Sabrina Amorim

Sabrina Amorim

Ph.D. Student at Virginia Tech

Virginia Polytechnic Institute and State University

About me

I’m an Animal Scientist, MSc in Genetics and Animal Breeding, and currently a Ph.D. Student in the Animal and Poultry Sciences Department at Virginia Tech. My research interest include quantitative genetics and image analyses of high-throughput phenotyping data. My overarching research interest is to understand the genotype-phenotype map in livestock species using bioinformatics, statistical genetics, and functional genomics. I’m interested in better understanding the genetic architecture of economically important traits, and also apply and develop statistical methods for prediction in the multi-omics era.

Download my resumé.

  • Phenomics
  • Non-additive gene actions
  • Whole-genome Prediction
  • PhD in progress in Animal Sciences, 2021 -

    Virginia Tech

  • MSc in Genetics and Animal Breeding, 2018 - 2020

    Universidade Estadual Paulista - UNESP/FCAV

  • BSc in Animal Sciences, 2013 - 2017

    Universidade Federal de Santa Catarina - UFSC


Improved MeSH analysis software tools for farm animals
The Medical Subject Headings, also known as MeSH, is a controlled life sciences vocabulary maintained by the National Library of Medicine to index journal articles. These headings are manually curated at the National Center for Biotechnology Information and used in the MEDLINE database accessible from PubMed. By mapping MeSH IDs to Entrez Gene IDs, MeSH turns into a powerful resource for enrichment analysis. In 2014, we developed MeSH annotation Bioconductor packages for over 80 species coupled with a MeSH enrichment analysis package that initially became available from Bioconductor 2.14 (April 2014). We reported the first detailed MeSH enrichment analysis of cattle, swine, horse, and chicken accompanied by reproducible R code to perform MeSH analysis. A unique aspect of MeSH is that the quantity and quality of annotations are driven by new knowledge that is disseminated via peer-reviewed scientific articles. In other words, MeSH analysis can be viewed as a community-driven annotation project that is expected to improve over time as more scientific articles get published. Recently, the MeSH framework went through a major change starting from Bioconductor 3.14 (October 2021), resulting in the previous R code being no longer functional. Thus, the objective of this work was to report the improvement of MeSH annotations over past years since the initial release in 2014 and update the animal genetics community regarding the new usage of MeSH Bioconductor packages.
Study of the Genetic Variability of Meat Fatty Acid Profile in Nelore Cattle Finished in Feedlot
Among the attributes of beef meat, the fatty acid profile is important because it affects not only the meat palatability, but also the human health. In recent years, fatty acids harmful to human health have received considerable attention. Several studies, working with taurine breeds, showed that there is genetic variability for meat fatty acid profile and, therefore the possibility of genetic improvement of fatty acid composition in beef cattle. Moreover, the results of these studies showed favorable genetic correlations estimates between fatty acids. However, genetic parameter estimates for fatty acid profile in zebu cattle are scarce. The meat fatty acid profile is difficult and costly to measure. For this type of trait is indicated the application of genomic selection, which is a type of marker-assisted selection. Despite the major advances in genetic molecular techniques that allow the genotyping of hundreds of animals in less time and in an automated fashion, there are still being developed methodologies that allow the incorporation of genomic information in animal breeding programs. The objective of this project is to study the genetic variability of meat fatty acid profile in Nelore cattle finished in feedlot conditions, and implement models and methods that use genomic information to improve the fatty acid composition of beef meat. To attain this objective we propose the following specific objectives: 1) Study and characterize the profile of meat fatty acids of Nelore cattle finished in feedlot 2) Estimate genetic parameters for meat fatty acid composition in Nelore cattle finished in feedlot 3) Implement genome wide association studies between single nucleotide polymorphisms markers (SNPs) with meat fatty acid composition 4) Predict the genomic breeding values for meat fatty acid profile considering different models 5) Verified the expression patterns of genes involved in lipid metabolism and fatty acid synthesis 6) Study the influence of polymorphisms (functional polymorphisms) on the expression of several genes of interest. Approximately from 800 to 1,000 Nelore males finished in feedlot conditions (minimum 90 days), aged around two years old, it were utilized. From the individual concentration of fatty acids, it will be calculated the proportion of saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, the ratio of polyunsaturated fatty acids and saturated fatty acids, fatty acids of n-6 and n-3 series and the n-6/n-3 ratio. In addition, the desaturation index (ID) (adding a double bond) and elongation index (IE) (conversion from 16 to 18 carbon chains atoms) will be calculated. The genetic parameter and (co)variance estimates for these traits will be estimated by restricted maximum likelihood method. The Bovine HD SNP BeadChip, with more than 777,000 SNP, will be utilized to genotyped the animals. To perform the genome wide association analyses, single and multiple regression analyses, will be done. These analyses will allow a genome scan in seeking areas of interest in the genome. Then, SNPs with significant effects obtained in the multiple regression analysis, it will be analyzed by an animal model including the effects of SNPs in the model. The genomic breeding values for meat fatty acid profile will be predicted using different a prior distributions for variances and SNPs effects. The estimation of SNP effects will be performed with BLUP, BayesB and BayesLasso models. The results of this project will establish new elements to differentiate, develop and promote the attributes of Brazilian beef meat on the basis of a solid scientific and technical support. In addition, this project will allow the formation, training and qualification of students and teachers that will be important for future research and for teaching in undergraduate and graduate courses.
Genomic Evaluation in Commercial Beef Cattle Population Using Real and Simulated Data
This project aims to evaluate the impact on commercial beef cattle genetic evaluation by the inclusion of genomic information in the evaluation considering different uncertain paternity situations and different genotyping strategies in a real and simulated population. Will be simulated two herds, where one is a commercial beef cattle considering different proportion of genotyped young animals with unknown sire and maternal grandsire, and the second one will be a population belonging to a breeding program with complete pedigree and genomic information. The main idea is to use genomic information from young animals to quickly establishing genetic links between the commercial beef cattle population and a population from breeding program. The relationship matrix (A and G matrix) will be created considering different proportions of young animals (calves) with unknown sire (25, 50 and 75%) and unknown grandsires (0, 25, 50, 75 and 100%) to evaluate the implementation of BLUP and single step genomic BLUP (ssGBLUP). In this population, calves from the last three generations will be genotyped considering the proportion of 25, 50, 75 and 100% of the animals. Will be simulated phenotypic records for two traits with low (0.12) and moderate heritability (0.34) similar to stayability and yearling weight, respectively. Also, will be used real data with records for stayability and weight yearling traits from a Nelore cattle population provided by Nelore Brazil breeding program from National Association of Breeders and Researchers (ANCP). The database available contain genomic information of 11,000 animals and 30,000 and 60,000 phenotypic records for stayability and weight yearling, respectively. These information are from 18 farms located in the Southeast, Midwest and North of Brazil. The results of this project will allow: 1) To assess the impact of the use of genomic information in commercial beef cattle herds with different population structure; 2) Check the proportion of young genotyped animals necessary to obtain reliable genetic evaluations in commercial beef cattle herds; 3) evaluate the technical and economic feasibility of genomic evaluations using the ssGBLUP in commercial beef cattle herds; 4) To form and consolidate a research group at national and international level, consisting of different experts of recognized trajectory, this group will form a solid platform for the consolidation of future research projects; 5) Implement statistical methodologies for the use of genomic data in animal breeding. The information generated in this project will be widely disseminated at regional, national and international levels, through different strategies (presentations at national and international conferences, technical and scientific articles, abstracts, etc.), taking into account the different audiences (students, technicians, designers). In this process several actors of the beef industry will participate, from the farmers, researchers, technicians, graduate students, insemination centrals and industry. This project will outline strategies and tools that will support the genetic improvement of beef cattle commercial herds for several traits that influence the profitability of beef cattle systems. In addition, the results of this project should help to design strategies using genomic information that will identify genetically superior animals with higher reliably in commercial herds.
Epistatic Interactions Associated With Fatty Acid Profile of Beef From Nellore Cattle
The meat is an important source of amino acids, vitamins, minerals and fatty acids (FA). Health consciousness among consumers has grown the more information and misinformation is publicized about the effects of food components, the demand for healthier products increased, and recently the composition of fatty acid contents in beef cattle. Several studies with taurine breeds indicate the existence of genetic variability in the fatty acid profile of beef. Genomic selection has been suggested as an alternative to deal with complex traits such as fatty acid composition of meat. Epistasis is a non-additive genetic effect, and it occurs between SNPs (single nucleotide polymorphisms), genes or QTLs (quantitative trait loci). Approaches have already been made to model epistatic effects in genomic selection, however, studies that consider the identification of epistatic effects among genomic regions are still rare in livestock animals. The variation of fatty acids can be explained by the characterization and identification of epistatic interactions that may be useful for phenotype prediction for commercial animals. In this sense, the aim of this study is to identify epistatic interactions for each fatty acid trait in Nelore cattle meat. Data from 943 Nelore male animals from farms that integrate DeltaGen, CRV PAINT and Nelore Qualitas breeding programs and that also participates in the Thematic Project (FAPESP Process: 2009/16118-5) and Young Researcher Project (FAPESP Process: 2011 / 21241-0 ) will be used. The FA profile will be analyzed in Longissimus thoracis samples using gas chromatography, and capillary column of 100m. Animals were genotyped using the BovineHD BeadChip (High-Density Bovine BeadChip) Illumina® with 777,000 SNPs. The software EPISNPmpi and EPISNP will be used to analyze FA traits and to identify epistatic interactions. For the windows analyses, the BayesC model will be used in GenSel software to adjust SNPs and epistatic effects. Enrichment analysis of the genes and GO terms identification will be searched at ENSEMBL Biomart. Terms associated with metabolic processes of FA will be used to identify genes associated with epistasis of FA. The present project provides a unique opportunity for study and genomic association based on epistatic interactions for fatty acid profile in the meat of Nelore cattle.
An Assessment of the Relationship Between Genomic Connectedness and Prediction Accuracy for Fat Composition Traits in Nellore Beef Cattle
Genetic connectedness assesses the extent to which estimated breeding values can be fairly compared across management units. Connectedness in genetic evaluation is important if management units differ in their genetic mean. The concept of genetic connectedness in the whole-genome prediction era can be extended to measure the connectedness level between the reference and validation sets. In genomic prediction (GP), several statistical models address additive and non-additive effects parametrically and nonparametrically. When non-additive genetic variation is accounted, it can be used to predict total genetic values, to increase the efficiency of mate allocation procedures as well as crossbreeding or purebred selection schemes. However, the relationship between the estimated level of connectedness and prediction accuracies in the presence of non-additive genetic variation is less well understood and little is known about the impact of non-additive gene action on genomic connectedness measures. Despite the recent achievements in GP, there is still a drastic shortage of non-additive gene action studies in cattle breeds. The objective of this study is to investigate the relationship between genomic connectedness and prediction accuracy from additive and non-additive gene actions for fat composition traits in Nellore cattle. For fatty acid profile, data from 943 Nellore male animals from farms that integrate DeltaGen, CRV PAINT, and Nelore Qualitas breeding programs, the Thematic Project (FAPESP Process: 2009 / 16118-5), and the Young Researcher Project (FAPESP Process: 2011 / 21241-0) will be used. The fatty acid profile was analyzed in Longissimus thoracis samples using gas chromatography, and capillary column of 100m. Animals were genotyped using the BovineHD BeadChip (High-Density Bovine BeadChip) Illumina® with 777,000 SNPs. The data set for fat composition traits encompasses records from 66,496 females and their relatives, totaling 176,069 phenotypic records for growth, carcass, reproductive, and feed efficiency indicator traits. The pedigree contained information from 244,254 animals, born between 1977 and 2016. A total of 8,652 animals were genotyped with the low-density panel (CLARIFIDE® Nellore 2.0). Genotypes were imputed to a panel containing 735,044 markers using the FIMPUTE 2.2 software. We will assess genome-based connectedness across management units by applying prediction error variance of difference and coefficient of determination. The present project provides a unique opportunity to characterize non-additive gene actions associated with fat composition traits in Nellore cattle. Given that connectedness and prediction accuracies have important influences on genomic selection, this project will be of interest to wider community members including academics and industry professionals.

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