Genetic mapping of quantitative trait loci affecting bodyweight on chromosome 1 in a commercial strain of Japanese quail

2012 ◽  
Vol 52 (1) ◽  
pp. 64 ◽  
Author(s):  
A. K. Esmailizadeh ◽  
A. Baghizadeh ◽  
M. Ahmadizadeh
Keyword(s):  
Quantitative Trait Loci ◽  
Japanese Quail ◽  
Quantitative Trait ◽  
Average Daily Gain ◽  
Interval Mapping ◽  
Chromosome 1 ◽  
Commercial Strain ◽  
Kleiber Ratio ◽  
Trait Loci ◽  
Sib Families

This study was conducted to identify quantitative trait loci (QTL) affecting growth on chromosome 1 in quail. Liveweight data were recorded on 300 progeny from three half-sib families created from a commercial strain of Japanese quail. Three half-sib families were genotyped for nine microsatellite loci on chromosome 1 and QTL analysis was conducted applying the least-squares interval mapping approach. Significant QTL affecting bodyweight at 3, 4, 5 and 6 weeks of age, average daily gain, and Kleiber ratio, an indirect criterion for feed efficiency, were mapped at 0–23 cM on chromosome 1. The detected QTL segregated in two of the three half-sib families and the size of the QTL effect ranged from 0.6 to 1.1 in unit of the trait standard deviation. This is the first report of liveweight QTL segregating in a commercial strain of Japanese quail.

Quantitative trait loci underlying hatching weight and growth traits in an F2 intercross between two strains of Japanese quail

2012 ◽  
Vol 52 (11) ◽  
pp. 1012 ◽  
Author(s):  
S. S. Sohrabi ◽  
A. K. Esmailizadeh ◽  
A. Baghizadeh ◽  
H. Moradian ◽  
M. R. Mohammadabadi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Japanese Quail ◽  
Quantitative Trait ◽  
Growth Traits ◽  
Interval Mapping ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Cross Model ◽  
Trait Loci ◽  
Qtl Effects

A three-generation resource population was developed using two distinct Japanese quail strains, wild and white, to map quantitative trait loci underlying hatching weight and growth traits. Eight pairs of white and wild birds were crossed reciprocally and 34 F1 birds were produced. The F1 birds were intercrossed to generate 422 F2 offspring. All of the animals from three generations (472 birds) were genotyped for eight microsatellite markers on chromosome 1. Liveweight data from hatch to 5 weeks of age were collected on the F2 birds. Quantitative trait loci (QTL) analysis was conducted applying the line-cross model and the least-squares interval mapping approach. The results indicated QTL affecting hatching weight and several growth related traits on chromosome 1. The F2 phenotypic variance explained by the detected additive QTL effects ranged from 1.0 to 3.7 for different traits. Modelling both additive and dominance QTL effects revealed additional QTL with significant dominance mode of action affecting pre-slaughter weight. However, there was no evidence for imprinting (parent-of-origin) effects. The variance due to the reciprocal cross effect ranged between 3.0 and 19.1% for weight at 1 week of age and hatching weight, respectively.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

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.

scholarly journals Identification of Quantitative Trait Loci Influencing Traits Related to Energy Balance in Selection and Inbred Lines of Mice

Genetics ◽  
1999 ◽  
Vol 152 (2) ◽  
pp. 699-711 ◽  
Author(s):  
D E Moody ◽  
D Pomp ◽  
M K Nielsen ◽  
L D Van Vleck
Keyword(s):  
Quantitative Trait Loci ◽  
Energy Balance ◽  
Heat Loss ◽  
Quantitative Trait ◽  
Subcutaneous Fat ◽  
High Heat ◽  
Chromosome 1 ◽  
Direct Calorimetry ◽  
Resource Population ◽  
Trait Loci

Abstract Energy balance is a complex trait with relevance to the study of human obesity and maintenance energy requirements of livestock. The objective of this study was to identify, using unique mouse models, quantitative trait loci (QTL) influencing traits that contribute to variation in energy balance. Two F2 resource populations were created from lines of mice differing in heat loss measured by direct calorimetry as an indicator of energy expenditure. The HB F2 resource population originated from a cross between a noninbred line selected for high heat loss and an inbred line with low heat loss. Evidence for significant QTL influencing heat loss was found on chromosomes 1, 2, 3, and 7. Significant QTL influencing body weight and percentage gonadal fat, brown fat, liver, and heart were also identified. The LH F2 resource population originated from noninbred lines of mice that had undergone divergent selection for heat loss. Chromosomes 1 and 3 were evaluated. The QTL for heat loss identified on chromosome 1 in the HB population was confirmed in the LH population, although the effect was smaller. The presence of a QTL influencing 6-wk weight was also confirmed. Suggestive evidence for additional QTL influencing heat loss, percentage subcutaneous fat, and percentage heart was found for chromosome 1.

scholarly journals Time-Related Mapping of Quantitative Trait Loci Underlying Tiller Number in Rice

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 297-303 ◽  
Author(s):  
Wei-Ren Wu ◽  
Wei-Ming Li ◽  
Ding-Zhong Tang ◽  
Hao-Ran Lu ◽  
A J Worland
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Tiller Number ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Recombinant Inbred Population ◽  
Trait Loci ◽  
Final Observation

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

scholarly journals PSV-13 Genetic parameters and quantitative trait loci for heat stress related traits in sheep

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 163-164
Author(s):  
Devin R Jacobs ◽  
Claudia E Silvera-Rojas ◽  
Jennifer M Bormann ◽  
Terry A Gipson ◽  
Arthur L Goetsch ◽  
...  
Keyword(s):  
Heat Stress ◽  
Quantitative Trait Loci ◽  
Rectal Temperature ◽  
Quantitative Trait ◽  
Fixed Effects ◽  
Univariate Analysis ◽  
Average Daily Gain ◽  
The United States ◽  
Nucleotide Polymorphisms ◽  
Trait Loci

Abstract Greater selection emphasis has been placed on efficiency than on fitness in livestock populations over the last several decades. Heat stress is a concern in production systems due to the negative effects on production, reproduction, and immunity. The objective of the study was to estimate variance components and identify quantitative trait loci (QTL) for heat stress related traits in sheep. A total of 125 Dorper, Katahdin, and St. Croix ewes originating from four regions of the United States were selected for the experiment. Animals were separated into four trials due to facility limitations. Data were collected for each trial over four consecutive two-week periods in an environmentally controlled facility with targeted heat load index (HLI) for daytime/nighttime of 70/70, 85/77, 90/77, and 95/81. Body weight was collected three times per week and rectal temperature was collected weekly. Black globe temperature and humidity were measured every 15 minutes. Animals were genotyped using the Illumina OvineSNP50 BeadChip. After quality control, 49,396 effective single nucleotide polymorphisms were included in the univariate analysis performed with the BLUPF90 suite of programs. Fixed effects in the models included region of origin, breed, trial, and age as a covariate. Traits analyzed included rectal temperature at 95 HLI (RT95), feed intake at 95 HLI (FI95), and average daily gain for the period for HLI between 90 and 95 (ADG). Heritabilities for RT95, FI95, and ADG were 0.35, 0.10, and 0.10, respectively. Largest effect QTL were identified on chromosomes 23, 9, and 6 for RT95, chromosomes 9, 2, and 20 for FI95, and chromosomes 6, 1, and 5 for ADG. Many of the regions identified have also been associated with weight and carcass traits in other studies, but few had obvious connections to the heat stress related response. In conclusion, results suggest selection could improve heat tolerance in sheep.

Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Inbred Line Cross Data

Genetics ◽  
1998 ◽  
Vol 148 (3) ◽  
pp. 1373-1388
Author(s):  
Mikko J Sillanpää ◽  
Elja Arjas
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Backcross Progeny ◽  
Random Variable ◽  
Interval Mapping ◽  
Mapping Method ◽  
Structure Mapping ◽  
Standard Interval ◽  
Trait Loci

Abstract A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL-intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the location wise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/~mjs.

scholarly journals A Rank-Based Nonparametric Method for Mapping Quantitative Trait Loci in Outbred Half-Sib Pedigrees: Application to Milk Production in a Granddaughter Design

Genetics ◽  
1998 ◽  
Vol 149 (3) ◽  
pp. 1547-1555 ◽  
Author(s):  
Wouter Coppieters ◽  
Alexandre Kvasz ◽  
Frédéric Farnir ◽  
Juan-Jose Arranz ◽  
Bernard Grisart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Real Data ◽  
Interval Mapping ◽  
Mapping Method ◽  
Residual Variance ◽  
Data Set ◽  
Wilcoxon Rank Sum Test ◽  
Holstein Friesian ◽  
Trait Loci

Abstract We describe the development of a multipoint nonparametric quantitative trait loci mapping method based on the Wilcoxon rank-sum test applicable to outbred half-sib pedigrees. The method has been evaluated on a simulated dataset and its efficiency compared with interval mapping by using regression. It was shown that the rank-based approach is slightly inferior to regression when the residual variance is homoscedastic normal; however, in three out of four other scenarios envisaged, i.e., residual variance heteroscedastic normal, homoscedastic skewed, and homoscedastic positively kurtosed, the latter outperforms the former one. Both methods were applied to a real data set analyzing the effect of bovine chromosome 6 on milk yield and composition by using a 125-cM map comprising 15 microsatellites and a granddaughter design counting 1158 Holstein-Friesian sires.

scholarly journals Mapping of Quantitative Trait Loci Controlling Adaptive Traits in Coastal Douglas Fir. III. Quantitative Trait Loci-by-Environment Interactions

Genetics ◽  
2003 ◽  
Vol 165 (3) ◽  
pp. 1489-1506
Author(s):  
Kathleen D Jermstad ◽  
Daniel L Bassoni ◽  
Keith S Jech ◽  
Gary A Ritchie ◽  
Nicholas C Wheeler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Moisture Stress ◽  
Interval Mapping ◽  
Douglas Fir ◽  
Adaptive Traits ◽  
Growth Cessation ◽  
Trait Loci ◽  
Growth Initiation

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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