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Dr. Ayman Gamal Fawzy EL Nagar :: Publications:

Title:
USING GWAS (GENOME WIDE ASSOCIATION STUDIES) FOR GENETIC IMPROVEMENT OF ECONOMIC CHARACTERS IN BUFFALO: A REVIEW ARTICLE
Authors: M.H. Khalil and A.G. EL Nagar
Year: 2025
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: Local
Paper Link: Not Available
Full paper Ayman Gamal Fawzy EL Nagar_EJAP_Volume62_Issue3_Pages205-227 (1).pdf
Supplementary materials Not Available
Abstract:

Traditional progeny testing programs practiced on cattle in the developed countries require a lot of time and expense to be practiced for buffalo in the developing countries. Presently, GWAS (genome wide association studies) for economic characters in buffalo are still very limited compared to dairy cattle. This genomic method makes use of the entire genome to identify the genes associated with economically significant traits. The recent availability of medium density SNP platform (90K array) for buffalo facilitated the integration of genomic information and the phenotypic data to improve the accuracy of the individual buffalo evaluation and to accelerate the genetic improvement program. The present article provides the methodology and applications of GWAS in buffalo in terms of: 1) Website searching for candidate genes associated with lactation, reproduction, semen, growth and carcass traits, 2) Goals and uses of QTL and GWAS approaches, 3) The SNP chips and genetic markers used for GWAS analyses, 4) Genomic databases and Software used in GWAS analyses, 5) The methodology to be performed in GWAS analyses, 6) Implementing single-step GWAS to analyze GWAS dataset, 7) Reliability of GWAS analyses, 8) The advantages of ssGBLUP over classical GWAS, 9) Plotting the Manhattan Figures from GWAS results, 10) Putative candidate genes identified by GWAS analyses in the Egyptian buffalo studies, 11) Candidate and novel genes identified by GWAS analyses in non-Egyptian buffalo studies, 12) Estimating Genomic Breeding Values (GEBV) and Genomic selection (GS), and 13) Highlighted conclusions concerning GWAS.

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