I’m Mehdi Momen, a Statistical and Quantitative Geneticist and a post-doctoral research associcate at the Department of Animal and Poultry Sciences at the Virginia Polytechnic Institute and State University (VT). I am profoundly interested in the world of Statistical Genetics and Genomics and its connection with the organism life. The highly-specific relay of information processed from the outside of the DNA deep into its appearance intrigues me. I’m currently conducting research in this field in the Dr. Morota Lab. I’ve been trained in various aspects spanning computational genetics and genomics, now it’s time to extend many of which I’ve learned during my time as a PhD student at SBUK university (Dr. Ayatollahi Mehrgrardi Lab) and visiting student at Wisconsin-Madison University (Prof. Daniel Gianola and Prof. Guilherme Rosa Lab).
My research interests span a broad range of topics including animal breeding, plant breeding, and human quantitative genetics. I am particularly interested in applying statistical learning methods to multi-layer omic data, dissection of the genetic architecture of complex traits, predictive ability of dense arrays of variants for animal, plant and human populations. The primary research areas I want to focus on, deriving the prediction error variance by using bootstrap aggregation sampling and investigated the relationship between empirical connectedness measures and prediction accuracy in the cross-validation framework. Also demonstrated the potential of non-parametric relationship matrices to quantify genomic connectedness and prediction accuracy in the presence of non-additive gene actions. Subsequently, development of algorithms finds an optimized training set from a larger set of candidate individuals such that it minimizes the prediction error variance in the test set.