I am a senior postdoctoral researcher involved with different projects related to molecular breeding, statistical genomics and systems genetics. My prime interest lies in Pine genomics, which was fueled by the opportunity I had to play a critical part in the development of a SNP array for tropical pines. Ultimately, I would like to establish an informatics framework towards genome-assisted breeding in Pines – for growth, wood quality and resilience to pests and diseases as well as climate change – using data science and machine learning approaches.

In 2020 the FMG Population Genomics Team together with the international tropical pine SNP consortium, developed a genotyping array for tropical pine species, their hybrids and other closely related species. The SNP array is currently being used to establish pine species references that will aid in species and hybrid identification. The first pine genetic linkage map has been constructed and we can now perform marker-trait association analyses. As a result of advanced phenotyping platforms, next-generation sequencing technologies and cost-effective genomic resources, plant breeding has become a data science. We are therefore setting up an informatics resource in the form of a database with a front-end for hosting SNP genotype, phenotype and environment data as a basis for machine learning and genomic selection pipelines. Other data science projects that I am involved with include combining systems genetics and artificial intelligence to dissect the regulatory pathways of wood formation in Eucalyptus; using machine learning approaches to predict lumber yield and lumber quality from Pine growing conditions; and simulating breeding populations to apply machine learning in genomic selection.

YouTube videos: 

• A genome-wide 50K SNP chip for tropical pine tree species
• FSA Women in Science profile: Nanette Christie