Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken. In total, 475 birds (203 and 272 individuals from the lean and fat lines, respectively) from the 11th generation population of Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) were used. These 475 birds were genotyped by the chicken 60k SNP chip and PennCNV method were used to perform genome-wide CNV detection.

Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken.

Project description:Chickens divergently selected for either high abdominal fat content (fat genotype) or low abdominal fat content (lean genotype) at SRA-INRA, France were used to profile abdominal adipose gene expression during juvenile development (1 to 11 weeks of age) and to identify differentially expressed genes associated with genotype and age. The fat line (FL) and lean line (LL) chickens are different in various phenotypic and metabolic measurements, including abdominal fatness, plasma glycemia and T3. The FL and LL chickens represent unique models for characterizing biomedical and agricultural traits. The Del-Mar 14K Chicken Integrated Systems microarrays were used for a transcriptional scan in abdominal adipose during juvenile development using a balanced block hybridization design. Fluorescence intensities were normalized within array (without background subtraction), and between array (aquantile method) in LIMMA package R [Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Vol. 3, No. 1, Article 3] producing M-values (log-2 expression ratios) and A-values (average log-2 expression values). Normalized values were analyzed using a two factor ANOVA model. A total of 1,020 differentially expressed functional genes were identified (FDR<0.05). Genes were determined to have a significant effect of age (422), genotype (344), or an age by genotype interaction (254). The differentially expressed genes include metabolic enzymes, acute phase proteins, growth factors, coagulation factors, immune factors and transcription factors involved in various pathways. Several of the functional genes are also identified as positional candidate genes within QTLs in an F2 population established from an intercross between the FL and LL lines. Keywords: Divergently selected chickens, fatness, transcriptional profiling, differentially expressed genes A balanced block design was used for microarray hybridizations, where half of the birds of each genotype and age were labeled with Alexa Flour 647 (red) and the other half with Alexa Flour 555 (green). Four biological replicates were used for each genotype (FL or LL) at six different ages (1, 3, 5, 7, 9 and 11 wk).

Project description:Chickens divergently selected for either high abdominal fat content (fat genotype) or low abdominal fat content (lean genotype) at SRA-INRA, France were used to profile abdominal adipose gene expression during juvenile development (1 to 11 weeks of age) and to identify differentially expressed genes associated with genotype and age. The fat line (FL) and lean line (LL) chickens are different in various phenotypic and metabolic measurements, including abdominal fatness, plasma glycemia and T3. The FL and LL chickens represent unique models for characterizing biomedical and agricultural traits. The Del-Mar 14K Chicken Integrated Systems microarrays were used for a transcriptional scan in abdominal adipose during juvenile development using a balanced block hybridization design. Fluorescence intensities were normalized within array (without background subtraction), and between array (aquantile method) in LIMMA package R [Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Vol. 3, No. 1, Article 3] producing M-values (log-2 expression ratios) and A-values (average log-2 expression values). Normalized values were analyzed using a two factor ANOVA model. A total of 3,669 unique differentially expressed functional genes were identified (FDR<0.05). Genes were determined to have a significant effect of age (3,222), genotype (344), or an age by genotype interaction (254). The differentially expressed genes include metabolic enzymes, acute phase proteins, growth factors, coagulation factors, immune factors and transcription factors involved in various pathways. Several of the functional genes are also identified as positional candidate genes within QTLs in an F2 population established from an intercross between the FL and LL lines. Keywords: Divergently selected chickens, fatness, transcriptional profiling, differentially expressed genes

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Gallus gallus

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