Causal inference
With applications in genomics and neuroscience
Most significant loci discovered in genome-wide association studies are located in non-coding regions of the genome, with likely regulatory functions. This underscores the importance of characterizing molecular mediators that connect genotypes to disease phenotypes. Furthermore, many regulatory effects are tissue- or cell-type dependent and involve the complex interplay of multiple biological processes. This necessitates leveraging multi-tissue, multi-omics, and single-cell data to deepen our understanding of the genetic basis of complex diseases.
Mendelian randomization, the application of the instrumental variable method in genomics, has been successfully employed to uncover the causal roles of genes or other molecules at the population level. The application of Mendelian randomization to emerging data types presents new challenges, such as sparsity and heterogeneity, which demand the development of novel statistical methods to uncover meaningful insights from these complex datasets.
