With the development of high-throughput sequencing, whole-genome analysis, such as genomic prediction and genome-wide association studies, plays an important role in animal, plant, and human studies. As the amount and diversity of omics data continue to grow, several challenges arise for the linear mixed model. First, there is a need to extend mixed models to incorporate multiple sequential layers of data as one connected network (e.g., the regulatory cascades). Second, due to increasing concerns about data privacy, there is a need to adopt mixed models for encrypted data, enabling collaborative genomic analyses with data confidentiality. Also, there is a need to address computational costs for large data analysis. We proposed new methods to solve these challenges.
Dr. Tianjing Zhao joined the Department of Animal Science in August 2023 as an assistant professor in theoretical quantitative genetics.