| Grant number: | 17/00462-5 |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| Effective date (Start): | May 28, 2017 |
| Effective date (End): | May 27, 2018 |
| Field of knowledge: | Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals |
| Principal Investigator: | Humberto Tonhati |
| Grantee: | Daniel Jordan de Abreu Santos |
| Supervisor: | Li Ma |
| Host Institution: | Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil |
| Research place: | University of Maryland, College Park, United States |
| Associated to the scholarship: | 15/12396-1 - IMPUTATION OF MARKER GENOTYPES IN BUFFALO GENOME AND ITS IMPACT ON GENOMIC EVALUATION OF MILK YIELD AND CONTENT, BP.PD |
Abstract The homology between the buffalo and bovine species is especially important because it may enable transferring of developed genomic technologies between the two species. The homology principle may be applied to cover the lack of a reference genome for buffaloes because the bovine reference genome has been widely studied. Thus, this project aim at determining existing homologies among bovine species to propose a rearrangement of the buffalo genome. A panel of bovine SNPs will be used for principal component analysis (PCA) and admixture cluster analyses with genomic data of others bovids. Other aim of this project is to assess the genotype imputation in buffalo using machine learning methods (LM). Considering the imputations methods depend on the ordering of the markers in some genomic region, methods based on artificial intelligence can be a good alternative for genotype imputations in buffalo genome. Thus, we will also compare the traditional methods for imputation that needs the order of the SNPs with alternative methods based on machine learning (LM). The LM methods do not require any priori information about the relationship between the predictions and the original data set, and they can to learn this relationship by the own data according to the LM algorithm used. For this study, we will use Support Vector Machines and Random Forest algorithms that represent the supervised learning and unsupervised learning algorithm, respectively. Different scenarios with few numbers of markers and reference populations will be used in this study. The traditional methods can to show litter better results than ML methods, but for the cases with markers with unknown position, as for species without genome reference assembled, the accuracy of genotype imputation could be lower than ML. Thus, considering the absence of imputation studies and the specific genome reference for buffaloes, the genotype imputation using ML methods can be viable alternative. (AU) | |
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