2017 | Yehudit Hasin,1,3 Marcus Seldin1 and Aldons Lusis1,2,3*
This review provides an overview of multi-omics approaches in disease research, highlighting the integration of various omics technologies to understand the complex flow of information underlying diseases. The authors discuss the evolution of omics technologies, from genomics to proteomics and metabolomics, and their integration into biological research. They emphasize the importance of multi-omics in understanding the multifactorial nature of diseases, which often involve genetic, environmental, and developmental factors. The review covers the design considerations for multi-omics studies, including the complexity of disease etiology, the use of human and animal models, and the integration of data across multiple omics layers. It also explores different approaches to integrative analysis, such as the genome-first, phenotype-first, and environment-first approaches, and the challenges and future directions in this field. The authors highlight the need for large-scale datasets, technological advancements, and improved analytical methods to fully leverage the power of multi-omics in disease research and personalized medicine.This review provides an overview of multi-omics approaches in disease research, highlighting the integration of various omics technologies to understand the complex flow of information underlying diseases. The authors discuss the evolution of omics technologies, from genomics to proteomics and metabolomics, and their integration into biological research. They emphasize the importance of multi-omics in understanding the multifactorial nature of diseases, which often involve genetic, environmental, and developmental factors. The review covers the design considerations for multi-omics studies, including the complexity of disease etiology, the use of human and animal models, and the integration of data across multiple omics layers. It also explores different approaches to integrative analysis, such as the genome-first, phenotype-first, and environment-first approaches, and the challenges and future directions in this field. The authors highlight the need for large-scale datasets, technological advancements, and improved analytical methods to fully leverage the power of multi-omics in disease research and personalized medicine.