IntroductionDue to the limited amount of agricultural production area available, animals are being rehabilitated to get the maximum performance from each animal with expectations to be able to meet the increasing nourishment needs of humanity [1]. Fast and accurate estimations of existing animal husbandry data are essential to be able to increase future product performances in agriculture and livestock practices [2]. Since the 1990s, phenotype and parent information of the animals have been used to estimate their breeding values, and all genetic progress was achieved based on the phenotypic selection of animals for breeding purposes [3].Genetic improvements of efficiency characters of farm animals take place very slowly due to (a) certain efficiency features being only measurable for 1of the sexes, (b) expression of numerous genes being obtained by the sum of their effects, and (c) major impact of environmental effects on the quantitative characters. This situation reduces the accuracy of genetic evaluations as well. Furthermore, generation range gets longer and the rate of genetic improvement slows down due to the fact that productivity qualifications can only be measured on adult animals [4].The possibility of utilizing genome analysis to provide faster genetic progress on productivity characters has been investigated in recent years. Genome analyses are performed in 3major types as structural, functional, and comparative analyses. Functional genomics studies, biological functions of genes, and the formations and products thereof [5].Genes in a cell give directions to the interior systems of the cell to perform their functions. Various biochemical events occur in the cell as a result of these directions. Metabolites arise from these biochemical events. A phenotype for the aforementioned features can be presented by revealing their relationship with the diseases, efficiencies, and their adaptation.Factors that affect the appearance of metabolites must be fully determined in order to be able to use them in selection studies. The factors that affect metabolites are generally the species, race, age, productivity period, and health conditions of the animals.Metabolomics can be used as biomarkers for some diseases. The relationships between the metabolites and the productivity performances of animals, their adaptation capacities, and their resistance to diseases were identified, and the potential use of metabolites on selection studies was
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