SaLLy - Statistical Genetics

Statistical genetics is a field of study that applies statistical methods to genetic data in order to understand the genetic basis of complex traits and diseases. It involves the analysis of data from genetic studies, such as genome-wide association studies (GWAS), linkage analysis, and sequencing data, to identify genetic variants that are associated with a particular phenotype.

Statistical genetics also involves the development of statistical models and methods for analyzing genetic data. This includes methods for estimating genetic parameters, such as heritability and linkage disequilibrium, and for detecting genetic variants that are associated with complex traits.

Overall, statistical genetics plays an important role in advancing our understanding of the genetic basis of complex traits and diseases, which has important implications for personalized medicine, drug development, and public health.

Meet Our Statistical Genetics Team

Vanda Lourenço

Vanda Lourenço

Research Coordinator & Team Leader

Key Publications

- Inácio, V., M. Lourenço, V., de Carvalho, M., Parker, R. A., & Gnanapragasam, V. (2021). Robust and flexible inference for the covariate‐specific receiver operating characteristic curve. Statistics in Medicine, 40(26), 5779-5795.

- Lourenço, V. M., Ogutu, J. O., & Piepho, H. P. (2020). Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data. BMC genomics, 21(1), 1-18.

- Rodrigues, P.C., Heuvelink, E., Marcelis, L.F.M., Chapman, S., and van Eeuwijk, F. (2021). An analysis of synthetic yield data for pepper shows how genotype by environment interaction in yield can be understood in terms of yield components and their QTLs. Crop Science, 61:1826-1842

- Lourenço, V., Rodrigues, P.C., Pires, A.M. and Piepho, H.-P. (2017). A robust DF-REML framework for variance components estimation in genetic studies. Bioinformatics. 33:3584-3594.

Rodrigues, P.C., Monteiro, A., and Lourenço, V.M. (2016). A Robust additive main effects and multiplicative interaction model for the analysis of genotype-by-environment data. Bioinformatics 32:58–66.

The complete list of publications from SaLLy members can be found in our Google Scholar profile.

Ongoing Projects and Activities

- A hybrid robust-weighted AMMI modeling approach with generalized weighting schemes;

- A robust modeling framework for the study of QTL-by-environment interaction: Evaluation using simulation and empirical data.