In this project, we will study whether the sperm RNA code can be used to predict causal effects on the offspring phenotypes and to provide a tool for epigenetic risk management. Benefiting from the special expertise of our team gained of the semiconductor sequencing technology, we will streamline the RNA mapping using a novel semiconductor microarray approach. By combining the closely human-like animal model with advanced sequencing and array technologies, our study contributes to the decoding of the small non-coding RNA signature. This understanding may help elucidate and avoid the intergenerational factors which influence the rising trend of metabolic diseases and obesity in the Western world.

2024

2028