• Journal of Crop Science and Biotechnology
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• v.11 no.1
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• pp.7-12
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• 2008

Quantitative trait loci, a genetic concept for explaining the inheritance of non-Mendelian traits in 1940s, have been realized as particular fragments of chromosome even unique genes in most crops in 21st century. However, only very a small portion of QTL has been screened out by geneticists comparing to a great number of genes underneath the quantitative traits. These identified QTL even have been seldom used into breeding program because crop breeders may not find the QTL in their breeding populations in their field station. Several key points will be proposed to meet the challenges of QTL analysis today: a fine mapping population and the related reference genetic map, QTL evaluation in multiple environments, recognizing real QTL with small genetic effect, map integration.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.11
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• pp.1505-1510
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• 2001

A method for mapping quantitative trait loci (QTL) was introduced incorporating the information of mixed progeny from complex pedigrees. The method consisted of two steps based on single marker analysis. The first step was to examine the marker-trait association with a mixed model considering common environmental effect and reversed QTL-marker linkage phase. The second step was to estimate QTL effects by a weighted least square analysis. A simulation study indicated that the method incorporating mixed progeny from multiple generations improved the accuracy of QTL detection. The influence of within-genotype variance and recombination rate on QTL analysis was further examined. Detecting a QTL with a large within-genotype variance was more difficult than with a small within-genotype variance. Most of the significant marker-QTL association was detectable when the recombination rate was less than 15%.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.12
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• pp.1651-1659
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• 2011

This study was conducted to detect quantitative trait loci (QTL) for thirteen growth and meat quality traits in pigs by combing QTL experimental populations. Two F2 reference populations that were sired by Korea native pig (KNP) and dammed by Landrace (LN) or Yorkshire (YK) were generated to construct linkage maps using 123 genetic markers (mostly microsatellites) and to perform QTL analysis on porcine chromosomes (SSCs) 1, 2, 3, 6, 7, 8, 9, 11, 13, 14, and 15. A set of line-cross models was applied to detect QTL, and a series of lack-of-fit tests between the models was used to characterize inheritance mode of QTL. A total of 23, 11 and 19 QTL were detected at 5% chromosome-wise level for the data sets of KNP${\times}$LN, KNP${\times}$YK cross and joint sets of the two cross populations, respectively. With the joint data, two Mendelian expressed QTL for live weight and cooking loss were detected on SSC3 and SSC15 at 1% chromosome-wise level, respectively. Another Mendelian expressed QTL was detected for CIE a on SSC7 at 5% genome-wise level. Our results suggest that QTL analysis by combining data from two QTL populations increase power for QTL detection, which could provide more accurate genetic information in subsequent marker-assisted selection.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1826-1835
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• 2019

Objective: Testicular growth and development are strongly influenced by androgen. Although both testis weight and plasma testosterone level are inherited traits, the interrelationship between them is not fully established. Males of DDD/Sgn (DDD) mice are known to have extremely heavy testes and very high plasma testosterone level among inbred mouse strains. We dissected the genetic basis of testis weight and analyzed the potential influence of plasma testosterone level in DDD mice. Methods: Quantitative trait loci (QTL) mapping of testis weight was performed with or without considering the influence of plasma testosterone level in reciprocal $F_2$ intercross populations between DDD and C57BL/6J (B6) mice, thereby assessing the influence of testosterone on the effect of testis weight QTL. Candidate genes for testis weight QTL were investigated by next-generation sequencing analysis. Results: Four significant QTL were identified on chromosomes 1, 8, 14, and 17. The DDDderived allele was associated with increased testis weight. The $F_2$ mice were then divided into two groups according to the plasma testosterone level ($F_2$ mice with relatively "low" and "high" testosterone levels), and QTL scans were again performed. Although QTL on chromosome 1 was shared in both $F_2$ mice, QTL on chromosomes 8 and 17 were identified specifically in $F_2$ mice with relatively high testosterone levels. By whole-exome sequencing analysis, we identified one DDD-specific missense mutation Pro29Ser in alpha tubulin acetyltransferase 1 (Atat1). Conclusion: Most of the testis weight QTL expressed stronger phenotypic effect when they were placed on circumstance with high testosterone level. High testosterone influenced the QTL by enhancing the effect of DDD-derived allele and diminishing the effects of B6-derived allele. Since Pro29Ser was not identified in other inbred mouse strains, and since Pro29 in Atat1 has been strongly conserved among mammalian species, Atat1 is a plausible candidate for testis weight QTL on chromosome 17.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.4
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• pp.473-480
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• 2003

In order to control the genetic background noise in QTL mapping, cofactor markers were incorporated in single marker analysis (SMACO) and interval mapping (CIM). A simulation was performed to see how effective the cofactors were by the number of QTL, the number and the type of markers, and the marker spacing. The results of QTL mapping for the simulated data showed that the use of cofactors was slightly effective when detecting a single QTL. On the other hand, a considerable improvement was observed when dealing with more than one QTL. Genetic background noise was efficiently absorbed with linked markers rather than unlinked markers. Furthermore, the efficiency was different in QTL mapping depending on the type of linked markers. Well-chosen markers in both SMACO and CIM made the range of linkage position for a significant QTL narrow and the estimates of QTL effects accurate. Generally, 3 to 5 cofactors offered accurate results. Over-fitting was a problem with many regressor variables when the heritability was small. Various marker spacing from 4 to 20 cM did not change greatly the detection of multiple QTLs, but they were less efficient when the marker spacing exceeded 30 cM. Likelihood ratio increased with a large heritability, and the threshold heritability for QTL detection was between 0.30 and 0.05.

• Journal of Crop Science and Biotechnology
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• v.10 no.4
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• pp.243-248
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• 2007

Rice is a facultative short-day plant that flowers in response to reduced day lengths. This study was conducted to identify quantitative trait loci(QTL) for the early heading date(EHD) using H143 line showing extreme EHD compared to other regular cultivars in rice. The japonica H143 was crossed with a japonica cultivar 'Dongjinbyeo' as well as a tongil cultivar 'Milyang23' to measure the inheritance mode of EHD and identify major QTL conferring EHD, respectively. Pooling test revealed that segregation distortion occurred on chromosome 7 and subsequent linkage map was constructed using 10 SSR markers. QTL analysis using Q-gene 3.06 revealed that the EHD trait in H143 was largely controlled by two major QTL, EH7-1 and EH7-2, accounting for more than 40% of genetic variation that were closely related to the previously reported QTL, Hd4 and Hd2, respectively. This result suggests that these two QTL markers may be a useful source for the control of heading date in rice breeding programs.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.6
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• pp.651-655
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• 1995

A simulation study on detection of linkage between genetic markers and QTL in backcross design was conducted. The effects of various sample sizes and the degree of QTL dominance on detention of linkage were examined by using a simple regression analysis. The results indicated that as sample size increased, the standard error of the estimated slope became smaller. When the dominance effect of QTL was complete, the estimated slope tended to be negative but was statistically not significant at all with type I error of greater than 50%. With complete linkage between genetic Marker and QTL, the estimated intercept value was smallest but the estimated slope was largest as expected. In most cases with various degree of dominance and sample sizes, when the actual recombination rate became larger, greater values were obtained for the slope except in the case of complete dominance of QTL.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.6
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• pp.772-779
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• 2005

The objectives of this study were to examine problems with using F$_1$ data by simulation, association of marker loci and QTL from crosses of inbred parental lines and to enumerate the preliminary characterization of genetic superiority within inbred parental lines. In this study, the association between markers for QTL used as covariates and estimates of variance components due to effects of lines was investigated through computer simulation. The effects of size of population to develop inbred lines and initial frequencies and magnitudes of effects of QTL were also considered. Results show that estimates of variance components due to line effects are influenced by including marker information as covariates in the model for analysis. Estimates of line variance were increased by adding marker information into the analysis, because negative covariances between effects associated with the markers and the remaining effects associated with other loci existed. However, the fit of the model as indicated by the log likelihood improved by adding more markers as covariates into the analysis. Marker assisted selection will be beneficial when markers explain unexplained genetic difference during selection procedure. Markers can be used to identify QTLs affecting traits, and to select for favorable QTL alleles. To efficiently use genetic markers, location of markers at the genome must be identified. The estimates of variance due to effects of with and without marker information used as covariates in the analysis were investigated. The estimates of line variances were always increased when markers were included as covariates for the model because a negative covariance were existed.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.533-541
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• 2005

We identify the correct chromosome and locate the corresponding markers close to the QTL in the linkage analysis of a quantitative trait by using the SSVS method. We consider several markers linked to the QTL, as well as to each oyher and thus the i.b.d. values at these loci generate collinear predictors to be evaluated when using the SSVS approach. The results on considering only closely linked markers to two QTL simultaneously showed clear evidence in favor of the closest marker to the QTL considered over other markers. The results of the analysis of collinear markers with SSVS showeed high concordance to those obtained using traditional multiple regression. We conclude based on this simulation study that the SSVS is quite useful to identify linkage with multiple linked markers simultaneously for a complex quantitative trait.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1507-1510
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• 2012

Quantitative trait loci (QTL) associated with fat deposition traits in pigs are important gene positions in a chromosome that influence meat quality of pork. For QTL study, a three generation resource population was constructed from a cross between Korean native boars and Landrace sows. A total of 240 F2 animals from intercross of F1 were produced. 80 microsatellite markers covering chromosomes 1 to 10 were selected to genotype the resource population. Intervals between adjacent markers were approximately 19 cM. Linkage analysis was performed using CRIMAP software version 2.4 with a FIXED option to obtain the map distances. For QTL analysis, the public web-based software, QTL express (http://www.qtl.cap.ed.ac.uk) was used. Two significant and two suggestive QTL were identified on SSC 6, 7, and 8 as affecting body fat and IMF traits. For QTL affecting IMF, the most significant association was detected between marker sw71 and sw1881 on SSC 6, and a suggestive QTL was identified between sw268 and sw205 on SSC8. These QTL accounted for 26.58% and 12.31% of the phenotypic variance, respectively. A significant QTL affecting IMF was detected at position 105 cM between markers sw71 and sw1881 on SSC 6.