis of sib pair data for QTL detection." Genetical Research 78, no. 2 (2001): 177–86. http://dx.doi.org/10.1017/s0016672301005225.

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Haseman and Elston (1972) developed a robust regression method for the detection of linkage between a marker and a quantitative trait locus (QTL) using sib pair data. The principle underlying this method is that the difference in phenotypes between pairs of sibs becomes larger as they share a decreasing number of alleles at a particular QTL identical by descent (IBD) from their parents. In this case, phenotypically very different sibs will also on average share a proportion of alleles IBD at any marker linked to the QTL that is lower than the expected value of 0·5. Thus, the deviation of the p

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14

Zhao, C. H., H. Sun, C. Liu, et al. "Detection of quantitative trait loci for wheat (Triticum aestivum L.) heading and flowering date." Journal of Agricultural Science 157, no. 1 (2019): 20–30. http://dx.doi.org/10.1017/s0021859619000200.

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AbstractHeading date (HD) and flowering date (FD) are critical for yield potential and stability, so understanding their genetic foundation is of great significance in wheat breeding. Three related recombinant inbred line populations with a common female parent were developed to identify quantitative trait loci (QTL) for HD and FD in four environments. In total, 25 putative additive QTL and 20 pairwise epistatic effect QTL were detected in four environments. The additive QTL were distributed across 17 wheat chromosomes. Of these, QHd-1A, QHd-1D, QHd-2B, QHd-3B, QHd-4A, QHd-4B and QHd-6D were m

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15

Walling, G. A., A. D. Wilson, B. L. McTeir, P. M. Visscher, G. Simm, and S. C. Bishop. "QTL Detection in the UK Suffolk and Texel Sheep Sire Referencing Schemes." Proceedings of the British Society of Animal Science 2002 (2002): 22. http://dx.doi.org/10.1017/s1752756200006785.

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Genomic research and the detection of quantitative trait loci (QTL) provide tools to enhance genetic progress and improve understanding of the biology of commercially important traits. The large sire reference schemes in UK terminal sire sheep breeds provide a unique opportunity to investigate QTL segregation within commercial populations. This study aims to identify QTL for performance traits in commercial Suffolk and Texel sheep.

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16

Keightley, Peter D., and Grahame Bulfield. "Detection of quantitative trait loci from frequency changes of marker alleles under selection." Genetical Research 62, no. 3 (1993): 195–203. http://dx.doi.org/10.1017/s0016672300031906.

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SummaryA method was developed to estimate effects of quantitative trait loci (QTL) by maximum likelihood using information from changes of gene frequency at marker loci under selection, assuming an additive model of complete linkage between markers and QTL. The method was applied to data from 16 molecular and coat colour marker loci in mouse lines derived from the F2of two inbred strains which were divergently selected on 6-week weight for 21 generations. In 4 regions of the genome, marker allele frequencies were more extreme than could be explained by sampling, implying selection at nearby QT

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17

Alfonso, L., and C. S. Haley. "Power of different F2 schemes for QTL detection in livestock." Animal Science 66, no. 1 (1998): 1–8. http://dx.doi.org/10.1017/s135772980000878x.

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AbstractThe power for detection of quantitative trait loci (QTL) using marker information was compared in several schemes differing in the mating type and the number of parents involved. An experiment based on an F2 population of fixed size obtained by crossing two lines differing phenotypically for a single trait was simulated, assuming that QTLs could be fixed or segregating in the lines crossed. Different additive and dominant QTL effect values and allele frequencies were considered covering a range of different favourable situations for the detection of the QTL. Comparison was done by regr

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18

Yuan, Y., M. Zhang, H. Zheng, et al. "Detection of QTL for phosphorus efficiency and biomass traits at the seedling stage in wheat." Cereal Research Communications 48, no. 4 (2020): 517–24. http://dx.doi.org/10.1007/s42976-020-00067-4.

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AbstractPhosphorus (P) is one of the most vital nutrient elements in crop output and quality formation. In this study, four biomass, four P uptake efficiency (PupE), and three P-utilization efficiency (PutE) traits were investigated using a set of recombinant inbred lines (RILs) derived from a cross of “SN0431 × LM21”, under hydroponic culture trials at low P (LP) and normal P (NP) levels in two different seasons, respectively. A total of 85 QTL were identified on 18 chromosomes except for 1D, 2A, and 3D. Among them, 36 and 42 QTL were detected under LP and NP, respectively, and seven QTL were

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19

Bink, Marco C. A. M., and Johan A. M. Van Arendonk. "Detection of Quantitative Trait Loci in Outbred Populations With Incomplete Marker Data." Genetics 151, no. 1 (1999): 409–20. http://dx.doi.org/10.1093/genetics/151.1.409.

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Abstract Augmentation of marker genotypes for ungenotyped individuals is implemented in a Bayesian approach via the use of Markov chain Monte Carlo techniques. Marker data on relatives and phenotypes are combined to compute conditional posterior probabilities for marker genotypes of ungenotyped individuals. The presented procedure allows the analysis of complex pedigrees with ungenotyped individuals to detect segregating quantitative trait loci (QTL). Allelic effects at the QTL were assumed to follow a normal distribution with a covariance matrix based on known QTL position and identity by des

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20

Rebai, Ahmed, Bruno Goffinet, and Brigitte Mangin. "Comparing Power of Different Methods for QTL Detection." Biometrics 51, no. 1 (1995): 87. http://dx.doi.org/10.2307/2533317.

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21

Piyasatian, N., R. L. Fernando, and J. C. M. Dekkers. "QTL detection and marker-assisted composite line development." Livestock Science 143, no. 2-3 (2012): 233–41. http://dx.doi.org/10.1016/j.livsci.2011.09.021.

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22

Koning, D. J. de, P. M. Visscher, S. A. Knott, and C. S. Haley. "A strategy for QTL detection in half-sib populations." Animal Science 67, no. 2 (1998): 257–68. http://dx.doi.org/10.1017/s1357729800010018.

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AbstractA statistical analysis strategy for the detection of quantitative trait loci (QTLs) in half-sib populations is outlined. The initial exploratory analysis is a multiple regression of the trait score on a subset of markers to allow a rapid identification of possible chromosomal regions of interest. This is followed by multiple marker interval mapping with regression methods within and across families fitting one or two QTLs. Empirical thresholds are determined by experiment-wise permutation tests for different significance levels and empirical confidence intervals for the QTLs' positions

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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 (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 interva

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Lehmensiek, A., P. J. Eckermann, A. P. Verbyla, R. Appels, M. W. Sutherland, and G. E. Daggard. "Curation of wheat maps to improve map accuracy and QTL detection." Australian Journal of Agricultural Research 56, no. 12 (2005): 1347. http://dx.doi.org/10.1071/ar05126.

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Three Australian doubled haploid populations were used to illustrate the importance of map curation in order to improve the quality of linkage maps and quantative trait locus (QTL) detection. The maps were refined and improved by re-examining the order of markers, inspection of the genetic maps in relation to a consensus map, editing the marker data for double crossovers, and determining estimated recombination fractions for all pairs of markers. The re-ordering of markers and replacing genotypes at double crossovers with missing values resulted in an overall decrease in the length of the maps

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Schulz, Dietmar, Marcus Linde, and Thomas Debener. "Detection of Reproducible Major Effect QTL for Petal Traits in Garden Roses." Plants 10, no. 5 (2021): 897. http://dx.doi.org/10.3390/plants10050897.

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The detection of QTL by association genetics depends on the genetic architecture of the trait under study, the size and structure of the investigated population and the availability of phenotypic and marker data of sufficient quality and quantity. In roses, we previously demonstrated that major QTL could already be detected in small association panels. In this study, we analyzed petal number, petal size and fragrance in a small panel of 95 mostly tetraploid garden rose genotypes. After genotyping the panel with the 68 K Axiom WagRhSNP chip we detected major QTL for all three traits. Each trait

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Knott, S. A., and C. S. Haley. "Maximum likelihood mapping of quantitative trait loci using full-sib families." Genetics 132, no. 4 (1992): 1211–22. http://dx.doi.org/10.1093/genetics/132.4.1211.

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Abstract A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers

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Wittenburg, Dörte, Volker Guiard, Friedrich Liese, and Norbert Reinsch. "Linear and generalized linear models for the detection of QTL effects on within-subject variability." Genetical Research 89, no. 4 (2007): 245–57. http://dx.doi.org/10.1017/s0016672307008968.

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SummaryQuantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is a

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Cervantes-Flores, Jim C., G. Craig Yencho, Kenneth V. Pecota, Bryon Sosinski, and Robert O. M. Mwanga. "Detection of Quantitative Trait Loci and Inheritance of Root-knot Nematode Resistance in Sweetpotato." Journal of the American Society for Horticultural Science 133, no. 6 (2008): 844–51. http://dx.doi.org/10.21273/jashs.133.6.844.

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Resistance to root-knot nematodes [Meloidogyne incognita (Kofoid & White) Chitwood] in sweetpotato [Ipomoea batatas (L.) Lam.] was studied in a mapping population consisting of 240 progeny derived from a cross between ‘Beauregard’, the predominant cultivar in the United States, and ‘Tanzania’, an African landrace. Quantitative trait loci (QTL) analyses to locate markers associated with resistance to root-knot nematodes (RKN) were performed using genetic maps based on parental segregation in ‘Beauregard’ and ‘Tanzania’ consisting of 726 and 947 single-dose amplified fragment length polymorp

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Uimari, Pekka, Georg Thaller, and Ina Hoeschele. "The Use of Multiple Markers in a Bayesian Method for Mapping Quantitative Trait Loci." Genetics 143, no. 4 (1996): 1831–42. http://dx.doi.org/10.1093/genetics/143.4.1831.

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Abstract Information on multiple linked genetic markers was used in a Bayesian method for the statistical mapping of quantitative trait loci (QTL). Bayesian parameter estimation and hypothesis testing were implemented via Markov chain Monte Carlo algorithms. Variables sampled were the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage or nonlinkage, and the parameters. The parameter vector included allele frequencies at the markers and the QTL, map distances of the markers and the QTL, QTL substitution effect, and polygenic and residual variances. The c

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Rebaï, A., B. Goffinet, and B. Mangin. "Approximate thresholds of interval mapping tests for QTL detection." Genetics 138, no. 1 (1994): 235–40. http://dx.doi.org/10.1093/genetics/138.1.235.

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Abstract A general method is proposed for calculating approximate thresholds of interval mapping tests for quantitative trait loci (QTL) detection. Simulation results show that this method, when applied to backcross and F2 populations, gives good approximations and is useful for any situation. Programs which calculate these thresholds for backcross, recombinant inbreds and F2 for any given level and any chromosome with any given distribution of codominant markers were written in Fortran 77 and are available under request. The approach presented here could be used to obtain, after suitable calc

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Esmailizadeh, A. K. "Detection of chromosomal segments underlying scrotal circumference in ram lambs and age at onset of puberty in ewe lambs." Animal Production Science 55, no. 8 (2015): 1018. http://dx.doi.org/10.1071/an14008.

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Scrotal circumference (SC) is highly correlated with total sperm production and quality of the produced semen. In ewe lambs, puberty is an important reproductive trait and extreme delay in reaching puberty will have a negative effect on breeding efficiency. To identify genomic regions (QTL) underlying variation in SC and age at onset of puberty in ewe lambs (AP), a whole genome scan was performed with 169 microsatellites covering the ovine autosomes. Progeny (360 animals) from six half-sib families in a population of Kermani sheep, an indigenous fat tailed sheep breed in south-east of Iran, we

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Zhao, L., K. Zhang, B. Liu, and J. Tian. "Detection of quantitative trait loci for paste viscosity characteristics based on the doubled haploid progeny from a cross between two Chinese wheat varieties." Canadian Journal of Plant Science 89, no. 5 (2009): 837–44. http://dx.doi.org/10.4141/cjps08201.

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In order to understand the genetic basis of starch pasting viscosity characteristics (the RVA profile, which is produced by the Rapid Visco Analyser) of wheat grain samples, a doubled haploid (DH) population (Huapei 3 × Yumai 57; Yumai 57 is superior to Huapei 3 for RVA profile parameters) and a linkage map consisting of 324 marker loci were used to search QTL. This program was based on mixed linear models and allowed simultaneous mapping of additive effect QTL, epistatic QTL, and QTL × environment interactions (QE). Mapping analysis produced a total of 35 QTL for 6 RVA profile parameters with

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de Koning, Dirk J., Luc L. G. Janss, Annemieke P. Rattink, et al. "Detection of Quantitative Trait Loci for Backfat Thickness and Intramuscular Fat Content in Pigs (Sus scrofa)." Genetics 152, no. 4 (1999): 1679–90. http://dx.doi.org/10.1093/genetics/152.4.1679.

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Abstract In an experimental cross between Meishan and Dutch Large White and Landrace lines, 619 F2 animals and their parents were typed for molecular markers covering the entire porcine genome. Associations were studied between these markers and two fatness traits: intramuscular fat content and backfat thickness. Association analyses were performed using interval mapping by regression under two genetic models: (1) an outbred line-cross model where the founder lines were assumed to be fixed for different QTL alleles; and (2) a half-sib model where a unique allele substitution effect was fitted

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Khanal, R., A. Navabi, and L. Lukens. "Linkage map construction and quantitative trait loci (QTL) mapping using intermated vs. selfed recombinant inbred maize line (Zea mays L.)." Canadian Journal of Plant Science 95, no. 6 (2015): 1133–44. http://dx.doi.org/10.4141/cjps-2015-091.

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Khanal, R., Navabi, A. and Lukens, L. 2015. Linkage map construction and quantitative trait loci (QTL) mapping using intermated vs. selfed recombinant inbred maize line (Zea mays L.). Can. J. Plant Sci. 95: 1133–1144. Intermating of individuals in an F2 population increases genetic recombination between markers, which is useful for linkage map construction and quantitative trait loci (QTL) mapping. The objectives of this study were to compare the linkage maps and precision of QTL detection in an intermated recombinant inbred line (IRIL) population and a selfed recombinant inbred line (RIL) pop

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Crepieux, Sébastien, Claude Lebreton, Bertrand Servin, and Gilles Charmet. "Quantitative Trait Loci (QTL) Detection in Multicross Inbred Designs." Genetics 168, no. 3 (2004): 1737–49. http://dx.doi.org/10.1534/genetics.104.028993.

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Yagil, Yoram, and Chana Yagil. "Problems with linkage analysis and QTL detection in hypertension." Journal of Hypertension 21, no. 2 (2003): 247–49. http://dx.doi.org/10.1097/00004872-200302000-00008.

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Lee, Hakkyo, Jack C. M. Dekkers, M. Soller, Massoud Malek, Rohan L. Fernando, and Max F. Rothschild. "Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits." Genetics 161, no. 2 (2002): 905–14. http://dx.doi.org/10.1093/genetics/161.2.905.

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Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR

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Piepho, Hans-Peter, and Hugh G. Gauch. "Marker Pair Selection for Mapping Quantitative Trait Loci." Genetics 157, no. 1 (2001): 433–44. http://dx.doi.org/10.1093/genetics/157.1.433.

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AbstractMapping of quantitative trait loci (QTL) for backcross and F2 populations may be set up as a multiple linear regression problem, where marker types are the regressor variables. It has been shown previously that flanking markers absorb all information on isolated QTL. Therefore, selection of pairs of markers flanking QTL is useful as a direct approach to QTL detection. Alternatively, selected pairs of flanking markers can be used as cofactors in composite interval mapping (CIM). Overfitting is a serious problem, especially if the number of regressor variables is large. We suggest a proc

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Brown, Garth R., Daniel L. Bassoni, Geoffrey P. Gill, et al. "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 (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 amon

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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 (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

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Mott, Richard, and Jonathan Flint. "Simultaneous Detection and Fine Mapping of Quantitative Trait Loci in Mice Using Heterogeneous Stocks." Genetics 160, no. 4 (2002): 1609–18. http://dx.doi.org/10.1093/genetics/160.4.1609.

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Abstract We describe a method to simultaneously detect and fine map quantitative trait loci (QTL) that is especially suited to the mapping of modifier loci in mouse mutant models. The method exploits the high level of historical recombination present in a heterogeneous stock (HS), an outbred population of mice derived from known founder strains. The experimental design is an F2 cross between the HS and a genetically distinct line, such as one carrying a knockout or transgene. QTL detection is performed by a standard genome scan with ~100 markers and fine mapping by typing the same animals usin

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Mayo, O. "Interaction and quantitative trait loci." Australian Journal of Experimental Agriculture 44, no. 11 (2004): 1135. http://dx.doi.org/10.1071/ea03240.

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Parallel searches for quantitative trait loci (QTL) for growth-related traits in different populations frequently detect sets of QTL that hardly overlap. Thus, many QTL potentially exist. Tools for the detection of QTL that interact are available and are currently being tested. Initial results suggest that epistasis is widespread. Modelling of the first recognised interaction, dominance, continues to be developed. Multigenic interaction appears to be a necessary part of any explanation. This paper covers an attempt to link some of these studies and to draw inferences about useful approaches to

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Rodolphe, F., and M. Lefort. "A multi-marker model for detecting chromosomal segments displaying QTL activity." Genetics 134, no. 4 (1993): 1277–88. http://dx.doi.org/10.1093/genetics/134.4.1277.

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Abstract A statistical method is presented for detecting quantitative trait loci (QTLs), based on the linear model. Unlike methods able to detect a few well separated QTLs and to estimate their effects and positions, this method considers the genome as a whole and enables the detection of chromosomal segments involved in the differences between two homozygous lines, and their backcross, doubled haploid, or F2 progenies, for a quantitative trait. Genetic markers must be codominant, but missing markers are accepted, provided they are missing independently from the experiment. Asymptotic properti

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Hwang, J. T. Gene, and Dan Nettleton. "Investigating the Probability of Sign Inconsistency in the Regression Coefficients of Markers Flanking Quantitative Trait Loci." Genetics 160, no. 4 (2002): 1697–705. http://dx.doi.org/10.1093/genetics/160.4.1697.

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AbstractEstimates of the locations and effects of quantitative trait loci (QTL) can be obtained by regressing phenotype on marker genotype. Under certain basic conditions, the signs of regression coefficients flanking QTL must be the same. There is no guarantee, however, that the signs of the regression coefficient estimates will be the same. We use sign inconsistency to describe the situation in which there is disagreement between the signs of the estimated regression coefficients flanking QTL. The presence of sign inconsistency can undermine the effectiveness of QTL mapping strategies that p

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Korol, Abraham B., Yefim I. Ronin, Alexander M. Itskovich, Junhua Peng, and Eviatar Nevo. "Enhanced Efficiency of Quantitative Trait Loci Mapping Analysis Based on Multivariate Complexes of Quantitative Traits." Genetics 157, no. 4 (2001): 1789–803. http://dx.doi.org/10.1093/genetics/157.4.1789.

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AbstractAn approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this ap

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Zhang, Wengang, Xue Gao, Xinping Shi, et al. "PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy." Animals 8, no. 12 (2018): 239. http://dx.doi.org/10.3390/ani8120239.

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Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based

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Freyer, G., C. Kühn, and R. Weikard. "Comparison of different statistical-genetic approaches of QTL detection by Evaluating results from a real dairy cattle data set." Archives Animal Breeding 46, no. 5 (2003): 413–23. http://dx.doi.org/10.5194/aab-46-413-2003.

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Abstract. Recent reports on estimating QTL positions and effects on milk production traits show several chromosomal regions, for example on BTA6, being putative QTL regions, for milk yield and traits of ingredients. The statistical methods and genetic models on which these results were based on, show different advantages and limits. Thus, it is sometimes difficult to compare and evaluate such results. Confirmation studies are inevitable, before drawing conclusions towards finemapping analyses or practical use of such results. We compared three published approaches, realizing up to five differe

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Lou, R. Nicolas, Nina O. Therkildsen, and Philipp W. Messer. "The Effects of Quantitative Trait Architecture on Detection Power in Short-Term Artificial Selection Experiments." G3 Genes|Genomes|Genetics 10, no. 9 (2020): 3213–27. http://dx.doi.org/10.1534/g3.120.401287.

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Abstract Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTL) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a more real

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Lee, G. J., A. L. Archibald, G. B. Garth, et al. "Detection of quantitative trait loci for locomotion and osteochondrosis-related traits in Large White ✕ Meishan pigs." Animal Science 76, no. 2 (2003): 155–65. http://dx.doi.org/10.1017/s1357729800053418.

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AbstractData from the F2 generation of a Large White (LW) ✕ Meishan (MS) crossbred population were analysed to detect quantitative trait loci (QTL) for leg and gait scores, osteochondrosis and physis scores. Legs, feet and gait score were assessed in 308 F2 animals at 85 ( + 5) kg and osteochondrosis and physis scores were recorded for the right foreleg after slaughter. A genome scan was performed using 111 genetic markers chosen to span the genome that were genotyped on the F2 animals and their F1 parents and purebred grandparents. A QTL on chromosome 1 affecting gait score was significant at

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Varona, L., L. Gómez-Raya, W. M. Rauw, A. Clop, C. Ovilo, and J. L. Noguera. "Derivation of a Bayes Factor to Distinguish Between Linked or Pleiotropic Quantitative Trait Loci." Genetics 166, no. 2 (2004): 1025–35. http://dx.doi.org/10.1093/genetics/166.2.1025.

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Abstract A simple procedure to calculate the Bayes factor between linked and pleiotropic QTL models is presented. The Bayes factor is calculated from the marginal prior and posterior densities of the locations of the QTL under a linkage and a pleiotropy model. The procedure is computed with a Gibbs sampler, and it can be easily applied to any model including the location of the QTL as a variable. The procedure was compared with a multivariate least-squares method. The proposed procedure showed better results in terms of power of detection of linkage when low information is available. As inform

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