qtl

One of the important issues in genomic selection is estimating the effect of markers. In recent years, various methods have been proposed to estimate the effect of markers, each of which estimates genomic breeding values with different accuracy. One of the methods used in genomic evaluation is the ridge regression (rrBLUP), which has been used in different studies to predict genomic breeding values. Recently, by applying changes in the parameters of the rrBLUP method, a variety of this method so called Ridge Regression method 6 (RR-m6) has been proposed to solve regression problems. However, so far, this method has not been used in the genomic evaluation of threshold traits with additive and dominant genetic architecture, and its performance in this field is not known. Therefore, in this research, the prediction performance of this method was compared with other common methods of genomic evaluation.

A genome consisting of 10 chromosomes, each containing 1000 bi-allelic single nucleotide polymorphism (SNP) was simulated at heritability level of 0.5. All quantitative trait loci (QTLs) were given additive genetic effects, and their effects were modeled by gamma distribution. Two scenarios of the number of QTL were considered as 1 and 10% of the total number of SNPs (100 and 1000 QTL, respectively). Also, in different scenarios, 0.0, 50 and 100% of QTLs were given dominance effect. Genomic breeding values were estimated using RR-m6, rrBLUP, GBLUP, BayesA, regression tree (RT), Random Forest (RF) and boosting, and the indicators of LR method, including prediction accuracy, bias and dispersion or inflation of genomic breeding values (inflation) were used to analyze the breeding values estimated by different methods. In addition, the computing time and the amount of memory needed to execute the codes of each method on the CPU were calculated.

The results showed that the use of a purely additive model when the genetic dominance effects contributed to the phenotypic variation of the trait lead to decrease in accuracy and increase in the bias and dispersion of the genomic breeding values, amount of which depend on the number of QTLs that have dominance effect. Compared to other methods, the RR-m6 showed a very good performance, so that in all the studied scenarios, estimated genomic breeding values by RR-m6 had the highest accuracy and the lowest bias and dispersion, although in most cases the differences were not significant with BayesA. In terms of computational speed, the RR-m6 method was the fastest, and compared to other methods, it required less memory to perform the analysis.

The results showed that since the RR-m6 method predicted genomic breeding values with a high accuracy, and in the mean time it was very efficient in terms of the computing time and the required memory, it can be used for genomic evaluation of threshold traits.

The optimization of the reference population in genomic evaluation plays an important role in livestock breeding, because of its potential impact on the accuracy of estimating the marker effects and genomic breeding values. In the present study, seven different train set selection methods including selection of all, selection of the highest and lowest performances, random selection, selection of individuals with the most and least marker and QTL similarity were evaluated. In genome wide association study selection of all as train set detected common SNPs which make a high variation on the trait. However selective train set was just reported rare SNPs with a major effect on the trait. In genomic selection simultaneous use of high-density markers and selective train set in comparison with low-density and selection of all as train set reduced accuracy, but did not change the ranking of animals. There was also an interaction between train set selection method and generation (P≤0.0134) as well as the linkage disequilibrium (P≤ 2e-16). In general, selection of all animals as a train set resulted in higher accuracy compared to six selective train set methods. There were no differences between the methods of selecting train set in populations with a low effective size (r2 = 0.255, Ne =100), but in populations with a high effective size (r2 = 0.086, Ne =400) methods, with different accuracy predicted genomic breeding values. The highest and lowest accuracy were respectively belonged to most QTL and marker similarity methods.

The aim of this study was to compare different methods of Bayesian (parameteric) approaches for predicting genomic breeding values of traits with different genetic architecture in different distribution of gene effects, number of quantitative traits loci, heritability and the number of reference population using simulated data. A genome contained 3 chromosomes, with the length of 100 cM and 1000 evenly spaced single nucleotide polymorphisms (SNP) on each chromosome was simulated. For hypotheses simulation attributes, different distribution of gene effects (uniform, normal and gamma), 3 levels of QTL (50, 200 and 400), 2 levels of heritability (0.16 and 0.5) and 2 levels of the reference population (1000 and 2000) was considered. In order to predict breeding values of individuals in the population of reference and verification, 5 Bayesian methods including Bayes Ridge regression (BRR), A, LASSO (L), Cπ and B were used. As the distance between reference population and selection candidates increased, due to disruption of linkage phase, the accuracy of genomic breeding values in all methods decreased. As heritability increased from 0.16 to 0.5, all methods showed an increase of about 0.2 in accuracy of genomic breeding values. When the number of reference population had increased from 1000 to 2000, all methods showed increased accuracy by nearly 0.18. When the distribution of gene effects was gamma, the Bayes A method showed a clear preference along with both levels of heritability compared with other methods. When the distribution of gene effects was uniform, all methods showed similar accuracy. For high heritability traits, the accuracy of prediction reduced as the number of QTL increased from 50 to 200. Conversly, in low heritability traits, there was no visible influence in accuracy of genomic breeding values.

Introduction During years genetic improvement of economically important traits, which are amongst polygenic traits, has been based on the estimation of breeding values i.e. the total heritable effects of genes, based on pedigree and phenotypic records. This approach had limitations such as being time consuming and demanding massive phenotypic information. Nowadays, high throughput genomic technologies are available that provide genotypes of dense markers across genome towards estimating breeding values more accurately. Accurate estimation of allelic and genotypic effects of markers in linkage with QTLs needs a lot of phenotypic observations which is not always available in practice. Therefore, the amount of error of estimated QTL effect could be high. Further, the distribution of the effects of genes controlling traits might be non-non-normal. In case of overlooking these facts, the predicted genetic progress can be erroneous. The objective of this study was to find the influence of the accuracy of QTL effect estimation, considering the dominance deviation, on marker assisted selection response.
Materials and Methods A base population of 1000 unrelated, non-inbred individuals was simulated according to a trait with heritability of 0.1 and 0.3. The trait was affected by residual polygenic and QTL with additive effect associated with 0.0, 0.1 and 0.2 standard errors and complete or incomplete dominance effect. The genotypic effects of the three QTL genotypes were a, d and –a, respectively for dominant homozygotes, heterozygotes and recessive homozygotes. The QTL had two alleles and the dominance deviation was considered either equal to or half of the genotypic effect a. The population was in Hardy-Weinberg equilibrium. The polygenic variance was calculated as the difference between total additive genetic variance and QTL variance. Residual variance was equal to the difference between phenotypic variance and total additive genetic variance. Two selection was employed; one with polygenes and marker information, and the other one with polygenic variance without marker information. The difference between mean of selected group and the population mean was considered as response to selection. The selection response calculated by truncation selection based on the performance of top 20% with and without using QTL information over 500 repetitions.
Results and Discussion The results showed higher response for marker assisted selection compared to conventional selection without marker information, but it also showed the presence of dominance effect for QTL effect associated with estimation errors leads to decrease in marker assisted selection response. The superiority of genetic progress with marker assisted selection is proportional to the QTL variance contributing to the total genetic variance. Increasing standard error of QTL effect to 10 and 20 percent, led to lower genetic response to selection. When the contribution of QTL variance in total genetic variance is higher, with high levels of standard error of QTL effect, the response to selection was even lower than response to selection without marker information. Complete dominance further decreased the genetic response compared to incomplete dominance. This is because the genetic variance is more influenced by the dominance variance in case of complete dominance.
Conclusion This study showed that QTL information may be used in practical selection programs when estimated parameters are of high accuracy to be used in practical selection programs. Estimating QTL effects with error causes that selection response would be even lower than polygenic selection if the associated error rate is high. Estimated effects of genes controlling quantitative traits should have less error rate in order to be used in breeding programs.

Advances in high-throughput assays for genotyping single nucleotide polymorphisms (SNP) led to using these dense markers for predicting genomic breeding values, called genomic selection, and have been revolutionized both animal and crop breeding programs. The accuracy of genomic predictions is determined by the size of the reference population, extent of relationships between selection candidates and the reference population, linkage disequilibrium among markers and QTLs, the method used to estimate marker effects, and genetic architecture of the trait. Most of stuides on this topic focus on continuous traits, but some of economical important traits in livestock are threshold. Therefore, a stochastic simulation was used to compare 125 different scenarios for a threshold trait based on five 10, 50, 150, 500 and 2000 underlying QTL numbers, five normal, uniform, t, gamma and Laplace distributions for QTL effects and five BayesA, BayesB, BayesC, BayesL and BayesR methods for estimation of marker effects.
In order to compare the different methods, R software were used to simulate datasets. Simulation started with a base population of 100 animals, including 50 male and 50 female, which randomly mated for subsequent 50 generations. Generations were discrete and number of animals for each generation fixed at 100 animals. Thereafter, by randomly mating of animals in generation 50, reference population generation with 1000 animals were obtained and a threshold trait was simulated for each of them. Finally, Next validation population generation was produced by randomly mating of animals from the previous generation. Simulated genome for each animal consisted of 10 chromosomes with equal 1 Morgan lengths, each having 1000 evenly spaced SNPs. In each scenario QTLs were randomly distributed on genome and their substitution effects were drawn from one of five normal, uniform, t, gamma and Laplace distributions. Marker effects were estimated in reference population using five different Bayesian methods that differ with respect to assumptions regarding distribution of marker effects, including: Bayes A, Bayes B, Bayes C, Bayes L and Bayes R. These estimated markers effects were used for genomic breeding value predictions of animals in validation population which did not have any phenotype. The accuracy of the different methods was calculated as correlation between true and estimated genomic breeding values.
Results of this study showed that Bayes A, Bayes B and Bayes C predictions was affected by QTL numbers and distributions, while Bayes L and Bayes R had almost same accuracies for all scenarios. Scenario with gamma distribution for QTL effects and number of 10 QTLs had highest accuracy for Bayes A, Bayes B and Bayes C methods. Bayes C method was the best method when number of QTL was low. Increases in number of QTL from 10 to 150 were decreased accuracy of Bayes A, Bayes B and Bayes C methods and after that accuracy of different methods was constant and same. When distributions of QTL effects was uniform lower accuracy achieved rather than other distributions for same QTL number, instead distributions with unequal QTL variance like gamma increased accuracy of Bayes A, Bayes B, and Bayes C methods. These results were because of different extent of shrinkage of estimates of effects for different methods. Methods like Bayes B induced differential shrinkage of estimates relative to methods like Bayes R and Bayes L lead to higher accuracy for scenarios with low QTL number and unequal QTL variance.
Conclusion in a nutshell, different studied methods had diverse results for various QTL number and distribution, and its difficult to suggest a method that best in all situations. However, it is better to use methods like Bayes B when the number of QTLs was low and each QTL had not equal contribution to the trait of interest.

The purpose of breeding is to rear animals which are biologically and economically important. Among the domestic species, the poultry, due to the large number of offspring per parent and short generation interval, enable researchers to run breeding programs even on small farms. In this regard, quail because of certain characteristics: short generation interval, large numbers of eggs, disease resistance, and low cost farming is of great interest to biologists and commercial growers. Japanese quail (Coturnix coturnix japonica) has been grown for the production of meat and eggs in Asia and for its meat in Europe and America during the recent years. Despite the great diversity of traits in Japanese quail, only a limited number of genes and linkage groups related to the bird have been identified. Japanese quail belongs to the order Galliformes and the Phasianidae family. The considerable phylogenetic similarity between quail and chickens in the number of chromosomes and the genome size makes the quail qualified enough as a model for studies on poultry. Furthermore, the similarities between 9 chromosomes and chromosome Z in quail and chicken have been confirmed and following the divergence, only small numbers of chromosomes of the two species have changed. In recent years, the attempts at mapping of quail genome based on the segregation and identification of microsatellite markers has begun and 50 microsatellite markers were identified in quail. This study examined F2 population of Japanese quail to find the loci for carcass traits on chromosome 5 using microsatellite markers. The population included crosses of two strains of Japanese quail (white and wild strains) and the traits examined included weights of hot and cold carcasses, internal organs and carcass parts.
Polymerase chain reaction (PCR) amplifications of each marker for 472 birds were carried out on a Thermal Cycler (Eppendorf, UK). Afterward, the population was genotyped using AlphaEaseFC 4.0 software for each marker. Parental (P0), F1 and F2 individuals were genotyped with 3 microsatellite markers. A genetic model, line-cross, was applied for QTL interval mapping analyses using the regression method in the GridQTL software. Data analysis was performed with least squares regression interval mapping method. The line-cross analysis employed the models including joint effects of additive, dominance and imprinting. The QTL by sex interaction was assessed to determine whether the effect did differ between the two sexes. The additive QTL effect by hatch interaction was also analyzed.
Significant QTLs were identified for carcass efficiency, breast percentage and breast weight, liver percentage and liver weight, back weight and back percentage, spleen weight and spleen percentage, gizzard and head on chromosome 5. The QTL for breast weight and percentage were identified at 19 cM (in position marker GUJ0100) on chromosome 5. The QTL for head weight and percentage and weight of back were identified at 12 cM (in position marker GUJ049) on chromosome 5. Other QTLs were mapped at 10 cM for carcass efficiency and spleen weight, at 0 cM for Gizzard weight, at 17 cM for head weight, at 15 cM for weight of liver, at 16 cM for liver percentage and at 11 cM for spleen percentage. The proportion of the F2 phenotypic variation explained by the significant additive, dominance and imprinting QTL effects ranged from 2.22 to 11.11%. In the current study, QTL affecting different traits were mapped to similar chromosomal regions. These evidence are indicative of genetic correlation among traits and correlated response to selection. If these traits are really controlled by the same pleiotropic QTL or by closely linked QTL, therefore they are in linkage disequilibrium (LD). However, higher resolution analysis is required to distinguish LD from pleiotropic.
The present study identified informative QTL regions that may form a useful resource as part of our advance on developing DNA tests for carcass quality and internal organs in Japanese quail. However, it should be noted that to identify candidate genes and informative markers in linkage disequilibrium with QTL affecting carcass and internal organs traits, association studies using SNP markers may be needed for the significant QTL regions detected in this study. The results showed that the identified positions for some carcass and internal organs traits on chromosome 5 were similar to the characteristics of quantitative traits (including pleiotropic and epistasis effects) and can be effective in phenotypic differences of trait. Such results are indicative of genetic correlation among traits and correlated response to selection.

The aim of this study was to map body weight QTLs with some microsatellite markers on chromosome 2 of Markhoz goats. For this purpose a total of 255 offspring including 129 male and 126 female from 8 half-sib sire families were genotyped for 6 microsatellite markers on chromosomes 2. Quantitative traits were included weights at birth, 4 months, 6 months, 9 months and 12 months of age. Significant QTL effect was tested using the least squared regression approach using GridQTL (V3.3.0) software. In this study, one putative QTL were detected for weaning weight at 153 cM on chromosome 2 (P

This research carried out to identify effective quantitative trait loci in growth rate and Keliber ratio on chromosome 5 of Japanese quail by microsatellite markers. A three-generation resource population was developed by using two distinct Japanese quail strains, wild and white, to map quantitative trait loci underlying growth rate and Kleiber ratio. Eight pairs of white (S) and wild (W) 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 three microsatellite markers on chromosome 5. Phenotypic data including average daily again and Kleiber ratio were collected on F2 birds. QTL analysis was performed using least squares regression interval mapping method fitting five various statistical models. Significant additive QTL were identified for average daily gain from hatch to one week of age and Kleiber ratio between 3 to 4 weeks of age. Dominance and imprinting QTL effects were not significant. The F2 phenotypic variance explained by the detected additive QTL effects ranged from 1.1 to 3.6 for different traits Significant QTLs identified by doing various statically types for average daily gain and keliber ratio with various effect.

Ascites syndrome is a metabolic disorder in fast growing broilers. Due to high mortality, the poultry industry carries economic detriment. Ascites related traits have high heritability which shows impressibility of genetic factors. Therefore, genetic selection against ascites susceptibility is possible. The aim of the current study was to evaluate consequences of alternative selection indices to reduce ascites susceptibility using deterministic simulation. The traits investigated were partial pressure of oxygen in venous blood, bicarbonate, oxygen saturation in venous blood and ratio of right to total ventricular weight. In addition, the consequences of having information on the underlying genes (MAS) were identified. Alternative selection strategies were compared based on the selection response for body weight and ascites susceptibility, accuracy of selection as well as the rate of inbreeding. The results showed that by including blood gas parameters as indicator traits to ascites, reduced genetic response for BW5 based on indices number 1, 2, 3, 4, 5, 6 were 20, 23, 69, 16.8, 18.8 and 15.7 percent of genetic response for BW5 based on single trait selection, respectively. The results of schemes comprising QTL information of ascites susceptibility showed that ascites susceptibility can be reduced appropriately with considering different values of genetic variance due to QTL. This reduction was lower by including QTL information and was about 2.8 percent for BW5 in which QTL explains 50% of the genetic variance of ascites syndrome. The profitability of MAS was depended on economic analysis of animal breeding programs.

In comparison to traditional breeding methods, using genomic information causes considerable increase in response for selecting young animals which have no phenotypic records. Genomic selection refers to selection based on estimated breeding value (BV). The purpose of this study was to survey the effects of heritability of trait and the numbers of QTL on accuracy of estimating genomic BVs. For this reason, genome design and required population was done by computer simulation. The results of the effects of trait heritability on the accuracy of genomic BV estimation showed that the accuracy of genomic BV estimation of reference group via three strategies of trait heritability 0.05, 0.1 and 0.25 were 0.790, 0.827 and 0.876, respectively. Therefore, increasing the level of trait heritability increased the accuracy of estimated BV. In strategies for numbers of 4, 10, 20 and 40 simulated QTLs (in each chromosome) through the genome, the calculated accuracy in reference group was 0.813, 0.833, 0.826 and 0.798, respectively. Totally, with increasing the simulated QTL, the range of alterations of the accuracy was not significant. Also, the alterations of accuracy along the consecutive generations from 51 to 57 generation had a decreasing trend in all four strategies.

The litter size is a threshold trait that is controlled by many genes. Due to the low heritability (0.10), of litter size, classical breeding methods lead to slow genetic progress. Hence it is essential to use new technology to improve this trait. Length of chromosome 1 in goat is approximately 186 cM. Based on previously published data in addition to a high genetic homology between goat and cattle choromosome 1, a region between 16 to 169 cM of chromosome 1 was selected for QTL detection of litter size in the Markhoz goat breed. Sample population included 8 paternal half-sib families. The numbers of half-sib offspring per buck ranged from 30 to 40. All individuals were genotyped by six microsatellites specific to chromosome 1. QTL analyses were performedusing multiple regression method under a half-sib model. Our study indicated that a QTL in position 165 cM on chromosome 1 affect litter size rate in Markhoz goats. Its location was near to CSSM19 marker and was able to explaining 2.20 percent of the genetic variance of litter size. The QTL detected in this research could be related with POU1F1 gene. Hence this gene could be a candidate for the associated QTL on goat chromosomes 1.

Body Weight (BW) is a complex and important economic trait that may benefit from the implementation of Marker Assisted Selection (MAS). The objective followed in this study was to identify QTLs associated with BW at different ages as distinct traits based on an experimental half-sib cross of Japanese quails divergently selected for BW at 4 weeks of age. The body weights at hatch, and as well at one, two, three and four weeks of age were assessed in the F2 population. A total 220 F2 individuals were genotyped for 6 informative microsatellite markers located on chromosomes 1, 2 and 9. The interval mapping was conducted to identify putative QTLs. A QTL for BW at hatch while another at 1 wk of age were identified as significant on chromosome 2 (P<0.01) which may include 1.25% vs. 1.63% of the phenotypic variance, respectively. A QTL for BW at 3 wk of age can be susggested on chromosome 1. As for BW at 4 wk of age, one QTL was recognized as significant on chromosome 1 (P<0.01) while two suggested on chromosomes 2 and 9. A significant QTL explains 1.13% of the phenotypic variance. In conclusion, the results introduced some regions as harboring significant QTLs that affect BW traits. This study provided a potential for further work(s) on candidate gene(s), and Marker Assisted Selection for Body Weight traits.

In order to investigate the effect of genetic markers in genetic improvement of domestic fowl, seven breeding programs were simulated. In first breeding program called base program, phenotypic information of traits was only used. In this program, aggregative genotype equation consisted of body weight at 8 weeks of age, egg weight and egg number and selection index consisted of traits in aggregative genotype together with two other tarits including body weight at 12 weeks of age and age at sexual maturity. In other programs called marker assisted programs, QTL information that described 5, 10, 20, 30, 40 and 50% of genetic variation of egg number were also included in selection index. Based on obtained results, in program that QTL described 5% of genetic variance, economic response was improved by 3.37% compared to base program. Economic response in program that QTL described 50% of genetic variance was also improved by 14.28% compared to base program. Rate of Inbreeding was decreased from 0.69% in base program to 0.50% in program that QTL described 50% of genetic variance in each generation. The result of present study showed that QTL information, even with 5% description of genetic variance, can effectively improve economic traits in native fowl.

The present study was attempted to produce MAS software. This software uses a combination of quantitative and molecular information for the prediction of breeding values. Some genes that control a trait have major effects comparing with others. These genes are called major genes which are located on QTLs (quantitative trait loci). Our understanding on inheritance pattern of QTL can be helpful in selection programs via marker assisted selection (MAS). This provides an opportunity to increase the genetic progress of domestic animals by MAS. The marker assisted selection performance are based on an index, consists of phenotypic and molecular markers data. The MAS software based on mixed model methods (MMM) was established with the C# programming language. This software designed based on animal models with matrix form for predicting breeding values. To identify polymorphism in promoter region of DGAT1 gene, DNA was extracted from blood samples, the PCR process performed on DNA samples. Allelic and genotypic frequencies of animals were characterized. MASS software was used for the prediction of breeding values for 80 male lambs of Afshari sheep in research-education farm of Zanjan University. To compare the results, analyses were also performed using SAS software. Comparing the predicted breeding values obtained from MAS software and SAS indicated the effect of molecular marker in selection based on the combination of molecular and phenotypic information.

In order to investigate the effect of genetic markers in genetic improvement of domestic fowl، seven breeding programs were simulated. In first breeding program called base program، phenotypic information of traits was only used. In this program، aggregative genotype equation consisted of body weight at 8 weeks of age، egg weight and egg number and selection index consisted of traits in aggregative genotype together with two other tarits including body weight at 12 weeks of age and age at sexual maturity. In other programs called marker assisted programs، QTL information that described 5، 10، 20، 30، 40 and 50% of genetic variation of egg number were also included in selection index. Based on obtained results، in program that QTL described 5% of genetic variance، economic response was improved by 3. 37% compared to base program. Economic response in program that QTL described 50% of genetic variance was also improved by 14. 28% compared to base program. Rate of inbreeding was decreased from 0. 69% in base program to 0. 50% in program that QTL described 50% of genetic variance in each generation. The results of present study showed that QTL information، even with 5% description of genetic variance، effectively can be used in genetic improvement of domestic fowl.