  Mendelian randomization implemented through instrumental variable analysis is
frequently discussed in causality and recently the number of applications on
real data is increasing. However, there are very few discussions to address
modern biomedical questions such as the integration of large scale omics in
causality. While in the age of large omics, we face several hundred or
thousands of components with little knowledge about the underlying structures,
the focus of the field is on small scales and mostly with known structures. The
availability of large omic data accentuates the need for techniques to identify
interconnectivity among the omic components and reveal the principles that
govern the relationships. This study extends instrumental variable techniques
to identify causal networks in large scales and assess the assumptions.
Large-scale causal networks are complex and further analyses are required to
uncover mechanisms by which the components are related within and between omics
and linked to disease endpoints. This study will review these utilities of
causal networks for mechanistic understanding.
