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Dr. Rekaya is a Professor of Animal Breeding and Genetics at the University of Georgia. He received an agriculture engineer degree from the Hight Institute of Agriculture, Tunisia, a Master of Science from the International Center for Advanced Studies in Mediterranean Agriculture of Zaragoza, Spain, and a PhD from the Polytechnic University of Madrid, Spain.
Dr. Rekaya has an extensive experience in quantitative genetics, genomics, biostatistics and bioinformatics. His research program is centered in the development of statistical and computational tools for analysis for big genetic and genomic data sets with application in livestock, poultry and human. Dr. Rekaya’s research has been supported by several federal, state and private funding agencies including the USDA NRI, AFRI and NIFA programs, NAAB, the Georgia Agricultural Commodity Commission for beef, Cobbs-Vantress, Inc., Merial, Inc., and Tyson Foods. He has published 134 refereed journal articles, 4 book chapters, and over 170 abstracts and proceedings.
Dr. Rekaya is an adjunct professor at the Statistics department and an associate faculty member with the Institute of Bioinformatics at UGA. He has taught several international courses in Canada, Norway, Brazil and Kenya.
Agriculture Engineer, Ecole Superieur d’Agriculture de Mateur, Tunisia
M.S., International Center for Advanced Studies in Mediterranean Agriculture, Zaragoza, Spain
Ph.D., Polytechnic University of Madrid, Spain
Dr. Rekaya’s primary research interest is in the improvement of the statistical methodology used in the genetic evaluation of livestock and poultry species for the improvement of production, health and efficiency traits. Dramatic increase in the available amount of phenotypic, pedigree and molecular data has created unprecedented opportunities for genetic improvement and for a better understanding of the genetic basis of complex traits. However, it has created major management and analysis challenges. Solving these challenges requires the development of appropriate statistical and computational tools. Dr. Rekaya research is centered in developing these resources using classical and Bayesian statistics, bioinformatics, and machine learning approaches.
Bayesian Inferences with applications to Animal Agriculture
QTL mapping and Microarray Gene Expression Data Analysis - Syllabus
Selected Recent Publications
González-Cerón, F., R. Rekaya, and S.E. Aggrey. (2015). Genetic analysis of bone quality traits and growth in a random mating broiler population. Pout. Sci. 94(5): 883-889
Rekaya, R. and S. Aggrey. (2015). Genetic properties of residual feed intakes for maintenance and growth, and the implications of error measurement. J. anim. Sci. 93:944-948
Lee, J., A.B. Karnuah, R. Rekaya, N.B. Anthony, S.E. Aggrey. (2015). Transcriptomic analysis to elucidate the molecular mechanisms that underlie feed efficiency in meat-type chickens. Mol. Genet. Genomics 290(5):1673-82.
González-Cerón, F., R. Rekaya, N. B. Anthony, and S.E. Aggrey (2015). Genetic analysis of leg problems and growth in a random mating broiler population. Pout. Sci. 94 (2): 162-168.
Hay, EH, and R. Rekaya. (2015). A structural model for genetic similarity in genomic selection of admixed populations. Livest. Prod. Sci. 181:72-76
Hay, EH., and R. Rekaya. (2015). A multi-compartment model for genomic selection in admixture populations. Livest. Prod. Sci. 177: 1-72
González-Cerón, F., R. Rekaya, and S.E. Aggrey. (2015). Genetic relationship between leg problems and bone quality traits in a random mating broiler population. Pout. Sci. 94 (8): 1787-1790.
Toghiani, S., S. E. Aggrey, and R. Rekaya. (2016). Multi-generational imputation of SNP marker genotypes and accuracy of genomic selection. Animal, 10(7):1077-1085.
Rekaya, R., Smioth, S., E. H. Hay, A. Ling, S. Aggrey. 2016. Analysis of discrete responses with error specific misclassification. The application of Clinical genetics, 9:169-177.
Cui, Z., B. Marshall, N. Shi, S. Y. Chen, R. Rekaya, H. X. Liu. (2017). RNA-Seq analysis on chicken taste sensory organs: An ideal system to study organogenesis. Scientific Reports, 7(1): 9131. DOI:10.1038/s41598-017-09299-7
Aggrey, S. E., F. González-Cerón, R. Rekaya, Y. Mercier. (2017). Gene expression differences in the methionine remethylation and transsulphuration pathways under methionine restriction and recovery with D,L-methionine or D,L-HMTBA in meat-type chickens. J Anim Physiol and Anim Nutr 10/2017;, DOI:10.1111/jpn.12779
Toghiani, S., E. Hay, P. Sumreddee, T. W. Geary, R. Rekaya, A. J. Roberts. (2017) Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed. Journal of Animal Science 10/2017; DOI:10.2527/jas2017.1944
Habashy, W. S., M. C. Milfort, K. Adomako, Y. A. Attia, R. Rekaya, S. E. Aggrey. (2017). Effect of heat stress on amino acid digestibility and transporters in meat-type chickens. Poultry Science 96:2312-2319.
Toghiani, S., L. Y. Chang, A. Ling, S. E. Aggrey, R. Rekaya. (2017). Genomic differentiation as a tool for Single Nucleotide Polymorphism prioritization for Genome wide association and phenotype prediction in livestock. Livestock Science 205: 24-30.
Chang, L. Y., S. Toghiani, A. Ling, E. H. Hay, S. E. Aggrey, R. Rekaya. (2017). Analysis of Multiple Binary Responses Using Threshold Model. Journal of Agricultural Biological and Environmental Statistics 08/2017; DOI:10.1007/s13253-017-0305-6
Habashy, W. S., M. C. Milfort, A. L. Fuller, Y. A. Attia, R. Rekaya, S. E. Aggrey. (2017). Effect of heat stress on protein utilization and nutrient transporters in meat-type chickens. International Journal of Biometeorology 08/2017; DOI:10.1007/s00484-017-1414-1
Sumreddee, P., S.Togniani, E. H.i Hay, A. Roberts, S. E. Aggrey, and R. Rekaya (2018). Inbreeding depression in line 1 Hereford cattle population using pedigree and genomic information. J Anim Sci 97(1):1-18, https://doi.org/10.1093/jas/sky385
Hay, EH, R. Rekaya. 2018. Use of Observed Genomic Information to Infer Linkage Disequilibrium between Markers and QTLs. Agricultural Sciences 09(11):1470-1478.
Habashy, W., M. C. Milfort, R. Rekaya and S. E. Aggrey, 2018.Expression of genes that encodes cellular oxidant/antioxidant enzyme are affected by heat stress in meat-type chickens. Molecular Biology Reports 45(3):389-394. doi: 10.1007/s11033-018-4173-0
Chang, L.Y., S.Toghiani, A. Ling, S. E. Aggrey,and R. Rekaya. (2018). High density marker panels, SNPs prioritizing and accuracy of genomic selection. BMC Genet. 2018; 19: 4. https://doi.org/10.1186/s12863-017-0595-2
Vilar da Silva, J.H., F. Gonzalez-Ceron, E. W. Howerth, R. Rekaya and S. E. Aggrey, 2018. Inhibition of the transsulfuration pathway affects growth and feather follicle development in meat-type chickens. Animal Biotechnol. doi: 10.1080/10495398.2018.1461634
Ling A, EH Hay, SE Aggrey, and R. Rekaya. 2018. A Bayesian approach for analysis of ordered categorical responses subject to misclassification. PLoS One 13(12): e0208433 2018