Identification of disease-associated genes remains a major challenge in the study of primary
immunodeficiency disorders. Traditional genomic studies often rely on either genetic
association data or transcriptomic evidence alone. However, complex immune disorders
involve interactions among multiple genes, regulatory pathways, and environmental factors.
In this study we propose a multi-omics gene prioritization framework that integrates transcriptomic meta-analysis, genomic variant data, and protein interaction networks to identify candidate immune dysfunction genes. By combining heterogeneous biological datasets
and network-based ranking methods, the proposed approach improves the prioritization of
genes associated with immune disorders. Results demonstrate that integrating multi-omics
data significantly enhances the identification of biologically relevant genes compared with
single-data-type approaches. This framework provides a powerful strategy for studying
complex immune diseases and supports the development of precision medicine approaches
for immunodeficiency disorders.
