single nucleotide polymorphism

Broiler chickens are one of the important and strategic sources in the meat supply of Iran. The main origin of this vital industry is the line chicken, benefiting only a few countries. Iran is one of the countries with a broiler line called Arian. However, this line has not been able to satisfy customers due to some problems, including the high death rate due to the ascites syndrome, a key factor of mortality and losses in the poultry industry. Therefore, it seems necessary to pay attention to a sustainable solution to reduce this disease in the broiler farms of Iran. This syndrome involves different tissues, and an important tissue is the liver, which plays the main role in regulating metabolism in the whole body. The sustainable solution to reduce this syndrome in broiler farms is to create lines resistant to the ascites syndrome through breeding. The present study aimed to identify single-nucleotide polymorphisms (SNPs) in CPQ, B3AGT2, and HPSE genes in the liver tissue of Arain broiler chickens with ascites syndrome using RNA-seq data.

This research was conducted in two phases, including the first phase (ascites induction) and the second phase (normal rearing conditions). In the first phase, 817 one-day-old chicks from 71 paternal stepfamilies from the B Arian line, which were sexed and numbered on the first day, were obtained from the Arian line chickens located in the Babolkanar farms in Mazandaran province and were reared at the Khalat Pushan Poultry Breeding Station at Tabriz University. The birds that showed signs of ascites were classified as susceptible to ascites, and the rest of the birds were classified as healthy. Total RNA from the liver tissue was extracted from 32 birds, including 16 healthy (8 males and 8 females) and 16 diseased (8 males and 8 females) birds individually. An equal amount of RNA from four birds was merged with each other to obtain a total of eight merged samples, four samples from each of healthy and ascites-affected birds. The quantity and quality of the extracted RNA were measured by a UV-1800/SHIMMADZU nanodrop and 1.5% agarose gel. Then, the extracted RNA was treated and purified by DNase. The samples were sequenced using Illumina sequencing technology. The quality of the reads was controlled using FASTQC software, and the reads were aligned using STAR software. After aligning the sequences against the reference genome, the tools provided in two software packages, Picard tools, and GATK, were used to identify the variants in each sample.

All the quality control indicators of FASTQC software showed the appropriate quality of the studied data. After quality control, the RNA-seq data of the liver tissue were mapped on the reference genome. About 90% of the reads were mapped on the reference genome, which indicates the high quality of the sequenced data alignment compared to the reference genome. Based on previous reports, the CPQ gene was considered an important gene related to ascites in this study. To fully understand the importance of this gene in disease-related phenotypes, the gene network, and protein interaction with other proteins, the protein network of this gene was drawn based on STRING. The ontology (GO) of 49 genes related to each other based on the string was performed in DAVID software. According to our observation, CPQ, HPSE, and B3GAT2 genes are involved in important biological pathways related to ascites disease. Then, the genes related to CPQ, B3GAT2, and HPSE genes, which were also related to ascites and contained SNPs in the samples with ascites, were selected and studied in this step. In this study, eight and three SNPs were detected in the CPQ gene in the samples with ascites and healthy samples, respectively, which were identified and reported for the first time in this study. The CPQ gene is the most prominent ascites candidate gene identified to date, which can be used in marker-assisted selection to increase ascites resistance in breeding programs. Moreover, four and one SNPs were detected in HPSE and B3GAT2 genes in ascites samples, respectively. However, no polymorphism was found for these genes in healthy samples. Heparanase (HPSE) is an endoglycosidase that catalyzes the cleavage of side chains of heparan sulfate proteoglycans. Therefore, it plays a role in the regeneration of extracellular matrix and basement membranes, as well as the release of various molecules related to heparan sulfate as growth factors, cytokines, and enzymes.

The polymorphisms identified in this research can be used to select ascites-resistant lines of broiler chickens in breeding programs by further investigating and confirming their effects on ascites.

So far, several methods have been used to identify genetic factors affecting polygenic traits. Common methods are least squares regression analysis, maximum likelihood regression analysis, and Bayesian. The superiority of Bayesian methods over other methods is that it is possible to use all SNPs in the model simultaneously.  The simultaneous presence of markers can prevent overestimation of marker effects and increase the probability of identifying true positive SNPs. Therefore, in the present study, the BayesCpi method was used to identify SNPs related to body weight at early stages of growth (i.e. body weight at week 2) in an F2 population of mixed chickens. For this purpose, the Illumina 60K SNP bead chip was used to genotype the present population, including 312 chickens from the F2 population. According to the results of the analysis, 16 SNPs with a Bayes Factor (BF) between 20 and 150 were known and suggested as markers for body weight at early age. Results of post-GWAS showed that these SNPs were distributed across 4 chromosomes and were located close to, or inside the 16 genes. Among the identified genes, 12 genes were protein-encoding and 4 were noncoding RNAs. To identify genes associated with each SNP in candidate regions, 0.5 Mb around each significant SNP was considered.

Gene regulation can be assessed by allele-specific expression (ASE), which has the potential to cause phenotypic variation. The variants associated with gene regulation can be found using ASE. Transcriptomic variation in gene expression is known to play an important role in the formation of the phenotype. Single base variations resulting from transitions (C/T or G/A) or transversions (C/G, C/A, T/A, T/G) of nucleotides at the same position between individual nucleotides in genomic DNA sequences are known as single nucleotide polymorphisms (SNPs). SNPs are important molecular markers used in breeding and genetic research. Next-generation sequencing is advancing rapidly and provides a high-throughput approach for SNP discovery in the transcriptome or genome. Transcriptome studies can bridge the gaps between genotypes and phenotypes and provide insights into the mechanisms linking sequence and function. An effective transcriptome mapping and quantification technique for studying global gene expression is RNA sequencing or RNA-Seq. For this reason, RNA-Seq is a next-generation sequencing technology that can analyze gene expression profiles and the entire transcriptome. This study aimed to identify differential gene expression and SNPs in three tissues of the heart, muscle, and spleen of roosters adapted to low and high altitude regions based on open access RNA-Seq databases.

RNA-Seq data from 54 samples were collected from the SRA database in NCBI for chickens (NCBI GEO accession: GSE119387). The samples included 33.4, 30.3, and 35.3 million paired final reads of heart, muscle, and spleen tissues, each 100 bp in length. Illumina Hiseq 2000 was used to perform mRNA sequencing. The fastq-dump command in Sratoolkit 2.11 was used to convert data from SRA to FASTQ format  FastQC (version 0.11) was used to assess data quality and Trimmomatic (version 0.33) was used to trim the reads to eliminate adapters and low-quality sequences. Trimmed reads were aligned to the reference genome of the chicken species (Gallus gallus domesticus) and the gene annotation data (GRC6a) from Tophat2, whose core host is Bowtie2. By independently aligning and mapping the RNA-Seq reads of each sample to the chicken reference genome, the transcriptome was assembled. Then, the differential gene expression analysis was performed with cufflinks. The Samtools program performed SNP detection. Finally, using the R software (version 4.2.2), the chi-square test was used to compare the amount of expressed reference and alternative alleles in the polymorphic regions of heterozygous individuals to find the notable variations.

As a result of gene expression analysis, 2260 genes were significantly differential expression (P<0.0002). Gene ontology analysis showed that these genes are in pathways related to heat stress and immune responses to the cause of trying to maintain body temperature involved and this cause was the activation of immune pathways. Identification of 1,473,176, 388,224, and 1,169,394 SNPs in the heart, muscle, and spleen tissues was enabled by SNP calling and discovery in the assembled transcriptome. The chi-square test revealed that the ASE-SNPs in the heart, muscle, and spleen tissues were 48,906 (10.3%), 28,529 (7.3%), and 76,251 (6.3%) SNPs (P<0.05). These SNPs were associated with 7,919, 6,182, and 10,590 genes in the heart, muscle, and spleen tissues. In three tissues, the number of reference and alternative alleles was shifted by 4.5% in favor of the reference allele. This suggests that the superiority of the reference allele over the reference genome during mapping may be related to mapping bias. Among the twelve potential SNPs and ASE-SNP types found, four were transition types (Ts) and eight were transformation types (Tv). The transition type accounted for 74% of the most common polymorphisms in the heart and 77% in the muscle and spleen. For ASE-SNPs, transition mutations accounted for 70% of all mutations in the heart tissue and 75% in the muscle and spleen tissue. These mutations were also the most common. The Ts/Tv ratios for the heart, muscle, and spleen tissues were 2.3, 3, and 3 in the ASE-SNP and 2.9, 3.3, and 2.2 in all SNPs, respectively. This indicated a decrease in Ts/Tv for the heart and muscle tissues in the ASE-SNP compared to all SNPs.

The results of the current study support the validity of identifying SNPs in transcriptionally active regions of the genome using RNA-Seq data. Further research is needed to determine whether the expression differences between reference and alternative alleles found in heterozygous roosters raised in different environments are related to tolerance to environmental stressors such as low oxygen levels.

The purpose of present study was to detection and comparison of molecular markers in BOLA gene were between pregnant and non-pregnant Holstein cows. The studied animals included Holstein Friesian cows and did not have any clinical diseases or history of reproductive problems. The number of studied samples included 8 pregnant and 11 non-pregnant cows. The data analyzed in the present research were the result of RNA sequencing. After quality control, the reads were aligned and located against the reference genome and the software provided in two packages, Picardtools and GATK were used to identify the considered variants. The interaction network of studied gene was drawn at the protein level using the STRING database. The results showed that there are 4 different transcripts and a total of 15 SNPs in the BOLA gene in non-pregnant cows compared to pregnant ones, and these polymorphisms were observed in the BOLA gene and its upstream regulatory region which is the binding site of microRNA bta-mir-11979 and specifically, it has only been observed in non-pregnant cows. Among the observed mutations, a mutation at the position of 28723238 base pairs leads to the substitution of adenine with cytosine nucleotide and creating a stop codon in the BOLA protein. No deletion/insertion variants were observed in the studied gene. Considering the missense and nonsense mutations observed in the BOLA gene, which probably leads to fundamental changes in its protein structure, the polymorphisms identified in this research can be used for selection in breeding programs.

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.

Obstructive gastrointestinal (GI) malformations are one of the most important congenital problems resulting in calf mortality within a few days of birth. The most common site for atresia, after the esophagus, is the jejunum. Jejunum atresia is the congenital absence or complete blockage of a part of the jejunum lumen. Early detection of intestinal obstruction is essential to prevent further complications. Intestinal atresia is an underdiagnosed congenital defect in cattle. It results in complete occlusion of the intestinal lumen and, unless surgically corrected, results in death or euthanasia of the affected calf. There is limited information on the incidence of this condition or risk factors, including predisposing alleles, associated with the defect. Atresia is a well-known congenital defect of the gastrointestinal system in calves and investigations into the etiology of this condition are warranted. Domestication and selection have significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans left detectable signatures on the genome of modern dairy cattle. The identification of these signals can help us to improve the genetic characteristics of economically important traits in goats. Over the last decade, interest in the detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. This study aimed to identify the selection signatures using the unbiased theta method associated with gastrointestinal atresia in Holstein dairy calves.

For calves with intestinal atresia, muscle tissue (>1 g) was collected from the Latissimus dorsi muscle postmortem, and submerged in RNA later solution. DNA samples from 91 atresia cases and 377 control animals were genotyped using the Illumina 777K BovineHD beadchip (Illumina Inc). The work described here is a case–control association study. Single nucleotide polymorphism (SNP) missing 5% of data, with MAF of <1% and Hardy–Weinberg equilibrium P-values <10−6 were removed. The genotyping efficiency for samples was also verified, and samples with more than 5% missing data were removed. Grouping was done to infer selection signatures based on FST statistic. The bioinformatics investigations were carried out using the Ensembl database for bovine genes (assembly ARS-UCD1.2), to identify potential candidate genes which already have been reported in/or surrounding genomic regions containing the peak of absolute extreme FST values. The regions corresponding to the upper and lower 0.01% of positive and negative obtained FST scores were considered regions under selection. Genes within a 500-kb span of the start and end of the QTL were identified using Ensembl 108 on the ARS-UCD1.2 bovine genome assembly implemented in biomart. Then, using the PANTHER database, the general biological function of the genes was checked. At this stage, it is assumed that genes that belong to a functional class can be considered as a group of genes that have some specific and common characteristics, and the quantitative trait loci (QTLs) in the selected region were extracted using the Animalgenome database, and the genes were compared with other researches. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes.

with a 99.90 percentile threshold of the obtained theta (θ) values, eight genomic regions on chromosomes 7, 12, 13, 21, 22, 23 (two regions), and 29 in the Holstein calves were identified. Further investigation using bioinformatics tools showed these genomic regions overlapped with the genes (CSF2, SIAH3, TMEM14A, and SKIV2L) associated with embryonic development, small intestine length, apoptosis, and several tumors. The population used in our study is small, owing to the challenge of collecting a substantial amount of blood on calves on commercial herds having received the diagnosis of gastrointestinal atresia and ready to be culled. Diagnosis and culling of gastrointestinal atresia animals are ineffective preventive measures.  Further work is required to identify which farm-specific or management risk factors contribute to the incidence of intestinal atresia.

The results of this study may provide an important source to facilitate the identification of genomic regions and then, the genes affecting gastrointestinal atresia in claves. However, further studies are warranted to refine the findings using a larger sample size, whole-genome sequencing, and/or high-density genotyping.

The selection of animals by humans left detectable signatures on the genome of modern goat. The identification of these signals can help us to improve the genetic characteristics of economically important traits in goat. Over the last decade, interest in detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. A run of homozygosity (ROH) is a consecutive tract of homozygous genotypes in an individual that indicates it has inherited the same ancestral haplotype from both parents. Run of homozygosity one of the most methods were used to detecting the genomic inbreeding. The locations of ROHs which are under positive selection, or laboring favorable allele in population, tend to be fixed in the genome and formation of ROH Island during long times. Genomic regions enriched with ROH may be indicative of selection sweeps and are known as ROH islands. As detecting the ROH Islands, the genomic regions contain economic traits could be detectable.

In this research, the amount of genomic inbreeding and the effective size of the population were investigated using the information obtained from 879 goats of different breeds including Beetal, Daira Deen Panah, Nachi, Barbari, Teddi, Pahari, and Pothwari. In order to determine the genotype of the samples, Illumina caprine Bead Chip 50K were used. The genomic information of goat breeds was extracted from the figshare database. Quality control was conducted using the Plink software. The markers or individuals were excluded from the further study based on the following criteria: 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 10-3. After quality control, 36,861 SNPs from Goat SNP chip 50K on 827 goats were remained for the future analysis. Inbreeding coefficient was calculated using four methods including, genomic relationship matrix (FGRM), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI) using the GCTA 1.0 software and run of homozygosity (FROH) using the PLINK 1.9 software. The effective population size (Ne) was calculated from linkage disequilibrium data with SNeP software (version 1.1). GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes.

The lowest and highest inbreeding coefficient calculated by three methods (FGRM, FHOM, and FUNI) were related to Beetal and Barbari breed, respectively. The highest (0.159) and lowest (0.028) amount of FROH was estimated in the Barbari and Pothwari breeds, respectively. The average length of ROH ranged from 70.2 to 391.4 Mb, and the average number of ROH fragments varied between 8.19 and 48.65. Also, the highest and lowest number of ROH were observed on chromosome 2 and 29, respectively. The size of Ne in in the current generations (fifth generation) of the studied breeds was ranged from 35 to 365. The highest Ne was estimated in the Beetal breed (365 heads) and the lowest in the Barbari breed (35 heads). The average inbreeding coefficient in Beetal, Teddi, Pahari, Nachi, Barbari, Daira Deen Panah and Pothwari breeds was obtained 0.035, 0.081, 0.031, 0.052, 0.15, 0.11 and 0.02, respectively. In addition, the Ne of most of the studied populations has been decreased. The results of this study revealed that, the selection processes in different goat breeds for economic traits during several years, has led to the formation of many ROH islands in goat genome, therefore scanning these regions at the genome level can be an alternative strategy to identify genes and associated loci with economic traits.

our findings contribute to the understanding of genetic diversity and population demography, and help design and implement breeding and conservation strategies for study goat breeds. Therefore, it is necessary to economize production and planning a suitable mating scheme to control inbreeding and genetically conserve the remaining pure animals of these breeds.

The present study was conducted in order to select effective markers in breed discrimination and compare the performance of SNP marker selection methods with the data of 304 animals from 14 different breeds that were genotyped using the Illumina SNP50K marker panel. Knowledge of genetic structure are very important for better understanding of genetic changes in genomic studies. The information content of each marker is used as an index for selecting markers in reducing the size of marker panels. To estimate the information content of each marker, the following selection methods were used: Fst (pairwise & global), Theta, Delta, D, Gst, G'st, G"st and Principal Component Analysis. In this study, the logarithm of the likelihood ratio was used to select markers. According to the results, all selection methods for identifying markers had similar performance. The number of common markers between the methods was at least 42 markers and at most 499 SNP markers. In general, the F_ST statistical method required a smaller number of markers to achieve a successful assignment. G'st and G"st statistics showed poor performance with more than 350 markers to achieve 95% correct assignment. It should be noted that with only the top 60 selected markers, it is possible to achieve a success rate of more than 70%. According to the results, Wright's paired Fst had better performance than other SNP selection methods. The obtained results lead to the creation of exclusive panels to identify various breeds, which have great economic importance.

Varroa infestation is undoubtedly the greatest threat and challenge facing Apiculture today. This external parasite inevitably lives in the bee colony and causes irreparable damage to its colony and the subsequent honey production. One of the proposed strategies in this regard is the use of pesticides, which have a negative impact on the health of bees and honey consumers. To avoid these negative consequences, safer alternative methods of controlling mites are needed, including the use of resistant genetic strains and breeding selection programs to establish colonies with relative resistance to mites. In honeybee colonies, there are several physiological and behavioral mechanisms for Varroa resistance that by examining their relationship with identified resistance genes, the genetic basis of Varroa mite resistance in honeybees is identified and can used in breeding programs. The aim of the present study was to identify single nucleotide polymorphisms (SNPs), in a sample of the Iranian honeybee population, in the candidate gene (the dopamine receptor gene (DOP3)) effective on defense behaviors of honeybees against the varroa mite.

For this purpose, a total of 10 drone bees (5 susceptible and 5 resistant) were selected and their DNA was then isolated using a CTAB-based method. The PCR was carried out based on specific primers in the UTR region sequence of the DOP3 gene. The quantity and quality were then determined using the nanometer method. After a single band (900 bp) of the expected size, product purification and sequencing (Sanger method) were performed. The display of the outputs and the determination of the quality of the raw sequence (phred index) took place with the FinchTV software and the alignment with BLAST and clustering with the MAFTT software.

In this study, differences were observed in several regions of the nucleotide sequence of the UTR region of DOP3 gene, the most important difference in the nucleotide sequence between sensitive and resistant individuals in the two regions. One is in the nucleotide region of 428 to 437 forward readings as many as 9 bp nucleotides and the other is in the nucleotide region of 715 to 720 forward readings as many as 6 bp nucleotides, which in both, mutations of the deletion type have been performed.

Evaluation of the final results showed significant difference, in type of deletion/addition with the size of 6 and 9 bp between two groups (sensitive and resistant to Varroa), respectively which can be used in the molecular identification of resistant colonies and breeding programs to produce Varroa mite resistant colonies. No such deletions from this gene have been reported so far.

Generally, genetic markers, associated with metabolic pathways, might be used in marker-assisted selection to improve production and reproduction traits in farm animals. Especially, some traits with low heritabilities, such as reproduction and health traits could not be easily improved by classic selection strategies. However, the use of genetic markers and marker-assisted selection are considered powerful tools for the genetic improvement of low-heritable traits. Thus, detection of genetic markers associated with production and reproduction traits is an effective way to save endangered indigenous breeds, such as the Markhoz goat, a mohair-producing breed in Iran. The thyroid hormone-responsive (THRSP) gene is a candidate gene involved in thyroid hormone functions and the lipogenesis process. This gene is a protein-coding gene, with two exons, located on chromosome 29 in goats. There are some reports on the association of THRSP with production traits in farm animals. There are limited studies on metabolic pathways of THRSP and its associations with important economic traits in small ruminants and no study on the association of THRSP and reproduction traits was found in the literature. The aim of the present study was the investigation of the THRSP gene polymorphism and its association with body weight and litter size traits in Markhoz goats.

A total of 140 blood samples of Markhoz goats were randomly collected from the Research and Breeding Station in Kurdistan province in western Iran. Genomic DNA was extracted from whole blood samples. A pair of primers were designed using the Primer 3 online software to amplify a 486 bp fragment in THRSP gene exon 1. The designed primers were as follows:Forward: 5’-AGTCTGCGGGACTCCATATG-3’ Reverse: 5’-AAAATGGGACAGGCCATGT-3’ Polymorphism of the amplified fragment was investigated using the single-strand conformational polymorphism (SSCP) method and sequencing of three random samples for each SSCP pattern. The observed sequences were aligned to the GenBank reference sequence using the MegAlign module of DNAStar software and compared based on the Clustal W method. The sequences were translated by the Translate section of the ExPASy website (us.expasy.org/translatetool). Associations of body weight traits, including birth weight, three-month, six-month, nine-month, and 12-month body weights, with the observed genotypes were investigated using a general linear model, fitting genotype, birth year, birth type, and dam age as the fixed factors. The association of the observed genotypes with litter size was investigated using a two-way Chi-squared test. The SAS 9.4 program was employed for the association analyses.

In the studied samples, two different SSCP patterns and two single-nucleotide polymorphisms (SNPs) were detected in 148 and 173 bp locations of the studied fragment, as g.148G>A (resulting in Arginine to Glutamine amino acids exchange) and g.173A>G (a synonymous mutation), both as heterozygous loci. In the studied population, the frequency of the double-homozygous genotype, GG/AA (0.743), was noticeably higher than the double-heterozygous genotype, which is GA/AG (0.257). The alleles G and A had high frequencies, both equal to 0.871 in the 148 and 173 bp loci, respectively. Both loci had a significant deviation from Hardy-Weinburg equilibrium (P<0.0001). Polymorphism of the studied fragment did not have any significant effect on body weight traits. A significant association was observed between the detected genotypes and litter size. Whereby, litter size in double-heterozygous does (1.21) was significantly higher than the double-homozygous individuals (1.04), (P = 0.015). The present study is probably the first report on the association of the THRSP gene with litter size in goats. The THRSP is a protein which involves in thyroid hormone function and therefore might affect many metabolic pathways. However, a significant association of the observed genotypes with prolificacy is probably due to the role of THRSP in lipids metabolism and the association of lipids metabolism with reproduction performance.

The THRSP exon 1 is polymorphic in Markhoz goats. The studied fragment did not have any significant associations with body weight traits, but it had a significant effect on litter size. Based on the results of this study, the THRSP gene could be considered a candidate gene for litter size, and the detected SNPs at 148 and 173 bp of the studied fragment, could be used for marker-assisted selection in Markhoz goats. However, more studies on possible associations between THRSP polymorphism and production and reproduction traits are still needed in other goat breeds.

Genotype imputation in genomic selection schemes has been considered by researchers in recent years because it can reduce the costs of genomic selection without having a negative impact on the accuracy of genomic selection. In the genotype imputation process, markers that their genotypic information has been missed for any reason are imputed using various statistical methods.

To constructe genotypic matrix, a one morgan genome including one chromosome for 250 and 1000 individuals was simulated on which in different scenarios 250, 500, 750, 1000, 1500 and 2000 single necleotide polymorphismes (SNP) was distributed. In order to create genomic matrix including missing genotypes, genotypic information of respectively, 5%, 10%, 25%, 50%, 75% and 90% of SNPs was masked and then imputed with KNN. The percent of genotypes correctly imputed (the ratio of genotypes correctly imputed to total masked genotypes) as well as the correlation between primary genotypic matrix (no missing genotype) and imputed genotypic matrix were used as imputation accuracy.

In the population including 250 individuals, the accuracy of imputation in the scenarios of 5%, 10%, 25%, 50%, 75% and 90% missing genotypes, were 0.82, 0.82, 0.80, 0.76, 0.62 and 0.40, respectively, but by increasing the size of the population to 1000 individuals, the imputation accuracies as 0.83, 0.83, 0.82, 0.82, 0.71 and 0.54 were obtained which in the scenarios of 75% and 90% of missing genotypes the increase in imputation accuracy was noticable. The correlation between the primary genotype matrix and the imputed genotypic matrix also decreased with increasing percentage of missing genotypes. In a fixed population size, by increasing the number of SNP from 250 to 2000, imputation accuracy increased from 0.67 to 0.84. In addition, an inverse relationship was observed between MAF and imputation accuracy in a way that by increasing MAF from 0.01 to 0.5, imputation accuracy decreased by 15%. Computation time increased following increase in dimension of genotypic matrix. Bu increasing the percent of missing genotypes, the accuracy of predicted genomic breeding values decreased. In the scenarios of 5 and 10% of missing genotypes, no change in accuracy was observed, but in the scenarios of 75 and 90% of the missing genotypes, the accuracy of prediction of breeding values decreased by 16 and 32%, respectively.

In general, imputation accuracy of KNN was acceptable in such a way that up to 50% of missing genotypes, KNN imputed missing genotypes with 80% accuracy and therefore one could recommend this algorithm for genomic selection schems.

Due to the widespread distribution of SNPs throughout the genome, these markers are widely used in livestock breeding research. These markers were used to predict the disease risk in human, to localize genetic variations responsible for complex traits through genome wide association study (GWAS), and to predict the genetic values of economically important traits in plant and animal breeding (Zhang et al 2015). Mostly whole genome scanning methods are based on two SSGWAS (Single SNP Genome-Wide Association Studies) and multiple markers methods. The SSGWAS method is able to identify a large number of common variables affecting quantitative traits. However, a large proportion of the genetic variance remains to be explained (Shirali et al 2018). In quantitative traits the proportion of phenotypic variance explained by SNPs is related to the number of adjacent SNPs in the genomic region. The heritability created by these genomic regions is defined as the regional heritability. The RHM (Regional Heritability Mapping) method is used to identify small genomic regions. This method can capture more of the missing genetic variation (Nagamine et al 2012). In RHM, a mixed model framework based on Restricted Maximum Likelihood (REML) is used, and two variance components, one contributed by the whole genome and a second one by a specific genomic region, are fitted in the model to estimate genomic and regional heritabilities, respectively (Uemoto et al 2013). Also fastBAT (fast and flexible set-Based Association Test) is a method that performs a fast set-based association analysis (Bakshi et al 2016). The purpose of this study is compare SNPs and regions identified by the Genome-Wide Association methods, compare these results with the simulated QTLs and also investigate and determine the false positive results in each method.

In this study, markers and populations were simulated as a Forward-in-time process using QMSim software (Sargolzaei and Schenkel 2009). For this population, 27586 single nucleotide polymorphisms (SNPs) were counted on 3 pairs of autosomal chromosomes. Simulation was performed in 3 scenarios with 75, 150 and 300 quantitative trait loci (QTL). The minimum and maximum number of SNPs in the analysis after quality control were 19662 and 23817 SNPs, respectively. For each scenario, 10 replicates were simulated, in all scenarios, heritability was 0.2 which corresponded equally to the polygenic and QTLs effects. Whole genomic relationship and pedigree base genetic relationship matrices were used in all 3 methods to estimate genetic parameters. To create the whole genomic relationships matrix, whole genomic additive effects was estimated using all SNPs. Also the additive effect of genomic regions was estimated using the regional genomic relationship matrix. Whole genomic relationships matrix and regional genomic relationship matrix were estimated based on genetic relationships between individuals using SNPs by GCTA software (Yang et al 2011). Pedigree based genetic relationship matrix was created by the kinship relationship between individuals using pedigree package (Coster 2013) of RStudio software (RStudio Inc 2013). To perform RHM and to estimate variance components, windows containing 50 genotyped SNPs were considered. Also windows containing 25 genotyped SNPs to overlap between two consecutive windows throughout the genome were used. SSGWAS analysis were performed by MLMA (Yu et al 2006) method using GCTA software. MLMA results were adjusted based on P-value at 5% significant threshold using Bonferroni correction. To evaluate the results of SSGWAS using fastBAT method, GCTA software was used.

For each replication after identifying significant SNPs, the genetic variance explained by these SNPs was estimated by equation (Faulkner & McKay 1996). In Table 1, the number of QTLs detected by the SSGWAS method, the MAF of QTLs, the range and mean of genetic variance explained by significant SNPs and QTLs are reported. For 30 replicates of simulation in SSGWAS, 16 QTLs were detected containing 2 QTLs with MAF≤0.1 and other detected QTLs with MAF≥0.1. 107 Significant regions were identified in fastBAT method. In this method, 120 QTLs were detected in 3 scenarios containing 52 QTLs with MAF≤0.1. All QTLs detected in the fastBAT and SSGWAS methods were also detected in the RHM method. In RHM method, 612 regions containing simulated QTLs and number of 316 QTLs with MAF≤0.1 were detected. In all replications, the variance explained by SNPs was equal to the variance explained by QTLs. In SSGWAS, less number of QTLs were detected than the other two methods and the maximum variance explained by QTLs was 14.9%. The criterion used to determine false positive QTLs was the absence of significant QTL in the before and after significant windows containing QTLs. In SSGWAS method the percentage of false positive QTLs was higher than the other two methods. In fastBAT, unlike the other two methods, detected QTLs were not false positive. In table 5 Number of detected QTLs, MAF range of QTLs, range and mean of genetic variance explained by detected QTLs and SNPs in fastBAT are shown. Many QTLs and regions detected by RHM method were not detected by SSGWAS and fastBAT methods. The genetic variance explained by detected QTLs in the RHM was at the range of 7.26 to 46.86% that was higher than other two methods. In table 6 the three methods compared by the number of detected QTLs, number of false positive QTLs, number of stable QTLs and the number of detected QTLs with MAF≤0.1. We found that QTLs with MAF≤0.1 were more frequently detected in RHM than the other two methods. These results confirmed that the RHM method was able to identifying more of QTLs affecting the trait variance.

Defensins are small antimicrobial peptides that play an important role in innate immunity. Therefore, their coding genes can be used as markers in selecting markers to help improve the economic quality of domestic animals. Therefore, the aim of this study was to investigate the relationship between C2239T SNP of the intron region of the beta-4-defensin gene with milk production traits and the somatic cell count in Holstein cows.

For this purpose, genomic DNA was extracted from 182 dairy cows. Then, using a suitable primer pairs, a 393 bp fragment from intron of bovine B4-defensin sequence (2100 to 2493) was amplified by the polymerase chain reaction. Then, the amplified fragment was screened by single strand conformation polymorphism (SSCP) and DNA sequencing methods. The associations between genotypes of the SNP with SCC and milk production traits were analyzed using the GLM procedure of SAS (9.1).

  A total of 3 distinct SSCP patterns (A, B and C) were observed which confirmed single nucleotide polymorphism (substitution of C to T) at position 2239 upon sequence analysis in the population. The frequencies of CC, CT and TT genotypes were 0.79, 0.13 and 0.08, respectively. No significant difference was observed between SNP genotypes and milk production traits and SCC. However, the genotypes were tended to associate with milk yield (p=0.10). The highest average milk yield and somatic cell count were found in the TT genotype, whereas the lowest ones for average milk yield and somatic cell count were CT a CC genotypes, respectively.

In this study, no significant relationship was observed between the identified genotypes with milk production traits and the number of body cells, but this relationship showed a significant tendency for the amount of daily milk production. The results also showed that TT and CT genotypes for this mutation had the highest and lowest milk production, respectively.

This study was conducted to investigate the polymorphisms of the Prion protein gene (PRNP) exon 3 in Mehraban and Romanov sheep. Blood samples were collected from 124 sheep (85 Mahraban and 39 Romanov). After DNA extraction, polymerase chain reactions (PCR) were performed to amplify a 173 bp fragment of the PRNP gene exon 3 using a specific pair of primers. Polymorphisms of the studied fragments were explored using single strand conformational polymorphism (SSCP) and DNA sequencing analysis. In both Mehraban and Romanov breeds, three different banding patterns (AA, AG and GG genotypes) along with a single nucleotide polymorphism (g.625A>G) were identified, that deduced one amino acid substitution (p.171R>Q). Three haplotypes including ARR/ARR, ARR/ARQ and ARQ/ARQ were identified, which the most frequency (61.2 and 46.15% in Mehraban and Romanov breeds, respectively) was related to the ARR/ARR haplotype with high resistance to scrapie disease. The results of current study regarding the similarity of polymorphism in PRNP gene of domestic and foreign breeds could be used in sheep breeding programs.

The aim of this study was to investigate genome-wide Inbreeding and effective population size using the information obtained from 96 Zandi sheep breed using a density SNP panel (50K SNPChip). For this purpose, after quality control of SNP markers data, 40,879 SNPs were remained for computing inbreeding and effective population size. Effective number of breeders was estimated per each chromosome using NEESTIMATOR software based on heterozygote-excess method., and inbreeding coefficient was derived using four methods including, genomic relationship matrix (FGRM), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI) using GCTA software and run of homozygosity (FROH) using PLINK software. Average expected and observed heterozygosity ranged 0.393 and 0.407 respectively. Average chromosome-wise effective number of breeders was equal to 69 and corresponding average confidence interval was between 40.0 and 93.26. The magnitude of inbreeding coefficient using FGRM, FHOM, and FUNI was similar (0.064) and it was estimated 0.053 using Run of homozygosity. Generally, the results indicated that although a considerable genetic variation exists in Zandi population in case study, however effective population has been decreased strongly in Zandi sheep breed during recent years and designing of appropriate programs is necessary to conserve remaining purebred animals of this indigenous sheep breed.

This study was conducted to estimate the linkage disequilibrium (LD) and determine haplotype blocks structure in 93 Sarabi cow using SNP-chip 40k of Illumina company. After genotyping and quality control, 27386 SNP markers on autosomal chromosomes remained for analyzing. The LD was measured by r2 and D' statistics. In this study the average of r2 and D' for range less than of 2.5 kb were maximum with 0.505 and 0.927, respectively. The average of r2 and D' were minimum with 0.064 and 0.486, respectively in range of 2-5 Mb. 582 haplotype blocks were observed in the genome of Sarabi cow. 6.73% SNP from all of the SNPs were covered and 0.83% (21.43 Mb) of the autosomal genome were covered by the blocks haplotype. Population effective size was estimated about 40 that refer to four generations ago. The low number of haplotype block and also low LD level in Sarabi cow population showed high variation. In refer to the result and the number of haplotype blocks in this breed, applying the haplotype blocks could be improve results and high precision on genomic selection study so it was recommended that in study of genomic selection applying the haplotype blocks really useful than single SNP study

In compare with other dairy animal, the most important components of goat milk are protein and fat. Due to association between milk proteins and economic traits, there are a lot of studies about genetic structure of milk proteins. Nowadays, the effect of genetic polymorphisms in goats and other species and association with production traits and health widely was investigated. One of the most important topics for the genetic characterization of dairy breeds is investigation of the casein genes. The goat casein genes including CNS1S1, CSN2, CSN1S2 and CSN3 are clustered on chromosome 6. The Ks1-casein protein encoded by theCSN1S1 gene and known as candidate gene. The funding of different studies indicates the effect of ks1-casein gene on milk traits in goat. Khalkhali goat is one of the native dairy goats in Iran that is distributed in North West at Ardabil province. Due to less economically viable, the population of this breed have a declining trend. Therefore consider and attention to this breed is very important. K-casein gene is a useful genetic marker to improve animal breeding programs. The polymorphisms of k-casein gene have significant effect on milk composition and production. To the best of our knowledge, no study has yet considered the about casein gene in Khalkhali goats. This study aimed to assess the variation and polymorphisms at CSN3 loci on Khalkhali goats.
In this study for analyzing of polymorphisms in Kappa casein gene, 100 blood samples were collected from Khalkhali goats. Genomic DNA was extracted using DNA isolation kit for mammalian blood and the exon 4 of Kappa casein gene amplified using specific primers including: Forward primer F: 5’-GGT ATC CTA GTT ATG GAC TCA AT-3’ and revers primer R:5’-GTT GAA GTA ACT TGG GCT GTG T -3’. Genotyping were determined using PCR-SSCP and were sequenced through detection of different pattern. The sequences were analyzed using bioinformatics software’s. Estimates of evolutionary divergence between the sequences were conducted using the maximum composite likelihood method by using MEGA version 6.10 software. Phylogenetic tree was constructed using the Neighbor-Joining method by using the same software. Haplotype analysis and the network analysis of haplotypes was constructed using DnaSp 5.10 and Network 4.613 respectively.
The results of sequencing lead to identification of one polymorphic site in kappa casein gene sequences, that nucleotide C substituted by T in exon 4 of kappa casein gene which caused changing CCT codon to TCT, subsequently, based on IUPAC standard genetic code lead to conversion of Proline with Serine amino acid. Genetic diversity of Khalkhali goat was 0.524 which evidences intermediate genetic diversity in this breed. Comparison between species identified 41 mutations that cause 29 haplotype with 0.833 haplotype diversity. Nucleotide diversity and average nucleotide differences (K) among species were 0.04469 and 11.75436 respectively. The analyzing of 129 sequences of farm animal showed that the biggest haplotype is belong to Hap 1 and Hap 2 with 35 and 36 frequency respectively in capra hircus and Khalkhali goat population. Haplotype diversity, haplotype diversity variance, nucleotide diversity in each loci, , average of the number of nucleotide diversity (k) and tajima D were 0.833, 0.0004, 0.0447, 11.7544 and 1.5899 respectively. Tajima D was non-significant at p>0.1 that could be due to use of less samples in this analysis.
Genetic differentiate (Fst) and Genetic distance (DXY) between farm animal based on CSN3 gene were analyzed and the most genetic differentiate were observed between sheep and buffalo and also we estimated less and negative genetic differentiate between Khalkhali goat and capra hircus. That could be because of allele frequencies distribution in marker loci is not normal.
Results illustrated in this study could be useful in characterization of specific dairy products linked to the Khalkhali breed, which are certainly a suitable tool to conserve local breeds and biodiversity. based on mutation identified in k-casein gene and this substation caused changing in Amino acids and based on reports about this gene as good marker genetics in animal breeding programs, recommended that association of this mutation with milk traits would be investigated and if that is significant should be used in Khalkhali goat breeding program. These results could be preferred in future planning of breeding programs and selection schemes for enhancing economic traits.

Knowledge on the extent of linkage disequilibrium (LD) among populations is a useful tool to study the evolutionary history of populations and to detect the genomic regions associated with economically important traits. In recent years, high dense SNP data has been widely applying as the standard tools in LD analysis of livestock populations. In the current study, LD levels in the genome of Fars native cattle were investigated in order to provide basic information required to design genome-wide association studies and to survey on probable changes in the effective size of this population. Moreover, the applicability of the modern genomic technologies such as genomic selection can be evaluated using LD data.
Material and Ten individuals were randomly sampled from Fars native cattle population and were genotyped using Illumina Bovine HD beadchip for 777962 SNP markers. Data quality control was performed based on genotyping call rate, deviation from Hardy-Weinberg equilibrium and minor allele frequency. Thereby, a total of 55,718 SNPs were remained for further analyses.
MAF was calculated using PLINK v1.07 for all autosomal SNPs and the distribution of the allelic frequencies was graphed as the proportion of SNPs represented in 6 different categories of MAF: Number of SNPs located on X, Y, mitochondrial chromosomes and unknown sites were respectively equal to 39367, 1224, 343 and 1735 and all of those were excluded from further analysis. Average distance between adjacent SNPs was equal to 48.1 Kb and the average of MAF was equal to 0.223. Also, 44.5% of sites had MAF Results from this study can be applied to determine the optimum marker density required to achieve enough accuracy in the possible genomic studies on the Fars native cattle. Due to recent intense inbreeding and considerable reduction in effective population size of Fars native cattle population, the high r2 values found in this study were as expected.