جستجوی مقالات مرتبط با کلیدواژه « پویش ژنوم » در نشریات گروه « کشاورزی »

Identifying genes with large effects on economically important traits, has been one of the important goals in sheep breeding. A method to identify new loci and confirm existing quantitative trait loci (QTL) is through genome-wide association studies (GWAS). QTL-assisted selection and genomic regions affecting the production traits have been considered to increase the efficiency of selection and improve production performance. GWAS typically focuses 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 single nucleotide polymorphism (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 GWAS based on gene-set enrichment analysis for identifying the loci associated with economic traits using the high-density SNPs.

In this research, to identify genes and biological pathways associated with some economic traits, GWAS based on gene-set enrichment analysis was conducted in a F2 population derived from a reciprocal cross by using Illumina iSelect 4K Japanese quail SNP Bead chip. For each bird, traits including body weight gain, feed intake, feed conversion ratio, tibia ash, and foot ash were measured. The SNPs that were associated with traits were identified based on mixed linear models using GCTA software and no correction was made to adjust the error rate. The gene-set analysis consisted of three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and (3) the association analysis between each functional category and the phenotype of interest. In brief, for each trait, nominal P<0.005 from the GWAS analyses were used to identify significant SNPs. Using the biomaRt R package, the SNPs were assigned to genes if they were within the genomic sequence of the gene or 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 three components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge of 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, 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 to GWAS. 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. We hypothesized that the use of gene-set information could improve prediction. It is likely that a better understanding of the biology underlying meat production specifically, plus an advance in the annotation of the quail genome, can provide new opportunities for predicting production using gene-set information. 11 SNPs on chromosomes 2, 3, 4, 5, 10, 18, 20, 24, and 27 located in NPY, DRD2, PTPRN2, BMPR1B, MYF5, IGF2BP1, MYO1E, FGF2, LDB2, BMP4, ACOX1, PCK1, PLCB4, PLCB, and PLCG1 genes were identified. According to gene-set enrichment analysis, 23 categories from gene ontology and the KEGG pathway were associated with the traits (P˂0.05). Among those categories, Protein glycosylation, Myoblast differentiation, Positive regulation of muscle cell differentiation and Biological MAPK signaling pathway, and Calcium signaling pathway have a significant association with skeletal muscle fiber, feed intake, and availability utilization.

This study supported previous results from GWAS and revealed additional regions associated with these economically important traits. Using the findings of this study could potentially be useful for genetic selection to improve production in Japanese quail.

The present study aimed to conduct a genome wide association studies (GWAS) based on Gene-set enrichment analysis for identifying the loci associated with growth traits using the 50K arrays. For this purpose, phenotypes records and genotypic data related to birth weight (BW), weaning weight (WW), six month weight (6MW) and 12 month weight (12MW) were obtained from 240 Hu sheep. Genome wide association study was performed with growth traits using TASSEL software. Using the biomaRt2 package R 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. For the assignment of the genes to functional categories, the GO, KEGG, DAVID and PANTHER databases were used. By Gene set enrichment analysis, the biological pathways and candidate genes of regulation of skeletal muscle cell proliferation  and actin filament polymerization (MYOD1 and IGFBP4), regulation of anatomical structure morphogenesis, regulation of muscle system process and cell junction (BMP2, THBS1 and MYH10), regulation of skeletal muscle tissue development and regulation of anatomical structure size (FGF2, ANGPTL4, TGFBR3 and ACTR3), anatomical structure morphogenesis, positive regulation of ossification and cellular response to growth factor stimulus (MMP7, ACTA2, EGR1 and MYBPC3), for BW, WW, 6MW and 12MW were identified, respectively. The detected candidate genes played an important role in muscle structure development, glucose metabolism, Osteoclast differentiation, body size, skeletal muscle cell differentiation and regulation of ion calcium. These novel candidate genes identified in this study may be useful targets for clarifying our understanding of the molecular basis of growth traits in sheep. These results can provide new insight for future research into the genetic architecture of growth traits in sheep breeding program.

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 aim of this study was to identify the molecular pathways related to litter size in sheep using gene set enrichment analysis. For this purpose, information of high prolificacy sheep breeds including Wadi, Hu, Icelandic, Finnsheep, and Romanov and low prolificacy including Texel and Rahmani were used for genome wide association studies and gene set enrichment analysis. Genome-wide association study was conducted using GenABEL package of R program. Gene set enrichment analysis was performed with the goseq R package to identify the biological pathways associated with candidate genes. We identified different sets of candidate genes related to litter size: BMP5, DHCR24, BMPR1B, ESR1, ESR2 and PLCB1 in Wadi and Romanov; SMAD1, SMAD2, INSR and PTGS2 in Finnsheep and Hu; BMP7, NCOA1 and ERBB4 in Icelandic; BMP4, MSRB3 and SPP1 in Texel; BMP7, EGFR and KCNMA1 in Rahmani. According to pathway analysis, 30 pathways were associated with the litter size trait. Among biological pathways, the TGF-β signaling, Oxytocin signaling, Estrogen signaling, Prolactin signaling, and Insulin signaling pathways have significant association with ovulation rate and litter size trait. Overall, this study supported previous results from GWAS for litter size, also revealed additional regions in the sheep genome associated with litter size in sheep. These findings could potentially be useful for selective breeding for more litter size in sheep.

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 goals to dairy cattle breeding. Quantitative Trait Loci 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 was genome wide association studies (GWAS) based on gene set enrichment analysis for identifying the loci associated with milk yield and somatic cell score traits in Holstein cattle breed using the high-confidence SNPs that enable us to study 164312 SNP markers simultaneously.

In this study, the 1092 Holstein cattle and 164312 markers were analyzed with milk yield, fat percentage, and somatic cell score using plink software with no corrections 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 20 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 genome 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 three 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). It was 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. Eleven SNP markers  were identified on  chromosomes  5,  6,  7,  8, 14, 19, 22, 24, 25, 27, and 28 located  in  ASIC2,  ANXA3,  CCL2,  CCL11,  CCL24, IL33, TLR3, WWOX, EGFR, PRKCA, CAMK2A, KCNMA1, FABP2, SPP1, THBS4, HSP90B1, and ITPR1 genes. Some of the found genes are consistent with some previous studies and are involved in biological pathways related to milk yield and immune systems. According to pathway analysis, 25 pathways from gene ontology and KEGG pathway were associated with the milk yield and somatic cell score traits (P˂0.01). Among those pathways, the sensory perception of chemical stimulus, positive regulation of inflammatory response, and defense response biological pathway have an important role in the immune system and somatic cell score. Also, the GnRH signaling pathway, PPAR signaling pathway, oxytocin signaling pathway, and focal adhesion had a significant association with milk yield and fat percentage traits. Some of these regulatory regions, such as enhancers, are located far from the genes. Therefore, the gene might be part of the analysis, but 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 worth’s 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.

This study supported previous results from GWAS of milk yield and somatic cell score, also revealed additional regions in the cattle genome associated with these economically important traits. So, using these findings can accelerate the genetic progress in the breeding programs.

Identification of selection targeted genomic regions is one of the most challenging areas of research in animal genetics, particularly in livestock. We carried out a genome-wide scan for signatures of positive selection to identify genomic regions that had been under selection in Iranian Khuzestani and Mazandrani buffalo breeds. A total of 148 water buffalo from Khuzestani (N=121) and Mazandrani (N=27) buffalo breeds were genotyped using Axiom® Buffalo Genotyping 90K Array. Unbiased method of population differentiation index (FST) was applied to detect signatures of selection. In total, 23 regions exceeding the 0.1 percent threshold of the empirical posterior distribution were identified as extremely differentiated. These selected genomic regions were surveyed to find encoding putative candidate genes and 64 genes and 27 QTL were extracted from the corresponding areas in UMD3.1 Bos Taurus Genome Assembly. Some of these genes have previously reported as signature of positive selection in the last studies. Some of these genes were also found to be involved in milk production traits and domestication-related changes include sensory perceptions, brain and neural system development, pigmentation, and geographic adaptation. Also, survey on extracted QTLs was shown that these QTLs involved in some economicl important traits in buffalo such as feed conversion ratio, subcutaneous fat, body weight, average daily gain, type, Meat tenderness, milk production conentent, udder attachment, calf size and calving ease traits. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes.