"Mind the gap: From Genotype to Phenotype and the role of Modelling, Genomic Prediction and Development"

Abstract book available here.

The pad incl. Q&As for all talks and discussion notes is available here.

Understanding how natural variation in genetic information contributes to the diversity in organismal form and function is a major challenge in Biology. Thorough mechanistic insights into such genotype-phenotype associations can have a major impact on our understanding of evolutionary biology, plant- and animal breeding, biodiversity, and human diseases. Advances in sequencing technologies nowadays allow describing genetic variation on multiple scales ranging from genome architecture to gene transcript and protein abundance. Additionally, novel high-throughput imaging methods and computer-aided behavioral assays are being developed to quantify phenotypic variation present in nature. We, thus, reached exciting times with an accumulation of quantitative data on the genotypic and the phenotypic level. Such datasets are excellent sources to train and improve novel machine learning approaches that are being extensively employed to achieve high-quality predictions of natural phenomena. Accordingly, applications in establishing genotype-phenotype associations are at the doorstep. 

Most adult phenotypes are exposed to changing environmental conditions and therefore subject to natural selection. These adult phenotypes are shaped during embryonic and post-embryonic development. Therefore, developmental processes are excellent targets to link variation in genotypes with phenotypic divergence.

With this symposium we want to bring together leading researchers who employ quantitative dataset to gain insights into genotype-phenotype associations. We want to bring experimentalists and theoreticians together to evaluate the opportunities and challenges of quantitative data integration. Eventually, we want to come up with a tractable outline of requirements for genotypic and phenotypic data to make sure that future research directions will contribute as much valuable experimental data as possible to allow the integration of powerful machine learning-aided predictions.