Academic Profile

Biography 

Dr. Daniela Lourenco is an Associate Professor in Animal Breeding, Genetics, and Genomics at the University of Georgia in the United States. She was born and raised in Brazil, where she earned her MS and PhD degrees in animal breeding and genetics from Maringa State University. Daniela has been working in this field since 2004 and her current interests include the use of genomic information to increase rates of genetic progress, the development of methods for genomic evaluations, and the use of computational algorithms to analyze large data. She has authored over 275 conference proceedings, 162 peer-reviewed publications and 85 invited talks on five continents. She has taught several short courses and trained geneticists globally. Daniela has built a successful and solid international collaboration and has received over 40 visitors from different countries since 2015. Dr. Lourenco oversees a team of 15 graduate students and postdocs and received the UGA Fred C. Davis Early Career Award in 2020. Her research group has been working on genomic selection in beef and dairy cattle, swine, poultry, and fish. They have been involved in the development and implementation of single-step genomic evaluations for several breeding companies and breed associations. The software and algorithms developed by her group are being used for genomic evaluations worldwide.  

B.S., Biological Sciences, Maringa State University, Brazil
M.S., Animal Breeding and Genetics, Maringa State University, Brazil
Ph.D., Animal Breeding and Genetics, Maringa State University, Brazil and University of Georgia

Description of Research Interests

The increasing availability of genomic information for nearly all livestock species have led to increased interest in using this information to help selecting the best animals in genetic improvement programs. The genomic selection has been associated with additional accuracy in estimating breeding values and reduction in generation intervals, which lead to higher and faster genetic gains. However, the genomic response may be different depending on the species and the population structure, and the genomic strategy may be still unclear in some cases. My research has been focused on improving livestock production using genomic information, and addressing issues related to the implementation of genomic selection in beef and dairy cattle, poultry, swine, and fish. My work comprehends finding the most appropriated methodologies, models, and strategies to evaluate livestock populations.

Recent Awards and Activities

2020: UGA Fred C. Davis Early Career Award
2021 – now: CDCB's Genetic Evaluation Methods task force member
2022 – now: Section Editor for Journal of Dairy Science
2022 – now: Associate Editor for Genetic Selection Evolution
2022 – now: AGBT-Ag Scientific Committee Co-chair
2022 – 2026: WCGALP Permanent Committee
2022 – 2026: WCGALP Scientific Advisory Committee

Selected Recent Publications (2023)

Cesarani, A., D. Lourenco**, M. Bermann, E.L. Nicolazzi, P.M. VanRaden, and I. Misztal. 2023. Single-step genomic predictions for crossbred Holstein and Jersey cows in the US. J. Dairy Sci. Comm. https://doi.org/10.3168/jdsc.2023-0399

Hidalgo, J., D. Lourenco**, S. Tsuruta, M. Bermann, V. Breen, and I. Misztal. 2023. Derivation of indirect predictions using genomic recursions across generations in a broiler population. J. Anim. Sci. skad355. https://doi.org/10.1093/jas/skad355

McWhorter, T., M. Sargolzaei, C.G. Sattler, M.D. Utt, S. Tsuruta, I. Misztal, and D. Lourenco**. 2023. Single-step genomic predictions for heat tolerance of production yields in U.S. Holsteins and Jerseys. J. Dairy Sci. https://doi.org/10.3168/jds.2022-23144

Bussiman, F. C.Y. Chen, J. Holl, M. Bermann, A. Legarra, I. Misztal, and D. Lourenco**. 2023. Boundaries for genotype, phenotype, and pedigree truncation in genomic evaluations in pigs. J. Anim. Sci. 101: skad273. https://doi.org/10.1093/jas/skad273

Jang, S., R.Ros-Freixedes, J.M. Hickey, C.Y. Chen, W.O. Herring, J. Holl, I. Misztal, D. Lourenco**. 2023. Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs. Genet. Sel. Evol. 55:55.  https://doi.org/10.1186/s12711-023-00831-0

Leite, N.G., E.F. Knol, S. Tsuruta, S. Nuphaus, R. Vogelzang, and D. Lourenco**. 2023. Using social interaction models for genetic analysis of skin damage in gilts. Genet. Sel. Evol. 55:52. https://doi.org/10.1186/s12711-023-00816-z

Jang, S., S. Tsuruta, N.G. Leite, I. Misztal, and D. Lourenco**. 2023. Dimensionality of genomic information and its impact on GWA and variant selection: a simulation study. Genet. Sel. Evol. 55:49. https://doi.org/10.1186/s12711-023-00823-0

Hidalgo, J., D. Lourenco**, S. Tsuruta, M. Bermann, V. Breen, and I. Misztal. 2023. Efficient ways to combine data from broiler and layer chickens to account for sequential genomic selection. https://doi.org/10.1093/jas/skad177

Jang, S., R. Ros-Freixedes, J.M. Hickey, C.Y. Chen, W.O. Herring, I. Misztal, D. Lourenco**. 2023. Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs. Front. In Genet. https://doi.org/10.3389/fgene.2023.1163626

Cesarani, A., M. Bermann, C. Dimauro, L. Dagano, D. Vicario, D. Lourenco**, and N.P.P. Macciotta. 2023. Strategies for choosing core animals in APY and their impact on the accuracy of single-step genomic predictions. Animal. 100766. https://doi.org/10.1016/j.animal.2023.100766

Steyn, Y., T. Lawlor, D. Lourenco**, and I. Misztal. 2023. The importance of historically popular sires on the accuracy of genomic predictions of young animals in the US Holstein population. J. Dairy Sci. Comm. https://doi.org/10.3168/jdsc.2022-0299

Steyn, Y., T. Lawlor, Y. Masuda, S. Tsuruta, D. Lourenco**, and I. Misztal. 2023. Non-parallel genome changes within sub-populations over time contribute to genetic diversity within the U.S. Holstein population. J. Dairy Sci. https://doi.org/10.3168/jds.2022-21914

Garcia, A., S. Tsuruta, G. Gao, Y. Palti, D. Lourenco**, T. Leeds. 2023. Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing. Genet. Sel. Evol. https://doi.org/10.1186/s12711-023-00782-6

Leite, N.G., E.F. Knol, S. Nuphaus, R. Vogelzang, S. Tsuruta, M. Wittmann, and D. Lourenco**. 2023. The genetic basis of swine inflammation and necrosis syndrome and its genetic association with post-weaning skin damage and production traits. J. Anim. Sci. skad067. https://doi.org/10.1093/jas/skad067

Hollifield, M.K., M. Bermann, D. Lourenco**, and I. Misztal. 2023. Exploring the statistical nature of independent chromosome segments. Livest. Sci. 105207. https://doi.org/10.1016/j.livsci.2023.105207

Bermann, M., I. Aguilar, D. Lourenco, I. Misztal, and A. Legarra. 2023. Reliabilities of breeding values in models with metafounders. Genet. Sel. Evol. https://doi.org/10.1186/s12711-023-00778-2

Guinan, F.L., G.R. Wiggans, D. Norman, J.W. Dürr, J.B. Cole, C.P. Van Tassell, I. Misztal, and D. Lourenco**. 2023. Changes in genetic trends in US dairy cattle since the implementation of genomic selection. J. Dairy Sci. https://doi.org/10.3168/jds.2022-22205

McWhorter, T.M., M. Bermann, A.L.S. Garcia, A. Legarra, I. Aguilar, I. Misztal, and D. Lourenco**. 2023. Implication of the order of blending and tuning when computing the genomic relationship matrix in single-step GBLUP. J. Anim. Breed. Genet. https://doi.org/10.1111/jbg.12734

Ramos, P.V.B., G.R.O. Menezes, D.A. Silva, D. Lourenco, G.G. Santiago, R.A.A. Torres Jr, F.F. Silva, P.S. Lopes, and R. Veroneze. 2023. Genomic analysis of feed efficiency traits in Nellore cattle using random regression models. J. Anim. Breed. Genet. https://doi.org/10.1111/jbg.12840

Courses Taught

ADSC 8110 (Spring): Linear Models Applied to Breeding and Genetics
ADSC 8886 (Spring): Current Literature in Genetics and Computing Relevant to Applications in Animal Breeding