Browsing by Author "Ibelli AMG"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens
    (BioMed Central Ltd, 2018-05-21) Moreira GCM; Boschiero C; Cesar ASM; Reecy JM; Godoy TF; Trevisoli PA; Cantão ME; Ledur MC; Ibelli AMG; Peixoto JDO; Moura ASAMT; Garrick D; Coutinho LL
    BACKGROUND: Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken's carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds. RESULTS: ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs. CONCLUSIONS: This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.
  • Thumbnail Image
    Item
    Screening and Identification of Muscle-Specific Candidate Genes via Mouse Microarray Data Analysis.
    (Frontiers Media S.A., 2021-12-13) Raza SHA; Liang C; Guohua W; Pant SD; Mohammedsaleh ZM; Shater AF; Alotaibi MA; Khan R; Schreurs N; Cheng G; Mei C; Zan L; Ibelli AMG
    Muscle tissue is involved with every stage of life activities and has roles in biological processes. For example, the blood circulation system needs the heart muscle to transport blood to all parts, and the movement cannot be separated from the participation of skeletal muscle. However, the process of muscle development and the regulatory mechanisms of muscle development are not clear at present. In this study, we used bioinformatics techniques to identify differentially expressed genes specifically expressed in multiple muscle tissues of mice as potential candidate genes for studying the regulatory mechanisms of muscle development. Mouse tissue microarray data from 18 tissue samples was selected from the GEO database for analysis. Muscle tissue as the treatment group, and the other 17 tissues as the control group. Genes expressed in the muscle tissue were different to those in the other 17 tissues and identified 272 differential genes with highly specific expression in muscle tissue, including 260 up-regulated genes and 12 down regulated genes. is the genes were associated with the myofibril, contractile fibers, and sarcomere, cytoskeletal protein binding, and actin binding. KEGG pathway analysis showed that the differentially expressed genes in muscle tissue were mainly concentrated in pathways for AMPK signaling, cGMP PKG signaling calcium signaling, glycolysis, and, arginine and proline metabolism. A PPI protein interaction network was constructed for the selected differential genes, and the MCODE module used for modular analysis. Five modules with Score > 3.0 are selected. Then the Cytoscape software was used to analyze the tissue specificity of differential genes, and the genes with high degree scores collected, and some common genes selected for quantitative PCR verification. The conclusion is that we have screened the differentially expressed gene set specific to mouse muscle to provide potential candidate genes for the study of the important mechanisms of muscle development.