Roland A.

My research was focused on African cowpea, a valuable legume predominantly cultivated in Africa, accounting for more than 90% of its worldwide production. It primarily aimed to uncover the genetic mechanisms behind cowpea's adaptation to high temperatures, a critical concern in the face of climate change. My mission encompasses a wide range of tasks aimed at improving food and nutritional security. These tasks included conducting environmental association studies, utilizing genotyping and environmental data, exploring homologous genes in cowpea's related species, and contributing to the creation of innovative genomic datasets. To achieving these goals, I employed a variety of techniques, including evolutionary, population, and quantitative genetics, as well as plant breeding skills. I use UNIX shell, Python, R, and other programming languages to facilitate data analysis and interpretation.

France (SYSAAF) - Inra Centre-Val-de-loire 37380 Nouzilly. (September 2019 – June 2022). My work was threefold:

Advisory support to poultry breeder (40% of my working time). This mission involves collaborating with breeders to select animals for mating. To achieve this, I utilize advanced statistical toolsVCE, Wombat, the F90's family programs and the TM software to calculate the genetic parameters of successive generations, based on phenotypic and/or genomic dataset. These parameters are then used to calculate the genetic values of the candidate animals for reproduction. Using these genetic values, the most promising animals, those with high performance and low inbreeding are selected to create new generation.

I worked on the TE-GWAS project, which focused on using Transposable Elements (TEs) as markers in Genome-Wide Association Studies (GWAS). The goal was to explore rice genetic diversity to enhance food security in the face of global challenges by understanding the functional roles of TEs and leveraging GWAS. My role involved processing data, conducting GWAS analyses, and identifying rice varieties containing candidate transposable elements for pipeline validation. The dataset came from the Rice 3,000 Genomes Project, while the TE insertion dataset was sourced from a previous project in the host lab. I utilized various GWAS tools, including Lfmm, Mlmm, Emma, Gapit, and Cate. The innovative TE-GWAS approach was successfully published in the peer-reviewed journal New Phytologist.