candidate gene

The principal aim of the dairy sheep industry worldwide is to produce high-quality milk. Mastitis is an inflammatory disease in dairy animals that occurs in response to infectious factors and has substantial impacts on animal health, and economic profitability. Somatic cell count has been used as an indirect method to control mastitis. Genetic resistance to mastitis involves linked biological mechanisms resulting from differences in mastitis response, which activate and regulate different levels of the immune response. Over the last decade, interest in identifying genes or genomic regions targeted by selection has grown. Identifying selection signatures can provide valuable insights into 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 effective genes and genomic regions under positive selection in two sheep breeds using selection signature methods. For this purpose, FST and hapFLK analyses were performed using the genome-wide single nucleotide polymorphisms (SNPs).

In this study, data from 585 sheep of different breeds were used to identify genomic regions under selection related to the somatic cell counts in milk. Illumina ovine Bead Chip 50K was used to determine the genotype of the samples. The genomic information of sheep breeds was extracted from the Zenodo database. Quality control on genotyped samples was performed using PLINK v1.9. SNPs with a minor allele frequency (MAF) of less than 0.02 and those with a call rate of less than 0.97 were excluded. In addition, individuals with more than 10% missing genotype data were removed. SNPs that did not conform to Hardy–Weinberg equilibrium (P<10-6) were also eliminated. Following these quality control measures, 41,673 SNPs from 585 sheep were retained for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under the software packages FST and hapFLK, respectively. The candidate genes were identified using PLINK v1.9 software and the Illumina gene list in R by SNPs located in the 0.01 percentile of FST and hapFLK values. In addition, the latest published version of the animal genome database was used to define QTLs associated with economically important traits at identified loci. The GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes.

Using the FST approach, nine genomic regions on chromosomes 2, 5, 7, 10, 11, 13 (two regions), 17, and 22 were identified. The identified candidate genes associated with somatic cell count in these genomic regions included IL11RA, CDC16, CARD14, BTRC, OTUD4, COL23A1, LACTB, and PRELID3B. Some of the genes located in the identified selection regions were associated with the immune system, innate immune response, inflammation response, cancer disease, and milk production. Some of these genes in the selected regions were consistent with previous studies. The investigation of reported QTLs showed that these regions are related to QTLs of important economic traits, including milk somatic cell count, udder height and depth, clinical mastitis, bovine tuberculosis susceptibility, and heat tolerance. Moreover, the results of hapFLK statistics in this research led to the identification of six genomic regions on chromosomes 3, 4, 5, 7, 10, and 13. The identified candidate genes associated with the somatic cell count in these genomic regions included FAM49A, CDK6, and DLGAP5. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with known genes related to innate immune and various cancers.

Different genes that emerged in the aforementioned regions can be considered candidates for selection based on their function. It was found that most of the selected genes were consistent with some previous studies and were involved in production traits. However, further investigation is recommended to determine the exact function of the identified genes and QTLs. These areas should also be confirmed by other independent studies using larger samples. In general, the data from this study may be used in research on genomic selection and genomic regions associated with mastitis in dairy sheep and in further reviews and evaluations for the improvement of dairy sheep production.

Identifying selection signatures  can provide valuable insights into genomic regions that are under positive selection, which in turn leads to a better understanding of genotype-phenotype relationships Over the past few decades,, selection programs in broiler chicken have been based on fast growing and increased feed efficiency. On the other hand, this selection for rapid growth has been resulted an accumulation of fatty tissue and decrease of chicken meat quality, it has been new challenges in poultry breeding due to genetic correlation between rapid growing and fat deposition. The aim of the present study was to identify genomic regions under positive selection associated with body fat deposition in seven-week-old broiler chickens  using a statistical method based on linkage disequilibrium.

In the present study, a total 475 chickens from two chicken lines divergently selected were obtained using the Illumina chicken 60 K SNP chip. The broilers used in this study were from two Chinese broiler lines. In the first step, for the detected regions of the genome were evaluated using the XP-EHH method based on linkage disequilibrium using Selscan software v.2.0. Candidate genomic regions and genes were identified by SNPs located at 1% upper range of XP-EHH values in ten creeping windows. Finally, GeneCards and DAVID databases were also used to interpret the function of the obtained genes. Additionally, the latest published version of Animal genome database was used for defining QTLs associated with fat deposition traits in identified locations.

Candidate genes STAB2, TAPT1, JDP2, FNDC3B, PTPN11, ADIPOR1 and SLC44A3 obtained these regions. Further investigation using bioinformatics tools showed these genomic regions overlapped with lipid metabolism, fatty acid transport, lipoprotein receptors, glucose metabolism and homeostasis. Various genes that were founded within these regions can be considered as candidates under selection based on function. Also, a survey on extracted QTLs showed that these QTLs involved in some economically important traits in chicken such as abdominal fat weight and carcass fat weight traits.

However, will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. Identifying important economic traits and locating parts of the genome that have changed as a result of selection could be used in poultry breeding programs. The results of our research can be used to understand the genetic mechanism controlling fat deposition trait and using these findings could potentially be useful for genetic selection in chicken for increasing body weight while reducing body fat deposition during a broiler breeding.

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. The aim of this study was to identify candidate genes and genomic region related to litter size trait in Murcia-Granadina goats using the selective sweep methods.

In this study, data from 643 Murcia-Granadina goat genotyped using Caprine 50 K BeadChip were used. Quality control measures were performed in Plink by setting animal call rate of 0.90, SNP call rate of 0.90 and SNPs with minor allele frequencies (MAF) lower than 0.01. To identify the signatures of selection, two statistical methods of FST and hapFLK were used. Candidate genes were identified by SNPs located at 1% upper range of FST and hapFLK. Finally, GeneCards and UniProtKB databases were also used to interpret the function of the obtained genes.

Using FST approach, we identified nine genomic regions on chromosomes 4, 5, 6, 8, 12, 13, 14, 15, and 22 chromosomes. The identified candidate genes associated with litter size trait in these genomic regions included KMT2E, CAMK2D, CTNNAL, DACH1, DNMT3B, STK3. Some of the genes located in identified regions under selection were associated with the oocyte growth, development and differentiation of ovarian follicles, fertility and growth and development of granulosa cells, which can be directly and indirectly related to the trait of the litter size. Also, survey on extracted QTLs was shown that these QTLs in cow orthologous associated sperm count and calf size. The results of hapFLK statistics in this research led to the identification of four genomic regions on chromosomes 1, 2, 5, and 11. The identified candidate genes associated with the litter size trait in these genomic regions included EDA2R, KCNH7 and CNOT11. It was determined that they had different functions in folliculogenesis and spermatogenesis. Also, survey on extracted QTLs was shown in cow orthologous associated calving interval.

  By the way, various genes that were founded within these regions can be considered as candidates under selection based on function. However, will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. Finally, the results of our research can be used to understand the genetic mechanism controlling litter size trait.

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.

Molecular markers that reveal polymorphisms at the DNA level now play a key role in animal genetics. However, the selection of molecular markers is crucial depending on the purpose, viz. this depends on different molecular biology techniques and their effects. Over the last decade, interest in identifying genes or genomic regions targeted by selection has grown. Identifying selection signatures can provide valuable insights into 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. Type characteristics are important for breed identification and classification and are also positively correlated with body weight. This study aimed to identify effective genes and genomic regions under positive selection signatures in different goat breeds using selection signature methods. For this purpose, FST and hapFLK analyses were performed using the genome-wide single nucleotide polymorphisms (SNPs).
In this research, the information from 728 goats of four different breeds was used to identify genomic regions associated with type traits. To determine the genotype of the samples, Illumina caprine Bead Chip 50K was used. The genomic information of goat breeds was extracted from the Figshare database. Quality control was performed using the Plink software. The markers or individuals were excluded from 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, 36861 SNPs from goat SNP chip 50K from 691 goats remained for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under the software packages FST and hapFLK, respectively. Candidate genes were identified using the Plink v1.9 software and the Illumina gene list in R by SNPs located in the highest  FST and hapFLK values. In addition, the latest published version of the animal genome database was used to define QTLs associated with economically important traits at identified loci. The GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes.
The FST and hapFLK statistics were used to identify genomic regions subjected to positive selection associated with type traits in four goat breeds. Using the FST approach, we identified eight genomic regions on chromosomes 3, 4, 7, 13, 15, 18, 20, and 29. The identified candidate genes associated with type traits in these genomic regions included TGFBR3, CALCR, ACAD8, BCAR1, and ADAMTS6. Some of the genes located in the identified selection regions were directly and indirectly related to cell differentiation and proliferation, skeletal muscle growth and development, body length, calcium channel regulation, muscle fiber homeostasis, protein synthesis, and muscle cell size. Some of these genes in the selected regions were consistent with previous studies. The results of the reported QTLs in the selected regions and the bovine orthologous regions were QTLs located in the identified regions that were related to average daily gain, body weight, trunk width, and metabolic body weight. Furthermore, the results of the hapFLK statistics in this research led to the identification of five genomic regions on chromosomes 1, 5, 6, 13, and 30, and they were in the 99.9th percentile of all hapFLK values. The identified candidate genes associated with the type trait in these genomic regions included FNDC3B, STAB2, and CCNY. They were found to have different functions in fibroblast proliferation and bone cell differentiation.
Various/different genes that emerged in studied regions can be considered candidates for selection based on their function. By the way, various genes found in these regions can be considered candidates for selection based on their function. Most of the selected genes were found to be consistent with some previous studies and to be involved in production traits. A survey of extracted QTLs also found that these QTLs are involved in some economically important traits in goats, such as average daily gain and body weight in yearlings. However, further association and functional studies are required to demonstrate the importance of the genes obtained from association analyses. Leveraging these findings can accelerate genetic progress in breeding programs and help understand the genetic mechanism that controls these traits.
In this research, to identify genomic regions under selection associated with type traits were used the information obtained from 728 goats of different breeds including Beetal, Daira Deen Panah, Barbari, Teddi, 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 691 goats were remained for the future analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under FST and hapFLK software packages, respectively. Candidate genes were identified by SNPs located at 1% upper range of FST and hapFLK using Plink v1.9 software and the gene list of Illumina in R. Additionally, the latest published version of Animal genome database was used for defining QTLs associated with economic important traits in identified locations. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes. We used the FST and hapFLK statistics to identify genomic regions that have been under positive selection associated with type traits in four goat breeds. Using FST approach, we identified eight genomic regions on chromosomes 3, 4, 7, 13, 15, 18, 20, and 29 chromosome. The identified candidate genes associated with type trait in these genomic regions included TGFBR3, CALCR, ACAD8, BCAR1, ADAMTS6. Some of the genes located in identified regions under selection were associated with the cell differentiation and proliferation, skeletal muscle growth and development, body length, calcium channel regulation, muscle fiber homeostasis, protein synthesis and muscle cell size which can be directly and indirectly related to the trait of the type traits. Some of these genes in the selected regions were consistent with previous studies. Result of the reported QTLs in the selected regions and the orthologous regions of cattle were located in the identified regions, QTLs related to average daily gain, body weight, rump width and body metabolic weight. Also, the results of hapFLK statistics in this research led to the identification of five genomic regions on chromosomes 1, 5, 6, 13, and 30, and they were in the 99.9 percentile of all hapFLK values. The identified candidate genes associated with the type trait in these genomic regions included FNDC3B, STAB2 and CCNY. It was determined that they had different functions in proliferation of fibroblasts and differentiation of bone cells. Result of the reported QTLs in the selected regions and orthologous cattle in the identified regions, QTLs related to metabolic body weight were located. various genes that were founded within these regions can be considered as candidates under selection based on function. Most of the genes under selection were found are consistent with some previous studies and to be involved in production traits. Also, survey on extracted QTLs was shown that these QTLs involved in some economical important traits in goat such as average daily gain and body weight in yearling. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes and survey on QTLs related to selected regions. However, will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. Using these findings can accelerate the genetic progress in the breeding programs and can be used to understand the genetic mechanism controlling this trait.

The present study aimed to conduct a genome wide association studies based on Gene-set enrichment analysis for identifying the loci associated with conformation traits in some goat genotyped with the Caprine 50K SNP chip. For this purpose, phenotypes records related body length, body height, pubic bone length, heart girth and chest length were obtained. Genome wide association study was performed with conformation traits using GEMMA software. Using the biomaRt2 R package the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene. Finally, a gene enrichment analysis was performed with the KOBAS platform from online bioinformatics databases for the assignment of the genes to functional categories. In this research, genomic region related to conformation traits on chromosomes 1, 4, 5, 6, 10, 11, 16, 17, 22 and 27 were identified. Also, genes related to body conformations traits in our study included PDE5A, WDR1, ATF3, SIPA1L1, TMTC2, CHCHD3, SHROOM2, TBPL2, ADIPOQ, ASAH1 and LRPPRC. According to pathway analysis, 18 pathways from gene ontology and biological pathways were associated with the conformation traits. The results of our research can be used to understand the genetic mechanism controlling conformation traits and considering, this study supported previous results from GWAS of conformation traits, also revealed additional regions, using these findings could potentially be useful for genetic selection in goat.

Genomic selection has provided the dairy industry with a powerful tool to increase genetic gains on economically important traits such as milk production (Taylor et al. 2016). One way to identify new loci and confirm existing QTL is through genome-wide association analysis (GWAA). In addition identifying of genes loci with large effects on economically important traits, has been one of the important goal to dairy cattle breeding. QTL assisted selection and genomic regions affecting the production traits have been considered to increase the efficiency of selection and improve production performance. Genome wide association studies typically focus on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and deepen the understanding of the genetic background of complex traits, is to move up the analysis from the SNP to the gene and gene-set levels. In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS, is tested for over-representation in a specific pathway.

The present study aimed to conduct a genome wide association studies (GWAS) based on Gene-set enrichment analysis for identifying the loci associated with milk protein composition traits. For each cow, a total of eight traits including protein yield, protein percentage, αs1-casein, αs2-casein, β-casein, κ-casein, α-lactalbumin and β-lactoglobulin were recorded using plink software and no any correction to adjust the error rate. The gene set analysis consists basically in three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and finally (3) the association analysis between each functional category and the phenotype of interest. In brief, for each trait, nominal P-values < 0.05 from the GWAS analyses were used to identify significant SNP. Using the biomaRt R package the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene, to include SNP located in regulatory regions. For the assignment of the genes to functional categories, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into 3 components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge on molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set. The gene enrichment analysis was performed with the goseq R package. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in BioMart, Panther, DAVID and GeneCards databases.

Gene set enrichment analysis has proven to be a great complement of genome-wide association analysis (Gambra et al., 2013; Abdalla et al., 2016). Among available gene set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics (Morota et al., 2015, 2016). We had hypothesized that the use of gene set information could improve prediction. However, neither of the gene set SNP classes outperformed the standard whole-genome approach. Gene sets have been primarily developed using data from model organisms, such as mice and flies, so it is possible that some of the genes included in these terms are irrelevant for milk production. It is likely that a better understanding of the biology underlying milk production specifically, plus an advance in the annotation of the bovine genome, can provide new opportunities for predicting production using gene set information. According to gene set enrichment analysis, 20 categories from gene ontology and KEGG pathway were associated with the related to traits (P˂0.05). Among those categories, the Oxytocin signaling pathway, Glycerolipid metabolism, Response to progesterone, detection of calcium ion, Complement and coagulation cascades and amino acid binding including candidate genes CDH13, P4HTM, SPP1, CSN1S1, CSN2, SERPINA1, SLC35A3, ODC1 and PAEP have significant association with protein yield and content, phosphorylation of glycoproteins, coagulation and curd firming milk and lactose synthesis. Some of these regulatory regions, such as enhancers, are located far from the genes. Therefore, although the gene might be part of the analysis, the relevant variant would probably not be included in the gene set SNP class. Finally, linkage disequilibrium interferes with the use of biological information in prediction because irrelevant regions (regions without any biological role) capture part of the information encoded in relevant regions, causing both regions to exhibit similar predictive abilities. The use of very high density SNP data or even whole genome sequence data could alleviate some of these issues.Finally, it is worth noting that our gene-set enrichment analysis was conducted using a panel of SNP obtained from a single marker regression GWAS, which relies on a simplified theory of the genomic background of traits, without considering for instance the joint effect of SNP. Hence, other approaches (e.g., GWAS exploring SNP by SNP interactions) might provide a better basis for biological pathway analysis.

Our finding showed a genetic selection potential for improving milk quality to milk protein composition per animal. Since genetic improvements are heritable, cumulative, and permanent, this improvement would be permanent and beneficial.Keywords: pathway analysis, casein, α-lactalbumin, β-lactoglobulin, candidate gene

Milk production and reproductive traits are important economic traits in goat. The different approaches including candidate gene mapping and genome-wide association studies (GWAS) are now implemented in goats in an attempt to identify the molecular mechanisms affecting these economically important traits. The gene ontology (GO) analysis gives a controlled vocabulary for describing attributes of genes and gene products. Kyoto Encyclopedia of Genes and Genomes (KEGG) provides the mythology for the careful examination of gene functions in respect of networks of genes and molecules. Gene network analysis can also identify paths and processes shared by candidate genes. The purpose of the present study was to bioinformatics analysis (GO, path enrichment, and network analysis of the effects of protein-protein interaction) of genes effective in litter size and milk production in goat.

Candidate genes associated with studied traits were retrieved from literature review, review of candidate gene studies, GWAS and NCBI database. GO analysis and enrichment analysis of the signaling pathways of KEGG were performed using online database of G: Profiler. The String database was used to infer the network of protein-protein interactions (PPI) and the selection of performance module. Cytoscape software was used to draw the resultant networks of protein-protein interactions. Finally, cytoHubba was used to identify Hub genes.

GO analysis for litter size showed that, for molecular function the candidates genes enriched in signaling receptor binding, signaling receptor active activity, hormone activity, protein binding and transforming growth factor beta receptor activity. The effect of the transforming growth factor β (TGF-β) family on fertility and reproduction, embryonic development, organogenesis, and uterine growth and function is remarkable in a wide range of organisms. In mammals, even early stages of reproductive development, including male and female germline specification, are controlled by TGF-β-related proteins. Hormone binding play an important role in fertility and reproduction. Sex hormone-binding globulin (SHBG), specifically bound to the biologically active androgens and estrogens that are key regulators of the reproductive organs as well as other sex-differentiated tissues such as muscle, adipose tissue, and bone. KEGG analysis also identified some signaling pathways including ، PI3K-Akt signaling pathway، TGF-beta signaling pathway and ovarian steroidogenesis which are significantly associated with litter size. GO analysis for 33 candidate genes related to milk production traits identified only one GO category for biological process namely linoleic acid metabolic process.

In this study, we identified several significant biological and signaling pathways that can be used to better understand the biological processes associated with litter size and milk production in goats.

Sheep are used as a genetic model to study the relationship between genetic diversity and ovulation rate. Previous studies have shown that ovulation rate and litter size can be controlled by a set of genes called fecundity genes . Three fertility genes have been identified in sheep called BMPR1B or FecB, GDF9 or FecG and BMP15 or FecX. Tetra-ARMS-PCR is a suitable alternative method in the context of expensive methods such as sequencing and PCR-RFLP to identify SNPs whose sequences are known. The entry of the Booroola allele into the Afshari breed increased the frequency of litter size. The aim of this study was to investigate the polymorphism of two fertility genes including FecB and FecG in Mughani, Afshari and Baluchi sheep in Khorasan Razavi province.Blood samples were taken from 95 Afshari, Baluchi and Mughani sheep. Two pairs of control (outer) and specific (inner) primers were used to amplify FecB and FecG gene fragments by the Tetra-ARMS PCR method. Temperature cycling of PCR amplification for FecB started at 94°C for 4 minutes, followed by 35 cycles consisting of 94°C for 25 seconds, annealing temperature at 54°C for 35 seconds, extension at 72°C for 20 seconds and a final extension at 72°C for 5 minutes. For amplification of the G1 point mutation on the FecG gene, the annealing temperature was 54°C for 35 s, extension was 70°C for 40 s, and final extension was 70°C for 5 minutes. The PCR products were electrophoresed on a 2% agarose gel.Three genotypes have been observed for the FecB gene in Afshari sheep; wild homozygous (++), heterozygous (B+) and homozygous mutant (BB). All Mughani and Baluchi sheep were homozygous for this gene. A 108 bp band was detected for mutant homozygotes. A band of 213 bp was observed in wild homozygotes. The frequency of wild homozygotes was high in the three breeds,. The frequency of the wild allele in this breed was 0.39 and that of the mutant allele 0.61, the overall frequencies for the three breeds being 0.8 and 0.2, respectively. The result of Tetra-ARMS-PCR for the G1 point mutation of the GDF9 gene showed polymorphism in all three breeds, however, the frequency of wild homozygotes was high. In the Balochi breed, only two animals (4%) were heterozygous and 96% of the animals showed the wild homozygous genotype. In the Mughani breed, 16% were heterozygous and only one animal was homozygous mutant. In addition, only one heterozygote was observed in the Afshari breed. Several studies have shown the association of the FecB, FecG and FecX genes with litter size in sheep. Due to the high frequency of wild homozygotes and the fact that Mughani and Baluchi ewes mostly gave birth to single lambs, the role of FecB mutation in litter size of Afshari ewes was observed. The ram used in this herd was heterozygous for FecB. In general, it can be concluded that the mutant FecB allele has a correlation with litter size in Iranian sheep. However, the statistical confirmation of this issue requires a dedicated study with a detailed record of reproductive traits.The results of this study demonstrated the presence of a Booroola mutation in the Afshari ewes that were offspring of heterozygous rams. In addition, all of these ewes had litter size in at least one of the two pregnancies. Hence, the Booroola mutation may potentially increase the litter size of ewes. However, a more comprehensive study measuring biological indicators such as sex hormone levels, ovulation rate and follicle size is needed to demonstrate the effect of these types of mutations on increasing fertility.

In this study in order to estimate inbreeding coefficient and identify the ROH Islands associated with the genes under selection, the 50k BeadChip genotyped data of 206 sheep from 3 different Egyptian breeds were used. After quality control, 48361 SNPs in 204 sheep 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. One percent of SNP with the highest frequency in ROH were considered as ROH Islands. The lowest rate of inbreeding according to (FGRM, FHOM, and FUNI) was related to Wahati and the highest was related to Saidi breed. The highest amount of FROH (0.043) was observed in Saidi and the lowest amount (0.018) was observed in Barki breed. Average length of ROH ranged from 45.02 to 205.87 Mb, while the average number of ROH ranged from 8.14 to 14.07. The highest number ROH was observed on chromosome 2, while the lowest was on chromosome 26. Average inbreeding coefficient from FROH in Barki, Saidi and Wahati were estimated 0.018, 0.043 and 0.027 respectively. A total of 62 ROH Islands with length: 24.60 Kb to 13 Mb were identified, which covering less than 1% of the sheep genome. The ROH Islands was not distributed across the genome uniform and varied among breeds, but some common were identified. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with reported genes that directly or indirectly influenced traits for adaptation and immune system (SEMA3D, CSF2, ITPR1), development of the skeletal muscle (UGGT1, ITGA2), and reproduction (ABHD16B). The results of this study revealed that, the selection processes in different sheep breeds for economic traits during several years, has led to the formation of many ROH islands in sheep genome, therefore scanning these regions at the genome level can be an alternative strategy to identify genes and associated loci with economic traits.

Genetic architecture of sheep reproduction is increasingly gaining scientific interest due to the major impact on sheep production systems. The present study aimed to conduct for identifying the loci associated with total number of progeny born and first lambing age in highly prolific Chios sheep using the 50K arrays.

In this research, phenotypic and genotypic data related to total number of lambs born and first lambing age in lifetime and first lambing age were obtained from 538 highly prolific sheep. The gene set analysis consists basically in three different steps: the assignment of SNPs to genes, the assignment of genes to functional categories and finally the association analysis between each functional category and the phenotype of interest. Genome wide association study was performed with reproductive traits using GEMMA software. Using the biomaRt2 R package the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene. Finally, a gene enrichment analysis was performed with the KOBAS platform from online bioinformatics databases for the assignment of the genes to functional categories, the GO, KEGG, DAVID and PANTHER databases were used.

In this research, 11 SNP markers on chromosomes 1, 2, 5, 7, 8, 10, 13, 15, 17, 22 and 23 were identified. Also, we identified different sets of candidate genes related to reproductive traits: QULP1, AURKA, TEX12, DLG1T, ACVR1, LCP2 and PPFIA2 in Chios sheep. Some of these genes identified in this research were consistent with previous studies. In the enrichment analysis of gene sets, 19 biological pathways related to reproduction traits were identified. Some of these pathways have important role in fertilization, ovulation rate, estrogen biosynthesis, conception rate, skeletal muscle development, early development of the fetus and puberty.

The results of this research can further reveal the role of genetic mechanisms affecting reproductive traits. Considering the conformation of previous genomic region and the new genomic regions identified in this study, the findings of this research can be used more confidently in breeding programs for important economic traits in sheep.

Identifying of genes with large effects on economically important traits, has been one of the important goal to sheep breeding. Over recent years, advances in DNA-based marker technology have made it possible to identify genomic regions or quantitative trait loci (QTLs) underlying complex traits, such as fleece traits, in sheep. The present study aimed to conduct a genome wide association studies (GWAS) based on gene-set enrichment analysis for identifying the loci associated with wool traits in native Zandi sheep using the 50K arrays.
A total of 300 Iranian Zandi sheep used in this study came from the Zandi sheep breeding station. Wool sampling coincided with the maximum wool growth prior to the shearing of wool. In order to facilitate sampling, sheep were restrained in a lateral position and true wool from the left mid-side site was cut from a 5×5 cm2 close to the skin using regular scissors. Each sample was separately packaged and labeled with ear tag number of the sheep. We measured and recorded four wool production traits: staple length (SL), mean fiber diameter (MFD), fiber diameter coefficient of variation (CVFD), and the proportion of fiber that are equal or more than 30 µm (PR), kemp percentage (KEMP%) and outer coat fiber (OCF) were measured. Genomic DNA extraction from sheep blood was performed by the applying a modified salting out protocol and genotyping of the Sheep SNPChip 50 K SNP Bead from Illumina Inc. The gene set analysis consists basically in three different steps: the assignment of SNPs to genes, the assignment of genes to functional categories, and finally the association analysis between each functional category and the phenotype of interest. Genome wide association study was performed with wool traits using GEMMA software. Using the biomaRt2 R package, the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 50 kb up- and downstream of the gene and bioinformatics analysis was implemented to identify the biological pathways performed in GO, KEEG, DAVID and PANTHER databases. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into 3 components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge on molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in GeneCards databases.
The result from genomic control showed weak population stratification with for woot traits among Zandi sheep population. We identified different sets of candidate genes related to wool traits including: CEP290, PRKCZ, TMTC3, RHPN2, TNFSF4, NLGN1, SPHKAP, PLCE1, FAT1 and PIK3R4 in Zandi sheep. Some of the found genes, are consistent with some of the previous studies related to reproductive traits. According to pathway analysis, 21 pathways from gene ontology and biological pathways were associated with the wool traits (P˂0.05).  Some of the genes were found are consistent with some prior studies and to be involved biological pathways related to hair follicle development, keratinocytes differentiation, synthesizes an enzyme of threonine kinases, development of epidermal and Wnt signaling pathway.
In total, this study supported previous results from GWAS of wool traits, also revealed additional regions in the sheep genome associated with these economically important traits. These findings could potentially be useful for genetic selection in the breeding programs and can be used to understand the genetic mechanism controlling this trait.

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.

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.

The goal of genome-wide association (GWA) studies of quantitative traits is to identify genomic regions that explain a substantial proportion of the genetic variation for the trait, with the ultimate goal to identify causal mutations underlying the genetic basis of the trait. The standard GWA approach is to genotype a population that has been phenotyped for the trait(s) of interest and genotyped for many genetic markers across the genome and to analyze these data by estimating and testing the effects of marker genotypes on phenotypes using a regression-type of analysis for each single nucleotide polymorphism (SNP), one at a time. Bayesian methods such as Bayes A and Bayes B assume a heavy tail prior distribution for SNP effects and use Markov Chain Monte Carlo (MCMC) to sample from the posterior distribution. Although the objective of these methods was to predict the breeding value of selection candidates (genomic breeding values), they do that by estimating the effects of all SNPs. The estimated SNP effect, the proportion of variance explained by a SNP, or the number of times the SNP fits in the model with non–zero effect can be used as criteria to identify locations or genomic regions that affect the trait of interest. Results have shown that these Bayesian methods can effectively detect QTL in simulated and real data. Recently, a new methodology has been developed to address this limitation and allow for a better understanding of the genetic architecture of complex traits through a gene network analysis. For this purpose, to identify genomic regions and candidate genes associated with egg weight (EW), a genome-wide association study (GWAS) was performed in the present study using Affymetrix 600 K high density SNP array in 1,078 hens of 11th generation of Rhode Island Red. Data available for 1,078 pedigree-recorded hens were used to collect phenotypic EW-related data. Seven traits, including egg weight at the first laying of hens, and egg weight at 28, 36, 56, 66, 72, and 80 weeks of age were collected for each bird.  The analyses were performed using GenSel v4.73R, by fitting covariates for haplotype alleles in BayesA and BayesB models. A single Markov chain Monte Carlo (MCMC) chain of length 41,000, including burn-in of 1,000 first iterations, was computed for each analysis to obtain posterior estimates of covariate effects. These were used to obtain a direct genetic variance for animals. The primary analysis showed that correlations and regression coefficients had converged at this chain length. Annotation terms and pathway analyses were conducted using protein analysis through evolutionary relationships of PANTHER software version 10.0. The results showed that the BayesA method performed better in explaining additive genetic variance compared to BayesB method. Nine markers obtained from BayesA with the highest additive genetic variance were located on chromosomes 1, 3, 5, and 20. Genes that overlap in regions of interest were identified with the Ensembl BioMart data mining (http://www.ensembl.org/biomart/) based on the Galgal6 assembly and the Ensembl Genes 96 database. The detected SNPs were located close to 35 genes, among which, the candidate genes of BPIFB2, OCX36, CPT1A, TCF15, CECR2, SIAH3, FADS1, FADS2, and SGK1 play important functions in the egg production process through the albumen protein formation, fatty acids metabolism, and eggshell formation. It is noteworthy that the present study has detected an association in regions different from that reported by previous studies. This can be because of flock particularities, such as the extent of linkage disequilibrium, allelic frequencies, and statistical approaches. The results of the present study showed that when the genetic architecture of studied traits follows infinitesimal model assumptions, the BayesA method usually performs better than BayesB. Moreover, considering the identification of new genome regions and the key role of the mentioned genes on the development of egg weight, the efficiency of the BayesA method can be confirmed for GWAS in egg weight traits.

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.

Unlike evaluation of male fertility which is done based on the phenotype records of females e.g., conception rate, semen traits are directly measured in bulls. The objective of this study was to identify genomic regions associated with sperm traits in Holstein bulls using the genome wide association based on gene set enrichment analysis.

For this purpose, phenotype records included 1508 genotyped Holstein bulls were used for ejaculate volume, sperm concentration, number of motile sperm, progressive sperm motility, and number of progressive motile sperm in each ejaculation. The evaluation of genome-wide association was carried out PLINK software (v. 1.90). In the next step, using the biomaRt2 R package R the SNP was assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene. For the assignment of the genes to functional categories, the GO, KEGG, DAVID and PANTHER databases were used.

In this research, 11 SNP markers on chromosomes 1, 2, 5, 7, 8, 15, 18, 20, 21, 25, and 27 associated with SPEF2, SEPT12, SLC24A, BSP3, ETNK1, ERBB4 (sperm motility and number of progressive motile sperm), DSCAML, MYH3, RPM1 , RPM2, CAPSL (number of sperm and sperm concentration), DMRT1, and PPARγ genes (ejaculate volume) were identified. Some of these genes in the significant regions were consistent with some previous studies. In pathway analysis, 15 pathways from gene ontology were associated with the sperm traits. Among those pathways, the purine nucleotide metabolic process, cellular calcium ion homeostasis and regulation of fertilization biological pathway had an important role in the progressive sperm motility and number of progressive motile sperm. Also, the calcium channel regulator activity, PPAR signaling pathway, steroid hormone mediated signaling pathway and focal adhesion had significant association with ejaculate volume and sperm concentration.

The Identification of these candidate genes and selection bulls using genomic selection can considerable to improve male fertility and benefits of animal breeding centers and decrease production prices for consumers.

The aim of the present study was to evaluate the genetic architecture, genomic regions and candidate genes associated with body weight gain, feed intake and feed conversion ratio in Japanese quails. For detection the informative genomic windows, genotyping data on 920 quails was used in a single-step genome-wide association study. The BLUPf90 family software was used to perform related analyses. Theresults was calculated based on the proportion of additive genetic variance (agv) explained by genomic region with an average size of 1.5-Mb of adjacent SNPs. Windows with accounting for more than 1% of the agv were used to identify genomic regions and to search for candidate genes. A total of 13 significant windows over 8 chromosomes were explained 23% of the agv for the body weight gain and including SMYD1, ADGRG6 and CFL2 candidate genes. A peak on CJA2 explained the largest proportion of variance. For feed intake, we identified 20 informative windows across 8 chromosomes and including ACSL, PPA2, FGF2 and RBL2 candidate genes. These regions explained 38% of the agv and a peak on CJA4 explained of agv. Also, for the feed conversion ratio, 12 significant windows were identified on 7 chromosomes and explained 23.7% of agv, contained ATRNL1 and PTPN4 candidate genes. Four genomic regions had a pleiotropic effect. Considering the identification of new genome regions and the key role of the mentioned genes related to feed intake, the single step method can be validated for GWAS in feed efficiency traits.

Understanding the genetic control of growth traits is one of the most important breeding goals in poultry industry. Genomic selection has provided the poultry industry with a powerful tool to increase genetic gains on economically important traits such as meat production. One way to identify new loci and confirm existing QTL is through genome-wide association analysis (GWAA). In addition identifying of genes loci with large effects on economically important traits, has been one of the important goal to poultry breeding. QTL assisted selection and genomic regions affecting the production traits have been considered to increase the efficiency of selection and improve production performance. Genome wide association studies typically focus on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and deepen the understanding of the genetic background of complex traits, is to move up the analysis from the SNP to the gene and gene-set levels. In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS, is tested for over-representation in a specific pathway.

The aim of the present study genome wide association studies (GWAS) based on Gene set enrichment analysis for identifying the loci associated with related to body weight and shank length and diameter traits in advanced intercross line (AIL) using the high-confidence SNPs that enable us to study 161376 SNP markers simultaneously. For this purpose, the 599 advanced intercross line and 161376 markers were performed with mixed linear model (MLM) approach was used for the GWAS of the F9 generation, as implemented in the GCTA package (v1.92) (Yang et al., 2011) and no any correction to adjust the error rate. The gene set analysis consists basically in three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and finally (3) the association analysis between each functional category and the phenotype of interest. In brief, for each trait, nominal P-values < 0.005 from the GWAS analyses were used to identify significant SNP. Using the biomaRt R package the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene, to include SNP located in regulatory regions. For the assignment of the genes to functional categories, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into 3 components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge on molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set. The gene enrichment analysis was performed with the goseq R package. In the next step, a bioinformatics analysis was implemented to identify the biological pathways performed in BioMart, Panther, DAVID and GeneCards databases.

Gene set enrichment analysis has proven to be a great complement of genome-wide association analysis (Abdalla et al., 2016). Among available gene set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics (Morota et al., 2015, 2016). We had hypothesized that the use of gene set information could improve prediction. However, neither of the gene set SNP classes outperformed the standard whole-genome approach. Gene sets have been primarily developed using data from model organisms, such as mice and flies, so it is possible that some of the genes included in these terms are irrelevant for meat production. It is likely that a better understanding of the biology underlying meat production specifically, plus an advance in the annotation of the chicken genome, can provide new opportunities for predicting production using gene set information.11 SNP markers on chromosomes 1, 2, 4, 5, 7, 8, 10, 11, and 27 located in MSTN, CAPN3, PNPLA3, ANXA2, IGF1, LDB2, LEPR, FN1, ‌TMEM135, MC4R, EDN1, and ADAMTS18 genes were identified. Some of the genes were found are consistent with some previous studies and to be involved biological pathways related to muscle skeletal growth, energy metabolism and bone growth and development. According to pathway analysis, 19 pathways from gene ontology and KEGG pathway were associated with the body weight and shank length and diameter trait (P˂0.05). Among those pathways, the regulation of muscle organ development, regulation of cell growth and anatomical structure homeostasis biological pathway has an important role in the growth and skeletal muscle development. Also, the anatomical structure formation involved in morphogenesis, positive regulation of ossification and calcium signaling pathway significant association with body weight and shank length and diameter traits. Some of these regulatory regions, such as enhancers, are located far from the genes. Therefore, although the gene might be part of the analysis, the relevant variant would probably not be included in the gene set SNP class. Finally, linkage disequilibrium interferes with the use of biological information in prediction because irrelevant regions (regions without any biological role) capture part of the information encoded in relevant regions, causing both regions to exhibit similar predictive abilities. The use of very high density SNP data or even whole genome sequence data could alleviate some of these issues. Finally, it is worth noting that our gene-set enrichment analysis was conducted using a panel of SNP obtained from a single marker regression GWAS, which relies on a simplified theory of the genomic background of traits, without considering for instance the joint effect of SNP. Hence, other approaches (e.g., GWAS exploring SNP by SNP interactions) might provide a better basis for biological pathway analysis.

Considering, this study supported previous results from GWAS of body weight and shank length and diameter traits, also revealed additional regions in the chicken genome associated with these economically important traits, presented here should be contribute to a better understanding of the genetic control of growth traits in broiler chicken and using these findings can accelerate the genetic progress in poultry breeding programs.

The β-lactoglobulin gene (β-LG) has been considered to be one of the most important genes involved in the milk productin related traits. In the present research, the association of β-LG polymorphism with breeding values for milk production related traits was investigated in Brown Swiss cattle. The data of this study was collected from the Golpayegan dairy farm. In this study, genomic DNA was extracted from 150 blood samples by modified salting out method. Following this stage, a fragment of 247 bp was amplified and used for enzymatic digestion. The HaeIII restriction enzyme was used to digest of 247 bp of gene. The results showed that genotypes AA, AB and BB of the β-LG gene were estimated to have frequencies of 0.24, 0.48 and 0.28, respectively. These Genotypes were distributed according to the Hardy-Weinberg equilibrium.Genetic parameters were estimated by the restricted maximum likelihood (REML) method using DFREML program. Heritability and repeatability for milk yield traits were estimated to be 0.154 and 0.309, respectively. The statistical analysis based on the General Liner Models revealed that there is no significant effect for β-LG genotypes on the milk yield breeding values.