جستجوی مقالات مرتبط با کلیدواژه "marker" در نشریات گروه "علوم دام"

The genome-wide association study (GWAS) is a powerful approach to identify genomic regions associated with fertility traits that explain a significant portion of the genetic variance associated with these traits and identify the relevant causal mutations. Evaluating the correlation between each genotyped marker and trait is an essential strategy for GWAS studies that examine the effects of all markers by considering their possible interactions, environmental factors, and even mutual effects between markers. Recently, machine learning methods have been introduced to genomic topics, and the basis of these methods is different from the common methods of genomic evaluation. The machine learning method is used to estimate the genomic breeding values of the candidate animals by considering the training data (genotypic and phenotypic information of the reference population). One of the key advantages of this method is the ability to analyze large data. Machine learning is a branch of artificial intelligence whose goal is to achieve machines that can extract knowledge (learning) from the environment. A variety of machine learning methods (random forest, boosting, and deep learning) are used to model genetic variance and environmental factors, study gene networks, GWAS, study epistasis effects, and genomic evaluation. Random forest is one of the machine learning methods that has been successfully used in various fields of science. This research was conducted to identify markers and genes related to reproductive traits such as calving interval (CI), days open (DO), daughter pregnancy rate (DPR), and age at first calving (AFC) in Iranian Holstein dairy cattle. These traits have already been investigated with the ssGBLUP method and using a smaller sample size. However, in the present research, by using more genotyped animals, a random forest algorithm was used to identify markers and genes related to reproductive traits.
The records used in this research were provided by the National Animal Breeding Center and Promotion of Animal Products of Iran and included AFC, DO, CI, and DPR related to the genotyped bulls' daughters. In this research, the pedigree information of 2774183 animals was used. The genotypic information of the markers related to 2419 Holstein bulls was used. Genomic data quality control was performed using factors such as the number of genotyped SNPs per animal (ACR), the number of genotyped animals per SNP (CR), Hardy-Weinberg equilibrium (HWE), and minor allele Frequency (MAF). When filtering genomic data, the markers whose MAF was less than 5% were removed, and then the samples whose genotyped frequency was less than 90% were identified and removed. Then, the markers whose genotyping rate was less than 95% in the samples were identified and removed. Finally, the SNPs that deviated from the HWE test (P<10-6) were excluded from the analysis as a measure of genotyping error. To control the quality of genomic data, PLINK 1.9 software was used. Then Ranfog software was used in the Linux environment to perform analysis through random forest algorithm.
By using the random forest algorithm, a total of 21 important SNPs were observed, then important fertility trait candidate genes were identified by the gene ontology method, and 62 genes were within 250 Kb of these SNPs. The most significant SNP was observed for AFC. The main SNP for AFC is in ARS-BFGL-NGS-22647 BTA3, for CI is in ARS-BFGL-NGS-114194 (BTA11), for DO is in BTA-74076 -no-rs (BTA5), and for DPR is in ARS-BFGL-NGS-32553 (BTA26). The researchers, who studied fertility traits in Nellore cattle using machine learning methods, identified MPZL1 and CD247 genes on chromosome number 3 and this gene was associated with age at first calving. Many pathways of cell biology affect the performance of reproductive traits. Research has reported the relationship between the CD247 gene and pathways of biology, including cell development and function. Research has shown that the IFFO2 gene plays an important role in the molecular structure of cells, as well as in the mechanism of blastocyst formation, embryos, and the length of gestation in cattle. In a study conducted on the mouse population on the structure of the flagellum and the sperm maturation process, the role of the ALDH4A1 gene in the sperm maturation process was reported. The association of the RPS6KC1 gene with pregnancy rate and antral follicle number in Nellore heifers has been reported. The KAT2B gene is a transcriptional activator that plays an essential role in regulating the correction of histone acetylation and plays an important role in improving carcass quality, muscle and fat development, and metabolism in native Chinese cattle. In addition, they play a key role in regulating biological processes and are related to cell growth, metabolism and immune system function.
  According to the objectives of this research, new information on markers and candidate genes related to reproductive traits in Iranian Holstein dairy cattle was reported. The markers and candidate genes identified in the present research can be used in genomic selection to improve the reproductive traits of Holstein dairy cattle.

Reproduction is one of the most important economic traits in sheep with within and between-breeds variation. Reproductive traits normally show low to medium heritability and therefore response to conventional selection methods is not satisfactory for these traits. Considering the genetic information of the genetic variants underlying reproduction variability could efficiently increase the selection efficacy. Genome-wide association studies (GWAS) have been used to identify associations between genotypes and phenotypes as well as candidate genes for economically important traits. Statistical power in GWAS is mostly affected by sample size. The low sample size is hence a main obstacle in GWAS. Combining multiple data sets of different studies for joint (mega) GWAS provides an opportunity to increase the sample size required for GWAS. This study was performed to identify genomic regions affecting litter size in sheep using the mega-analysis of GWAS.
Multi-population joint GWAS was performed using genotypic and phenotypic data of six sheep breeds retrieved from the database. Quality control was performed using the Plink software. The markers or individuals were removed from the further study based on the following criteria: (1) unknown chromosomal or physical location, call rate <0.95, missing genotype frequency >0.05, minor allele frequency (MAF) < 0.05, and a P-value for Hardy–Weinberg equilibrium test less than 10-6. Before analysis, imputation of missing genotypes for combined data set was implemented by LD-kNNi method. Mega-analysis was performed using a mixed linear model in TASSEL software considering kinship and population structure (top five components of principal component analysis (PCA)) as confounding effects. The quantile–quantile (Q–Q) plot was visualized by plotting the distribution of obtained vs. expected log10 (P-value). The association results along the genome and the significant SNPs were visualized in the Manhattan plot. To account for multiple test problem and identify the genome-wide and chromosome-wide significance level, Bonferroni test was used based on the number of independent SNPs obtained from pairwise linkage disequilibrium analysis. After GWAS analysis, the 300 bp sequence upstream and downstream of the significant SNP was explored to identify the adjacent candidate genes using Ovis aries_v4.0 (UCSC).
In the present study, we implemented a mega GWAS using six different sheep breed data to identify the genetic mechanisms responsible for litter size in sheep. After quality control, 305 animals and 351,615 SNP markers with a mean MAF of 0.33 were kept for further analysis. The results of the mega-analysis identified one marker on chromosome 21 at the genome-wide level and 10 markers at the chromosome-wide level on chromosomes 1, 2, 3, 14, 17, and 22. The quantile–quantile plot that features the total distribution of the observed P-values (−log10 P-values) of quality passed SNPs vs. the expected values, showed the effective control for confounding effects. Many of the significant SNPs identified in this study were located in or very adjacent to known genes (OPCML, GULP1, RBP4, MMP2, and LPCAT2) that have been already reported for their contribution to fertility and pregnancy success. It has been reported that OPCML is more consistently expressed in cells lining the uterus, oviduct, and rete ovarii. OPCML has been reported as a tumor suppressor protein that is frequently inactivated in epithelial ovarian cancer. It has been reported that the RBP4 gene is expressed during the period of fast elongation of the pig blastocyst which is a crucial period for the survival of the embryos. Also, it has been suggested that RBP4 has the main contribution in uterine and conceptus physiology during the establishment of pregnancy and therefore can be considered as a candidate gene for litter size. MMP2 has an essential function during ovulation and pregnancy through extracellular matrix (ECM) components degradation and therefore enabling cell migration and angiogenesis.
Comparison of the results of this study with previous reports showed that the mega-analysis of GWAS, compared to the meta-analysis already reported for GWAS results, had comparable power in identifying genomic regions influencing litter size in sheep but identified fewer genomic regions than individual GWAS for each breed. No previously reported major genes controlling litter size in sheep were identified using our mega GWAS. The results of our research are suggested for further investigations in identifying causal genetic variants or genomic regions underlying the litter size variation in sheep and can be used to understand the genetic mechanism controlling this trait.

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.

Using SNP markers information and genomic evaluation approach, predicting the genetic merit of individuals without phenotypic records is now possible. However, using high-density panels for genomic evaluation of all individuals is not economically feasible. To achieve high genomic prediction accuracy with reasonable price, it is possible to genotype a proportion of animals with high-density panels and the rest of animals with low-density panels then impute them to high-density genotypes. In this study, the effect of three low-density panels (1k, 2k and 4k), genotype imputation to 10k panel and the relationship between reference and validation populations on the accuracy of genomic predictions and also the correlation between the estimated breeding values using panels with different densities in simulated data were assessed. The low density panels genotypes were actually consisting of 10, 20 and 40 percent of 10k markers selected randomly and FImpute was used for genotype imputation. As a general trend, by increasing the density of markers, the correlation between the estimated breeding values was increased using different panels. So, the accuracy of genomic predictions was similar using 4k and 10k genotypes. Moreover, imputing 4k to 10k genotypes, did not improve the accuracy of genomic prediction. However, the accuracy of estimated breeding values was increased after imputation from 1k or 2k to 10k. The accuracy of imputation was decreased when the reference and validation populations were more distant.

In order to determine the prediction equations for apparent metabolizable energy corrected for nitrogen (AMEn) of Iranian wheat, this experiment was conducted at two different ages of broiler chicks in 2014. At first, chemical composition including dry matter, ash, crude protein, ether extract, crude fiber and nitrogen-free extract of 16 widely used Iranian wheat cultivars were measured in the laboratory. To measure AMEn content of these cultivars at 10 and 24 days, 6 and 4 mixed sex ROSS 308 broilers per each treatment were used respectively. At these ages, the samples from the excreta and the contents of ileum were collected. Afterwards multiple regression equations for predicting wheat AMEn content were determined by SPSS software and stepwise method. The results showed that the AMEn estimation equations were determined by sampling of excreta at two ages of 10 and 24 days, respectively, in the form of AMEn = 37.855 × NFE and AMEn = 43.494 × NFE and ileum content was determined as AMEn = 41.173 × NFE and AMEn = 42.224 × NFE, respectively. Thus, using theses equations is recommended at the time of diet formulation for grower and finisher phases of broiler chicken.

Honeybee is a pollinator that has prominent role in agriculture products and nourishment for human. So, the most number of studies like genetic improvement were done on honeybee. Recent advances in knowledge of molecular genetics of bee emphasize the potential for using genetic markers to select favored traits. This research was done to determine polymorphism of loci of first foraging age (AFF) trait in Iranian honeybee (Apis mellifera meda). Two colonies with high production- long longevity and low production- short longevity were selected from 11th generation of “Iranian Breeding Honeybee Project”. In the high production colony, foraging initiation had started sooner (age of 9 33 day). Also‚ the length of foraging initiation period in comparison with low production colony (age of 12-29 day) was longer. Genetic polymorphism of the loci was assessed by using 4 primer pairs. PCR products were detected using polyacrylamide gel. 26 polymorphic alleles were observed using AFLP markers. Totally‚ the most amount of Shanon Index is related to E3M2-1 locus in low production population (0. 693) and also E M3-4 position in high production population (0. 693). Therefore‚ these two loci have shown most polymorphism. The least amount of Shanon Index is related to E6M3-5 locus in low production population (0. 156). The highest effective allele was belong to E3M3-4 position in high production colony and the lowest was related to E6M3-5 position in low production population. Heterozygosity was diverse between (0. 053) in E6M3-5 to (0. 05) in E3M2-1. Mean of (He) in low production colony was (0. 321) and in high production colony was (0. 324). Mean of Shanon Index were (0. 490) and (0. 491) respectively. Genetic variance between 2 populations was only ٪10. There was difference in production in the 2 populations but, genetic diversity is not very impressive. Genetic similarity in them may is because of using the small number of samples in this research.

The aim of this study was to investigate genetic diversity in Mazandaranian native cattle in compared to Holstein breed, using Inter microsatellite (ISSR) markers. A total of 175 animals, including 71 Mazandaranian and 104 cattle of Holstein breed were screened. The extraction of DNA samples were carried out using modified salting out method. (AG)9C was used as ISSR primer in PCR reaction. Analyzing of banding patterns showed 30 (93.75%) and 24 (75%) polymorphic bands in native and Holstein herds, respectively. Genetic variation indices, including observed number of alleles, effective number of alleles, Shannon index and Nei’s gene diversity in indigenous cattle were higher than in Holstein. Lower level of diversity in Holstein cattle may be of the consequence of applying intensive selection programs and artificial insemination; however native cattle have been less affected by artificial selection. Knowledge of genetic diversity in indigenous breeds provides important information for management of native resources in order to avoid inbreeding depression and maintain genetic variability.

Data on 300 Kermani sheep from Kermani Breeding Center of Shahrbabak were utilized in the present research. Horizontal polyacryle amide electrophoresis was employed to detect polymorphism of Transferrin and Albumin, while Acetate cellulose being utilized for Hemoglobin. Three genotypes of Hemoglobin were observed(AA, AB and BB with respective frequencies of 4.6, 43.8 and 51.6). Frequency of A allele of Hemoglobin in Kermani sheep was superior to those investigated in other Iranian sheep. Eight allels were determined for tranferrin, (B, C, A, D, E, G, Q and L in frequencies of 46.7, 27.11, 12.82, 9.16, 2.01, 1.28, 0.55 and 0.37 respectively). These alleles made up 19 genotypes from among which BC genotype possessed the most frequency while AL & LD genotypes the least ones. Albumin Electrophoresis results showed two genotypes SS and SW with frequency of 98% and 2% respectively. Following genotype determination of the sheep for the proteins, effect of proteins on body weight, daily weight gain and wool production was investigated. Trensferrin and Hemoglobin effects were singnificant on 12 month weight (P<0.05), from which the sheep with BB Hemoglobin and CE Transferrin types weighted the highest. Effect of proteins on other traits was not significant although some genotypic groups of hemoglobin and transferrin exhibited significant differences.