Prediction of milk in water buffaloes (Bubalus bubalis) by genomic models

Animal breeding includes necessary tools and methods for more economic and efficient animal production. Utilizing the breeding values, selection studies have led to significant genetic improvements in different species. Detection of genes location together with genomic selection and Marker Assisted Selection (MAS) may be used for economic purposes in animal breeding. In this study, different methods were used to estimate putative genes associated with milk yield by using DNA information. Bayesian methods use the posterior distribution that combines the information from the prior and the likelihood to make statistical inferences about unknown parameters. In genomic selection: precursors regarding gene effects have led to the discovery of different genomic selection methods. In this study: using the raw data set (529 buffalo, 61793 SNP), the most homogeneous layer selected as a result of multidimensional scaling analysis (247 buffalo, 60946 SNP), layers selected according to kinship (for coefficient 0.01, 526 buffalo 61774 SNP; coefficient 0.1, 503 buffalo 61707 SNP; coefficient 0.2, 437 buffalo 61628 SNP; coefficient 0.3, 259 buffalo 61435 SNP; coefficient 0.4, 80 buffalo 60969 SNP), and data sets arranged in terms of LD(Linkage Disequilibrium) (for r2=0.5, 529 buffalo 21173 SNPs; for r2=1, 529 buffalo 52417 SNPs; for variance inflation factor (VIF) = 1, 529 buffalo 177 SNP; for VIF = 2, 529 buffalo 9349 SNP), by making both a genomic relationship matrix and without making a genomic relationship matrix, and by testing different genomic relationship matrices that add other pre-assumptions to the model; LMM (Linear Mixed Model), BSLMM (Bayesian Sparse Linear Mixed Model) analyzes were carried out with the GEMMA program. Using the data obtained, multiple comparison tests were applied to prevent type1 and type2 errors. In the results, the strongest related genomic signals with milk yield were found from chromosomes 5 and 11.